Intro:

Hi. You’re listening to Coefficient, a brand-new podcast series brought to you by TheMathCompany. And I’m your host Nirupama, a journalist, researcher, and podcaster—currently the Content Lead for Thought Leadership & Podcasts at TheMathCompany. In every episode of Coefficient, we deep-dive into compelling topics related to the present and future of data analytics and AI for business intelligence.

This is Episode 5, and we’re talking about upgrading your Revenue Growth Management toolkit.

Revenue Growth Management—or RGM refers to a set of capabilities and strategies that large companies use to drive growth—that is, increase sales, profits, or market share for specific products at certain times and in certain markets.

So we’re talking about sustainable growth here. It’s not a plan to do or invest in everything, everywhere, at once. Rather, it refers to strategic decisions on pricing, and informed investments in promotion and marketing.

This is crucial to be successful in dynamic markets with lots of competition, new products getting launched, and customer demands fluctuating.

RGM is often understood in terms of the growth levers that the solutions target: pricing, promotion, and assortment & distribution, among others.

Now, all companies have some sort of an RGM capability, but to be competitive in today’s world, that’s simply not enough. Which is why it is important to digitize and upgrade your RGM toolkit. How does that work? That’s exactly what we’ll be discussing in this episode.

Our guests today are leaders of the RGM capability here at MathCo. I’ll take a moment to briefly introduce them.

Silvana Dimitrov – Partner and RGM Practice Lead 

Silvana Dimitrov has 20 years of working experience in both B2B as well as CPG companies across different geographies and functions. Silvana has held both market and regional RGM head roles in Africa and Asia. Silvana has also successfully set up and led RGM for Carlsberg Asia and led the implementation of the Carlsberg Analytics platform. Recognized as a leading expert in RGM, she is a regular speaker at RGM conferences.

Aditya Durai, Principal and RGM Solutions Lead 

Aditya has close to a decade of industry expertise in designing and executing organization wide data/digital strategies for multiple Fortune 500 companies across retail, CPG, finance and manufacturing. Aditya works on the solution side of things here at MathCo, responsible for delivering flawless solutions to a portfolio of multinational industry giants.

They’re joining us today from Amsterdam and Chennai. Let’s dive into the conversation.

Nirupama: Let’s get started. Hi, Silvana. Hi, Aditya. Thank you so much for joining Coefficient. This is episode five.

Today we’re talking about revenue growth management, which is such an important topic for all companies, right? And especially in the CPG industry. So, I have a few questions for both of you because you’re sort of specialists in the space and you’ve been working in the space for a while.

Let’s get started. The first question I have is for you, Silvana. You’ve worked in this space for a really long time, and I’m sure you’ve worked with so many companies. While everyone must have some kind of an RGM solution in place, I’m guessing that some are less desirable and maybe some are a little bit more upgraded and effective, if I can use those words.

So, I wanted you to maybe take us through the different maturity levels. Um, that you can see in RGM and how can companies sort of figure out where they stand.

Silvana: Thank you for having us. It’s a pleasure to be here and talk to you about RGM, which is a favorite topic of mine. When it comes to data and analytics in RGM, we’re still playing catch up in terms of where RGM is actually in terms of maturity.

What we’re seeing across the board is about probably four different levels of maturity where the CPG companies are operating today. The first phase of maturity is what we call the experimental phase. And that’s where companies are still trying things out. We’re talking about quick and dirty solutions to capture the low hanging fruits.

Companies are still operating largely in manual spreadsheets. There’s no tools or there are manual tools. And we’re not making fact-based decisions. So there’s still a lot of reliance on past experience in terms of future decision-making. Then the next phase that we see in terms of maturity is the tactical phase.

That’s where we have identified now some key processes where RGM needs to be plugged in. There are key markets that we want to support and those are being powered by analytics. And we’ve seen some advanced analytics coming into play, data partnerships for key markets, and again, key processes. Those are typically standalone off-the-shelf solutions.

And that’s where we start to see companies developing what we call Frankensteins. So solutions that typically die off at some point. And often markets are actually left on their own to choose the solutions they need to have for their own needs. And it’s very difficult to scale those across different markets at a point in the future.

