Please welcome Vice President, Investor Relations, Chris Genualdi.
All right. Hello, everyone. Welcome to Planet's 2022 Investor Day. We're glad that you all could join us today. We have a really exciting agenda for you today. You'll be hearing from Planet's executive team, Will Marshall, our CEO, Kevin Weil, our President of Product and Business, Charlie Candy, our Chief Revenue Officer, Andrew Zolli, our Chief Impact Officer, and Ashley Johnson, our CFO and COO. You'll also get to hear from a number of our customers and partners, including Bayer, Microsoft, and NASA Harvest. We're grateful for their participation in today's event. At the end of the session, we'll hold a special fireside chat with Robert Cardillo, the Chief Strategist and Chairman of the Board for Planet Federal. Finally, we'll wrap up with Q&A. Both in-person and online attendees will be able to participate.
For our online attendees, please use the raise hand function in Zoom to indicate that you have a question for the team. For those that are attending in person, following Q&A, we will hold a visit to our satellite manufacturing facilities and mission operations. It's gonna be a great day. Now, before we begin, I'd like to remind everyone that today's presentation contains forward-looking statements. Forward-looking statements are subject to risks and uncertainties as detailed in our SEC filings. We encourage everyone to review our filings, which are available on our investor relations website and on the SEC's website. Okay, at this time, I'd now like to hand things off to Will Marshall, Co-Founder and CEO of Planet Labs PBC. Thank you.
Thanks, Chris. Welcome, everyone. Good morning. It's great to have everyone here for Planet's first Investor Day as a public company. We're excited to have you all here. What I wanted to do briefly is remind you who Planet is and what we do. Recap a little bit about Planet's achievements from the past year, and tell you a little bit about the themes, specifically the new themes that you'll be hearing during today's presentation. I was trying to do this and put it into a broader perspective of where Planet is going and the opportunity that lies ahead for us. The three key messages I want you guys to take home today. Firstly, we've executed and successfully accelerated our growth rate.
We've executed in the business, in the product, in other dimensions too, growing the team, growing our partnerships, doing an acquisition. Secondly, we've strong tailwinds driving our business. We've seen two key drivers, in particular, sustainability and security, becoming increasingly intertwined, and we'll talk about some of that. Thirdly, what Planet's achieved to date, as much as we're proud of that, is just the tip of the iceberg of the potential of Planet. We've set in place an ambitious growth plan. We'll talk about some of the elements of that plan today, including greater emphasis on analytics. We're solidly moving up the stack. Greater emphasis on partnerships to accelerate our go-to-market. Greater emphasis on global security, both as a driver of our business and alliance to our mission.
Let's get this show on the road. How does this begin? Who is Planet? Well, it starts here with our mission statement. Many of you have seen this before, but it's to image the whole world every day and make global change visible, accessible and actionable. This was actually formalized into our public benefit corporation charter as we went public, which speaks to both the sustainability and global security aspects of our mission, and it cements how our business and our impact are aligned and reinforcing of one another. We're doing good and doing well at the same time. Last year, we spoke about this sort of three aspects of Planet as we took the company public. We surmised the fact that we have a unique dataset. We image up to 300 million sq km of Earth surface every day.
That's about twice the land mass of the Earth, and it's about 100 x the imaging collection area of any other commercial Earth observation company. That gives and opens up huge new markets, which gets us to that second point, a huge range of industries benefiting from this. Thirdly, because the daily scan is always on and we can sell each picture multiple times, we have a highly scalable and highly profitable business model. All three of these things has been proved out and bolstered by what we've done over the past year. A myriad of examples you'll hear about through the day that we'll share will help you see what we've learned and help you to understand Planet as a business.
Throughout, I really hope that you'll see that we've been particularly dedicated and unwavering in our focus on driving real customer value. Now, before we go to the next slide, I just wanna say that as some of you probably know, I was a scientist before coming and starting Planet. I like being driven by quantifiable and reliable metrics. It's really exciting to see that the numbers are showing how well we're executing. Let me turn to some of those. Firstly, and most importantly, we have growth. Revenue growth has grown substantively. That's the big one. We grew from the late teens year-on-year growth last year to 40-ish% growth.
That was a bold claim a year ago when we went public, but it was on the basis of a real business that we'd already established, over $100 million in revenue at that time, and from many different vertical markets. We knew that as we scaled the business, we could enable quite high margins due to that one to many business, and it was very scalable thereafter. We were at a tipping point, essentially, of reaping the rewards of the foundations we'd built, in particular the satellite fleet that we had erected. We had to prove it, and I now feel really good that these numbers show that. In particular, that we've moved to this higher growth mode, and we've done so in a way that is sustainable.
At the end of the second quarter, we also had 855 customers, and this gives the sense of the diverse set of markets we can go into. We're proving that the business can have the kind of scale that we're talking about. I'm also very proud of the retention number. The growth there is really showing that we're retaining and growing our customers, and what it says to me is that our efforts in the product fundamentals, as well as customer success are really paying off. To the margins point, I'm especially proud of this margins growth, because going from 38% non-GAAP gross margins last year to approaching 50% this year is quite a big improvement.
I believe we've got much further to go here, where you see a straight line path to 70%-80% gross margins. Finally, you can see that we have the cash we need to invest in all of our growth opportunities and initiatives and to get to profitability. All this means that we can really focus on making the right decisions for the long-term growth and going after the full market opportunity. We don't have to hold back in any way. Now on these numbers, it also just makes me wanna reflect that I often hear from investors and analysts struggling with the question of, is Planet a satellite company or is it a software and data company? While, of course, the answer to that at some level is both, right?
we as a management team have always maintained that we're a data and a software business. Why? Because although we have space assets, and that's the huge moat on the basis of which what it powers our business, and it's completely unique and that is very cool, and those of you here today will see some of that in the lab, we're a data and software business because we sell data and software. We do not sell satellites. And that's been a pretty important point we wanted to impress with various degrees of success. in my mind, both under the hood and by the numbers that you see here, Planet is a software company. what's driving the growth that we're seeing? in part, it's these significant tailwinds, these global tailwinds.
We see Planet as being a part of three foundational multi-trillion dollar global transformations that are happening around the world today. First is digital transformation. That's industries like agriculture undergoing efficiency improvements, where big data and AI come to them and enable improvements in their methodologies. Many industries are undergoing this. Secondly, sustainability transformation, and that's being driven by the fact that the largest challenge humanity is facing today is the twin problem of climate change and biodiversity loss. This is no small matter either, right? The European Union has put 25% of its budget in the next cycle to sustainable transformation of the European Union. Larry Fink of BlackRock has said companies won't get access to capital if they don't track their ESG targets. Finally, we have peace and security.
You know, we're seeing increasing security issues around the planet, and they're being exacerbated by climate issues. We have to create, and we're enabling creation of a more transparent planet that will help the safe life for all of its inhabitants on the Earth. With all of these shifts, these three here, the first step is measurement. Measurement enables you to manage these challenges, which is why our data is critical to supporting companies as they shift in these economic times. Planet's not just relevant to these three transitions, it's foundational to all of them. That makes Planet's data relevant to every company and every government around the globe.
Last year, we spoke about these eight markets that we're looking at, and the vast majority of Planet's revenue today is on the left-hand four of these markets, and the right-hand four are nascent but have huge potential. Let me share an anecdote from a couple of trips I've made recently. I went a few weeks ago to Washington, D.C., and we met with heads of agencies, of civil government agencies, as well as defense and intelligence. From FEMA for Emergency Management to the National Reconnaissance Office. The interest in Planet's data is just taking off there. They can see this value. Meanwhile, I also visited three African countries where I met with ministers, presidents, and it's clear that we can help them too, from helping agriculture efficiency to border security to park encroachment.
We had great reception there as well. From the smallest economies to the largest economies in the world, Planet's data is relevant. On the commercial side, we saw great progress so far this year in agriculture. It's really strong growth. With VanderSat, our acquisition, we've helped really open up that new opportunities in that market as well, and they've helped us get more serious about the insurance market. For example, Swiss Re, we've got a really rapidly expanding and exciting partnership there. Finally, in finance, I just wanna touch on that. I still think that that's the biggest market opportunity. We've said before that they don't want imagery, they want time series calibrated data.
Hence, you'll all the discussion we'll have, and Kevin will speak more to this, about how we're going up the stack, but he will also share a couple examples of how we're getting into that finance area. We're I think the most importantly, and you'll hear this from Charlie, we're being disciplined and systematic as we go after all of these markets. That's the most important point for you to take home. Last year, we also introduced this concept of the analogy to a Bloomberg Terminal. You see, we could speak for many hours about the toolset alliance and the often under appreciated data processing steps, 'cause there's quite a lot of that. But what really matters is what the value that comes to the users, right? And what value it drives for them.
Really what the users get is information feeds. They set them up and subscribe to them on our platform, and enables them to make smarter decisions. That's rather analogous to what Bloomberg Terminal does. Like Bloomberg Terminal, this enables a high margins, high growth business and high stickiness as well, because the data goes deep into the workflows of these organizations. That gives barriers to change, right? The only differences are that whereas Bloomberg primarily aggregates free data, we've got a unique and proprietary data set. Whereas Bloomberg only really serves finance, we're serving multiple markets. I call it Bloomberg plus plus. Let me talk a little bit about some of the achievements the past year. There's been myriad, but let me share some of the ones that stand out to me.
On the product side, we built a lot of the foundations. From image quality to APIs to interfaces. You'll hear from Kevin some of the improvements that we're seeing in NPS, and I think a lot of that is because of these foundations. On SkySat tasking, we made some major improvements that improved the total capacity of that system by over 50%, even with the same satellites in orbit. We also announced a new fleet, so two new fleets of satellites. Pelican, which is higher resolution than SkySat and higher revisit, and Tanager, which is our hyperspectral fleet of satellites. On the business side, we grew the team substantively, and we feel very much on track there. We've really got to focus on systematic scaling.
For example, you'll hear from Charlie about how we systematically analyze all the different use cases of the different vertical markets and where to focus and where to not focus. As I said, Charlie will tell you more about that shortly. On partnerships, we won a landmark deal, the EOCL contract with the National Reconnaissance Office. We also did this cool and pioneering partnership with the German government, which is basically the first government-wide contract across all their civilian government agencies, 400 federal agencies. I think many countries will follow suit. Finally, we had an expanded partnership with Bayer, who's a leading agriculture company. You'll hear directly from Bayer here today, I'm pleased to say. Finally, on partnerships and acquisitions, VanderSat, the acquisition that we announced last year, is helping us to move up the stack.
In particular, they have a Planetary Variable on water moisture, basically soil moisture content, and a couple of other Planetary Variables that are exemplars of what we've got to build on the back of our data, and they bring a team that's able to do that. It's really cool. We also recently announced an exciting partnership with Telesat and SES, funded by NASA to develop satellite to satellite communications. This is really cool. It basically enables us to go from hours getting the data down to minutes via space to space communications. It's win-win because NASA wanted this capability and we forward fund a capability that we already wanted to build and accelerate that capability. It's very cool.
Then I mentioned this here last, just a few weeks ago, we were at the UN for the UN Climate Week, and we announced a partnership with Microsoft and with The Nature Conservancy on mapping renewable energy facilities around the whole world. You'll hear more directly on that project here today from Juan from Microsoft, as well as, Andrew Zolli, our Chief Impact Officer. Of course, many other examples, but those are some of the ones I wanted to highlight, here today. I wanna spend a couple minutes on Ukraine, because our data has been helping there in perhaps a wider set of applications than people initially may realize. Planet has been helping organizations across three different ways. Firstly, assisting governments, both with the security operations and defense of Ukrainian sovereignty.
Our data is unique there in showing and finding new threats around the corner, and we saw that just this last week with the Kerch Bridge, that we had the before and after first data up. Helping NGOs. We've been helping NGOs in a myriad of ways for the humanitarian relief. You know, there's millions of internally and externally displaced people, and they've got a wide variety of challenges, from housing to medicine to shelter. Even sadly, we're tracking war crimes in that area. Then finally, where appropriate, we'd be sharing that data with the media and think tanks, so they can shed light on events as they unfold. There's simply no better example of Planet's data in action than this, which is a partnership with NASA Harvest.
NASA Harvest used our data to do a countrywide analysis of the crop health in Ukraine, field by field across the whole of the country. It's incredible work, and it's super important for food production, both for Ukraine, but also for global food suppliers, because many of the poorest countries in the world rely on Ukraine for their grain supplies. It's a really sad situation, but Planet Solutions can help. In fact, it's an exemplar of the unique capabilities that Planet's data enables. Because there's no one else that can do this data scan. Simply, no one else could do this assessment at this scale. I just wanted to tee up also that you'll hear from Inbal, the NASA Harvest project later today.
You're gonna see that presentation in more detail. As I mentioned, we have continued to shed light on Ukraine via our data to the press, and that is shedding light on what's happening across the country. It is also true that Ukraine, as an event, has shed light on the need for open and global information, both in general and Planet's data in particular, and how critical it is to solving problems, not just in the security area, but civilian areas, like I said, in food security as well. Our media program is, of course, not just limited to Ukraine. We shed light on various events around the world, and Planet's images routinely show up in major publications pretty much every day. Let me move a little bit to the future.
Last year marked the 10th anniversary of Planet. As we were taking the company public, for both of those reasons, Robbie and I, my co-founder, sat down and we thought, let's write a plan and vision for the next 10 years. I invite you to read it. It's online, so we call it the North Star. Let me emphasize just a couple of things. The first thing, as put on this chart, is that five years ago, Planet was known as a satellite company. Today, we're really known as a data company. I think in five years' time, we'll really be known as a platform company. We also said, and I want to emphasize it here, that though we're proud of what we have done so far, it's really just the tip of the iceberg of the potential of Planet.
We're serving about 1,000 customers today, but there are hundreds of thousands of customers that need Planet's data. There's whole markets we've barely touched. In short, massive untapped potential. Systematically enabling those is where we're going next. That means going up the stack to enable those markets. It means easing our go-to-market with better partnerships and easing partnerships. In all, what we set out was with the North Star building an earth data platform powering action, because action is what really matters. It enables people to make smarter decisions for a smarter planet. Again, Planet's goal is to systematically enable all of those users. What we see our role is creating the data and the tools, whereas partners build applications, and then people can take action.
Before I close, let me just touch on some of the newer themes that you'll hear, some of the focus areas that we'll focus on here today. To the point of just being the tip of the iceberg of the potential, we'll speak about analytics and going up the stacks, and Kevin will speak more to that. We need to go up the stack, right? Google, the National Reconnaissance Office, Bayer, NASA, they can get value out of our data today. Many of the large customers like that, but many others cannot. They see the value in the raw imagery, but they can't get that value in practice until they extract out some of the analytics. We need to be systematic about building the tools for some and partnering with others to enable those tools for others.
Which brings me to the second point, which is empowering partners. Charlie will speak more to this. We cannot possibly do this alone. We aim to build the foundations that power partners to build applications, and we really see this as an ecosystem approach. Finally, on impact. The most obvious area that Planet can help the world was always sustainability, helping that sustainable transition. Myself and my co-founders, right from the get-go, knew and could understand how bringing transparency was gonna have significant impacts in peace and security. These two are increasingly intertwined. We saw a drought causing a civil crisis, causing a war, and then refugees. Now we're seeing in Ukraine the sort of reverse, a war creating various challenges of food security and energy security. These two things are intertwined.
Andrew Zolli, our head of impact, will talk about some of these in some of the examples later here today. Before I hand it over to Kevin, I just wanna reiterate what I said at the beginning. We're executing and have successfully accelerated our growth, now to 42% of the midpoint for the forecast this year. We have strong tailwinds driving our business in digital transformation, sustainability transformation, and in security. What we've achieved to date is really just the tip of the iceberg of Planet's potential, which is why we're moving up the stack, focusing on partners and so on. This is a solid and ambitious growth plan.
