Revenue Growth Management in Practice: Capturing Hidden Value Across Price, Promo, and AI with Danilo Zatta

Aaron: Hello and welcome to Pricing Heroes, a podcast sponsored by Competera. This is a series of interviews with the best-in-class retail pricing experts driving bottom-line metrics for major retail brands and the industry as a whole. Today's guest is Danilo Zatta, whom the Financial Times has described as one of the world's leading pricing minds, and LinkedIn has named a top five pricing thought leader. Dan is a global pricing and top-line growth advisor to numerous blue-chip companies and private equity investors.

He's also the author of more than 20 books, including his international bestseller, The Pricing Model Revolution: How Pricing Will Change the Way We Sell and Buy On and Offline, and his popular 2023 publication, Ten Rules of Highly Effective Pricing: How to Transform Your Price Management to Boost Profits. In addition to his many well-deserved accolades, Dan is also a returning guest on Pricing Heroes. He first joined us on the podcast in January 2024, and we invited him back on the show today to discuss his most recent book, Revenue Growth Management: How to Capture Hidden Value in FMCG and Retail.

Dan, welcome back. Danilo: Thank you very much. Super excited to be here together with you. Thanks for having me.

Aaron: So, Dan, it's been more than two years since you were last on Pricing Heroes. What are some of the significant changes you've seen in pricing during that time? Danilo: Over the past two years, pricing has shifted from crisis response to disciplined value capture. We moved from emergency price increases during inflation spikes to building sustainable architectures around price, promotions, mix, and terms.

Retailers have become even more sophisticated and demanding, so joint business planning and true net revenue management matter more than ever. Growth now comes less from list price and more from smart pack and channel mix, promotional effectiveness, and tighter control of trade terms. Data and AI have moved from pilots to everyday use, although many organizations are still catching up on governance and change management. Finally, there's a stronger focus on fairness and compliance, which is reshaping how companies communicate and execute pricing.

Aaron: Dan, I want to ask a quick follow-up to this. Is there any particular lesson that companies should have learned, in terms of preparedness? Obviously you cannot anticipate what has happened over the past several years, so how can companies prepare for the unexpected in terms of pricing and revenue management? Danilo: The best approach is to get feedback from the market.

So rather than planning too much, testing is often the better response. To be better prepared, test what future strategies may look like before doing a complete rollout. Aaron: So can you tell us about your new book, Revenue Growth Management: How to Capture Hidden Value in FMCG and Retail? Danilo: This is a practical playbook for people who live in the P&L every day.

I integrate the five core RGM levers - namely pricing, promotions, mix across product pack and channel, trade terms, and innovation - with shopper insights and commercial execution. The book offers frameworks, diagnostic checklists, and real cases to help teams move from strategy to the store shelf and back into the P&L. It's written for sales, marketing, finance, and RGM teams who need tangible outcomes - margin growth and capability - not theoretical models. For example, among the many cases I cover, I also included the great Novus case in Ukraine, where inflation, fast-moving competitors, and the limits of rule-based ERP pricing pushed the retailer to modernize.

Novus implemented an AI pricing platform delivered by the great Competera team and integrated it into SAP, trained it on two years of sales and inventory data, and combined demand-based optimization, competitive intelligence, and configurable guardrails to generate ready-to-approve recommendations. They piloted this against a control group and saw an amazing profit uplift of 6.7% and sales slightly up, while manual errors dropped to near zero, even amid wartime volatility. The system adapted faster than static rules, with analysts focusing on exceptions rather than spreadsheets.

The real win wasn't a black box - it was an end-to-end workflow that sensed demand, respected strategy, explained outputs, and scaled through SAP, turning AI from a promising tool into a solid pricing engine. Aaron: Yes, I remember reading that case study. It's very impressive, and as someone who lives in Kyiv, Ukraine right now and shops occasionally at Novus, it's great to see how they've been able to manage prices in real time despite all the fluctuations in products and pricing here.

Danilo: Absolutely. I find the case fascinating, and again, a proof of how great AI solutions can be in helping companies monetize better the value they're delivering. Aaron: So, in the introduction to your book, you write that as the industry has evolved over the past couple of decades, pricing alone was no longer enough, driving the need for a broader strategic function. What change made pricing insufficient on its own?