The next phase that we do see companies operating at is the functional level of maturity where now the entire RGM function is actually supported by data-driven applications. However, those still tend to be more standalone apps. So they’re not connected. So we’re not connecting insights and the data coming out of those applications.

And we still have poor economies of scale. And because those are typically off-the-shelf solutions, there’s lower adoption because at some point markets realize that the off-the-shelf solutions are actually not really meeting their needs in terms of supporting key processes and answering key business questions.

And there’s lower adoption. The final, most advanced stage that I would say is where we see more integral, the integral phase, where we’re seeing companies starting to scale globally. Applications are connected; they’re talking to each other. We’re talking about data and insights and applications talking to each other, and also RGM is connected to other functions.

My favorite example here is actually with one CPG company that has connected RGM data and applications with supply chain data and applications. And before they plan a promo event, they actually have a look at what is out of stock and what SKUs are enroute to the store, and then they plan promotions accordingly, not to disappoint the shopper experience.

So those are probably the four levels of maturity, and we do see companies operating across all four. And there are some examples of companies actually being in the most advanced stage.

Nirupama: Right, thank you so much for taking us through that. Before we go further, I have a really basic question.Usually, when I’ve heard about RGM, like when we’ve had discussions about revenue growth management, it’s been largely about CPG companies, consumer packaged goods industry. So, is it just applicable for these companies? Or is it also something that’s applicable to other industries as well?

Silvana: RGM probably started with the airline industry. They invented RGM. The hospitality industry, they’re a lot more advanced when it comes to RGM. So, it’s not just CPG. Retailers, also B2B companies, are starting to use a lot of RGM. Dynamic pricing, management of discounts. So it’s not just limited to CPG companies.

Nirupama: Thank you so much. We’ll move on to the next question, which is a question that I have for you, Aditya. So, we have gotten a context of the different maturity levels that companies can fall into when we think about RGM. And, you know, it’s obvious that the last level is where everybody should try and get because it’s the most effective. So, let’s talk about what we mean by digitizing RGM, which is a word that we use a lot, which is the modern approach to RGM. So, could you sort of paint a vision of what this ideal digitized RGM suite would look like?

Aditya: Sure. That’s a very interesting question. And the reason why it’s so interesting is because there are so many versions of what a digital RGM could really mean that are there in the market. And the reason why so many versions exist is because of what Silvana mentioned previously, which is different markets, different organizations have different levels of maturity.

Over the period of time, I think we’ve struggled to put one good indicator to say that this is what an ideal digital RGM would look like, but over the last few years, we are getting much closer to what the answer could look like. And there are a lot more leading indicators to tell us what a good digital RGM suite would potentially look like.

Some of them are, let’s say, some of the questions that organizations can answer to get to this point is that. How many of their business processes are truly powered by RGM solutions, which are more digital in nature? We have the tendency to look at pricing, promotions, assortment as individual levers.

But what drives value is really the business process. So how many of these processes are powered by RGM applications is like a good indicator of how digital it is for a given ecosystem or a set of customers. So that’s one indicator. Another indicator is how much or how nuanced are their solutions with respect to maximizing the value that they get out of the data that they have.

Many years back, we used to do assortment planning to maximize sales, but over the last few years, we’ve had so many organizations making panel data available, helping us get shopper insights. So it gives us opportunities to also think about assortment planning to improve market share, to improve household penetration in a more holistic manner. Usually, it’s a thought that we do as a part of innovation, but now it can be a more holistic decision that can be made as a part of assortment planning. Now, the data is available. Not all organizations will have it. But the organizations that have it, are they thinking about it as a separate shopper insight or an analysis, or are they thinking about it as integrating it into their RGM levers and applications?

So that’s another good indicator of how these levers in itself can get maximized. But we’ve seen some good examples and we are on a journey of where some of these things can get really mature to make holistic organizational decisions. In some mature markets, let’s say like the US, we’re also working on embracing some of the connectedness associated with these processes.

The example that Silvana spoke about, where some of the choices that you make as a part of RGM will have a downstream impact on certain things like supply chain, or for that matter, what you do as a pricing decision will also have to be thought through well off as a promo strategy or an assortment strategy.