On this last point, I'm really glad that you'll get to hear from some of the customers here, Inbal from NASA, Nalini from Bayer, and Juan from Microsoft, to give you some of that flavor. Thank you to those people for spending the time to be here today. Finally, I wanna note that all of this progress depends on one thing, which is the incredible team that we have here at Planet. I'm in awe of the talents and skills of this team, and I'm privileged to work alongside them. It starts here with Kevin, Planet's president. He leads both products and the overall go-to-market strategy. He came from leading products at little companies called Twitter and Instagram, but he came to join the rocket ship called Planet. With that, I'll hand it over to Kevin.
Thanks, Will, and good morning, everyone. At last year's Analyst Day, we talked about our strategy to make daily change visible, accessible, and actionable, and to build a great business as we do. I'm gonna show you how we've delivered on that and the positive impact that it's made for our customers. Next, I'll show you a demo of some of our products, and then we'll finish with a glimpse at where we're going next. Let me start with a quick reminder of how our business works. PlanetScope is our daily monitoring product provided by our constellation of nearly 180 SuperDove satellites. PlanetScope customers typically subscribe to an area, so you'll hear us talk about an AOI or an area of interest, and they receive daily imagery of their AOI.
They access the daily data via our web tool, Explorer, via our APIs or via Planet's built-in integrations to all the popular GIS tools. For example, a large agricultural customer like Bayer might subscribe to an area made up of all the fields across all their farming customers. A defense customer might set their AOI to be their border areas, plus every hotspot around the world that they're looking to monitor. A state government might set their AOI to be their entire state, and so on. Note that this model is right from the beginning one to many because the same patch of land can simultaneously be an agricultural area, near a border, and within a state. We capture the data once, and we're able to sell it multiple times to multiple different customers.
As our customer base grows, that multiple increases and our margins increase with it. In addition to forward-looking, the forward-looking cadence that PlanetScope provides, we also have a unique deep archive of PlanetScope imagery going back six years. That's about 2,000 data points for every point on Earth, one a day, documenting change. Our customers use this data to go back and contextualize events that they're seeing on the ground today, as well as to train predictive ML models. Continuing down, Basemaps offer a cloud-free aggregation of the best imagery over an AOI over a particular timeframe. Customers get weekly, monthly, quarterly, or yearly cloud-free basemaps, which are fantastic for visual use cases, and I'll show you an example of this in the demo.
The last one I'll talk about here is our high-resolution tasking provided by Planet's constellation of 21 SkySats that offer 50 cm imagery over any point on Earth with the ability to revisit points up to 10 x per day. Most of our SkySat customers set up yearly or multi-year contracts, typically with quarterly or annual minimum commits, and revenue is recognized with usage. We'll talk more about Fusion and about planetary variables later, and I'll show you a demo that brings these products together into a single integrated experience. Together, though, they form the basis of Planet's one-to-many business model. That plus the fact that over 93% of our ACV is recurring, sets us apart from our peers.
I have a poster that hangs in the office of, in my office at home, and it says in big red letters on a white background, good work consistently over a long period of time. That, to me, is what delivering for customers is about. Your strategy points you in a direction, and then you put your head down, and you execute. Over the long run, it's execution that makes all the difference. What you see on the slide is just a sample of the improvements that we've made for customers this year. Not all of them make the front page news, but every single one of them is a big deal to our customers.
The best part about doing the kind of foundational work that's represented here on this slide is it doesn't only improve the product itself, it improves every analysis, model, and decision that the product powers. We upgraded our entire PlanetScope system from four bands to eight bands, which has opened up new use cases in environmental monitoring, coastal areas, and more. As we did that, we massively improved band alignment across our imagery, meaning from one day to the next, our imagery got visually and radiometrically sharper. Again, this kind of software work not only improves the core image quality, it improves every downstream analysis a customer does with the imagery. Leveraging that, we completely rebuilt our analytics pipelines, and the results are better, of course, but they're also faster. They get to customers more quickly, and I'll show you that in the demo.
As Will just talked about, we've substantially increased the capacity and reduced the latency of our SkySat constellation. That's an improvement to an existing constellation in space. We didn't launch more satellites. We did it by shipping iterative software updates, and there's a bunch more to come as our systems get smarter. Customers notice. These product improvements along with the number of positive changes on the commercial side that Charlie will talk about, have moved our overall Net Promoter Score up 24 points to a score of 62. Remember, Net Promoter Score goes from -100 to 100, and you only get positive points if a customer gives you a 9 or 10 out of 10 when asked if they'd actively recommend the product. 62 is a fantastic score that represents a lot of positive customer sentiment. It's even higher for our larger customers.
It's 70 across our top 50 customers, and it's 85 among customers with seven-figure ACV. These kinds of NPS numbers are an indicator of our ability to drive retention and expansion among our existing customers. This focus on the fundamentals is paying off in other ways too. We've long said that the true value of Planet's multiple constellations is in how they can be used together. You can see our customers increasingly agree. Our percentage of multi-product customers has grown from 63% in 2020 to 85% in 2022, with the largest growth being customers that are using 3 or more of our products. Just to illustrate what I mean here, an example would be an agricultural customer that uses to monitor their fields using PlanetScope.
They expand the following year using Fusion to do predictive analysis, predictive time series. Then they add SkySat to dive deeper into specific issues that they find anywhere in the world. Or say, a defense customer who starts using SkySat for border monitoring and expands that the next year to monitor their coastal areas using ship detection. Then from there, adopts PlanetScope across a broad region so that they can see around corners, understand unknown unknowns, and with our six-year archive, piece together historical information on events that they may not have originally known to look for. Let me show you how some of these use cases are coming together in the product today for customers. I'm gonna show you three different videos that I'll narrate as they play. We'll start in the Amazon rainforest, the lungs of the planet.
It blew my mind to learn that we lose 40 football fields worth of tropical rainforest to deforestation. Not every month or every day, not even every hour, every minute. 40 football fields worth of tropical rainforest lost every minute. By the way, deforestation in the Amazon, it's not just an environmental issue. Often, the underlying reason is narcotics traffickers creating roads and temporary runways for drug smuggling. It's just one more example of how sustainability and security are increasingly intertwined. If you're gonna make an impact here, you need PlanetScope. You need our ability to scan huge areas. You need our resolution to see roads. You need our machine learning to identify change, and you need our daily frequency so you can take action as it happens, not just show up after it's over. Let's go to the first video.
We're starting here looking at our global monthly base map. This is the world as seen by PlanetScope imagery. As we zoom into Brazil, you can see how massive the Amazon really is. It's about 10 million square kilometers, almost 7% of the landmass of the Earth. Keeping up with how it's changing over time, being able to find where deforestation or development is taking place, it's a really challenging task. Now we'll zoom further into an area in the east, and we can observe just how quickly change can happen. As we zoom in here, we'll change this monthly base map view into our daily PlanetScope view. We're gonna look at imagery from this past April. This is April of 2022. Here's what this area looked like on April 14th. You could see a fairly large amount of forests here.
Now let's go to April 15th. Not much change. April 19th, still intact. April 21st, still good. By April twenty-fourth, we're starting to see some significant harvesting. By April 29th, almost this entire area is cleared out. How do you, as a customer, even know to look here? Like I said, 10 million sq km, it's a massive area. How do you identify that this is a place where change is occurring? That's where our AI-powered road and buildings change detection product comes in, the analytics that Will's been talking about. We'll take a look at how that product works. One thing to say, detecting road and building change is powerful, and it's more cross-cutting and horizontal than you might think. 'Cause anywhere people go, they create roads, and the Amazon is no exception.
As the timber got harvested, the roads became more visible, and our ML model picked them up, including polygons, confidence scores for every detection. From here, with one click, you can submit a task for our high-resolution SkySats to go and get a closer look. With a single click, you're taken to the tasking dashboard with the location prefilled. You provide a name for a task, you hit go, and you've just submitted a task to the software back-end that powers all of Planet's high-resolution SkySat tasking. Now when that image is delivered, it provides more detail into the deforestation that just occurred. You can see significantly higher resolution image from SkySat. You can even see the piles of wood harvested from the area that were left by the road.
Here in this one example, you have Basemaps, you have daily PlanetScope imagery, you have change detection analytics, and you have tasked high-resolution imagery all in one workflow. One additional point I'll make on this: through our imagery, through our machine learning, our products can tell that there are new roads and buildings, and generally, just when change takes place. What our platform doesn't have is the additional context about what's supposed to be going on in this area. This could be a completely legal deforestation event, or it could be illegal deforestation. This is where our partners and customers come in. They use our APIs, and they can build products that add additional context to the Planetary Variables that we produce. That's what this next video will show.
It's about how a partner of ours, SCCON, builds on the building blocks that I just showed to create an end-to-end application for their customer in the Brazilian government. Let's roll that video. You can see. Oops, this is the wrong video. Can you show the SCCON video, please? All right. You can see, as they go into their alerting dashboard, this is built by SCCON on top of Planet. You see labeled dots, and the number on each dot is the number of changes or alerts that have been identified in a particular area. As they zoom in, these labels become more granular until eventually what you're seeing is an individual detected change. With a click, their application pops up Planet imagery showing the before and the after and including a range of relevant metadata.
Again, here's a deforestation event. The difference now is that SCCON has integrated local permitting data. They can characterize on their end each alert as permitted or not permitted. Their app generates automatic analytic reports on each alert, which provide the appropriate details in the formats relevant to the government of Brazil and other decision-makers on the ground. It's a level of detail that we don't have. It's work that's specific to a particular customer. It's a perfect example of the way that we look to provide scaled imagery, analytics, and horizontal platform services. We rely on partners who can go deep with customers and build end-to-end applications that integrate our products. As one last quick demo, I know many of you are interested in learning more about the products from VanderSat, who we acquired in December of last year.
We're thrilled with how that integration is going. That team is packed with remote sensing scientists. They fit right in, and they're already meaningfully adding to our capabilities. Let's roll the last video, please. As this video zooms in, Brazil again, you can see one benefit immediately. VanderSat products fuse in microwave data alongside optical imagery, and microwave data sees through clouds. It means it can provide an extra level of continuity day on day on day in agricultural regions. They're measuring something complementary. It's not just optical imagery. It's a proxy for the actual amount of carbon biomass in each region of each field. Now as we head to the U.K., here's another major difference. The product here isn't imagery. It's a data point per area per day, and you can chart that data point over time to get a graph.
Some businesses might not know what to do with imagery, but every business knows how to understand a graph. As with Planet, they have an archive going backwards, so it's easy to benchmark across multiple years. There's a lot coming on this front next year, and I'm gonna talk more about VanderSat and analytics and moving up the stack as we now switch gears and start to look forward. As Will has said, our mission is to image the world every day and make global change visible, accessible, and actionable. This is how we orient our product strategy as well. Making change visible is about agile aerospace, Planet's ability to build large constellations quickly. They produce new datasets the world has never seen before.
Making change accessible is about stitching that data together, making it analysis-ready, helping our customers and partners extract insights from it, so that they don't have to start from the imagery itself. Making change actionable is about bringing these building blocks to the customer wherever they are and providing an Earth data platform that enables them to integrate our solutions into their workflows. Let's start with making data visible via agile aerospace. This is the first of Planet's modes, and I still think we're the best in the world at it. What you see here is our near-term satellite roadmap. You know our two existing constellations. SuperDove on top with roughly 180 satellites imaging the entire world every day in 8 bands at 3.7-meter resolution.
The SuperDoves generate a unique dataset documenting daily change across the planet, which has never existed before. We have a six-year archive. 2,000 data points above every point on Earth. It's both irreplaceable global context and a unique training ground for geospatial ML models. There's SkySat, the 21 satellites that deliver at 50 cm resolution with the ability to see a given point on Earth up to 10 x per day. This bottom row shows two new types of satellites that we're building as we speak and that we'll launch starting next year. Both bring new and differentiated datasets to our platform. On the bottom left is Tanager, the hyperspectral satellite that we're doing in partnership with NASA JPL, Carbon Mapper, and others that will measure methane and much more across the world.
On the bottom right is Pelican, the successor to SkySat that's going to be better in every way. What do I mean when I say better in every way? Instead of 21 satellites, the Pelican constellation is planned to be 30, about 50% bigger. Instead of being 50 cm, Pelican will produce 30 cm imagery. Instead of 10 revisits to a given area in a day, Pelican is planned to do up to 30 revisits in many areas. That is a revisit every 30 minutes. 30, 30. 30 satellites with 30 cm imagery leading to 30 captures per day, one every 30 minutes. That's not to mention a host of other incredible features. Pelicans will be able to connect to satellite communications constellations, which will enable rapid tasking and data dissemination. They'll have more bands than SkySats. It's not just imagery.
Pelicans will be able to capture video. Really better in every way. How does this play out in practice? Imagine that you're in national security and you get a tip from a source, or you're monitoring the Amazon and our analytics just detected a new road being built. You can use Planet's tasking API to submit a task to Pelican. 2 minutes later, that task has already been uplinked to a Pelican in space, which 15 minutes later is over your area of interest. It collects the image, and without needing to wait to fly over a ground station, it downlinks that image immediately using a commercial satellite comms constellation. 15 minutes later, that image has been processed, analytics have been run, and 5 minutes after that, the results are sitting in a cloud bucket that you own.
In well under an hour, you went from having a tip to having 30 cm imagery and analytics of a point on the other side of the planet without you even needing a human in the loop. That's the kind of thing that becomes possible with Pelican. Now with Tanager, we move beyond the optical spectrum to hyperspectral data. It's not 8 bands in the visible spectrum. It's 400 highly precise 5 nm bands starting in the visible spectrum, extending through the near-infrared and into the shortwave infrared all the way out to 2,500 nm. With 400 spectral bands, you can think of these satellites as essentially being able to detect chemical signatures. The driving use case is greenhouse gases.
Tanager is designed to detect and locate over 80% of methane and CO₂ super emitters, whether from pipeline leaks, landfills, cows, or other phenomena. Measuring methane and CO₂ globally provides ground truth for the world, as well as an ability to hold governments to their promises and hold emitters accountable. These satellites will be able to do far more, including applications in mineralogy, biodiversity, environmental assessment, natural resource management, as well as in defense, where it can be used to detect a variety of material and chemical signatures. Taken together, Tanager and Pelican are gonna provide Planet, our customers, and our partners unprecedented data. I know I speak for our whole team when I say I can't wait for these launches next year. For those of you in person, I'm really excited for you to go down to the lab and see some of this in person.
We're not gonna stop at just producing new data sets. The even bigger opportunity is moving up the stack from imagery and data and APIs to machine learning and time series. We're building a platform on top of our proprietary data, layers that can be mixed and matched together like Lego blocks that provide new capabilities, extract new insights, and generally make our data more valuable and easier to use. I'm gonna show you how we're building this strategy from the bottom up with each layer stacking on and reinforcing the ones below it. Each of the icons here represents a single example of data or a service at each layer. You see here at the bottom, PlanetScope monitoring, our high-res tasking via Pelican and SkySat, Tanager's hyperspectral data, and our billion-image archive. We are creating over 30 TB of data every day.
We have to move beyond humans using their eyeballs to look at imagery. The future is about computers running algorithms and analysis to automatically identify objects, patterns, time series. All of our data is analytics ready, and that's a new Lego block that partners and Planet are using to train machine learning, remote sensing algorithms on top of our data. We're also beginning to fuse our data with that of other sensors, other optical sensors, microwave sensors, even radar in the form of SAR, that allows us to mix in new capabilities, surface temperature, the ability to see through clouds, things like that. Over the last year, with these blocks in place, we've begun to build more sophisticated building blocks that we call Planetary Variables. As we do, notice that these don't look much like imagery.
