Ask a question button. I would like to remind all participants that this call is being recorded. I will now hand over to Joanna Kennedy, Head of Investor Relations.
Good afternoon, and welcome to the first in our series of Bite Size events at CCH. I'm Joanna Kennedy, I'm head of Investor Relations here, and in a moment, I will introduce you to our speakers. First, though, to explain a little bit more about Bite Size. This new series of Bite Size webinars are going to be deep dives into areas of the business that are important drivers of our strategy and investment case, and the ones that you've requested to hear more about. We will be introducing you to our subject matter experts and opening up areas that we believe are key differentiators for the business. Today's event is the first in that series, and it's on one of our key capabilities: Data, Insights, and Analytics, D-I-A or DIA.
Many of you participated in our DIA breakout session at our Investor Day last year, or perhaps you watched the video on our website. We received really positive feedback and also a clear request for more information to build a greater understanding of the capability, how it benefits and differentiates Hellenic, and what we think it is enabling us to achieve, and that's what we're going to do today. The format today is going to be a presentation from our team, followed by Q&A. We'll be able to take questions live on this call, or if you've joined in listen-only mode, you can type questions, which I will then put to the team. So now, let me introduce you to the team today. I'm really pleased that we're joined by our COO, Naia Kalogeraki, who will share some thoughts on the strategic relevance of DIA.
Naya, Naya will then hand over to Ruchika Sachdeva, our Head of DIA, who will take us through a presentation, and after that, we'll all join back for Q&A with the combined team. As you know, we are in closed period ahead of our Q3 results, so please bear that in mind when you're thinking of your questions, and I must also remind you that this presentation and meeting may contain various forward-looking statements, which should be considered along with our cautionary statements at the end of this release. With that, I'll pass the call over to Naya.
Thanks, Jo, and hello, everyone. I'm really pleased to be back with you again, and also excited for us to share the considerable progress we've made on DIA. Let me explain a little bit about why this is one of the areas that our board and leadership are particularly excited about. The essence of DIA is the importance of understanding our customer and consumer, so we can serve them better. Many of you will have seen this slide before. It gives an overview of our bespoke capabilities, which underpin our growth ambitions. Our bespoke capabilities are critical for us to better understand the real and changing needs of our customers and consumers, drive profitable revenue growth, and anticipate or react to new challenges faster and smarter than our competition. Put simply, by accelerating our core capabilities, we drive our growth algorithm.
DIA works across every part of our business as one of these bespoke capabilities, acting as a connector and an accelerator of our other key capabilities. Through DIA, we are driving a better understanding of our markets, our customers, and end consumers at a wider scale and at faster speed than we could achieve before. All of this is allowing us to better personalize our execution for every single outlet, to be more agile as we go into the market, and to uncover opportunities that might not have been visible before. Ultimately, this all drives our growth algorithm, ensuring that we remain highly adaptive to the environment and stay ahead of the competition. It is one of the reasons CCH has delivered resilient results, even in challenging markets.
What's really exciting is that each layer of additional data allows us to go to the next level of understanding and personalization, which in turn drives better relationships with our customers and revenue growth opportunities. We have been very busy since the Investor Day and have made great progress, but this is just the beginning. There are some things that we're working on, which we will be able to share at a later date. But for now, I will hand over to Ruchika, our head of Data, Insights, and Analytics, to open up this topic in more detail, and I'm looking forward to join you after the presentation to answer any questions.
Good afternoon, everyone, and hello also from my side. I'm excited to be here today with you and share our progress on DIA. Since I last spoke to many of you at our Investors Day in May 2023, we have continued to collect more data and further evolved our analytics and AI capabilities to continue finding growth opportunities. Let's start with a quick recap on what DIA is. The capability that translates information into actionable business insights, leveraging statistics, machine learning, and artificial intelligence is called Data, Insights, and Analytics. This capability is relevant to all parts of our business, but the way data enhances our understanding of each customer is particularly important for Hellenic. Given we serve over two million customers in 29 markets, in a wide range of channels, and offer the breadth of 24/7 multi-category portfolio. That's why we are investing in this capability.
While there are many opportunities for leveraging DIA across the business value chain, the focus of today's discussion is on its application within the commercial part of our business. Data intelligence, enabled by AI algorithms, powers our other bespoke capabilities: revenue growth management and route to market. RGM defines what we offer to our customers, and RTM defines how we serve them. Data and algorithms allow us to proactively understand the customers and their shoppers' needs, so we can pivot more precisely and offer personalized services through segmented execution. This allows us to treat every customer as an individual segment. Let me tell you more about it. What do we mean by segmented execution? Segmented execution is all about segmenting customers, as one size does not fit all.
Our customers have different needs, so to be truly relevant, we first must understand our customers and have a holistic view from an internal and an external perspective. Data enables this with a 360-degree view of the customer, and AI algorithms help generate outlet-specific insights for personalization and customer-centric actions. We start by segmenting our customers and predicting their revenue potential. Then, we identify the right assortment to offer from our 24/7 portfolio, proactively offering the right portfolio to the right customer in the right pack size for the right location. We personalize in-store execution with customized displays and relevant in-store marketing, and we make sure that every customer interaction, irrespective of the channel, is relevant and personalized.