So how do you build applications? How do you connect the data? How do you maximize your infrastructure to embrace this connectedness within this ecosystem is also what a good example of an ideal digital RGM suite could potentially look like. I think the term that was used for the most advanced version was integral, integrated. It reveals what the aim here is. It’s a very telling term. So, it’s not just about individual apps that may form a part of specific levers, but all of them sort of coming together.

That is what an ideal RGM suite looks like.

Nirupama: Absolutely, yes. Okay. So, the next question I want to ask is to you, Silvana. It seems to me like this is a journey. Obviously, it’s not like a company today decides to go from using traditional RGM methodology and can suddenly make a shift.

So, does this journey look the same for all kinds of companies? And if not, what are some of the key success criteria that, as business leaders, people can look for?

Silvana: That’s a good question. Thanks, Nirupama. The journey really varies depending on where you are on the maturity curve and the industries that you operate in. CPG companies are largely behind in terms of digitization. Retail, insurance, high-tech industries, and banking are leading the pack when it comes to digitizing RGM. The success criteria we have identified include, firstly, access to data. Companies need to start closing their data gaps. The second criteria is about what you do with this data. Do we have access to insights at the shopper level, consumer level? What is happening with the competition, and what are we doing with those insights? It’s a lot about actionable insights, and many companies are struggling with turning insights into actionable insights. Here, generalized AI starts to come in handy as it allows working with large data sets and turning those into actionable insights quickly, instead of people going through screens or charts to find the root cause of a problem, like a dip in revenue. The third criteria for success is the type of technology that companies are using. Are we accessing advanced analytics? Are we tailoring our price and promo strategies? Some organizations are starting to tailor their assortment plans at a store level, designing store-level assortment planograms, as opposed to working at a channel level, or not working at all, which is where some companies still are. Integrating with other functions is essential because RGM is not a standalone function within the business. Digitization has to be able to connect RGM to other functions in the business. Lastly, organization design is a critical success criteria. A hybrid design, where we have a center of excellence setting the standards, methodology, and consistent language that a company uses, coupled with the RGM analytics function sitting within the business, closer to the market and what is happening on the ground, works well. These criteria, together, set the foundation for a successful RGM journey.

Nirupama: So, just to be clear, the hybrid model is a better way to do it?

Silvana: Yes, the hybrid model is a better way because you can actually implement, you can design the strategies and then implement them.

Nirupama: The previous episode we had in Coefficient was about Generative AI. And, you know, we were talking about what are the different ways in which it can be used in an enterprise. And, it’s really interesting that, in just the next episode, we’re talking about how to improve RGM and Generative AI has an application here.

Silvana: Taking it from merely having data, to converting that data into dashboards, and now potentially having a generator that conversationally provides those insights is a natural progression. We’ve observed this evolution in companies, whether they are implementing standalone apps or moving towards connected systems where apps communicate with each other, leveraging insights from every interaction. This leads to considering innovation. Essentially, GenAI is providing that next step on the natural RGM journey.

Nirupama: Right, that does make sense. I have a follow-up question for you, Silvana. You’ve discussed the journey and how to measure success, outlining the criteria for it. However, could you also shed light on the potential pitfalls that companies should be mindful of and try to steer clear of on this journey?