They start to look more like a matrix or a time series, something that anybody who knows how to use Excel can use. That's why with each step along this path, we're not only reducing time to value for customers, we're meaningfully increasing our customer base and our TAM. Last year at this event, I said we're about at the second layer, and we were just kind of peeking into the third layer. Today, I showed you examples of change detection for roads and buildings and how that can identify deforestation or aid in permit monitoring. With our integration of VanderSat's products, we now sell third-layer offerings like a biomass proxy product, soil water content, and a land surface temperature product. We're moving really quickly on this front. We call these building blocks because they really are.
For example, at the insights level, you can combine Planetary Variables like soil water content and land surface temperature to produce accurate estimates of crop yields. In fact, just a few weeks ago, Bloomberg published our crop yield estimates in the Bloomberg Terminal a few days ahead of the official USDA report on crop yields. They ran again on the terminal yesterday morning. We've seen major insurance players like Swiss Re and AXA use soil water content as a Lego block in building a parametric drought insurance product.
What they do is they price the insurance for a particular area by looking at the history of soil water levels in that area over a bunch of years, and then by monitoring the difference in soil water levels on an ongoing basis, they can determine whether a payout is due, all automatically using data from satellites orbiting Earth as we speak. To the point about enlarging TAM, if we had just stopped at the bottom layers and waited for the insurance industry to do all of the work in the middle themselves, it wouldn't be happening yet. They wouldn't be a customer. Farmers wouldn't have this innovative new drought insurance product. Because we're building up the stack, we're making these building blocks available in the form of Planetary Variables, we're creating a whole new market for Planet and for our partners. It continues.
Partners are already using these building blocks to produce measures of carbon sequestration, commodity pricing, and all kinds of other things. We're seeing this kind of up the stack work spur a lot of innovation. By the way, this triangle is not shaped like this. It's shaped like this, right? It's incredibly wide. There's so many applications, and we're really beginning to open people's minds to the value of Planet data. As we do all of this, we have an opportunity to speed time to value even further for our customers and partners. So far, everything we've talked about is data, but what about the workflows around that data? Just like we wanna build up the data pyramid so folks don't have to continually recreate the wheel to do analysis, our customers regularly ask us if we can simplify the way that they manipulate, understand, and integrate the data.
I referenced an agricultural customer earlier who's getting PlanetScope scenes, fusion data, and SkySat images. What if they want to quickly generate a time series of NDVI values across all their fields, filter that down to a particular country, and then group by region? What if they wanna correlate soil water content and crop yields over time? Take the defense customer that I referenced with broad area PlanetScope, ship detection analytics, and SkySat tasking. Maybe they wanna not just detect ships, but count them over time, establish a baseline, and then automatically alert when that value goes anomalously high or low. You can see what's needed here is almost like an analysis and alerting platform for the data pyramid itself. We have the basics of this in place.
It builds on our history of supporting open web and geo standards, offering rich APIs, doing integrations with prominent products like Esri and Google Earth Engine, but there's a lot still to build. The important thing is each step we take along this path is one thing that our customers will be able to do with a few clicks that today they have to do by hiring engineers and writing code. It makes our platform stickier and more valuable for every customer and every partner in the world. I hope that's given you a sense of the ambitious roadmap we have ahead, a roadmap that's truly unique to Planet, to our vertical integration, and to our growing customer base. Beyond that, we are building an execution machine here, and I'm excited for what the next few years hold.
Now let's talk about how we're gonna take all this to market. For that, I'm happy to hand it to Charlie Candy, Planet's Chief Revenue Officer, and one of the strongest commercial leaders that I have ever worked with.
Good morning. Thank you, Kevin, for that kind introduction. My name's Charlie, and I have the great privilege to support our global commercial teams. It's great to be with you in person this year to talk about the progress that we've made in scaling our commercial organization and delivering on our go-to-market strategy. The commercial team has a really tremendous role and huge responsibility, and that is to convert the amazing potential that Will and Kevin have been talking about. As it did when I joined this company, my mind still races with the unbelievable amount of opportunity that we have.
With so much to offer and so much opportunity and so much interest in what we do, it's imperative that we focus and to ensure that our teams are working on opportunities that we can land, we can expand, and that we can renew consistently. We already know which customers we can make successful, and we have identified high value, high impact use cases. Now it is about being systematic in our scaling. I'm especially excited that we actually have one of these key customers with us today. Nalini is gonna come and talk to us later on about the great work that Bayer is doing. In this section, there's three things that I would like you to take away from my section today. The first is that we did the things we said we would, and it's working.
The second is we are honing our sales motions and becoming more systematic in the way we approach industries and use cases. Thirdly, we are laying the groundwork to scale emerging market opportunities as they develop. Last year, I presented a slide very similar to this with five key areas, focus areas, that our commercial team was gonna focus on. I'm pleased to report that we've delivered on each of these, and we're seeing great results, and you can see this in our numbers. We now have 55 sellers globally, and we've grown our sales development organization to support these sellers with a well-qualified pipeline of high-potential customers that we know that we can serve. We've added vertical teams in sales, pre-sales, and customer success, and this expertise has helped build credibility and repeatability in our sales motions.
I'll share later how we have gone through an exercise with our go-to-market teams to identify and prioritize our sweet spot industries and use cases to help us become laser-focused. We're seeing great results from our global strategic accounts team, and we're expanding this team. As we do, we're reducing the number of accounts that these reps cover as we see that the dedication of resources is really paying off. We have invested in customer education and engagement, launching Planet University, which has a plethora of content, including learning access opportunities, online courses, live training, webinars, and this content is co-developed. It's created by our Planeteers, but also our users and our partners.
We also launched our Planet Community, and this is a platform that enables us to engage with the users, connecting all the people around the world doing amazing things with our data and on our platform. We've invested in rolling out the industry-leading sales methodology Command of the Message to help us position our value proposition and drive sales productivity. We use this alongside MEDDPICC, which help us qualify and forecast accurately. The acquisition that Will and Kevin talked about with VanderSat is really exciting for us. We've already developed this promising foothold in the insurance vertical, but more importantly, it's actually right now it's creating a compelling upsell opportunity into our large civil government customers and our agricultural customers. It's a very complementary product set. Lastly, we have been making targeted investments in both business and market development, preparing future opportunities.
As an example, we've made investments in the Middle East and Africa, and we're preparing the ground. We're making these markets ready for our future partners and sellers so that they can be productive faster. The numbers. You can see through our numbers that we're presenting here today that these things are all making an impact. At our sales kickoff this year, we talked about the importance of winning the right customers, qualifying harder, focusing on core industries, and where we're being asked to solve for unproven use cases that are harder to quantify, we've empowered our teams to say no, just to make sure that we're focusing our resources on activities that we know will pay off. As our customer base continues to grow, we are winning more and more customers in our target industries, and as a result, our average deal size is growing.
Our sales process has also evolved. We're spending more time building alignment and quantifiable value proposition, which is driving bigger deals and improving our renewal rates and expansion as we go. The combination of focusing on our highest potential customers with a values led sales process is leading to this growth in our, in our million-dollar plus customers. What excites me about these customers is that what we're seeing is that when we land a large opportunity with a customer, it makes it even easier to land the next larger opportunity with the same customer or other customers in their industry. Our momentum is feeding itself. It's not just about winning customers, it's about making them successful so that we can retain them and that we can grow them.
Our investments in customer success, combined with the improvements that Kevin talked about in our products and our operations, is leading to a much better customer experience. You can clearly see this in the NPS results that Kevin presented earlier. 62 is good, but it's not where we're aiming for, and we're gonna continue to strive to make that better quarter on quarter. I'd like to share with you just one example of a customer that is in civil government, and we're just seeing an explosion of use cases in civil government. This is an example from a customer in Asia. However, it could be any country in the world. There is not a national, state, or local civil government customer that could not get value from these use cases, and they are uniquely served by Planet.
Our local partner has established large-scale monitoring solutions for agriculture, forest, water resources, mining, waste disposal, all supporting policy-wide implementation at a country-wide scale. Additionally, our data is used for disaster management, such as wildfire and flooding events. This has created huge demand for our data, but also visibility and raised our profile at the highest level. Our team was actually invited to visit the country recently, meeting with senior ministers and deputy ministers for digitalization, innovation, and space industry to discuss this project and the success that we're achieving. We've been asked to submit a proposal, which is now a multimillion-dollar proposal for country-wide monitoring, which sees 5x the growth in the next 12-24 months. This is just representative of a customer with all of these civil government customers globally that we can serve.
We're relevant at those highest levels, so meeting with politicians and world leaders is common at Planet, and it demonstrates the relevance of our value at the highest levels of government. This customer is a passionate advocate for Planet. We're making a difference, and they are a reference for us to help us go out to other national civil governments. The partner is working with us on opportunities in neighboring countries, and we've been using their success to demonstrate what is possible and to shorten sales cycles. I'd like to talk about the results that we're seeing in our global strategic accounts team. This is a new team, and we've invested in experts with industry focus in sales, pre-sales, and customer success. It's worth noting this is still a new team.
It was formed in July last year when we saw the opportunity of really focusing dedicated resources on a key list of strategic customers. In this chart, you can see the results. This is our A&D cohort, which has grown 225% year-on-year ACV. This has been achieved from investing in these customers, understanding their needs, and building shared roadmaps with aligned goals with dedicated teams that understand those customers to make them successful. It's a partnership, and that's the way most of our customers and our partners see us is strategic partnership.
The improvements that we're seeing in quality, consistency, and user experience from our eight-band SuperDove update is accelerating adoption in these customers, and our new capabilities like Fusion and Planetary Variables are creating exciting expansion opportunities for us with many, many projects with these kind of key customers on the way. The new constellations, Pelican and Tanager, they're inspiring for our customers. They can see that this is a future company that these partners want to invest with a compelling roadmap which will unlock significant future opportunities. I'm excited to, as we continue to invest in expanding this team, because the return on investment has already been very strong. I'm truly proud of our commercial team and the culture that we have created.
I'm surrounded by the most talented people that I've worked with in my career, and they are highly motivated and doing amazing work here at Planet. Planet is an exciting place to work. We offer purposeful roles, and we offer high earning potential, and this enables us to attract and retain amazing talent. Our growth is facilitating career advancement opportunities for our teams. Supporting that progression for our teams is something that gives myself and my leaders great pride to offer people these opportunities. My commercial leadership team have all worked in public technology companies, mostly in the SaaS arena, and they are building these high-performing teams, and as you can see, they're delivering the results, and they're making it fun in the process.
There's a lot of great work happening across the team, but I would just like to call out a couple of the teams that are the fastest-growing in the organization this year. The first is our sales development team, which is our pipeline generation machine. Our Global Director of Sales Development, Leslie Mertz, is building a world-class sales development organization, which is aligned with marketing and sales and sales operations. This focus is on quality and a sufficient quantity that we know we can make sure our reps can achieve and exceed their quotas. In this team, we have many people that are early in their careers. I'm delighted to see that this team is actually feeding other teams as we open new roles. It's, you know, again, it's very rewarding to see these people progress in their careers with opportunities at Planet.
I'd like to call out the customer success organization. Mike Merritt, we recently appointed him as Chief Customer Officer at Planet, again, demonstrating our commitment to the customer. Mike has built a very sophisticated organization with an amazing culture. In the past year, we've added CS operations to help us be more data-driven. We've added our customer education team, which has launched the Planet University and Planet Community, which I referenced earlier, which is enabling us to develop and engage and inspire our users. We've added industry experts that have walked in our customers' shoes, and having experienced the same challenges, they're able to offer a very credible and valuable advice. This is a passionate team that fully embodies that customer success mindset and is relentless in their pursuit of making sure that we continue to improve our customer experience.
Now, I'm delighted to introduce Nalini, who is Vice President for data science at Bayer, and she's come all the way in from Chicago just for today. Nalini's gonna be joined by Jackie, who's one of our superstar global strategic account managers. Please come up to the stage. I'm excited to hear the insights.
All right. Nalini, thank you so much for being here. We really appreciate you making the trip in and very excited to have you here for this investor day. Speaking of excited, I am very excited about the work we've been doing with your team over the last couple of years, and not just the outcomes that we've produced, but really the collaboration that we're able to work on with your team together. Thank you for that.
Same here. It's a pleasure to be here and, we came a long way, isn't it? We started with discussions here and there, and now we became a strategic partner and collaborators.
Yes.
Thanks for having me here.
Something we're all very excited about, and thank you again for coming. I think, you know, we have a couple of questions for you and we'd love to get your input on. For example, specifically right now, if you think about your day-to-day operations and some of the challenges that might be associated with that, how do you think that having access to, you know, the satellite imagery and how is that going to change your overall global operations?
That's a great question, Jackie. One thing I would say is satellite imagery, particularly at scale, is a game changer for us. It provides us an opportunity to advance our goals for reliable and solid seed production and also helps us innovate in agriculture in new and exciting ways. I'll give you a little bit about how our day-to-day operations are run today. Today, we have people visiting the fields, and we do not know fully what is happening in these fields with these visits because these visits happen every two weeks, and there's a limit to how many fields that can be visited or how much data that can be collected. With satellite imagery, why we are excited so much is we'll have the ability to know what is happening in the fields, every field, I mean, every single day.
That is game changing, and it's a major acceleration to what we do and how we work. With this data, we'll be able to inform the organization, make informed decisions, and we will know the diseases that are coming up, or we'll know about the quality of the produce, or we will also know in advance what the potential of that field is going to be. This will also help us know what will be delivered to our customers in advance. When I say that, it's both quality and quantity. It also helps us take more proactive and reactive measures to change the outcomes, which is very important.
Because this information we didn't have until after the growing season, at harvest. If you think about North America, that is September, and it's already too late. If we know this information through the season, then we can do something to adjust that outcome. That is something we are very excited about. Above all is the scale that this provides. We simply don't have enough people to visit every field every day. With imagery, we can do that.
Thank you. That's great, and that's really exciting. Thanks for sharing that. Second, we know that Bayer is one of the largest farmers in the world. Can you help us kinda talk through the role that remote sensing plays in your organization from research and development to in-field analysis? All the way to, you know, actually delivering the products to your end users?
Yeah. The main thing is, it helps us make our operations better ourselves. As I said, that's incredibly important. It helps us make more informed decisions, and we can also have more innovative ideas into our operations. Through all this, we'll be able to deliver better products to our customers. It helps us know what is happening in the field, which is something that we didn't know. In the past, we had to wait until after the growing season, and we had to make best guesses there. Now knowing this information through the season is quite important because we can make changes when it is needed.
The other area where it also helps is it helps us design and deliver better products in our R&D. Bayer is going through a shift. In our R&D, we are moving more from selecting the products that perform better in our pipeline to designing the products that perform best in farmers' fields. Satellite imagery, together with other techniques, will aid in this shift. The operational enhancements that we receive with satellite imagery will become hugely important for us, and it also helps us advance our sustainability goals. One thing we are looking at is this technology has the potential for us to build innovation to reduce our carbon footprint, while at the same time delivering better products and more innovation to our customers.
That's great. That's really exciting as well. You know, I know another big trend that's, you know, being discussed across all the industries is, you know, about this supply chain disruption.
How do you think about earth observation data for monitoring your supply chain specifically? Is it all about maximizing as much supply as possible?
That's one piece of it. When you talk about supply chain disruptions, we are hearing a lot about those these days, isn't it? Field monitoring will help us in many ways. One is maximizing the yield. Like what you called out. It also helps us deliver quality products to our customers, and it helps us build resilient supply chain. A big part of the equation for us is yield, the productivity of the field. Through the season, if we know what are the factors that are impacting the yield and drive that up, that's going to be hugely beneficial. If you think about supply chain disruptions, they're going to be the new norm. This environment of uncertainties or disruptions will continue.