Danilo: The battleground shifted from price points to total value realization. Shopper behaviour, fragmented across channels and occasions, made portfolio and pack price architecture as important as the price itself. Retailers started managing their profitability with far greater sophistication, which forced suppliers to consider promotions and trade terms holistically. Discounts, funding, and portfolio complexity created leakage between list and pocket price.

So unless you manage the entire system, you give away value. In short, net revenue - what actually lands in the P&L - depends on coordinated decisions across price, promo, mix, and terms, not a single lever. Aaron: I imagine managing across all of these various functions is extremely challenging - not only in terms of integration due to different data sets and tech stacks, but also just change management itself, with the various functions and teams operating across the organisation.

How are retailers thinking about making this shift towards revenue growth management that integrates all of this and streamlines the process by creating visibility and accessibility to information in order to make these decisions? Danilo: Absolutely, and that's a great point. I'm advising a number of retailers in different geographic areas at the moment, and what I see the most successful retailers doing is making sure they create joint teams from different functions - both RGM, but also finance, category management, and purchasing - bringing all these minds together.

Making sure there is a coordinated response and approach helps them win this game. Aaron: What different teams are you usually pulling from? Danilo: A very strong and key team is the finance team, because at the end of the day, we also need to understand the results of the actions we are taking. And then of course you have the RGM team who is driving all these activities, but then you also have the sales team who is negotiating the trade terms, for example, and is telling you what is possible and what is not, what they tried, and what the reactions were.

So it's really different teams bringing different parts of the mosaic together that at the end give you the full picture. Aaron: And do you ever see marketing as part of that team? Danilo: Absolutely. Marketing is also a key element of it.

Aaron: So I also found it very interesting that you emphasise that RGM isn't a function, a toolkit, or a one-off project, but it's actually a way of working. What prompted you to draw this specific distinction? Danilo: RGM only delivers when it's embedded in weekly decisions and shared routines. It's inherently cross-functional - sales, marketing, as you just mentioned, finance, and supply chain - and one-off projects can spark improvements, but without clear decision rights, a steady cadence, and aligned incentives, the gains evaporate.

Treating RGM as a discipline with governance, KPIs, and behaviours that reward value creation turns sporadic wins into repeatable performance. Aaron: You also write in the book - and you just echoed this - that this is for people in the real world of P&Ls, customer meetings, and trade reviews, not for theorists or academics. This made me wonder: where do you see the biggest gap between how RGM is talked about and how it's actually executed in practice? Danilo: That's another great question.

The biggest gap is between the language of being data-driven and the reality of firefighting at month end. Many teams talk about shopper-centric, ROI-based promotions, yet still fund last-minute deals on shaky data to hit volume targets. Companies declare pocket price discipline while off-invoice and special terms proliferate without proper controls. They advocate portfolio architecture but carry bloated SKUs with unclear roles and high cannibalization.

And although we all say joint value creation with retailers, negotiations too often devolve into tactical haggling rather than shared profitability planning. Closing that gap requires better data foundations, tighter governance, and the courage to stop unproductive mechanisms. Aaron: So you also cover AI in the book, and in that chapter you describe the technology as a force multiplier in RGM that amplifies decisions rather than eliminating them - which may seem counterintuitive to some people who think that AI is supposed to simplify the work of teams and allow them to perhaps shrink the scope of their focus.

You say it does the opposite by amplifying decisions. How should companies think about the role of AI within RGM today, and how can they separate the hype from reality? Danilo: Let me share my view on this. AI should be seen as a speed and scale engine for human judgment.

Today it can accelerate insight generation - estimating elasticities or promo lift, surface category recommendations on promo depth and timing, and automate routine analytics. To separate hype from reality, start by cataloguing the decisions you want AI to support and define what good looks like for each. Invest first in data readiness - clean, connected POS, promo terms, and cost to serve - because weak inputs yield seductive but wrong outputs. Keep humans in the loop with clear guardrails, approval rights, and exception paths.

Test rigorously with A/B designs and measure true incrementality, not just top-line spikes. And insist on explainability so commercial teams trust, challenge, and improve the models over time. Aaron: And is there a specific area in RGM where you see a misapplication of AI - where companies are investing money and time trying to implement some feature or function that has just flat-out failed? And if so, how are you identifying the opportunities that are most ripe for adoption at this time?