Silvana: Yeah, so I spoke about data and data being one of the criteria for success. But that’s where companies start getting paralyzed in terms of not having the perfect data and they wait until they have the perfect data. And we often use data as an excuse of not to get going. You know, we would just say we don’t have data. So my suggestion here is do not wait until you have the perfect data because you’re never going to have the perfect data. Start small and then build from there because only once you start building you realize what else you need and that’s how you start closing the data gaps. But the companies that have done RGM digitization successfully have invested in getting the right data up front. Some of the next pitfall to avoid is, think about economies of scale. Scale from the beginning, so do not think about a company start with a market or two markets and they’re thinking about the most advanced markets, they would get an off the shelf solution and then they’re not thinking about scaling, so you need to think about scaling right up front when you actually start designing the RGM digital journey or the type of applications that the business needs to have to support RGM. The third pitfall that I would urge for companies to avoid is basically you need to find the right balance between customization and standardization, and it’s the right balance because an off the shelf solution would be 100% standardized, but then you’re going to struggle with adoption. Whereas if you go too far the other way, in terms of customization, you’re not going to have consistency within the business. And that’s where you also end up with what we spoke about the Frankenstein’s early on. The fourth suggestion would be to think about the market archetypes early on. So, start with the more advanced markets, but think about what are the different markets, market archetypes need, and what is the level of talent? What is the level of tools that we have, what is the readiness of a certain market in terms of implementing digital tools. And then also do not get stuck in the prioritization of use cases. So sometimes this gets can be actually analysis paralysis where decision making gets paralyzed because we cannot decide what is more important, use certain criteria weights them in terms of what’s important to do like organization where it’s.

Whether it’s strategic fit, whether it’s value, whether it’s customization, standardization, ability to scale and and make a decision on that prioritization, do not leave it until, basically just going around in circles.

Nirupama: Wow. That’s a lot of points packaged in such a short answer. Yeah, like I can understand a lot more about the topic already. So, let’s get going with the conversation. The next question is for you, Aditya. So, I’m curious to know what kind of problems, you know, as MathCo, we are solving today in this space and how we are adding value to clients.

Aditya: Sure. So, the good part about our set of engagements is that we work with some of the major CPG organizations in the world today. And the second is also that in a lot of these engagements and builds that we do, we work across multiple markets. So that gives us a gamut of problems to solve, and each of them creating value in very different ways.

We do build solutions across all the pillars of RGM like assortment planning, distribution planning, pricing, promotion, so on and so forth. But more importantly, the value we create and how we do this for each of these markets and CPG organizations are slightly different. I’d broadly classify it maybe into three parts.

The first one is probably markets and organizations which are on the lower side of the maturity curve. And that goes back to what Silvana was talking about before. Instead of giving data as a reason to get started, you don’t have to do store-level assortment planning but create a plan. They have a central strategy. That’s one part of it. You don’t have to know, let’s say, as many details of how to understand the elasticity of a new product that I’m going to launch, but at least start with understanding elasticities of your own products and competitive products because that data might be available to you.

You might not have data in one market extensively, but learn from some of the other markets to at least take learnings, let’s say, in similar market situations, product groups, launches, so on and so forth. So getting them started on that journey with smaller implementations, more focused on getting answers and implementing it in the market. And making sure that decisions are made. So that’s one part of solutions that we do. And all the insights that are generated are directly creating business value because they’re all decisions, which in turn reflect in business value.

Then there are the second type where markets which have existing solutions, there are certain products that they’ve bought, there are certain things that they’re already used to running. Like if you look at some of the major organizations and some of the major markets, there’s no way that they’re not doing RGMs, but they will definitely have certain inefficiencies or certain lack of capabilities that is stopping them from answering certain business questions.

If you look at TPO for that matter, the decision probably is that I would like to go to the understanding of which store do I offer this on the aisle or do I keep it at the front of the store as an offer, each of them will drive different results with respect to promotional patterns. But can I get to this level of granularity? Maybe not all TPO solutions that they bought off the shelf will be able to do that. There’s a customization that they would require. So how do you make sure that some of these solutions that they have are padded with more enhanced capabilities and how do you make sure that the digitizing that part that I spoke about where all their business processes are powered by custom applications, retaining the context of that organization and their RGM world.

So that’s the second type of value that we create, where most of their business processes are run by these kinds of applications and all of them powered by data and insights, keeping their organizational context in mind. The third one is more about global programs, global scaling, keeping the context of individual markets in mind.

The value there that we bring in is the same point that Silvana brought up, which is when you start small, don’t lose sight of how it will look like when you make it a global program and make sure that all markets are using it. Because if what you build in some of the major markets is going to cost as much to replicate in some of the smaller markets, then the value of the solution is not justified, and usually, that entire initiative gets canned.