We have to build a resilient supply chain to work through these disruptions, while at the same time driving the yield and the quality up in our fields. Where the field monitoring will help us is it will make disruptions less painful.
We know we will have disruptions, but when a disruption is happening, we will be able to inform the organization and then make necessary adjustments. In some cases, it may also help us avoid the disruption altogether. Just think of an example. If a field is drying up or some field needs attention because of some agronomic pressures, then we will know it, and we will know it sooner if that is the issue. We can do something about it. In this case, either irrigate the field or apply more fungicides. That will be really valuable for us. What I say is the field monitoring, through this field monitoring, it not only helps us maximize that supply, but it also helps us deliver those quality products to our customers, and it also helps us build a resilient supply chain.
Okay. You know, another very important thing that we all talk about is how the world is changing, and we're going to need to feed an estimated 10 billion people by 2050 around the globe. Interested to get your take on how, you know, we might be able to leverage satellite imagery to help us get there? You know, especially as it relates to the smallholder farms.
That's a big number, isn't it? 10 billion people. To deliver innovation, to provide food security for the estimated 10 billion people by 2050, that's on the top of our mindset Bayer. That's what we live and breathe.
Right
At Bayer. A big part of that is yield, the productivity. Deliver innovation to produce more with less input. That's one aspect of it. You also talked about smallholder farmers.
When it comes to smallholder geographies, as important as yield though is quality, because much of the produce there is used as food, so we need to drive the quality up. Through the season, knowing what are the environmental factors that are impacting the quality and being able to increase that is going to be hugely important. Second, I would say the scale that this provides. I talked about the hard time that we have with commercial farms. We have hard time collecting data in commercial farms. Now just imagine the smallholder farms. There are many of those. No one person or we don't have enough people to go to these farms and collect all their data. But with imagery, we'll be able to do.
Putting it even further, if you think about what happens in smallholder geographies is there is leapfrogging of technology. This is a technology that leapfrogs. This is a technology that gets introduced in commercial farms and smallholder farms at the same time. A good example of that is cellphones. If I put this in context to make this more stick, cellphones were introduced much faster and were adopted much faster in smallholder geographies. When you think about the innovation that solves for agronomic problems, which is seed or crop production or trade, which is majority of Bayer portfolio.
That doesn't leapfrog. Whereas digital technology leapfrogs. What I talked about cell phones or think about UAVs, so those are the ones that are used or they are more prevalent or more used in Southeast Asia than any other places of the world. This technology has the same potential. Finally, when it comes to smallholder geographies, one peculiar thing there is the owner or the holder of that farm, they know their farms very well. They're very intimately connected. It's not more data that will help them there, but it's better information. Giving them better information that would drive the quality up and also giving them information that can help adopt sustainable practices on their farms, and also giving more insights into the challenges that they may be facing. I'll give an example there. Think about the insect invasion that happened in Africa.
With imaging and predictive modeling, if we can get ahead of it and stop it, that will be beneficial.
Right.
Right now we do not know until it's coming to a given area. If we know through modeling that this is something that is spreading and then apply insecticides and stop it, then the people in those areas will benefit hugely. When you think about using this data to smallholder geographies, there are different nuances that are associated with it, but it's just knowing what those are and delivering innovation for that is what it takes. The potential here is huge.
I absolutely agree. Thank you, Nalini, for sharing all of that. I know we gotta bring Charlie back up here. Again, thank you so much for your time and for traveling all the way in and doing this session with us. We really appreciate it.
Yeah. Thank you for having me.
All right.
Good day.
Go back this way.
Thank you Nalini, for coming all this way, and thank you also for the partnership. I mean, I hope you saw there some of the amazing work and impact that Planet's data working with partners like Bayer can make. It's really just the beginning of the journey still in terms of there are so many more possibilities for our companies working together in the future. I mentioned earlier on in the beginning of my presentation that our sales kickoff, we talked about selling to the right customers and empowering our teams to say no and to qualify. I thought I'd take an opportunity to share the exercise that our go-to-market teams worked through together to prioritize the use cases that we're going to serve.
We developed a methodology and a framework, and then we surveyed our teams to create a list of core use cases, prioritizing based on four factors. The first is, do we have proven partners that we know that we can make our customers successful and that will lead to these higher renewals and expansions? The second is, can we prove it? Can we measure it? Are we demonstrating quantifiable returns? Is it something that's interesting, or is it something that's really valuable to this organization? Because if you're competing with other investment decisions, you have to be valuable. The third is, do we have competitive differentiation in coverage, cadence, resolution, or other capabilities? Where we know we have an advantage, we can achieve higher win rates, and we can avoid being drawn into price discussions.
The fourth, lastly, is making sure that we avoid one-offs or niche use cases. We want to serve the customers with large, attractive market segments so that we know that we can repeat and that we can scale. In the numbers, this is leading to bigger deals, higher retentions, better win rates, and low cost of sale. We will continue driving this forward. The summary of this exercise is some of the information that you see before you here. On the left-hand side of the screen, you can see our target core industries and verticals. This is where you can see that we've made the biggest investments to support these customers directly. You slide across to the right of the slide, this is where we are developing opportunities and industries and becoming more established.
This is where we're leveraging our regional teams, and we're working with our partners. This gives us a very efficient, well-targeted go-to-market sales motion. Along the bottom of the slide, you can see that there's horizontal use cases that Planet can serve, and these span industries. How we use this information is when we're prospecting outbound or if we're qualifying inbound, we look at this framework for deciding how we engage with which resources, so that we're understanding what is our chances of success. This is the result, a large, well-qualified pipeline, which is concentrated around our core industry verticals. This means that we can be confident of continuing to improve our conversion rates in a typical enterprise sales motion of creation to close between three to six months.
This gives our business the predictability that we need and the repeatability to scale. It's helping us achieve the numbers, as you can see. It's also meaning that we are a reliable partner to Ashley and the financial team in managing the growth of our business. We are not doing this on our own. We talked about partners a lot through today, and this is something that we will continue to talk about. They are critical in every corner of our business, and we encourage all of our sellers to lead with our partners in a co-pilot sales motion. Our guiding rule is do what is best for the customer.
We are making it easier for our teams to identify existing partners with solutions that solve customer problems, so that we can connect our sellers, our teams, with the opportunities and customers to help us deliver that value faster. Our partners bring to us industry expertise, technical skills, local knowledge, relationships, and an ever-expanding set of use cases that's solving real-world problems using our data and our platform. We have many kinds of partner types, but I'd just like to highlight four for you today. The first is our solution providers. Partners that have developed specific solutions for customers in certain industries.
As an example, Sinergise or F-Trust or others in Europe right now that are helping us with the 90+ paying agencies that are implementing the change in the Common Agricultural Policy, the biggest single budget line item, as they move from random spot checks to wall-to-wall monitoring across all agricultural areas. They're using our daily Fusion products to support with that process. We're excited to see the growth there. Secondly, we have our OEMs, and these are partners that are building derivative products as one-to-many solutions that are powered by our data and platform. Partners like LiveEO, where they have developed a linear infrastructure solution to support large-scale asset monitoring in energy, utilities, telecoms, and transport. The third partner type that I'll mention is the cloud service providers.
They bring great synergies in terms of scale, offering complementary services like compute, storage, and new paths to procure through their marketplaces. We've done extensive work with Google, and we're excited to see this partnership develop, and the door is open to others. The final partner type that I'll mention today that we're seeing rising interest from is the system integrators. We're investing to support the SIs because they're approaching us with large opportunities with significant enterprises, where they're leading programs that they need to leverage Planet's data to support use cases. To nurture and feed this ecosystem, we are investing in our partner program, Planet Orbit. Orbit is run by our growing channel partnerships team, whose responsibilities include the identification, onboarding, enablement, and ongoing support for our partners. We see a lot of potential also in the startup community.
We see a huge amount of innovation because so much of this opportunity is new. These industries are nascent. To support that, our business development team launched a startup program last quarter. In this startup program, these partners get access to data, services, and support to help them be successful in building businesses on top of Planet's capabilities. In addition to this internal initiative, Planet also recently announced a partnership with European Space Agency, ESA. In that effort, in Europe, they're looking to support commercialization using Earth observation data. Up to 300 companies are going through this each year. Through this partnership, we will offer startups and companies from ESA incubation centers, business and technical support, access to data packages, to support with their product development and go to market.
We're excited about this opportunity to support the growth in the industry in Europe working with ESA. To summarize, this really isn't rocket science. That is another department. This is about systematic scaling through disciplined focus on repeatable use cases, and making proportion investments to stimulate and prepare new market opportunities. These are the five core tenets that I'll leave you with today that drive our commercial organization. The first is prioritization of industries and use cases, not getting distracted, being empowered to say no, focusing the majority of our resources on the customers we know we can make successful. The second is never-ending pursuit of sales excellence. When we have the right prospects, we engage with a value-led sales process with quantifiable outcomes. Customer success.
It is at the heart of everything that we do, and we are relentless in the pursuit of making our customers successful. We only deliver our mission and our potential through our customers meeting their objectives. Striving for operational efficiency. Always looking to improve the way that we work and remove friction from our sales process and deliver the best possible customer experience. Lastly, partnerships. We are not doing this alone. Planet can only achieve our potential through working with partners that will give us the scale to reach all of the customers, potential customers globally. My own personal goal is for us to be the easiest company in the industry to do business with. This is a journey without an end. We will continuously look to innovate and strive to be better as I support our commercial teams.
To summarize and close, we did the things that we said we would, and they're working. We have made great progress in honing our sales motions, becoming systematic in the way that we approach industries and use cases. We are continuing to lay the groundwork to be ready to scale for these emerging market opportunities as they develop. For this next section, I'm excited to have our esteemed colleague, Andrew Zolli, who's gonna be joining us online from Paris, our Chief Impact Officer, to share some real-world examples of some of the inspiring work that our partners are doing to enable, to help life on Earth through space. Andrew, are you there? Oh, you are. Excellent.
I am. Hi, Charlie.
Hi.
Hi, everyone. It's such a pleasure to be here with all of you today, albeit virtually. I would be with you there in person in San Francisco, but I'm here at the global board of directors meeting for Human Rights Watch, one of the world's leading human rights organizations, which Planet is proud to support. Every day, I and my colleagues at Planet have the immense privilege of working to ensure that the remarkable tools and technologies that you've heard about from Will and from Kevin today are deployed in ways that make a meaningful and impactful difference in the world. That's what a chief impact officer does. After all, that's our mission. It's to use space to help life on Earth. Next slide, please. It's worth asking, how's life on Earth actually doing? The outlook, unfortunately, is worrisome.
As you heard Will mention, we live in an age of converging planetary scale emergencies, including climate change, the loss of biodiversity, and challenges to human security in all of its forms. These challenges are significant. They're global, they're moving fast, and they're interconnected. Where once we talked about single issues, now we regularly discuss the climate security nexus, for example, or the impacts of biodiversity on a company's ESG risk score. If we're gonna successfully address these challenges, we're gonna have to move in exactly the same way, collaboratively, at unprecedented speed and scale, and in a way that's deeply science and data driven. According to the UN, for instance, we're currently living in the most important decade, indeed the most important year in the most important decade, for climate change in the history of contemporary civilization.
The moment when we will either lock in or avoid the worst effects of climate change, like the extreme wildfires you see on the screen in front of you. That means working together. It means driving the right information to the right coalitions of the right people and institutions who can collectively make an impact. That requires common standards that drive common points of view and new kinds of feedback loops and accountability so that we can, in turn, drive systems change. How do we do that? Next slide, please. The front end of the funnel for Planet's impact is our work with the scientific community. Our team has worked tirelessly to enable thousands of scientists and researchers all over the world to discover new ways to use Planet's earth observation data to illuminate change.
It's kind of amazing, but from a standing start, there's now a peer-reviewed paper published with Planet's imagery as a principal source roughly every 15 hours, and the rate is growing literally by the day. You can see it here. This work helps us improve the state of human understanding, which is a social good all by itself. It also helps us train and recruit future industry leaders and users. It helps us validate new use cases in industries and sectors that are important to our work. It also gives us the opportunity to translate and scale those scientifically validated use cases into programs with enormous real-world outcomes. Next slide. You can see this at play in one of our signature programs on monitoring deforestation and supporting healthy, sustainable forests around the world, run by our Global Director of Forests and Land Use, Tara O'Shea.
With the support of the Norwegian government, Planet and its partners make data available covering all of the world's tropical forests between 30 degrees north and 30 degrees south, a huge swath, basically the entire tropical belt, covering all 64 countries, and some of them as big as India and Brazil. We make this data available to UN agencies and to governments, to NGOs, and to scientists and many others. This in turn creates a common standard for monitoring tropical deforestation and for the development of new kinds of policies on top of it that are working to reverse it. Next slide. The numbers here speak for themselves, and they are genuinely astounding. In just two years, the NICFI program that we run with our partners has grown to serve more than 14,000 users in 155 countries, all working together to stop deforestation.
Together, that community has made Planet data the gold standard surrounding one of the most important challenges on the planet, monitoring deforestation. There are network effects here. The more people use this data on that problem, the more it becomes a standard. The more it becomes a standard, the more people use it, and you get the kinds of data gravity that really allow us to have one common view of what's happening on the Earth. Doing this confers immense credibility on our organization, and it sets up entirely new commercial opportunities, like helping companies deliver deforestation-free supply chains, which is one of the top ESG metrics in the climate-aligned economy of the future, using exactly the same kinds of tools that Kevin demonstrated for you earlier. Delivering this kind of impact is worth doing, again, just like scientific research for its own sake.
We wanna live on a healthy planet, and we wanna pass that on to our children. It also has the pleasant upside of helping Planet and our customers and partners win commercially. Creating this kind of impact is always a team sport. As Charlie noted, it requires working closely with all kinds of scientific, technical, commercial, governmental, and NGO partners. Planet is honored to work with some of the best. Today, it's my great pleasure to introduce you to two of our close colleagues who've been doing just really amazing things with our data, and I've been looking forward to this for months as we planned this to share some of this work with you today. Next slide. First up is our friend and colleague, Juan Lavista Ferres.
Juan is the Chief Data Scientist, Vice President, and Head of the AI for Good Lab at Microsoft. Planet and Microsoft have been working together closely over the last year on some of the world's most pressing challenges, and I just wanna say, welcome, Juan. Are you there?
I'm here. Thank you.
It's great.
Thank you for having me. Thank you for the invitation.
Great to see you. Well, listen, you know, we're gonna dig into a couple of, I think, pretty breathtaking stories of how AI and data can be brought together profitably to deliver extraordinary impact in the world. We should just start with the foundations for everybody. Tell us a little bit about the AI for Good Lab at Microsoft. What do you do?
This is a complete philanthropic effort that we have at Microsoft. Our job and my job is to help using AI and data to help with some of the world's greatest challenges. We work in four areas. Accessibility, humanitarian action, sustainability, and health. One of the things very early on we realized was the power of satellite data. In actually these four areas, we actually been working for the four areas from health accessibility to sustainability and to Earth. We've been working with satellite data to solve some of the problems and challenges we face.
That's great. You know, I'm curious, maybe we'll get into this as we share a couple of these examples with our colleagues. Maybe let's do this. Let's jump right to the problem that we started working on together very intimately this year. That's the work that we've been doing with the United Nations. For those of you who are watching and listening online and who are assembled in San Francisco, I'm really excited to be able to share this with you because we've really kept this work under wraps. The work that brought Juan's team and our teams together to work was extraordinarily high impact, high value work for the Office of the Secretary-General of the United Nations in Ukraine. Juan, why don't you just share a little bit about how satellite imagery and AI can actually make an impact in such a complex situation?
