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Gurkan Munsuz, Head of Global Revenue Growth Management, McCain Foods, in conversation with Shiraz Mishra, Head of International Markets at Polestar Analytics. This is the latest in a series of conversations Polestar Analytics is running with commercial leaders across CPG and retail on what actually makes Revenue Growth Management work.
Ask ten people in a consumer goods company what Revenue Growth Management actually does, and you'll get ten half-answers. Something about pricing. Something Finance owns, or maybe Sales.
RGM has a strange reputation for a discipline that, done well, moves more profit than the entire advertising budget, quietly, like a well oiled engine. But rarely do people understand it simply.
So, we sat down with Gurkan to close that gap, walking through RGM from both a technical angle and a people angle: how the discipline actually works, and what it takes to build a career and a team around it.
Gurkan: In different times, RGM has been relevant for different reasons. During the inflationary period, it was really about pricing activity, because that's where the pressure was. But now we're having to grow demand organically through volume. So instead of being the "pricing police" or the "discounts gatekeeper" there's a shift to being the "value creation team."
This approach requires, as I was talking in my previous RGM roundtable, driving volume penetration and optimizing price-pack architecture to create value for both consumers and customers.
The problem is it's still a black box for a lot of the organization. Part of what we're trying to do is humanize that, so people understand what's actually driving those decisions instead of just seeing a black box that spits out numbers. But we should look at RGM now like an attacking midfielder (number 10) in soccer, because the role connects different lines, creates chances, and requires both technical understanding and creative vision.
Gurkan: Part of the reason we've seen challenges across the CPG industry is that we leaned on pricing as a lever without truly understanding what the consumer was going through. Now, we're getting significant learnings from other parts of the globe. For example, in India or some cash-strapped areas globally, we learned that sometimes it isn't even about elasticity.
The consumer would go into an occasion with a hard cap on what they're willing to spend. So, our role shifts. It's not "what's the right price for this SKU," it's "how do we build a commercial or meal solution for that occasion that fits inside that cap." That's not a discount, it's empathy toward how someone budgets their own hard-earned money. And we're seeing that same principle apply whether it's a high-inflation environment or lower-income consumers here in the US.
Obviously, there are frameworks like OBPPC, Triple Win, Price ladders, Price Waterfalls, etc; which we use in our systems and practices. But leading with the customer needs and demand is the best way.
Shiraz: This is true for our RGM solutions, frameworks are embedded into the applications itself. For example, PricePulse is based on Price Pack Ladder, Price Elasticity Modeling, Positioning matrix, etc. and PromoPulse has a Classification Engine (Scale / Optimize / Experiment / Stop) for Promotions.
Pattern seen in the real world: this pattern shows up elsewhere too. Hindustan Unilever hit the same wall in rural India in the 1980s, where standard-sized shampoo bottles simply didn't match how villagers spent money day to day. The fix was the now-famous shampoo sachet, priced at roughly a rupee, which grew to account for close to 70 percent of shampoo sold in India by the early 2000s, according to de Jong and Zatta's account. Coca-Cola ran a similar playbook more recently in Latin America with a 1.25-litre bottle and a 200ml mini can sized to hit price points households could commit to, a move Coca-Cola FEMSA's own disclosures tie to a 2 to 4 percent annual revenue lift in some markets, even where overall volume was flat.
Gurkan: AI's role is to augment human judgment, not replace it. The moment a recommendation comes out of a black box and everyone just implements it because "the machine said so," you've got a real problem, because trust doesn't work that way. What we're building is a way to improve our effectiveness with AI, but the human element, the EQ and SQ, is what actually builds trust with cross-functional partners once you've used AI to augment the work. It's the journey from pure data, to insight, to narrative, to storytelling that actually lands with people. You use different parts of EQ and SQ at each step of that.
I even tried writing a version of this out mathematically. AI on its own doesn't equal trust, so the equation becomes something closer to AI plus EQ plus SQ plus everything else, over self-orientation, equals trust.
There's a well-known version of this outside RGM entirely, the Trust Equation from Maister, Green, and Galford's book The Trusted Advisor, which frames trust as Credibility plus Reliability plus Intimacy, divided by Self-Orientation.
Mine maps onto it fairly directly:
- AI stands in for credibility, but only when it's explainable, not a black box people are just told to accept.
- EQ is the intimacy piece, actually reading what a commercial partner or a consumer needs.
- SQ carries the reliability and storytelling side, the consistency and social fluency to get an AI-informed recommendation to actually land across Sales, Marketing, and Finance.
- And we'd add one more term for RGM specifically: execution discipline, because a brilliant recommendation that dies in the warehouse or on the truck never gets the chance to earn anyone's trust in the first place.
- The denominator, self-orientation, is really about departmental interest here, Sales protecting its number, Marketing protecting its budget, Finance protecting its margin.
You can have great AI, real empathy, and flawless delivery, and still get nowhere if the room senses RGM is protecting its own turf instead of solving a shared problem.
Shiraz: This is similar to the principle Polestar Analytics builds toward in its own RGM tooling, pairing elasticity and pricing models with explainable AI outputs specifically so commercial teams can see the reasoning behind a recommendation, not just the number.
Gurkan: This is something I care a lot about as someone who builds and leads teams. We need to create a real path where people can see themselves excelling in an RGM role, and then understand what's next once they're in it. What skills do they need to develop? What should be in their development plan? That's where EQ and SQ come back in, this time on the people and HR side, not just the trust-in-decisions side. Building those skills is what lets someone grow into change management and broader influence across the organization, not just execute a pricing model well.
Gurkan: Curiosity, and a continuous improvement mindset. To succeed in RGM, or really anywhere in consumer goods, you have to keep asking the right questions, because the external environment never sits still. Consumers change. Retailers change. The countries we operate in change. If you're not curious, you end up working off an old picture of a market that's already moved on.
Gurkan: We're all human, we all make mistakes, and honestly those mistakes are what shape who we are. I go back to two things a mentor told me a long time ago. One, clarity is your superpower. Two, don't be a purist. That second one matters more than people give it credit for, because it's easy to get weighed down in data and wait for the perfect data set to present itself. While you're waiting, you miss the moment to actually provide the solution.
Humanizing RGM doesn't mean stripping out the technical rigor behind pricing and pack architecture. It means building systems, and people, that can act with clarity before the data is perfect, and trust each other enough to do it together. Polestar Analytics partners with CPG and retail teams on the pricing, promotion, and portfolio work behind conversations like this one.