So how do you build solutions in a more modular way? You pick up a few markets to pilot it. Make sure that you interview as many people as possible. Make sure that you build a solution that’s more holistic, find the right balance between modularity and customization. So that one, you keep the organizational context standard across all markets. But you also keep into account some of the data, maybe availability and unavailability within each of these markets and tailor those solutions for those markets at a much lesser cost so that it’s an entire global RGM practice as opposed to localized individual markets that they’re doing.

And how do you measure RGM is really like, how do you measure each of these things as value is fundamentally that if truly an organization is growing and all of those are powered by these applications. I think that’s where we take a lot of pride in where all of these are multi-year programs where at the beginning of the year, we commit to creating certain growth because of certain applications and capabilities that we do. And we measure how much of that is created over a period of time. And that’s what we take a lot of pride in as people who build these solutions and also drive adoption of these solutions within the client organization.

Nirupama: Right. As a follow up to this, I want to ask you, what do you think lies ahead? And how do you see this field evolving?

Aditya: So, one thing that we already visibly see is a lot more organizations are embracing the idea of connected systems and making all of these interactions between apps, business processes, data, infrastructure all of them, and they are seeing the benefits of this. So that’s one big trend that we’re definitely seeing, more and more organizations are beginning to adopt it. But what I’m most excited about is how some of these newer technologies, let’s say, like generative AI, can truly impact some of these spaces. The conventional way of thinking about generative AI is that it probably might end up being an efficiency game because some of these things can be automated and your questions can get answered faster than what it used to be.

But I think the potential is much beyond that. That’s probably a starting point. That’s probably more foundational in nature. But there is a lot more potential in some of these avenues if you explore deeply. A few examples that I can think about is that today, the richness of data. We don’t have a data limitation problem in CPG anymore.

We used to have that. But right now, some of that is getting solved with different dimensions of data that is coming in. With that dimensionality of data increasing, there is merit in thinking of like having crawlers which come and tell you that a new product has been introduced three months back locally in one region or let’s say one set of stores and that’s eating away quite a bit of market share.

We need to constantly understand if let’s say that’s a temporary trend based event, or is it something that is going to stay on for the longer run so which means we probably have to think about an NPD or let’s say strategy around that. So that’s something that probably organizations take a lot more time to catch.

But let’s say it’s a native AI engine that’s running in the background can pick it up and share this with us as an insight. So that’s one part of it. There is another part of it, which is also a lot more associated with providing recommendations and following through on actions where TPO systems, I think, as things stand today, the most mature systems probably recommend a promo plan for the next 52 weeks.

But if you integrate generative AI components to it and attach workflows to it, can you think about recommendations? Can you think about what shortcomings of those things could potentially look like? Because any recommendation will have its own advantage and disadvantage, right? Like, what are the pros and cons?

Synthesis of those recommendations. And even for that matter, once approved, can it go through a round of implementations measurements almost automatically, like how most analysts would even do for that matter. So those are all avenues and opportunities that I’m looking forward to unblocking when we think about generated AI in itself as what it can add value to our RGM.

So, it will unlock that. I mean, like value or avenues of RGM that we have not explored as such right now, because we’ve always been limited by data applicability and like, can we really do this kind of things, which I think is a barrier that generative AI will unlock for us.

Silvana: Another example that I wanted to bring into Aditya’s examples is, we’re busy developing a pricing co-pilot in one of our pricing apps, and that’s basically almost like having another person making pricing decisions and recommendations together with you. But it’s basically leveraging AI in terms of the ability to work with large data and coming up much quicker with recommendations. So, thanks Aditya for that.

Nirupama: Thank you so much, Aditya and Silvana. This was a great conversation. I think this is a much bigger topic and perhaps down the line we’ll have another follow up conversation in a few months and see how some of these things have evolved. Thank you so much for taking the time.

Aditya: Thank you for, thank you for inviting us, Nirupama. It was very exciting to talk to you about this. Both Silvana and I are very passionate about this, so very excited about doing this and looking forward to, you know, our future contributions as well.

Outro: 

Thank you for tuning in to Coefficient. I hope you found this conversation insightful. If you’d like to know more about how to digitize your RGM suite, get in touch with us on the mail address in the description.

silvana.dimitrov@themathcompany.com

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Until then, goodbye and have a good day.