Yeah. I can show you some of the slides on this. One second. Well, that was. We can see them here. I don't know. We've been working with Planet and the Office of the Secretary-General at the UN. Early on, they contacted us, and they contacted, like, Planet to say, hey, we have the problem that we need to monitor three different entities. There's schools, hospitals, and water facilities across Ukraine. And we want to understand whether these facilities have been affected or not by the war. The example that is really important, they don't have nobody can track that. They have some early data from basically like people that will take pictures, but there was no ground truth. We start looking at that. We got all the geolocation data from them.
What we did was basically getting like our experts experts on the other side to come to help us label data. We just develop a system that will connect automatically through Planet API. We could identify where this is a particular hospital in Ukraine. Across Ukraine, we could identify every single hospital, every single school, and every single water facility, and determine if they were affected or not by the war, they were destroyed or not. On top of that, we could actually assess when this event happened, like what was the date, at least what was the last time that they destroyed. Then we got some data that was not affected. So this was incredibly important for them.
Before that, they could have anecdotal evidence of these issues, but now they have a full recount of every single one of these hospitals and like water facilities and schools. On top of this, we did an analysis to identify how many children were affected by this, because we have not only the school location, but also have the school size and how many students they have. This has been tremendous. The Secretary-General of this has shared this across the UN, and these three entities are also protected by the Geneva Conventions. It's also important for not only these entities like UNICEF, but across the UN.
Now we continue to work with them, and we are in the next phase where we are actually mapping every single building in Ukraine. This is something that we're doing with the UNHCR. We're using AI models on top of the whole buildings and actually do the same exercise, but not just only for these entities, but every single building in Ukraine.
You know, it's incredibly powerful that this ability to monitor all of the building infrastructure, all of the X and Y location at a very fine-grained degree of both spatial and temporal accuracy, to be able to understand change and making that change visible, accessible and actionable. This is kind of the living manifestation of the proposition that Kevin mentioned earlier. This work, I will just say, for everyone who's watching it, you know, one thing that Juan's team and our teams share in common is a very strong bias to action and a bias around doing the most good at the largest scale.
That thinking led us very quickly once we were able to see just how impactful this work could be, not just technically accurate, but impactful in the right hands. When you put the data in the right hands, what happens after that we immediately began to think about bigger stories, bigger other things we could do. What were other problems we could go after? We've, of course, every time Juan and I talk, we come up with two or three new ones. This conversation has led us in a couple of really interesting directions, and we wanna share one other one with you, because it speaks to this ability to measure things that have just not been measurable in principle at planetary scale before.
That's a huge program we just unveiled at the UN General Assembly a few weeks ago, with our partners at the Nature Conservancy called Global Renewables Watch. Juan, why don't you share a little bit about the new platform we're developing?
Yeah. I have to say that I have been impressed by the agility of our three teams working together on this solution. We just released the early version of Global Renewables Watch. The objective of this is to measure every single at the global scale solar panel, solar farm, and every single wind farm installations across the world. This will help to track and forecast the progress towards the SDG, the Sustainable Development Goal number seven, and we will be able to do this at the country level, at the region level, and it will help countries understand progress toward the goal.
It's also important, this is something that is really important for The Nature Conservancy and of course important for the world, is that we want to make sure that like solar, particularly solar farms, we use a significant amount of land. In the case of, for example, in the case of U.S., the target for the U.S. is 207 gigawatts, and that's 1.4 x the amount to like a state like Rhode Island. It's important for them to be able to track this and to understand what was there before. We know at the global scale where we are using, how we are optimizing land to get solar farms and wind farms.
In order to do this is one of the problems that you will require, of course, satellite data, and you also require AI. Because this is a problem that you cannot solve using humans. Like a single person looking at the screen and finding windmills and solar farms would take over 100 years working full-time to do this exercise. This is something that thanks to technology, thanks to AI, and thanks to compute, we can do this in less than, I mean, a couple of hours basically. We use AI models on top of the satellite imagery. We connect to the satellite image from Planet. We use the models on top, and with this we generate. I'm gonna actually show you the demo. It's important to actually see the results.
What the demo will look like right now, we have four countries completely mapped: Brazil, Germany, Egypt, and India. The output of the models you can consume from here, you can see, for example, this is in the case of India. We can see what has happened in India in the case of solar farms, where has been the change. This is by region. Here in the left you can see the amount of capacity that we are predicting that is happening in each one of these regions. What was there before, what type of crop and like what type of land was there before. Even at this scale you can actually observe the actual features. You can, let me show you this. Give me one second.
This is the case of India. You can identify here in blue are all the windmills detected and all the solar farms detected. You can actually zoom in and see the actual imagery, and you can even see the imagery over time. Here in 2018, there was basically almost no solar farms installation. If you go to 2022, then you see all the changes. The same thing you can see actually, let's go to Egypt now. This is Egypt. We found this very big solar farm installation in the south of Egypt. It is huge. It's a huge solar farm installation. You can actually also see here in the north, there's some very big installations of windmills. This is all the windmills installations.
We still don't have a feature, but if you zoom right now, this is at the 4.3 m resolution. But if you switch here, you can actually see the actual windmills in high resolution. We are really excited about this project. Again, in the next couple of months, we will have the full world mapped, and we will open source collectively all this data, the full model for the world. Yeah, very excited about working with Planet and The Nature Conservancy group on this project.
Thanks, Juan. You know, it's a really stunning example, and it's part of a, I think, a playbook that we're getting close to at Planet. Finding the right partners and finding the right problems, and then driving toward public goods that help establish standards so that we are helping. We are becoming together, not just by ourselves, but all of us together, the canonical go-to dataset to understand a very particular form of change or an urgently needed form of understanding of change in these fields. I can't tell you how many institutions have just the phone is sort of rung off the hook as people understand now the way in which we're able to illuminate this critical area.
Juan, I wanted to say thank you on behalf of everyone at Planet. We're really thrilled at our partnership and excited to see where it goes next.
Well, thanks, Matt.
Speaking of right next, thanks, Juan. I'd love to now move on to our next colleague who we're gonna have in conversation, and that's Dr. Inbal Becker-Reshef, who leads NASA Harvest. NASA, that's NASA's signature food security and agriculture program. This year, also in the context of Ukraine, Inbal and her colleagues have been doing similarly breakthrough work, but in this case, in the area of food security, which some of you may know, it has profound consequences. The conflict is having all kinds of direct and indirect consequences, which we'll discuss in a bit later. I really think you're gonna be impressed with the amazing things that NASA Harvest and this team has been able to show.
Again, collecting information where in principle it's impossible to get access to. With that, I'd love to welcome Dr. Inbal Becker-Reshef. Are you there, Inbal?
Yeah. Hi. Great to be here.
Terrific. Well, before we dig into the details of this work, why don't you just tell us a little bit about NASA Harvest, your mission, how you're oriented, what you do?
Sure. NASA Harvest, as you mentioned, is NASA's food security and agriculture program. We were launched in 2017, and our primary mission is to enable and advance the adoption of satellite data in order to benefit food security, agriculture, sustainability, across the globe, and really helping to bridge the gaps between science and impact. We're focused on several areas really across the agricultural sector, including looking at transparency in agricultural markets and trade and supply chains, climate finance and insurance. We're doing a lot of work on sustainable agriculture, and early warning for early action are just a few of the areas that we're very much focused on and very much with an emphasis on end users.
Every activity we have is very much driven by a need and guided and with ownership by those that we're working with to ensure that we have impact.
Thanks, Inbal. You know, I think for those of us who are not global food security experts, we've heard that there are enormous potential implications from this conflict. I think many of us don't have a full handle on just how broad the impacts could be from this conflict. Could you help us understand sort of how does this conflict actually affect the world, and how are you tracking it?
I think today we mostly all recognize that our food system is very much a global one, right? It's very interconnected. If you have a drought, if you have a major disruption in one country, that can really have severe implications all the way across the globe, and actually oftentimes really have the largest impact on those that are already vulnerable and most food insecure to begin with. Particularly when we think about the war in Ukraine and Russia and that whole area is a really important and critical breadbasket for the world.
When you have uncertainty and when you have conflict and when you have disruptions to production or to transport of that grain out of that region, that has had a massive implication across the world, on food prices and on a range of commodities in the agricultural sector. It is very much important, and where we sit at NASA Harvest is of course to do very specific analyses, as I'll show, but also to have a broad view across the globe because of this interconnectedness.
Mm-hmm. Well, I mean, it's obviously a tragedy on a tragedy. The idea that poor and vulnerable people could be made more food insecure by the suffering that's associated with the conflict is obviously a terrible thing. But you were able, I think, to really build, I think, something very powerful. You know, obviously you do world-class work in all different sectors, but Planet I think was helpful to you as you and your team were looking specifically in Ukraine. Can you tell us a little bit about it? I know we have a few visuals that might help drive the point home.
Yeah, sure. I think we actually were working on Ukraine long before the war, given that it's such an important agricultural region. As soon as the war broke out, we started to work on Ukraine, and we're working very closely in support of Ukraine's Ministry of Agriculture at the moment, given the uncertainty. Ukraine is a very cloudy country. I think Planet has been a partner for Harvest from the very first day, from before we launched Harvest. We recognized the importance of Planet data. Admittedly, I will say that initially I really thought that we'd be using Planet data primarily for smallholder agriculture, for very small fields or for looking at very field-specific activities.
Never really thought we'd work at a national scale in a country like Ukraine with such big fields. Instead, Planet data turned out to be absolutely crucial for what we were doing, especially because we were trying to do this in real time, in a rapid response manner. What you're looking at here on the left is an area of agricultural area in Poltava oblast in Ukraine, an important agricultural region. On the left, basically you see a lot of white squares. What those are, all the acquisitions that we had from, in this case, Sentinel data, but they were all cloudy essentially.
If we look at that same time window with the Planet data, given the daily cadence, if you think about every time you had even a small area that didn't have a cloud, and you start to composite those like a puzzle, at the end of the two weeks, what we ended up with is a cloud-free image. If you go to the next slide, these are what the biweekly composites, what we call our composites, so these kind of coming together of imagery look like for Ukraine. We turned to you, of course, and we said, c an we start to look at these? We started to look at these biweekly composites. That means every two weeks we had this almost clear view of Ukraine. That became really important for us because you need time series, right?
If you think about agriculture in any field in your region or that you drive by or you see, they change very quickly. That means we need to be able to look at these through time. Looking at them through time means that we can start to use this kind of information to map, for example, where are all the croplands, right? There were a lot of big questions in the beginning of the war. Ukraine is a very big wheat exporter, for example. There was a lot of concern about wheat. Wheat was planted before the war. There was a lot of concern about, well, what was gonna happen to the wheat. You know, there were a lot of speculations that a lot of that land was gonna be abandoned.
The next big question was how much area was gonna be planted to their summer and spring crops, right? Like corn, like, sunflowers. Again, we know that Ukraine is a very important global player in those markets. Next slide. What we did is we used these Planet time series data to map agricultural crops across Ukraine at the 3 m resolution. We mapped both the winter cereals, primarily wheat. We mapped rapeseed, that's an important oil seed. We mapped the spring-planted crops, so we could actually map what had been planted and start to answer that question of how much was never planted, how much land was actually left bare. If you go to the next slide. That's looking at what wasn't planted.
Yes, if you see here, you can look at the area that's in red are the occupied territories or the Russian-controlled areas, and this was in the summer. We know that that border has now shifted, and I'll talk a little bit about that. You can see the concentration of non-harvested fields was primarily, in fact, in the occupied territories, but much, much less than what was thought. By and large, agricultural cultivation was continuing in the occupied territories. Next. We could map, for example, here we were looking at the distance between the different fields that were left unplanted and what stood out to us, and so that blue line is the front line.
Anything going to the left towards Russia is going towards Russia, in fact, the distance and then to the right is into free Ukraine. What we saw very quickly was the concentration of those fields that weren't planted, yes, was in the occupied territory, but really primarily focused on the front line and similarly, going into free Ukraine. Next. We saw very much the same when we were looking. We've all heard about the burning fields, and so that image on the left is showing the agricultural fires in croplands. Again, you could almost trace that front line with that. Similarly, one of the big questions was how much would be harvested versus how much is gonna be abandoned and couldn't be harvested because farmers fled, because they didn't have machinery, because their fields were damaged.
Again, what we found was very contrary to what everybody else was seeing and contrary to all the statistics that were being released, which was, by and large, fields were all or largely getting harvested. That was very important because there is no access to any information right now in the occupied territory. This information is very important for the ministry to be able to look at, one, how much production is happening. It's important for agricultural markets. Also understanding what kind of losses Ukraine might have to on the financial side, we've made some estimates in terms of what that looks like just for wheat for this year. I think just one final slide. What we were able to look at is how much of agricultural lands are under Russian control.
We've done that previously in the summer, and then we've been able to also look not only at how much of their croplands, and specifically, for example, wheat or rapeseed or other summer crops are under Russian control, but also been able to look at how that's changed now, after Ukraine has liberated a fairly significant amount of land. We've actually seen that they have liberated overall around 2.5% of their croplands and about 3.6% of winter wheat. But still here in the pie charts, what you're looking at is the blue area is the Ukrainian-controlled. The orange area is the newly occupied, so this year occupied and under Russian control, and then the gray area is the Donbas and Crimea.
Very significant areas of croplands for Ukraine are currently occupied. Planet data was absolutely fundamental to what we have done and to be able to respond very rapidly and provide information really where you cannot be on the ground.
I really appreciate that, Inbal. You know, as you're speaking, the thing that becomes so clear is just the order in which you tell that story, where we go from the lowest level form of information gathering, which is, you know, the differentiation between collecting information every day from space versus every few weeks from space, all the way up through producing the derivative indicators that tell us what it means to being able to then produce the kinds of programmatic and policy outcomes. It's a kind of perfect mirror of Kevin's inverted data pyramid that he was presenting a little bit earlier.
I wanna say, too, that one of the things that's especially meaningful about this work, as with some of the work with Juan and his team in Ukraine, is how we can tie, you know, the ability to monitor the system as fast as it's changing, as it's undergoing these shocks and disruptions, how essential that is to actually reducing human suffering. I mean, all of the other things are important. The pricing on commodities is important. The kind of ability of the system to manage itself is important at an international policy level. It also comes down to whether or not people put food in their bellies. I just wanna thank you on behalf of everyone at Planet.
We're really thrilled with this work and see lots of application to of it well beyond the scope of the conflict. Thank you so much, Inbal. We really appreciate it. Could you go to the next slide, guys? I appreciate that. Listen, as I hope all of you have seen, with just these two quick vignettes that we've seen over these three different projects in areas of humanitarian aid, food security, and global renewable energy, Planet's data and products and services are not just relevant, they are essential to bending the curve on some of the world's most pressing challenges.
The same kinds of considerations that predominate in commercial activity, like how do we get time to value, how do we shorten time to value, are exactly the kind of concerns that we have when we think about creating social and environmental impact too. We're relentlessly focusing right now on how to reduce the time it takes for us to get to those kinds of outcomes, how we can efficiently get to scale, how we can drive the kind of velocity that allows us to move faster than the problems themselves that we have to solve. Our rate limiting factor now is actually not our relevance. We're relevant to all kinds of problems. It's our ability to get our products, our data products and services, into the world's hands, and we're getting better at it every day.
That's why even with the kinds of breakthrough successes that we've been sharing with you this afternoon, I'm absolutely certain that Planet's most significant impacts are still ahead of us. I wanna thank all of you for listening today, and especially thank Juan and Inbal for joining us. Now I'd like to hand things over to someone you all know well, Planet's Chief Financial Officer and Chief Operating Officer, Ashley Johnson. Ashley, over to you.