We also guide profitable promotions and investment with our customers, deciding where to place a cooler or a coffee machine, or how best to promote our products. Today, data and AI-enabled insights power our two million active customers across 29 markets, personalizing our 24/7 assortment and execution approach. This, supported by our partner network, The Coca-Cola Company, Monster, Brown-Forman, Costa Coffee, Caffè Vergnano, gives us a distinct competitive advantage. In the last three years, we have invested significantly in purchasing a lot of external data, and combined this with our internal data to develop this 360-degree view of every customer we serve. We have wealth of internal data. We know the sales of our customer, their location, address, number of coolers in the outlet, number of times the coolers are opened, amongst several others.
We complemented this data with location intelligence of our customers, about who is around these outlets, the profile of shoppers, information about the neighborhood, purchasing power of people living nearby, traffic around the outlet, and many more. We went a step further and got even more specifics within the outlet, specifically in HoReCa channel, collecting information on traffic within the outlet, peak hours, popularity of the outlet, reviews of the customers, consumers, offering and occasions served. The power of combining all this internal and external information makes that holistic understanding of our customers possible and allows us to use data as a competitive advantage. The picture that you see here will continue to be enhanced as we are continuously scouting for more data that enriches this understanding of our customers, helping us generate insights that allow us to be more precise, targeted in our personalization approach.
So how do we leverage these insights and make them usable for our markets and teams? We support our marketing, sales, and business developers in the market with these data-driven insights to personalize actions for each and every outlet. And let's bring this to life today for you through some of the cases and show how this is enabling three important objectives for the business. Firstly, meeting consumer demand for both affordability and premiumization. Secondly, driving growth in HoReCa channel by leveraging our full 24/7 portfolio. And finally, seizing our vast growth opportunities in Africa while navigating the complexity and scale of these markets. Let's talk about the first one. We have a varied portfolio to tackle consumer demand for both premiumization and affordability. Affordability is always relevant, particularly so in these times and last few years of significant price inflation.
Leveraging our RGM capabilities, we are continuously evaluating new packs that can add value, ensuring we focus on areas with real opportunities. With data-driven segmented execution, we enable better targeting of consumers and customers that would benefit from affordable packs. Premiumization is equally important for certain consumers and customers. Here again, a data-driven, segmented execution approach allows us to target premium products to the most relevant customers. We also use promotions across the portfolio as a way of managing affordability, encouraging newer customers to join the category. With DIA algorithms, giving us now the ability to measure promotion effectiveness, we invest where it pays off most, improving returns for both us and the customer. Let's see how we do this. The first step to enabling segmented execution is all about segmenting customers.
AI algorithms analyze all the external data that we have for every single outlet and categorize outlets into distinct clusters, which we call microsegments. Here, you see an example for Bulgaria, where we serve 54,000 outlets. Each of these outlets differ in terms of location, traffic, points of interest, purchasing power, and so many more other dimensions. The algorithms work with this data to better understand specificities of each outlet and classifies each outlet into one of these six microsegments, allowing us to understand distinct characteristics and making this extremely relevant for our marketing and sales teams, that can then drive targeted portfolio and execution priorities. Microsegments give a face to our outlets, allowing us to drive targeted actions with a segmented approach at scale.
For example, knowing the outlets that are highly urban, with high traffic, high purchasing power, these characteristics allow our teams to activate Schweppes better and drive other premiumization activities. And we are glad to share that this capability of microsegmentation is live in every one of our market, and each market has a custom-built segmentation based on market specificities and, of course, the availability of data they have. So let's move forward and show you how these microsegments are used and how they connect to affordable and premiumization proposition. Here, you can see how we use DIA for the launch of 300 ml PET in Bulgaria. You've heard us talk about this in the past, about the launch of this pack in Bulgaria, and for a good reason.
This was a new pack specifically designed to hit an affordable price point, to target out-of-pocket spend for young consumers in Bulgaria. With our segmented execution outlet intelligence, we were able to select the best seventeen hundred outlets that would benefit most from this pack. This is how we did it. Firstly, microsegments helped us identify rural and town outlets with low to medium purchasing power, low food and beverage spend, and also low urbanicity, all of them making them pretty relevant from an affordability standpoint. Then, we selected styles of consumers, in this case, young and middle class. We then selected purchasing power per capita, in this case, low and average level. We further selected outlets having minimum two cooler doors to ensure there is enough chilled space for this innovation.
Finally, we selected priority channels as local and traditional to focus on smaller basket sizes, four to five items, and on-the-go drinking occasion. So from a market of 54,000 outlets, DIA enabled us to select 1,700 most relevant outlets for the launch of a specific affordable pack. This is a good example of how our bespoke capabilities work together in executing a new launch. RGM guides the pack and price choices. DIA segmented execution intelligence identifies the target outlets based on specific criteria, and route to market allows us to execute the plan in the targeted outlets. Combining all these bespoke capabilities, we executed a customer-centric plan, and the results were very encouraging. Nearly 100% of the targeted 1,700 outlets agreed to the distribution, versus a historic market average of 50%, demonstrating the relevance of our segmented approach.