Danilo: Another great question, and indeed there is a risk that companies either do too much or too little with AI. Doing too little means saying, "We will do this when we have better data quality" - but there is no company with perfect data. So rather than doing too much or too little, start by identifying some use cases and then test AI applications. A great use case, for example, is understanding which promotions should be killed because they are not delivering the desired return on investment, and which ones should be the focus so that we reduce overall promo spend, avoid cannibalization, and achieve a higher uplift.

If you focus on such cases and then roll out what you've found, you'll be on the winning side. Aaron: That makes a lot of sense. That's also what I've been doing with my team in my role - finding those use cases that are the lowest-hanging fruit, and experimenting and iterating to find how you can replicate and scale these processes. Because AI can create as many problems as it can solve, but it certainly is a very powerful tool if used in the right way.

And we've found that by dedicating specific teams to exploring the application of AI, we are able to adopt it more quickly. So it is worth the investment, either with internal teams or by bringing in consultants who have seen it firsthand and are able to expedite that process of adoption. Danilo: Absolutely - 100% agreement. Aaron: Okay.

So on the topic of AI, I've heard that you are working on a new book about AI pricing. Is this true? Danilo: Yes it is. I'm working on a book focused on how AI reshapes elasticity management, promo optimization, negotiation support, and governance - with practical blueprints for CPG and retail, with ethics and compliance built in.

I also want to explore how companies can be successful in introducing pricing software, giving guidance on what to keep in mind, how to make it a success, and how to roll it out, also taking into account change management. And I would love to include another great Competera case in the book - like the one I included in the RGM book - because you have so many successful cases and there is a lot to learn from them. Aaron: I'm sure we could find plenty of case studies to contribute to the book, and that would be great.

We'll have to have you on again once that book is published to discuss this topic. I'm curious - will you also be exploring the pros and cons of building versus buying solutions? Danilo: Yes, indeed - it sounds like you've already seen my book outline! This will be in the first chapter, where I'd like to outline what the typical expected return on pricing software looks like, what the pros and cons of make versus buy are, and why pricing software is now a priority for so many companies.

This will also be a key consideration for companies as they take decisions related to maturing their pricing. Aaron: So Dan, if you had to advise a business on the first steps they should take to validate their current RGM strategy, where would you suggest they start? Danilo: I would recommend beginning by clarifying the value equation. Define the price-pack architecture, the price ladders, and the role of each SKU by channel.

Then run a customer-level net revenue waterfall to quantify where value leaks - from list to profit - through promotions, terms, mix, and compliance. Follow with a hard-nosed promo effectiveness audit to stop non-incremental mechanisms, and bring in the true customer profitability view by integrating cost to serve. Finally, formalise the decision cadence - who decides what, when - and launch a 90-day test-and-learn on two or three high-impact use cases with clear KPIs. That sequence grounds strategy in P&L reality and builds momentum fast.

Aaron: Great. So, final question for you: what books, podcasts, or resources would you recommend to our Pricing Heroes community? Danilo: For books, I'm a bit biased. I would recommend Ten Rules of Highly Effective Pricing for those seeking a full background on pricing transformations, The Pricing Model Revolution for those wanting to understand how pricing models are becoming the new source of competitive advantage, and Pricing Decoded for great cases and case studies across industries.

For podcasts, I must say I love your Pricing Heroes, which is a must for me, along with Impact Pricing by my friend Mark. I'd also add a thoughtful newsletter called The Price Point - it's free of charge and you can subscribe by visiting my LinkedIn profile and clicking the link there. Aaron: That's great. Thank you so much.

I really appreciate the recommendation for Pricing Heroes, and Impact Pricing is a phenomenal podcast. I love Mark Stiving's perspectives there and all of the incredible guests he features. And I'll be sure to link the books in the show notes for this podcast for anyone who's interested in finding and purchasing them. Danilo: Thank you so much.

It is always very enjoyable to be with you - always very insightful. Thank you very much for having me. Aaron: Dan, thank you for being on the show and sharing your insights with us today. Danilo: My pleasure.

Thank you. Aaron: I hope you enjoyed our conversation with Danilo Zatta. You can find the link to his latest book in the show notes. Be sure to follow and connect with our guest on LinkedIn.

For more information about AI pricing solutions, visit competera.ai. Remember to subscribe to the show on your favourite podcast app to ensure you don't miss future episodes. And please help us reach others in the pricing community by leaving a five-star review.

Thanks for joining us on this episode of Pricing Heroes. Take care - until next time.