Great. That was incredible. I don't actually get to spend a lot of time with our customers and partners, so to hear from Nalini, Juan, and Inbal and see just how incredibly impactful our data is, whether it's for commercial use or government use or just pure impact work, obviously Planet is critical on many levels, and I'm just really proud to be part of this company. I'll bring us home with an overview of our financials now, and I wanna start by just reminding everybody what we've been saying since we first started speaking to you, which is that we are fundamentally a data subscription business. We have a powerful platform and ecosystem, and we address a wide array of very large vertical markets.
We went public last year to provide us with the capital we needed to accelerate our growth. We've demonstrated our ability to achieve that goal over the last several quarters. I'll now walk you through some numbers that support our recent growth acceleration and how we plan to sustain it. I'll also spend some time explaining how our data business model, which is absolutely unique to our industry, combined with our agile, vertically integrated business, facilitates a high return on invested capital, continued margin expansion, and cash flow profitability without needing to raise additional capital. Let me walk you through some of our Q2 highlights. Our growth is accelerating, and it's important to call out that this has been achieved through execution on our fundamentals, investment in our sales coverage, customer success, and product.
Our business model resembles data infrastructure or SaaS subscription businesses, which is to say we have strong predictability of revenue and our incremental cost to serve customers is low, resulting in continued gross margin expansion as our top line scales. We have a strong balance sheet, some of which we're investing in short-term, highly liquid securities. We have over $450 million in cash. We're seeking to capitalize on the current interest rate environment, and we have no debt. Our fundamentals point to a long-term operating model and high return on invested capital. We'll talk about how our agile systems model enables us to flex our capital allocation of the investments to align with market demand, enabling us to target very high ROIC.
We've been increasing investments in operating expenses to accelerate our growth, but through efficiencies and focus, we believe we are on track to achieve a highly attractive long-term operating model. Here we go. I'll start by reminding you all we are a January fiscal year end. When we talk about fiscal year 2023, we're talking about this January, this year. The bar in green represents the guidance range we provided for the full year fiscal 2023 revenue on our last earnings call. As you may recall, we went public with a desire to invest in our sales team because we saw strong demand for remote sensing data and high inbound pipeline development.
We referenced three core growth levers requiring investments in areas that Charlie referred to as the non-Rocket clients part of our business that we believed would expand our TAM, increase our share of wallet with customers, and inflect our growth. We invested in customer success to drive faster time to value for our customers, which results in higher customer satisfaction and in some cases faster revenue recognition. We also expanded our sales teams globally, enabling us to focus a number of the team around core vertical markets. We created a global strategic accounts team, and we invested in sales development and marketing activities, which Charlie walked you through earlier. Finally, we invested in core product capabilities to improve image usability and quality, expand our APIs, facilitate ease of use for our tasking systems, and optimize fulfillment.
We pointed to these factors in our Q2 earnings call as having driven upside in our results. The impact is clear as we achieved 59% year-over-year growth in Q2 and provided annual guidance reflecting strong growth at the midpoint. I want to underscore that the acceleration in our growth isn't driven by one-off factors, but rather fundamental changes that we believe should enable us to continue to drive strong growth in our top line going forward. One of the most important growth levers for our business is net dollar retention rate. It's a metric we obsess over. We drill into gross retention, on-time renewals, time to value with our customers.
Net dollar retention rate is a core business metric that has continued to improve over the last several years, and we were excited to achieve a 127% net dollar retention rate, including win-backs at mid-year. As we continue to lean into investments in customer success, we're focused on sustaining and improving on this achievement. A 127% net retention is a great indication of the outcome of the investments we've been making, and even more importantly, it's an indication of the value we're driving to our customers. Just because companies define this metric differently, I wanna touch quickly on a slide that we include in every one of our quarterly earnings decks, which details our own methodology. We start each year at 100%.
We look at the book of business that we have contracted as of year-end, and then we track the retention and expansion of those contracts throughout the year. If we had passed a renewal date without renewing the contract on time, then we write that contract down to zero, and if and when it renews and/or expands at a later date, then that gets captured in the metric net dollar retention rate, including win backs. We're very detailed in how we review this metric. As you may recall, last year we saw a decline in NDRR, specifically due to government contracts and most specifically around a contract continuation that occurred last summer when a government customer ceased to exist.
This year we saw improvement across the board on retention and significant boosts from larger contracts in both commercial and government markets, that brought us to 127%. This is an exciting new bar and one that I hope we continue because it is a critical lever for growth. Another key lever is entering new markets and expanding within current markets with new customers. Our market diversification provides a lot of hunting ground for our sales reps. The use cases we address span both commercial and government markets, and within government, we're focused both on defense and intelligence as well as civil government applications such as emergency response, land and water management, and sustainability use cases. This market diversification is critical to sustaining strong growth rates, even in the face of potentially challenging global economic factors.
We've been investing in AE coverage to build and close pipeline in these markets all over the world, as well as investing in customer success subject matter experts that enable customers in specific verticals and in specific geographies to ramp more quickly. We're also building out the vertical market partner ecosystem to develop solutions specific to vertical markets on top of our data. All of these elements are core to sustaining our growth rate in landing new customers in existing and in new vertical markets. As a side note, I should mention about 40% of our business is international, and about half of that is denominated in non-USD. We've definitely felt some of the currency headwinds, but this is partially offset by a natural hedge that we have because about 10% of our expenses are also denominated in non-USD.
Turning quickly to just revenue visibility. We sell data subscription contracts typically on an annual or multi-year basis. Over 90% of our ACV comprises this type of contract. Within these annual contracts, we have a mix of subscription, usage, and one-time upfront revenue. As you can see, subscription contracts make up the bulk of our business and are recognized ratably over the contract term. In addition to these contracts, we also have usage-based contracts, often with minimum commitments on an annual or quarterly basis, so use it or lose it.
That gives us a strong base of expected revenue that we can count on in our forecasts. The usage-based nature of many of our contracts is why even though our customers sign fixed price contracts and generally pay upfront annually or quarterly, the revenue recognition can still vary quarter to quarter based on customer usage patterns. Finally, upfront one-time revenue primarily relates to selling access to our archive. Under ASC 606, we recognize a portion of our archive revenue up front, and this typically represents about 7% of our total revenue. When you look at our business on a quarterly basis, you can see the acceleration that we noted, as well as some of the variability that we may see quarter to quarter, given the size of some of our contracts that have this usage-based revenue recognition.
For example, some of the usage pattern of larger customers historically resulted in Q2 being a down quarter versus Q1. However, with some of the product improvements we made this year, as well as timing of new business, we actually saw Q2 be much stronger this quarter than what we've seen historically. As we continue to diversify our business with new customers, as well as shift customers more to this ratable revenue recognition model, we expect to see some of this seasonality become less variable. On our last earnings call, we noted that some of the revenue upside in Q2 this year was timing of consumption, and some of that may or may not continue in the back half of the year. Some of this was due to truly new business and true business acceleration.
The degree to which we took up guidance in the back half of the year reflected these two counterbalancing forces. Let's shift now to margins. Our data subscription business means we have a very low cost to serve new customers with an estimated direct margin of 94%-96%. As a quick note, we report non-GAAP gross margins inclusive of depreciation and amortization, as SaaS companies do. We just simply caution those who are new to the space that other space companies report gross margin excluding D&A. If you are comparing numbers, make sure you do so on an apples-to-apples basis. Our cost of goods sold are relatively fixed and include depreciation and amortization, which is based on our core satellite assets, and I'll turn to that in a minute.
It also includes our hosting costs, which grow as our data assets grow, but not nearly at the pace of our revenue growth. With the increase in staffing for software engineering, we've been able to assign teams to evaluate opportunities to make our data hosting and compute systems more efficient. Finally, our customer success teams and professional services teams are also a component of COGS. These teams are relatively small and often based in lower-cost geographies. With some of the product investments we've been making in support of customer experience, we expect to achieve scale in this part of the business as well. All of that is to say, with relatively fixed COGS, as revenue expands, so do our gross margins. We proved this out quite effectively last quarter when our overachievement in revenue essentially dropped to the bottom line.
As we think about our operating model, I obviously think about that inclusive of capital expenditures, and I think it's important to illustrate the flexibility that we are afforded because we are vertically integrated. We believe there are obvious competitive advantages to having the space systems in-house, including the virtuous feedback loop into core data sets and capabilities that both Will and Kevin outlined. There's another important factor, which is our ability to flex capital expenditures to market demand. On our last earnings call, I referenced that we're pulling forward some key purchases in order to mitigate potential supply chain disruption, especially around critical components. Also, because we're a relatively low-volume purchaser, we can achieve some cost savings by making advanced purchases to get better volume pricing. These advanced commitments are relatively small relative to the entire fleet.
We plan around what elements maximize our future flexibility. If we were to see an uptick in demand, where could we potentially be constrained, and where might we again want to make earlier commitments to maximize the flexibility of the pace at which we can move in the future? The Space Systems team tells me it's easier to plan to go fast and then slow down than vice versa. We do some advanced commitments in the spirit of being programmatically ready. The lion's share of the procurement costs for our build-out can be timed to the market need, whether that's driven by the maintenance schedule of the existing fleet, for example, the end of life of SkySat, or whether that's driven by surging demand for our newer capabilities. We always have an eye to this one-year payback.
Because it's our own team that will put in the extra time to go faster, we can do it without incurring extra costs or potential delays that you get from third-party manufacturing. We believe this is a competitive advantage, and I also view the flexibility, the ability to flex our capital expenditures to time with market demand as a significant financial advantage of our business model. This flexibility ultimately rolls into our gross margin structure. As our fleets get to steady state, like our PlanetScope Doves are, D&A is constant, and margins expand with every incremental dollar of revenue. We have visibility to the end of life of our fleets, for example, SkySat, and we build our R&D and manufacturing processes to be ready in advance of that, to be able to continue to support existing customers while adding capabilities to expand market opportunity.
You can see that as Pelican comes on board ahead of when we expect to see SkySat roll off. There is a modest increase in depreciation and amortization expense as we bring the fleet to scale. Again, the timing of this is relatively in our control, and we can align this build-out to the market. We have that same ability to control the pace of build-out for Tanager. Incurring incremental depreciation and amortization expense as we bring on those capabilities, but timing that to when we see these new markets and new customers come into our fold. Importantly, as revenue scales, our gross margins continue to expand, which enables us to achieve our long-term model while also opening up space for us to continue potential new fleets and capabilities that could unlock new markets. Which brings us to our target model.
Our data subscription business model and our agile space missions capabilities enable gross margin expansion as our top line grows. We're also focused on driving efficiency in sales and marketing, as Charlie highlighted, with a focused straight-sales strategy around proven high-value use cases and an expanding partner ecosystem, all of which leads to higher ACV achievement per rep. Our investments in technology always have an eye to customer value, maintaining our competitive lead, and enabling an ecosystem via our platform. On the G&A side, we've been investing in our public company infrastructure over the last 12 months, and we're turning now to those investments that can enable us to achieve operational scale and excellence. We measure ourselves each budgeting cycle around making progress towards cash flow breakeven without raising additional capital and achieving the high-margin, capital-efficient operation that our data subscription business model enables.
With that, I am told that I now get to do something fun and outside of my usual financial interactions with you all and engage in a fireside chat with my esteemed colleague, Robert Cardillo, who is Chairman of our federal board and Chief Strategy Officer of our federal business unit.
Hey there.
Hey, Robert. Thank you for being here.
Absolutely.
You have had quite a distinguished career in government, including having the opportunity to brief our 44th president on an almost daily basis on security matters. Can you walk us through a little bit of your career journey and how that brought you to Planet?
Thank you. I appreciate the distinguished comment. It also means long. I'm actually entering my fortieth year of trying to help people make better decisions on our planet from access to the planet from space. The first 20 years of my career was using 95% of my kind of bandwidth was government only. I say that not to say anything about our current state today, we'll get to that, but it was more about the reality of who owned the assets, how they were developed and built, and how they were controlled. Quite frankly, most were highly classified because the focus at the time was bipolar competition. We needed to see things that the Soviets couldn't see, et c., and so there was a race there as well.
It served that time very well, but at about my 20-year mark, I began to experience the emergence, especially in the U.S., of a commercial imagery industry. I think because of the model I just described, much of that industry that emerged followed the similar model the government had employed. You can imagine larger satellites carrying larger mirrors, getting higher resolution of very important places on the planet. Again, that helped and extended the ability because it was more useful being unclassified, and we'll talk more about use. Then came along about 10 years ago, the gentleman who opened our session today, Will Marshall, and his co-founder, Robbie Schingler, and they had what I consider to be an audacious idea. They said, look, we admire all that. You know, we're from NASA.
We're part of that proud history, but what if we could see everything once a day? What kinds of problems could we address? What kinds of solutions could we create pursuing a different model? Again, I can't compliment them enough for having that idea, taking that risk, taking that leap, quite frankly, from the garage to where we are today. Because now for six years, this company with this constellation has been doing exactly that. Kevin talked about the 2,000 records of every place on our globe in a way that not only creates obviously that archive of history, so that you can go back and do forensic analysis of events, etc .
What excites me more is that those realities of our globe, by the way, these realities are whether we're talking about harvests in Ukraine or, you know, armored, you know, tank production, in an adversarial country. Those models can be pushed into the future and help us anticipate what's next. A year and a half ago, when I was invited to join Planet as the chair of the federal subsidiary and really flesh out the commitment that was there, sustainability, security, and prosperity as part of a public benefit corporation, I couldn't have been happier. I just have to say, after 18 months, and quite frankly, after seeing all the results and hearing from our partners and customers today, my enthusiasm meter just continues to rise.
I think the same is true for us having you here. One of my favorite anecdotes that you tell really hits home for me and especially around our strategy. Around kind of a presentation you gave when you were the head of the NGA about kind of the paradigm shift on how to think about the imagery data that's being collected. Would you mind sharing that anecdote now?
Happy to, but let me give a little more context, just to remind how I got to that moment. Again, 40-some years ago, I was welcomed into the highly classified world. Sent to a basement, no windows, light tables, film, that was the era. I evolved my career over time. I ended up leaving 4 different analytic organizations in the intelligence community. You mentioned one of them, managing the National Intelligence Council, editing and presenting the President's Daily Brief. Enormous opportunity to serve, right? At the highest level. After I left the White House, coming back to my home agency and leading the National Geospatial-Intelligence Agency. To remind, that agency, biased view here, is the premier producer of geospatial intelligence on the planet.
Very purposeful for security, safety of navigation of our country and our allies. It's 10,000 people. They work in 18 time zones. We used to say the sun never sets on our day because it continues to present new challenges. Because I had been away from NGA for 10 years, it also gave me some perspective because I had grown up in that world, very closed, compartmented. I had gone outside and been both a partner and a customer of NGA. At the White House, I was just a demanding customer, and you were as good as the last five minutes of, you know, was the mentality.
When I came back in, I realized, or I felt that while I too shared the pride in our past, which I've described, I was worried that that pride would inhibit our future because we would continue to pursue what we did yesterday. To get to your question, I decided to run an exercise and model the labor requirements if we were to exploit every image that was coming into the building on a given day, the way that Cardillo did it in 1983. By the way, I was a pretty good analyst, but that's beside the point. It takes a certain amount of time for a human, right, to interact with an image, whether it's hard copy or soft copy, and extract information of use. Sometimes it's, you know, as simple as counting objects.
Other times, it's as difficult as interpreting a camouflaged, concealed location or a network of activity that you've got to piece together. We averaged it all out. We took all the imagery, we divided by the time required, and we got to the lovely number that we needed to have 6 million imagery analysts on our staff. Right. Everyone had the same reaction. Well, actually, some of my imagery analysts got very excited. You know, oh, my goodness, we're gonna go on a huge hiring spree now. I said, well, obviously, we're not gonna do that. The point was, it's still our responsibility to extract the information from those datasets. We're not gonna do it the old way, right, team? Okay. We're not gonna hire another 6 million imagery analysts. It's gotta be machine-first mentality.