Cannibalization of other packs was significantly lower than what would usually be the case, ensuring the sales from the new pack were truly incremental to the business. And perhaps most exciting was that we saw a 35% increase in completely new shoppers in this category. This is really important, because the purpose of this pack was to recruit new shoppers, and that's exactly what we did. So let's move forward and now take another example, this time from Poland, and focus on premiumization. How we drive premiumization with Kinley. As part of Kinley relaunch, we wanted to increase the distribution of single-serve packs, including new flavors. DIA allowed us to quickly identify the outlets suited not only for Kinley relaunch, but also for potential of single-serve packs. And this is how we enabled it.
Firstly, we selected highly urban microsegmented outlets, meaning outlets with high traffic, high population density, lots of points of interest. Then we looked at outlets already having high sales of Kinley. We call them gold. Third, we used AI algorithms that predicted revenue potential for this category, and we selected outlets with high probability of Kinley sales. Finally, we selected outlets having enough space to accommodate a large range of this innovation. So we went for large format modern trade outlets. Through this approach, we identified targeted 930 outlets from 181,000 total outlets, based on the segmented execution intelligence, and the results were encouraging here as well. For the 500 ml pack, we achieved 80% distribution in selected outlets, versus a country average of 45%.
For the 250 ml cans, we saw 55% selected outlet listing of this pack, versus country average of 30%. And finally, 70% of the selected outlets listed new Kinley flavor, versus thirty-seven percent of the market average. With both the Bulgarian and Polish cases, we emphasize the relevance of our segmented execution approach, enabled by data and AI-enabled outlet intelligence. And now, let's continue our journey forward and talk about promotions. Promotions are a key tool in our RGM framework, allowing us to drive joint value with customers while meeting demand for affordability from consumers. We now use advanced analytics algorithms to provide our modern trade customers with promotion effectiveness insights by brand, pack, promotion type, and depth of discount.
We can decompose the sales and quantify the real contribution from promotions, netting out the impact of weather, seasonality, holidays, cannibalization that happens because of our own promotions or competition promotions. We calculate ROI per customer, brand, pack, type of promotion, and depth of discount. The example you see here is for a particular customer, where we decompose weekly sales for last three years. We are able to see contribution from promotion peaks highlighted in yellow. We understand the impact of seasonality, weather, and the loss or the cannibalization that happens when our packs steal volume from each other, or we have a competition promotion that is happening. This is allowing us to have insights for joint value margin improvement, emphasizing when to promote, how often to promote, how much to promote, and how to promote.
Let's take an example from Italy, where we have rolled out this promo optimization tool. We involved our customer management team to use insights from this tool to amend promo plans of a large modern trade customer. As a result of these insights, we took following actions. We increased promotion slots on the 300 ml can over 600 ml PP, as it showed a 10% higher ROI and 24% higher volume uplift than the other packs. We increased promotion slots for purchasing multiple packs of Powerade, as ROI and volume seemed stable, even with deeper promotions. And we launched new promotions for Monster, identifying the optimal discount range that we could play, in this case, between 20%- 30%. These actions helped us drive joint value creation with customers, driving profitable growth while navigating affordability challenge for our consumers.
This capability that you saw for promo is already live in eleven markets of CCH, primarily for sparkling category, and we look to expand this to our coffee and premium spirits business. We have been progressing, adding more customers to this capability. As of now, 75% of our promotion spend with modern trade retailers is already analyzed by algorithms, which is an increase from the 50% that we shared with you on the investor day sixteen months ago, so we continue making progress, a lot more to come. Now, moving on to the second example for today, driving growth in HoReCa channel by leveraging our twenty-four by seven portfolio. Let's show you how we are enhancing opportunities in this important channel by leveraging the power of data and insights. We see two main areas to enhance revenue growth in HoReCa. First, by recruiting more outlets.
We call them leads. We want to recruit the right leads, prioritizing our effort on the outlets that have highest potential for the categories we offer. Secondly, we want to maximize potential within the existing customers for additional product and categories they could buy. We call these opportunities. Let's see how DIA supports these strategic priorities within the HoReCa channel. We show you an example here from Greece, one of the biggest markets for us in HoReCa. To enable the priorities I talked about before, there are two things that are needed: visibility of total universe of HoReCa outlets in the market today, and prioritization of this universe so that we focus on right leads and opportunities. In the past, universe information was collected manually and maintained in Excel databases, making it infrequent and static, often an issue for HoReCa, where the turnover outlets can be quite rapid.
We had hunters manually scanning the markets every now and then, but making this is a very extremely costly exercise. Today, we have AI algorithms scanning external sources every week and giving us this information in an automated way at the click of a button. As you see with the map on the left, our route to market team gets full visibility of the HoReCa universe of Greece, and they can understand which outlets we serve and those that we do not serve, the potential leads. Next, we prioritize the right leads to focus for each category and the right customers within our existing customer base to pursue for additional categories. This prioritization is enabled by two dimensions: one, category-specific segmentation on each outlet, and algorithm-enabled estimation of potential revenue we can make on every outlet for every category.