To be clear, there was cultural resistance, you can imagine, because some people heard machine first, my job last, right? Like you know, machine comes in, I go out. We had conversations that went as follows. I said, look, we really need to let the machine do what the machine's really good at. You saw some of it today. In the NGA world, it's really good at what, where, and when. Detecting objects, classifying them, and then ascribing a timestamp to their location. Which by the way sounds pretty pedestrian straightforward, but is difficult to do and very time-consuming for a human, very straightforward for a machine. Where I need my analysts is on top of that, okay?
Let the machine lay down that context of that reality, if you will, of the tangible, whether they're man-made or natural, and let the humans go after the much more difficult questions of why and what's next. It isn't, you know, man or machine or human or machine. It is machine and human. That became the mantra. NGA continues to be on a journey, by the way, because it's a large government organization, and it takes a while. I will tell you that, you know, from my partnership with NGA then and the partnership with NGA now here at Planet, this company is accelerating the thinking again.
You know, again, the audaciousness of trying to image the whole planet and then the application of that and some of the use cases you saw today literally is now, you know, really being a very healthy two-way street with government.
I think it's great. I love the image of 6 million analysts flooding the gates. Let's shift gears because there's obviously a lot going on in the world and we're seeing a crisis in Europe like we haven't seen in 75 years. How do you see the impact of what's going on with the war in Ukraine and how that's affecting either the government's view of commercial satellite imagery or Planet's role in the world?
Happy to. Just to reiterate what is obvious but should be stated again and again, there's nothing but a tragedy happening in Eastern Europe right now, and there are no winners here. I do believe strongly that because of everything that we've just talked about, okay, the audaciousness of the idea, the implementation, moving up the stack that Kevin described, the expanse of applications that you saw from Andrew and our partners, has now been able to be exposed in a way that it never had been before, thankfully, because we hadn't had an event like this. One way to think about it is everyone will be familiar with the fog of war, right? Which is the reality of the confusion of battle space, of camouflage, of misinformation, of deception, et c.
That is the nature of conflict when societies or nations clash. Planet, commercial imagery companies, oh, by the way, and governments, right, have come together to not lift it, okay, because the fog is still there, but at least to eliminate, reduce it. To me, what it's been able to do is it's been able to expose some of that misinformation, some of that deception in a way that both better alerted ourselves to the reality of the threat, right? So this is Putin's buildup pre-February 2022. Because as you'll recall, he made many statements about what his objectives were, and was this just an exercise or was, you know, was he gonna call up reserves and et c. That unblinking eye was able to at least hold some of that, okay, at risk.
Planet contributed as other commercial imagery companies with a rather famous use case. On the day before the invasion, Putin had made an announcement that he was gonna redeploy. He said, look, the exercise is finished. We're gonna move some forces back. On the same day, that unblinking eye, right, of the global scan, which by the way, isn't pointed, right? It's just always looking, caught a pontoon bridge from Belarus into Ukraine that wasn't there the day before. Well, suffice to say, you don't use pontoon bridges when you're redeploying out, okay? You use them when you're coming in. Again, it wasn't just Planet community had writ large is that he is coming, right? Again, there were other indicators.
To me, again, we, you know, we saw in the use case with the harvest, it also has helped us to actually see what we were feeling. Whether that was a bread crisis in Cairo, okay, or increased starvation rates in Central Africa, what happens when that breadbasket shrinks. The great work that was done with the UN, again, was able to take that sense, right, and add some data to it, add some rigor to it so that it. Again, I'm not saying this got everyone at the same point of view, and everyone came to the same conclusion, but what I love about what we here at Planet contribute is we kinda raise the level of understanding so that that common context, that common frame of reference is just a bit higher.
I'm well aware that there's still gonna be confusing things above that frame, but at least it gets us closer to a place where we can have a reasonable conversation, we can have a reasonable discussion about sanctions, about alliance security, about next steps with respect to holding accountable. Again, you mentioned 75 years. You know, we obviously had no way like this to hold people accountable in that war. Again, Andrew and his team are doing a great job of setting those conditions, working with the institutions that do exactly that. I'll finish with this. Again, because sometimes because we live here at Planet, and we assume everyone knows that, you know, this is the way the world works.
There is no other company that provides the access to this globe that Planet does. That advantage is now six years old, okay, or six years new. By the way, tomorrow it'll be six years and one day old and new. To me, that's what's so different and what's so exciting about not just what we've done, but what we'll do next.
Yeah. Well, let's spend a quick minute. We've talked about what's changing from the past and what's unfolding in the present. What excites you the most about what's coming in the future?
Everything. The reason it's everything is because I think you take audacious idea one, and I didn't rehearse this part well, so go with me. There's audacious idea number two and three coming. You take the, hey, let's do the whole globe." Well, the way I see Tanager now, the spectrum, we were kind of peaking at the spectrum, okay, just the way we were peaking at the globe. Let's do that spectrum. Why not 400 bands? Why not? Well, we'll see soon because it'll be up, and it'll be doing that. Then if you look at Pelican, 30-minute revisit at 30 cm. We're not quite staring, but we are looking quite often at the areas of interest. Now you take that combination of global scan, global awareness with that next level of exposure and understanding.
To me, the upside is so big. I mean, just, you know, I'll finish with this. The first time I met Will was via YouTube, and he had done that Mission One, TED Talk, right?
Yeah.
Dramatically unveiled the Dove on the stage. This was what? Three years, I think, before. With the idea, well, to me, there's more unveiling to do, and the company that we are now on the cusp of now not just covering the globe, covering the spectrum and the clock. The combination of those three things just feels like the future is unlimited.
That's awesome. Thank you for being here. Thank you for being such an important part of Planet.
Pleasure.
Yeah.
Thank you, Ashley.
Thank you.
Thank you, everyone.
All right. Now we'll bring everybody back up on stage and give you guys a chance to ask some questions. I'm sure you don't have any, but just in case. Give me just a second. One moment.
All right. We've got time for some questions. Thanks, everyone, so for holding them till now. If you're online, I think you can raise your hand online, and we're happy to get them. For here, just wait until the mic comes around. Why don't we start with you in the front. Where's the mic?
She's coming. Right there.
Super.
Just in staying with your Bloomberg analogy, one of the great things about Bloomberg is if you ask someone something about a company, in like 5 or 10 seconds, if they're proficient at Bloomberg, they can give you the answer. It takes a minute to learn the language, but once you learn it's really kind of efficient and easy to use. You're at the point where you're sort of fire hosing people data, and you need to get to the point, I think you mentioned, where it becomes much like Bloomberg. What is the timeframe, the investment, the number of hires you need to make? Do you build this all internally? Can you partner with someone in-house? Can you just talk a little bit about that whole process?
Yeah, absolutely. Maybe Kevin can talk a little bit just to tee it up. I would say it's interesting to note that I think the Bloomberg Terminal interface is kind of clunky. It doesn't have to be hard to use. We can take some tricks out of the books of some of the companies that made reading things really user. But it does a fantastic job of getting, as you say, data right now, and if you know the system, you know, you can really specify exactly what you want and get it. Yeah, we're on a journey to get there. It's never gonna be black or white. I mean, I certainly measure it in years, not, you know, decades and/or months. I don't know if you. What do you add to that?
I think one thing that's important to add is this is not something that we have to build every single piece of ourselves, right? Charlie talked a lot about our partnerships. There are so many different use cases. Unlike a company where you wanna know a handful of different things, but it's pretty consistent across companies. Here, you have so many different opportunities and use cases. Our role, what we look to do, is we look across all of our customers, and we look at the kind of things that they're having to build in order to get value out of our data today. Many of those things they wish they didn't have to build, right? Some of them are their special sauce, others are just things that they need to do.
When we see that repeated across customers, we can build those building blocks, and we can help all of our customers and partners get to answers faster. There are going to be thousands of others that don't make sense for us to build that we'll rely on partners for. The net effect of all of that is we're building, we have partners building, and the ecosystem moves faster toward answers.
I just have one thing to that. Already today, our sellers have moved from just going out there and positioning and talking up the data and the things that you can theoretically do to actually leading with this partner can deliver this specific solution for you. The fact that it's powered by data is kind of irrelevant. It's actually solving a specific job for that customer. Our job, well, Kevin's mainly, is to make that easier. How do you build that solution, more solutions like that, the faster we can scale?
Do you have a question?
Yeah. Thank you. Sorry to go into a boring topic, but can you help us understand the data storage cost model? How does that scale? I guess the data is constantly coming, and you're archiving, and I think you're using different cost type of storage for archive and things like that. Could you just lay out the architecture there? How does the cost scale over time? When you went to eight bands, for instance, from four bands, what kind of impact that had? Where I'm trying to go to is you're gonna launch Pelican and Tanager, and they have data that might be a lot more complex or higher in volume. How is that gonna scale, you know, the data cost part of the business? How what's gonna happen to that?
There is. Yeah, indeed. The data rates are going up, and you'll see some of that when you go downstairs. Kevin, tell us how are we gonna handle all of that and what's the scaling? I know it's not nearly as fast as we're gonna scale the revenue, that's for sure. But.
Yeah. We generate a lot of data every day, that's for sure. I would say relative to places that I've worked in the past, it is a drop in the bucket at some level. We put a lot of thought into how we store the data for both to make it easy for analysis, but also so that it's efficient in a storage sense. You think about compression and so on. One major trend over the last however many years has been the cost of storage comes down and down and down and down. It's something we think about. We certainly look to optimize it, but it's not something that I personally worry a lot about in terms of our long-term business model.
Do you have a question about that?
Can you give us a little bit more meat around the bone? I understand.
Okay.
Yeah, just a little bit more meat around the bone, if it's possible. Just, you know, maybe, Ashley, you can help. Just, you know, what kind of numbers are we talking about, you know, in terms of the cost structure? How does Tanager and P elican changed that. I understand that it's small compared to some social media companies, but our revenue levels are small also. We need to know a little bit better picture.
Actually, the overall data rates, I think, of even Pelican and Tanager are gonna be smaller, much smaller than the PlanetScope daily scan, because that just covers such a dramatically large area. I don't think you're gonna see that dramatically changing. Overall, all of the databases are increasing with time. I mean, we just went up from four bands to eight bands, right? That doubles the data rate roughly. We will continue to do that, but it's not like it's suddenly it's gonna jump an order of magnitude because of this. In fact, it's more layering in. The bigger dataset is the scan. But I don't. Did you wanna.
Yeah. I mean, I'd just point you to our disclosures, so you can see some of the breakdown in our Qs and Ks in terms of what we do disclose in terms of our hosting costs and our relationship with GCP in particular. What I will emphasize is that we've been spending a lot of time internally looking at ways to continue to optimize that. Similar to Kevin, it's not something that I'm necessarily losing a lot of sleep over, but we obviously do look to see the teams managing that line item and continually finding ways to make it more cost-effective.
Yeah.
I think, Chris also has one online.
Yeah. Great. We'll go this one first, and then we'll get to you.
Thank you. As you develop your analytics tools, what kind of techniques are you putting in place to address. I'm assuming that bad actors use all kinds of interdiction techniques to camouflage their activities, whether it's military or deforestation or things like that. How are you addressing those issues? If you could even just talk about those issues of dealing with camouflaging activities from those types of elements.
Yeah. Well, obviously, when we're doing accountability, it's always a bit of a cat-and-mouse situation. The machine learning is getting better and better, and it's harder and harder to avoid being seen. To your point, this affects sort of sustainability and security use cases alike. In both cases, it's accountability. I think it's harder and harder to hide from this. Anything to add to that?
Yeah. I just go back to the building blocks approach. If you think about change detection, we're producing building blocks around how roads are evolving and things like that. We make that available via APIs. Partners that are going to go deep into particular defense use cases, for example, can take that at the beginning of the product, not the end of the product, and continue to iterate it on from there. As we launch things like Tanager, our hyperspectral satellite, you start to be able to see new things, right? You can start to see things like this ground has been moved. Even if there's no longer anything there was something that happened here.
Especially as we've become able to fuse these different constellations together, it becomes even more powerful.
To detect camouflage, as you mentioned. Yes, over here.
Thanks so much. Dave Edelman, Endurance. First, Will and team, I just wanna say it's rare that we see a company that's doing good and an exciting growth company. Thanks for all the hard work and putting together today. My question is directed really to Kevin and Charlie. Ashley, I'll lift you off the hook. It's really if I come back to the Bayer example, my broad question is really about pricing. You know, we heard in the Bayer example, you know, there's productivity because you don't have to put as many people out in the field. Maybe it helps on the R&D process, so there's new innovation that Bayer can do. My question to Bayer is, who's buying the service? 'Cause I don't.
Is there a chief data officer that goes across all Bayer, that buys this? 'Cause we're used to chief technology officers and all the other. Then for Planet, and especially, picking up on Elias' question with Tanager and Pelican coming, how do you price the value? I mean, how do you price this data service? I mean, all of us in this room use Bloomberg. We've got a price increase. There's an integrated, you know, the terminal. It's. Yes, a lot can be done on usability. We all know that. How do you price this stuff? And obviously, there are models out there, whether it's FactSet or D&B, but you're doing brave new stuff, as Robert has showed us. So I'd really. You're not gonna announce price increases, I understand that.
The concept of how, as you add value, imagery, frequency, how you capture that on a pricing decision, and I really wanna know who's paying for this at Bayer. Is it an operating expense? Does it span, you know, Monsanto to everything else you guys do? We have some understanding of how all this is purchased.
Yeah. Thanks. Let me just tee it up so by saying that, you know, we're obviously market making here, and so there's a fair bit of flexibility that we have to change the price. Certainly, the price per unit image sold is much, much greater than our cost to acquire that image. So we could come down an order of magnitude because there's elasticity of demand, and we expect the number of users to go up orders of magnitude, say, or, and vice versa. We're thinking very carefully and systematically about it. Yeah, do you.
Yeah. No, I think it's two aspects to your question. I heard is kind of like who is it that actually is making decisions and purchasing and running. Well, people like Ashley write the checks, but it's so who's making decisions and paying for it. That's number one. The second one is about like value and quantification of value. This is a tricky one I come to. So
I won't talk specifically for Bayer, but it tends to be different groups. If there are data science organizations or heads of agronomy, or it depends whether it's an internal customer or external customers. A company like Bayer can be many. You know, they have internal use cases. They also support the customers externally. You know, it depends what it is that they want to do with the data. There's many, many different ways that they could look at it. It varies is the short answer. People on the business side, if they're actually...
It's part of a cost of goods sold in terms of a product that they're actually selling to their customers that delivers a solution. It is very common in the agriculture space versus internal R&D, where it could be someone that's actually running R&D, or Nalini actually runs data science, data science and not just for agriculture, actually across Bayer. It really can vary, which is an opportunity, but also challenging when working with these very, very large customers, which is why we're adding dedicated people so we can serve them at the scale they need to be served.
Your second question was about the value, and this is a tricky one, and that's why investing in things like the startup program, the partnership with ESA, but also we're actually doing more and more contracts with our customers with kind of R&D type packages where we control our costs. We're removing that kind of cost hurdle because these businesses are building applications on top of our data, and we wanna work with them to help them to understand what the value can be. With that, we wanna move away from the paradigms of square kilometer pricing and move towards revenue share models or part of, like, an OEM type model where we're just a cost item.
What we're trying to do is make sure that we're proportional to the value that our customers can deliver to their customers. We're in a bit of a sensing mode. Honestly, that's challenging because there's so much opportunity, and that's why my role coming in is like making our team focused, is what do you do when you can do everything? You end up doing nothing. It's just making sure that we're laser-focused on where we know we can be successful, and then we have smart people in the business and smart customers helping us develop new markets. That's very much gonna be the case for applications like Tanager and also Pelican.