For example, a platinum outlet for sparkling may not have the same value for coffee, which is why it's critical to assess each category individually. In this case, let's take the example of spirits. We have set characteristics from the data that the algorithms evaluate when deciding that it's a gold outlet for spirits. We look at the type of outlets, bar, restaurants, pub. Each channel offers different potential for spirit sales. Then, operational factors like open and closing hours of the outlet. Outlet traffic is a critical factor, particularly when we can analyze traffic distribution by hour. We further assess the presence of dedicated bar, professional bartender, which serves as a strong indicator that the outlet can actually promote premium spirits. We leverage algorithms to analyze customer reviews, consumer feedback, which allows us to understand mentions of cocktails, spirits, bartenders, and the sentiment of consumer behind this.
A high level of positive sentiment, specifically for these categories, further validates outlet potential in the category we are looking for. Now, imagine the algorithms are analyzing this data in a holistic perspective and able to give a data-driven outlet segmentation for every single category. The more closely an outlet aligns to the criteria we are seeking for every category, the higher the segment it will be placed in, and this capability is now allowing us to classify HoReCa outlets into platinum, gold, and silver for every single category we offer. And having this supported by the estimation of potential revenue we can make allows us to focus on right portfolio, right outlets, activate right occasions, differentiate our offering, and allocate right resources, so we maximize the twenty-four by seven portfolio opportunities in both new and existing customers.
Now, this math, this capability, hopefully have given you a sense of the scale we are talking about, but the power comes from the granularity of the data, which is feeding the three sixty degree view and the algorithms that have been built behind it. You can see here an example from a bar in Athens, highly urban, dense, high traffic area, highly popular, having high consumer sentiments. Our algorithms show it's relevant for all categories, with best incremental opportunity for coffee, followed by adult sparkling and premium spirits. Let's take a closer look at how these tools are working. As you see in this slide, at a click of a button, the sales team is seeing 48,000 HoReCa outlets in Greece, which we currently don't serve, please.
We are also able to have an estimate of the potential, as I said before, but resources are not unlimited, so we will not go after all these outlets. We will prioritize based on the segmentation and the potential. At the click of a button, we can understand, of these 48,000 outlets, we have 1,480 outlets that are platinum or gold for all our categories, making this a right choice for twenty-four by seven portfolio activation. We quickly identify these relevant outlets and put the right effort behind conversion, converting these, and this shows how, with clarity and precision, our sales teams can now effectively focus on adding new HoReCa outlets to improve our revenue potential within this critical channel, and what about the existing outlets?
Today in Greece, we work with over 70,000 HoReCa customers, but that doesn't mean that we are maximizing the potential revenue from each of these outlets. Here you can see another example of estimation of potential revenue and how we are able to execute in the marketplace and get to a very focused approach, even for our existing customers, to ultimately drive profitable business growth. We are happy to share this HoReCa intelligence, that I just explained to you for Greece, exists already in 15 markets established this year, and to show you how the business developers are interacting with these capabilities, let us show you a short video.
Here we see the business developer in front of a potential new HoReCa customer, checking out a new lead directly on her iPad. She reviews the outlet's profile and prepares her personalized selling story using the output from the segmented execution algorithm, suggesting the most relevant and specific product categories, as well as activation opportunities. She can see this premium HoReCa outlet is a high revenue potential lead for sparkling and coffee. She can see the personalized sparkling assortment recommended for her to suggest to the customer, and she is prompted to tag her colleague, the coffee business developer, to follow up this lead for coffee. Equally, she knows there isn't potential in premium spirits for this outlet, and therefore no need to tag her premium spirits colleague. So how did the business developer find this outlet?
With just a few clicks, the sales team manager can pinpoint the outlets with the highest incremental revenue potential for each of our categories. Outlets are classified platinum, gold, and bronze by our AI algorithms. The team manager can then prioritize our sales team's actions, creating high-potential sales leads in a fraction of the time of traditional methods, and with confidence that we have up-to-date visibility of the entire market, and that we are prioritizing the right outlets for relevant categories. And how did the business developer create her personalized selling story for the customer? Outlet-specific intelligence enables the assignment of micro segments, each with distinct characteristics. For example, this is a premium outlet in a densely populated urban environment, charging relatively high prices with an upmarket atmosphere and positive consumer reviews.
Peak hours within the outlet, captured by the data, show high weekday traffic before early evening, making it relevant for breakfast and lunch occasions. Algorithms tag this as a relevant outlet for sparkling and coffee, but not for Premium Spirits. Algorithms also estimate the revenue potential per beverage category for the outlet, aiding in prioritized actions. Once the customer is onboarded, the RGM and trade marketing teams use the microsegments to tailor activation or marketing plans for the business developers. The Route to Market manager prioritizes outlet visits based on the revenue potential and the relevant categories and shares this with the business developers to act on. Customer-relevant assortment and quantities per product are suggested to the business developer when she visits the outlet, or when a customer speaks to the call center, helping them sell the right portfolio to every customer in every order and in every interaction.
This is Segmented Execution. Every customer, whether a recruited lead or an existing partner, receives personalized suggestions for product orders and recommended execution activities, taking into account our outlet-specific insights, including the shoppers and consumers they serve.
Hope you enjoyed that video. And finally, to our third focus for today, Africa. CCH is excited about long-term growth opportunities in Africa, but we also know that these markets face challenges and greater volatility than our other markets. We have to navigate these short-term challenges while remaining focused on long-term growth opportunities. Along with all our bespoke capabilities, DIA supports this. Our two Africa markets are at very different stages in their CCH journey. We have operated in Nigeria for over 73 years. It's well developed, also with our bespoke capabilities, including DIA. Whereas in Egypt, our recent acquisition, only in January 2022, we are still on a journey to embed our capabilities and bring the best practices from Nigeria and also across other markets of CCH. So let's move forward and first talk about Nigeria.
Nigeria is a critical part of our long-term success, and also for us to grow in the market, it's critical to drive recruitment, which in turn will help us continue expanding the per capita consumption expansion within Nigeria. To help achieve this goal, we have been collaborating with the Coca-Cola Company to link consumer and customer data sets. It's all about knowing who we need to recruit and where we can find these consumers around our outlets, so we drive personalized execution for targeted recruitment. Coca-Cola Company has detailed consumer insight studies, and this allows us to create and understand better these consumer profiles, understand the relevant occasions. This helps answer the who we are looking for. In case of Nigeria, we have identified six consumer profiles. We then work to match these identified profiles with our data-driven outlet intelligence.
When we understand the consumers, we can specifically identify the outlets they are most likely to shop at. We adjust the portfolio for the relevant location and activate the outlets with relevant messaging. This way, we are able to identify the priority outlets for each of the consumer profiles and drive targeted activation at scale. This linkage of who to where is one of the first type ever done in the Coke system. The initial results from this innovative data-driven approach shows that average sales per shopper from our initial pilot were 40% higher in the outlets we targeted compared to the control outlets. Importantly, sales actually remained higher in these outlets even after the pilot, and this is a great example in many ways of how we are collaborating with the Coca-Cola Company, and our teams are driving common objective of recruitment together in Nigeria.
As I said before, this case is particularly exciting because this collaboration is one of its first, first type happening within the system in sharing data and insights. We continue to raise the bar when it comes to how we use DIA in Nigeria, and we've got many more initiatives already underway jointly with the Coca-Cola Company. Now, let's turn the directions to Egypt, where we have rapidly rolled out DIA capabilities, much of this already being embedded in the market ahead of the target. When we entered the Egyptian market, outlet segmentation was very basic, existed only by regions. We rapidly brought in our micro-segmentation capability and other these levers of segmented execution I explained before. Algorithms were set up to predict the revenue potential of the outlet, and we leveraged this intelligence to inform cooler placements and resource allocation.
Further, we set up the capability to analyze cooler pictures from the outlet on a daily basis, giving us insights to improve occupancy and usage of existing and new assets within the outlets. Enabled by these insights, we are driving understanding of more than two hundred thousand outlets in Egypt through data, rather than the manual processes that existed before. We refresh this outlet segmentation even more frequently, at a six-month basis, giving us ability to check the market direction, which is very particular, you know, particularly important in a market like Egypt, where outlets or a whole area can transition from being less to more affluent in a very short period of time. All of this is providing insights to segment our portfolio of a differentiated brand and pack options and match this to the different level of affluency.
Our sales force is also prioritizing high-potential outlets, supported by targeted recommendations, and let me give you a practical example of how these capabilities are driving a segmented approach in Egypt. Earlier this year, we used data to prioritize investments in Cairo and Giza to drive higher visibility of our brands, to improve execution and sales. This included investments in terms of branded signs, marketing materials, placement of branded coolers. We call this initiative Own the Street, and it was a fully data-driven approach. Outlet segmentation informed who we go after. We went after outlets with high traffic, high estimated revenue potential in sparkling, as suggested by the algorithms, identifying 11,000 kiosks and grocery stores to activate and place coolers. We leveraged algorithms to monitor cooler pictures taken by the sales force on a daily basis.
Analyzing these pictures and the cooler door opening helped us identify gaps in time, so we could improve merchandising standards, ensure coolers are stocked with our product, and placed in the right position in the outlet. Further, we suggested specific in-store activation recommendations to our sales force. Results? We saw a 33% increase in sales from the outlets we activated, compared to the benchmark outlets not activated. Strong results with efficient resource allocation. We are now looking to scale this approach to more outlets in Egypt in 2025. We hope with these examples, we have helped you understand how we translate data insights and analytics to actionable insights and targeted actions for our business. I'm coming to the end of my presentation, but before we move to the Q&A, let me also take a few minutes to explain and ex...
to help you understand the investments we are making behind the team and the internal talent, and how we are developing a capability which is best in class now and for the future. Algorithms and data is important, but it's our people and the talent that truly makes a difference. Over the last four years at Hellenic, we have developed a best-in-class expert team of more than 50 people, including data and AI scientists, business analysts, and data quality experts, who are building these solutions at scale. We invest behind this talent so that they are skilled with the latest and the greatest capabilities. Also, we are focusing on upskilling the rest of the organization that needs to leverage these insights from DIA. We already have a data and analytics online learning platform open to all employees, and we have more than 2,000 people now investing time in learning.