I've had some very interesting conversations about Pelican with large agriculture customers because they wanna move towards kind of more proactive type services rather than just giving an image of the field. It's like, I can tell you what's happening in your field. You need to look here. These customers are gonna be helping us in terms of developing the quantification for these use cases moving forward.
Okay. Yeah. Here at the front.
Let's go to online.
Yeah.
Yeah. This is just a quick follow-up to some of that commentary. My question is really gonna be about channel conflict, right? You're partnering with people.
Yep.
You bring the data, and oftentimes they can bring scale on converting your data into insights that your customers can use. Where do you find the most value in that chain to be, today? Are you getting most of the value? Are your partners capturing most of the value with the end customer? How are you gonna de-conflict yourself in the future, where you find areas that look like are going to add a lot of value on that analytics side, you wanna build it out yourself? You've got channel partners that want your data to do the same.
It's a great question. I mean, we don't really have this kind of concept of direct or indirect teams here because everything we do is through partners. Even often our customers will, you know, think of themselves as partners because our data is powering something they're doing in their business for their customers. That actually de-conflicts it quite a lot already. We just have a program with, you know, we're very consistent in the way that we, you know, share opportunity with our partners. Obviously, we're focused on the data aspect of it, but there's a lot more that they're offering on top. I think that's why we're a very exciting proposition, because, you know, we're a part of a solution that they're building, and they're capturing a lot more value than we will, and that's okay.
That's a good thing because they're motivated to work with us because they see that opportunity. I think that helps with the conflict. The second part of your question was about?
Where's the most value in the chain?
Where's the most.
The value in that value chain, who's capturing the most of the value?
Could you repeat the question?
Yeah, sorry. The question was about like, where's the most value in the chain? Actually reminds you of the other part of your question, which was thinking about the conflict between us wanting to go after vertical applications, you know, with our platform, which maybe Kevin can add to. Our partners will be creating more value because they are delivering the end use cases to the customer, and that's ultimately what the customers want. If you speak to these customers, do they want the data? No, the data is a means to getting to the solution, and we're perfectly okay with just supporting that. In the future, Kevin talked about we're creating building blocks.
You know, the example that we showed with SCCON is that using a horizontal analytic to identify change. Actually, they are bringing the information from the customer about, you know, permitting and, you know, what's in the register within the customer. There's so much additional value on top that they're building. And that, you know, I don't think is gonna change in the future dramatically, even as we move up the stack. There's gonna be so much room on top of us in these different markets that we serve, that there's plenty of space for our partners. And in fact, actually, it will give them more time to focus there because they'll be doing less of the repetitive tasks that they have to do today to work with our data. Kevin, do you wanna add anything?
Yeah. I guess I'd just say, there's a famous Bill Gates quote about how you're not a platform unless the aggregate value of all the partners that are building on top of you is significantly bigger than the actual value of the platform itself. I think that's a model that we aspire to. That's a good thing, right? It means you're creating a really valuable ecosystem with good incentives for everyone to come in and drive value here. You might be surprised when you think about channel conflict. Today, if someone's gonna come in and build on our data, there are a bunch of things that they need to do on the path to building the end.
The end facing use case they're looking to build. When we go to them and say, hey, we see that you have this challenge, and we see nine other partners also have the challenge. What if we just did it? That's not threatening to them. They come back and say, oh my God, could you please? We've got, you know, a small team. We've got person half time building this. If you could build that for us, that would be fantastic because we could focus on what really differentiates us. In practice, I don't see a lot of that channel conflict. It's just we're helping accelerate time to value for everybody, and everybody benefits.
Well, perhaps now we'll switch to some questions online. Just add something that we've said a couple of times before, that you know, data is the new oil, The Economist quipped, right? Hopefully, it's a less dirty revolution for the planet. I mean, it is true in the sense of data is powering all these different industries, and we're refining it to get it better to enable people. It's really broad like that, and that's great that they're all adopting it and getting more value out of it. Chris, let's go to some online questions. What have we got?
Great. We have a question from Jeff Van Rhee of Craig-Hallum. Jeff, please unmute your mic and ask your question.
Great. Can you hear me, guys?
Yeah.
Okay, great. Thanks for taking the question. Thanks for today. Super productive, super helpful. Ashley, on the fireside, you killed it. Two questions, and you got the entire download. I'm gonna have to take notes on that. So I'm gonna start with you, Ashley, maybe two questions. I think on the target model, in the September 2021 target model, the midpoint of the gross margin target was 83%. It's now 75%. Talk about maybe what's driving that revision. Secondly, if you think about the business in the next three to five years, how do you see the evolution of the task versus systematic capture in terms of being revenue drivers?
Okay. I'm not 100% sure. I'd have to look at the slide that you're referencing, but I think what we showed before was an EBITDA model, so it would exclude depreciation and amortization. What I showed today was inclusive of D&A and was an operating margin view of the world. You can take a look at it and tell me if I'm wrong there, but I'm 99% certain that that's the delta. Why don't you take the question on where we see future revenue?
Yeah. I think the important thing on when you think about. Right now, you look at the world as we have PlanetScope and we have task imagery. Increasingly, you saw the slide about how our customers are using all of these products, and the demo that I showed today was how they come together. Increasingly, I don't think it's about is it this or is it this? It's how you use the different capabilities together to solve problems.
Yeah. Could I sneak one other question in there? Just, Charlie had.
Sure. All right. We'll let you get away with it this time.
All right. Thank you. Thank you. Charlie, just one for you. I want to go back to the ag example. You talked about your methodology is focusing on targeted, repeatable processes. If I look at ag, it just put a real fine point on it. How are you meeting the farmer? Because, you know, exactly how are they purchasing, right? Because you're displacing people that want to sell fertilizer and irrigation equipment, and so there's a lot of different motivations that people they typically deal with. So how do you actually get the product in the farmer's hand? Have you figured that out yet?
I don't think we're displacing those people. We're working through them. They actually want to provide farmers insights in how to irrigate or to fertilize. You know, they're looking to use our data because we offer that cadence and resolution that allows them to be much more targeted. Actually, if you look at our partners, the most of them are, you know, they're ag tech companies that are working, and they might have inputs or they might be independent ag techs, but they're looking to support the farmer making better decisions. I don't see that displacement actually. We reach the farmer through our partners and our customers.
Yeah.
It's not something we directly.
We don't directly talk to farmers. Exactly. Chris, other questions online? Okay. You had a question in the front there.
Yeah, Chris Quilty. Will, for you.
Yeah.
For what is a Tanager? Google search looks like a bird.
It is a bird. Yeah.
When you think through the phenomenologies that, you know, what follows optical, you know, whether it's SAR or thermal or, you know, hyperspectral or AIS, ADS-B, why hyperspectral? Which has historically been purely a government military technology. It's mostly aircraft. There's not a lot of use cases. So walk us around that. For Ashley, kind of the same question, which is, it's a great slide on Pelican, you know, 30 satellites, 30 x revisit, 30 cm. But when you look at the capital efficiency of that, where you've got 50% more satellites, and I'm guessing probably more expensive satellites, do you think the hyperspectral constellation generates the same types of capital returns as the core optical business? Then a final question. I think it was Kevin.
You were doing a customer use case example where you were talking about there was a civil government customer using ag and all the different applications they're adding on. Your wallet keeps growing with them. The question is, where does that incremental wallet come from? In other words, are you taking money from somebody else who they were paying for that service? Or are you finding that customers are actually saying, well, we'll find more internal dollars and create new dollars for something you haven't been doing?
Yeah. Well, on Tanager, yeah, it is named after a bird. We named all of our satellites after birds, the Doves and the Pelicans because they're low-flying. The Tanager is a very colorful bird in South America that is also endangered. It's very colorful like hyperspectral, and it's endangered. That also speaks to the biodiversity mission. Yeah, we always have fun with that. The markets there, look.
The use cases are just dramatic for that. I mean, 400 spectral bands. I mean, we sort of targeted a bit like Doves, we targeted it with a sort of canonical, can we see a tree so that we can stop deforestation? Then we went for this and other applications. The canonical use case here is methane and detecting methane leaks across the planet and at a small enough scale that we can get 90% of those emitters. The world just needs this. When we talk, I mentioned our trip to D.C., I mean, on the civil government side, the needs for that are just dramatic and accelerating, right? Because we need to track all of those emission emitters, and we don't know where it's coming from exactly. But there's all these other applications of all.
That uses, like three of the 400 spectral bands. There's all these other spectral bands that have all these other applications, and there's a reason the military have been doing this for a while. They can tell different materials, and they can tell different bio and chemical signatures, and that's kind of important. You think that's only useful for the military? Absolutely not. That's useful for huge number of other industries, from energy to transportation to agriculture to other and biodiversity tracking to name. You know, we're really excited about tapping them all. Ashley?
Coming back to, you know, what I talked about, just the flexibility around the pace of build-out. I get a lot of insight from Charlie and, you know, frankly, from the entire team as to what the thesis is behind a new fleet. If we're adding new capabilities. Pelican is actually less expensive than SkySat because we're building it ourselves, building it in-house and designed it to be a more efficient satellite. That gives us a little bit more headroom in terms of, you know, potentially sizing the fleet larger. I'm still looking at that ability to capture return on investment for that incremental spend within a year.
You know, gross margin, excluding D&A should pay back the cost of the satellite within a year, and that's how we modeled it out. We look at something like Tanager or like Pelican and say, "What do we think the market opportunities are?" We can watch that pace of development and size the fleet accordingly. That's where I said the harder part would be actually going faster. If Charlie's coming to us and saying, "There is so much demand for the capabilities that we're talking about bringing online with Pelican, we want to get to that size of fleet faster to bring all of those capabilities in, we can't just go down to the space systems team and say, "Okay, no, now that the orders are flowing in, move faster, unless we've planned for that scenario.
It's much easier to say, Okay, we're gonna scale this, scale this back, slow this down, because, you know, the pace of growth isn't as accelerated as we thought the opportunity might be. That flexibility is absolutely key. You know, being a vertically integrated business is not easy, but also offers a lot of advantages, and I think that's absolutely one of them.
If there's anyone that can react fast, it's us, as you'll see, for those of you here today, how quickly we can go down there. Did you wanna.
Yeah, just I guess real quick on the customer issue. If you think about ag as an example, we used to live in a world where in order to understand what was happening in a particular field, you had to go walk the field, right? Now we image every field on the entire planet every single day, and we can automatically say, hey, it looks like this vegetative index here has dropped. You might have a sprinkler out. We can do that automatically. You think about civil gov. In a lot of cases, it's replacing manual use cases where there's a lot of money and inefficiency, you know, being where our solution can replace what's already happening, but do it in a much more efficient way.
There's just increasing awareness that satellites can solve problems that people never realized they could before. You're seeing budgets and markets grow on top of that.
Yeah. If I could just add, like, I mean, if you're in a big domain like agriculture, which is a multi-trillion-dollar a year industry, and you're doing a 20% or 40% improvement in efficiency, there's plenty of room for paying for Planet's data and services. Okay. Yeah, another question at the back.
Yeah.
Perfect.
Hey, Colin Canfield, Barclays. I have a multi-part question. If you can just talk about the pricing trends that you're seeing across market, sensor and product type. Discuss all the pricing increases that you're assuming in your long-term model, and then maybe refresh us on when the free cash flow positive timing should be.
Yeah, sure. The easy one at the end. Ashley, to you.
Let's see. We don't really when we think about the long-term model and pricing, we're not baking in an assumption around price increases, but rather we're looking at the size of market opportunity and our ability to capture it. Do we have the pipeline? Do we have the teams, and do we ultimately have the solutions that will fulfill the market need? I haven't, you know, put an overlay on top of that and said, since we are the only one that has this data set, we're gonna, you know, hike up prices once we become fully ingrained. I'm not just saying that for Noel's benefit. That's point number one.
In terms of timing of cash flow break even, you know, the guidance we gave on the last earnings call is the only guidance I'll be giving today. You know, I do reiterate every call that we've got a very sharp eye to getting to that cash flow break even, and making sure that we do so with the capital that we have on the balance sheet.
Yeah. At the back. Going further back.
Hey, all. Rob Heilaue r, thanks for having us. I think Charlie mentioned earlier some of the use cases around Pelican and the 30 by 30. Can you just talk a little bit about some of the technical developments that have gone into getting to 30 cm, whether it's hardware or software and a nything else that needs to be done to get there? Thank you.
Well, perhaps I can speak to that a little bit. I mean, it's really cool, and it's really hard to do that sort of capability. As Pelican, the name indicates, we're flying lower. We have ion engines at the back to get that, and we've increased the telescope size. You'll get to see some of that downstairs. I encourage you to ask some of those questions when you go around the lab. There's also exciting improvements in the radio speed to get all the data down. Of course, we've got the radios to do the uplink that we're working on per the NASA project that I mentioned earlier. It's across the board. There's 12 subsystems of a satellite, roughly, and most all of them have to improve.
Like, even the reaction wheels to spin the satellite faster to enable more capacity, you know, every single system like that has been improved. Next question to your.
We'll make this the last question since we're at the.
Oh, it's true. Yeah, we're at time.
That's okay. We'll take the last question.
Good. Right. You're lucky. Just under the wire.
Yeah, no, it's definitely not worth it.
Well, you better make it worth it.
Yeah. I guess the question I had, David Sacks, how important is it for you to be at the leading edge of imaging satellite technology? I mean, do the customers say, look, we're picking the one with the highest revisit rate, we're picking the one with the best resolution, and every two or three years, we're sort of re-doing that competition? Or is it, once you've built this analytical application and you're embedded like the Bloomberg we can't get rid of, the resolution and the revisit rate are important to some people, but it's not the key determinant of what they purchase?
Well, I mean, I do think that we have this, you know, significant advantage on this daily scan, which is what's opening up these new markets like agriculture, forestry, and other things that just needed the wide scan, right, to even get to them. Now, it's fully our intent. That's in a way our first moat, you know. It's fully our intent to get to this second moat around this platform stickiness. You know, it's our prerogative to go after it. I mean, I very much think that no one has, you know, done a great web geo platform, and it's our prerogative with that data advantage to go after it. We haven't done it yet, and that's what we're building. You know, I think in time, we never wanna rest on our laurels about having better data.
We want to continuously invest in getting better and better data and then continually invest in the platform, as well. Do you wanna add anything to that?
Yeah, just add one thing. As Will said, it is our first moat, and it will continue to be a moat of ours. I think we're better at agile aerospace and manufacturing large constellations of satellites quickly and cheaply than anybody else. We're never going to build every satellite. There's a proliferation of satellites happening, different modalities, different data sources and types. As we talked about, we're increasingly fusing the different kinds of data together to produce a better end product. Taking our optical data, fusing it with microwave data, fusing it with SAR, and at the end of the day, you can build a better product using all of the data sources.
We look to say, where can we find unique advantage by building specific kinds of constellations that we think nobody else can build? Then everywhere else, let's look to bring in all the data to make sure that we can build the best product possible.
Well, great. That wraps our Q&A. Let me just end by saying what I said at the very beginning, which is I think, we're executing really well, and the numbers show it, so very proud about that. But we've got these tailwinds and a huge opportunity ahead. Thanks everyone here for your interest and for analyzing or investing in Planet as whichever is appropriate. Those of you online, thank you for sticking with us all these hours. I hope you've learned a lot. For those of you in person, you get to do this tour. I think we're gonna split into two groups.
I think that's gonna be really fun because, you know, really the foundation of what differentiates Planet is downstairs, three floors, and you'll get to go and see that. It's the foundation of what enables us to really open up the markets that we're doing. In fact, I think it's hard to understand Planet fully without seeing downstairs, in fact. You guys are in for a treat. Thanks a lot.