This is helping us build data and AI literacy, which is critical to support usage of these solutions and drive data-driven actions on a daily basis. As I look forward, I'm super excited about the journey ahead. Let me highlight for you a few forward-looking areas that we are already working on. We continue to gather more data to allow us to go deeper in our customer segmentation, and our ambition is to move from micro to nano segments. We are expanding the HoReCa intelligence to the rest of the out-of-home channel, including at work and vending... We are working to enhance execution in high-potential new categories. For example, for Powerade, we will identify non-traditional ways to prioritize outlets, focusing on areas with younger population, outlets closer to the parks, gyms, sports centers.
We are strengthening the collaboration with the Coca-Cola Company to drive hyper-personalized marketing, also including generative AI, for high-revenue potential outlets, where we can really combine forces of bringing segmented consumer marketing with segmented execution, this time to drive customer transactions. Having this 360-degree view of the outlets helps the Coca-Cola Company to drive hyper-personalized marketing around the right outlets. Finally, as I said, we are already working with generative AI, unique opportunity, and we will continue to improve our current algorithms and bring in semi-structured and unstructured data, like outlet images, consumer feedback, customer feedback, comments, and all of this will further amplify the 360-degree approach and improve all the decision-making we are already embedding with the prioritized use cases.
So these are some of the exciting cases, you know, that we are working on already, and, you know, happy to bring this forward as we continue our sessions. With that, I will conclude my presentation on DIA, and I thank you for your time and attention today. But before we close, let me bring it back all to our bespoke capabilities. As Naya shared at the start of our presentation, the progress we are making on each of our capabilities, and particularly the way they interact together, is allowing us to better understand the real needs of our customers and consumers, drive profitable revenue growth, and anticipate and react to new challenges faster and smarter than our competition. Overall, this interconnection is driving personalized execution for every outlet, allowing us to continue to win in the marketplace.
Thank you very much, Naya and Ruchika, for the presentation. We will now start the Q&A. If you wish to ask a question, please use the Raise Hand function at the bottom of your Zoom screen. Well, a reminder, participants can also submit questions through the webcast page using the Ask a Question button. As I mentioned at the start, we're currently in closed period ahead of Q3, so please bear that in mind as you're thinking of your questions. And can I ask that you stick to one question and one follow-up? While we're assembling the list of live questions, let me take the first question from the webcast, and it's a question for Ruchika. Ruchika, can we ask a question about scaling this capability? How scalable are the tools in terms of being able to roll them out swiftly across countries and categories?
Thank you for the question, Jo. Let me start by saying scaling has two dimensions. There's first aspect of building the tools and rolling this out in the market, and second is getting people to use them in their daily decision making. As I shared before, with the best-in-class capability and the prioritization that we are doing behind this capability, the examples of promo, segmented execution, HoReCa multi-category, they are already available in many of our markets, demonstrating our ability to scale and roll these capabilities out. We continue to invest behind the second pillar, which is increasing the usage of these tools and continuous effort to build capabilities and changing the behavior of the users that can benefit from these from these insights. And that's the second part of scalability that we are continuously working on.
Thanks, Ruchika, and it now looks like we have a question on the Zoom call, so let me pass back to the operator to moderate this part of the Q&A.
We'll take our first question from Edward Mundy from Jefferies. Please go ahead.
I think Edward's muted.
Hi, can you hear me?
Yes, we can hear you now.
Good. Great. Thank you. Thanks for the presentation. Naya, the first question is perhaps for you. You know, this is a huge differentiator for you. Are you able to sort of talk to, you know, what are you doing better than peers, either in terms of your capabilities or any particular parts of the execution? And I guess as part of that same question, I mean, how important has data, insights and analytics been in your ability to drive that big step up in revenue per case that we've seen over the last couple of years, without much impact on volumes? That's my first question.
The first view here when it comes to your question is why it's different overall and why we call it bespoke. It's mainly because of four elements. One, we are achieving local relevance while having overall group framework. So there is a discipline at the group level, but at the same time, we're trying to commercialize this in the most local relevant manner. The second thing is what Ruchika mentioned, we're working together with the Coca-Cola Company in complementary partnership. There's a third part, which has to do with delivering fast results when it comes to method and segmentation. So we go as targeted as possible, taking into consideration all the different dynamics from consumers, from shoppers, from customers.
And last but not least, there is an element of obviously investment when it comes to that, both in resources as well as tools. So this is a little bit how it's working. And it goes without saying that all this work together in order to deliver better quality when it comes to the growth.
I guess apart from that same question, I mean, you know, I suppose one way of asking the same question is, you know, you've managed to get through quite a big step up in revenue per case without much volume impact over the last couple of years, unlike a lot of your CPG peers. I mean, if you didn't have some of these tools, you know, do you think you would be able to sort of execute in quite that same manner?
These tools are really making us more well-informed to ensure that our choices are done in a way where we prioritize them towards the right sort of play to win, if I would say. So, yes to your question.
Okay. And then my follow-up question for Ruchika. Yeah, again, thanks for the presentation. You know, clearly, the insights and analytics are only as good as the data that goes into them. I mean, how do you ensure the data, which then drives the outcomes, is up to date? And perhaps you could, you know, give an example, you know, when you saw consumer sentiment soften a bit in Bulgaria, and you started to launch the 300 ml PET pack. I mean, what were the inputs into that data that then allowed you to, you know, mobilize so quickly?
Absolutely, and you rightly said, I mean, the algorithms are as good as the data that goes into it, so that's why, you know, when we were even establishing the capabilities, we have a specific team that's working on governance of data, so we make sure that we do thorough validation, especially on the external data that we are purchasing in the market, before we start rolling out these capabilities, so to your example on Bulgaria case, which used extensively the external data I talked about, before these capabilities were created, we actually did extensive validation within the market, going to sample areas, sample customers, you know, validating what the data was showing us, and that gave us confidence to move forward at scale, and that's where, across all pillars that I talked about, we invest in validating the information we use.
The second thing that we do, which is a big call-out, you know, to our technology teams, is we make sure data remains secure. And we have the best privacy, you know, controls around this. We allow access from an access management to only people that can see the data, you know, at the right level. And the third thing, you know, really from, from that perspective, is how we drive usage of these insights and the interpretation. And that's the third thing, you know, that we work on. I would also like to call out, you know, the partnerships that we have established with a lot of these external providers, which are, which are key in the sourcing we are doing.
And having worked with them over the last few years, we have established a way in which we are validating the data upfront with them, and then, you know, scaling this out in the market. So I think this whole ecosystem approach is allowing us to go faster in maintaining the quality of the data. And probably one more thing I would call out, you know, which is very important, we always have humans in the loop. No algorithms, no quality of data, you know, allows us to localize the part that Naya mentioned, unless the relevant people standing in front of the outlet have a say, you know, and can give us input back. And that's the last critical driver that we absolutely amplify in any of these solutions.
Thank you. Our next question comes from Mandeep Singh from Barclays. Please unmute your line and ask a question. Please go ahead, Mandeep, and ask your question.
We're yet to hear Mandeep. Maybe in the meantime, shall I ask one question from the chat, and then, I can pass it back to the Zoom call after that? We have a question from Mitch Collett at Deutsche Bank, on the webcast, which is around the rapid development of AI. What other capabilities you expect to be able to deploy in the next twelve to eighteen months, as the tools you have at your disposal improve?
I'm going to start, and Naya, please feel free to add. I think, first of all, AI is not new. You know, we've been using AI for a fairly long period of time. Obviously, this is first step of machine learning. A lot of solutions we talked about are running at scale. We are already experimenting with the new AI, which is about generative AI, and I'll talk about specific examples in which we are already deploying these in a very targeted but business impactful manner. So there are three dimensions in which you can think about, you know, this new form of AI and how this impacts. Either it's allowing us to automate repetitive tasks, it's allowing us to augment human intelligence, and third, it's supporting the acceleration of the competitive intelligence that, you know, example of what we shared.
So the first one would be where we have tools like Microsoft Copilot already deployed within the organization with our technology teams, and we are using this to summarize meetings, read through documents, so giving already a productive, you know, edge. The areas where we are complementing this with our human intelligence, a very good example is the work we do now in digital marketing. So we have a big pillar on driving engagement with our consumers and customers through digital marketing. And we have the power of segmented execution that I shared, which is precisely telling us how to understand the customer and what to sell. We are now complementing this with generative AI-created marketing visuals and communication, which is allowing us to personalize the communication for every customer, making sure we just don't sell the right product, but we sell it in the most compelling way.
And this is a true game changer in how we will personalize the approach with our customers going forward. The third thing is all algorithms, you know, that we have already invested and we are continuing to invest behind, we are bringing unstructured data at a speed not possible before. Just to give you an example, already all customer feedback, comments, transcripts from the call center, they are already being monetized and connected to the Segmented Execution algorithm, so we can really understand this dimension of the customers, which were looked at, you know, in a separate way before. So those hopefully, you know, will give you a flavor of some of the capabilities we are already building in monetizing and establishing AI at scale.
Maybe also to add here, to give it an example, to give a little bit of a color on what do we mean by embedding generative AI capabilities in the analytics insights tools. Like, this allows overall to really extract insights in a more conversational way. For example, getting into outlet intelligence segmented execution tool and asking questions like: What is the revenue we can capture if we list Powerade in high potential convenience stores within a radius of 200 m from the gyms? You get an answer in seconds, as Ruchika mentioned earlier, and this is powerful for people that are still building data literacy and may not be actually conversant with use of complex data and analytic tools.
And on top of this, of course, what is critical when it comes to AI is one part that we keep an eye on, which is what do we do to continuously upskill and reskill our talent overall, to make sure that we make them as relevant as possible by using all these type of tools. So this would be the approach at the moment.
Thank you. We'll now try and take Mandeep's question again. Please unmute your line and ask your question. Okay, it looks like we're struggling to connect with Mandeep. I'll hand over to Jo for the written questions.
Yeah, and Mandeep, maybe for starting, you can just email me as well, and we can ask it directly. Yeah. Yeah, I can go through a few questions that we have here on the webcast. So, one probably for Ruchika, maybe Naya would like to build also. Can you give us a bit more color about how you collaborate and share data with the Coca-Cola Company? That seems to be a really big opportunity for the overall system.
Absolutely. First of all, because we are building capability on both sides, we are driving active collaboration in the mindset of really looking at data and insights in a holistic approach. Nigeria example, as I said, one of its first kind, is exactly in this direction, where we are looking at the full value chain of consumer and customer, bringing data and insights together, and drive a common objective of recruitment within Nigeria. And we see very encouraging results. We want to continue scaling this to all other markets. And these are the examples where, you know, we, we're really creating very innovative approaches to, you know, sit and really show the insights that we have, both for both from consumer and customer side, and really drive, you know, next generation steps within the market.
I mean, nothing to add here. I mean, our system is so powerful, when we are together, and we're trying to leverage this, and, we're working in full complementarity and in a completely end-to-end approach.
Great. And I now have Mandeep's question, which was around the potential for the tool to identify gaps in the portfolio. So specifically, he asked a bit about Finlandia, but does the tool help you identify that there's a significant opportunity in your outlets that gives you confidence around certain categories or brands?
Yes, absolutely. And specifically for Finlandia, I can already share some live examples that we have from our existing customers, but not only, where we recently used in Poland and Czech a selection of almost 10,000 very targeted outlets, where we are now having discussions on onboarding Finlandia. And it's again, exactly with the segmented approach that we shared, where we could tailor pick these outlets, you know, and drive a more focused approach into building the distribution of Finlandia.
So, a question from Andreea Condrea at UBS. Given the high frequency of data coming in and the higher speed of processing, how much quicker do you see yourself reacting to sudden events in a given market compared to before having these tools? So we're really looking for a little bit of a span of capability over the period, and maybe, yeah, we could also ask Naya to give a bit of a view, because you've been obviously here longer than us, and a sense of how it's changed.
I mean, it's a tectonic change. If you compare anyone, not only us, the last ten years. Post-2020, I mean, there have been so many changes, and it goes so fast that, you know, done right, this thing, and in a more segmented way, you can only, like, go after more opportunities and at the address at the same time, you know, all the volatility that we're going after. So in the past, it used to be only, I would say, a mono-dimensional approach, a little bit of elasticities, a little bit like, consumer data, sales data.
Now, with this type of information, we go to the granularity of understanding, what is it that we're trying to solve and what is the solution to go after, and always, as we mentioned earlier, and how Ruchika actually explained it, achieving local relevance. That is why we're talking about micro-segmentation. That is why the next level is nano-segmentation. It's all about achieving this local relevance while keeping a discipline when it comes to what we're after, and always with the execution excellence at the very core, uniquely tailored for every single outlet.
Great. Thanks, Naya. A question from Olivier Nicolai at Goldman Sachs, around how we use this for regulatory changes, like sugar taxes or DRS schemes. So can we collect and leverage data between countries that allow us to model the impact of those events, and then, think about how we adapt on making price pack adjustments or thinking about adjusting the volume next?
I don't know whether you would like to start, Ruchika, and I can follow.
Sure. I can give some examples, specifically, you know, on how we've already supported the market, especially when, you know, DRS and sugar tax, these instances happened, and there was an important element that connects to how we, you know, managed these big changes through the use of promotions, in fact, so where the promo insights really helped us understand customers, regions, you know, and type of promotions that would help us complement, you know, some of these bigger events, allowed us to go with the customers in the right confidence, but at the same time, always maintaining, you know, a profitable approach to the promotions that we were doing.
Using promotions, for example, to cater, you know, the impacts that happened when we went into sugar tax or when the DRS were launched on incentivizing the customers, you know, to get used to this new way of, you know, collection and incentivizing them through the right promotion. These are some of the examples where we are already connecting to these components.
Yeah, and I think the message here is that the beauty of these tools is that they can be applied at the granular level. So back to what we mentioned before, it's all about knowing what we're trying to solve, and then go after the combination of solutions, always making sure that we are actually having the end result there. So that is why it is very specific, very country relevant and also within a country, very much area-based. So it's not a full country. Even a full country is a one-size-fits-all. We get into areas, and we're trying to actually demonstrate based on these tools the solution.
Great. Thank you, Naya. We currently have no more questions in the webcast or on the Zoom call. I'll give it a moment just to see if we get one more. I think with that, that's great. I think that was the last question. So we thank you all for joining the event today. We hope it's given you a great insight into DIA and the benefits it brings to the business, and we look forward to speaking to you again soon. I will now hand back to the operator to close the call.
Thank you for joining today's call. We are no longer live. Have a nice day.