Throughout your career, you've helped organizations translate consumer understanding into commercial decisions. Looking back, was there a project where consumer research completely challenged what the business believed a product should be priced at? What happened, and what did that experience teach you about consumer value?
One that stands out was a new smaller/thinner sized-format (let’s call it X) launch in a restricted category. The category was dominated by an illicit, low-priced player that had built substantial share purely on price. The business wanted to counter it with a legitimate ‘X’ offering, and the working assumption internally was straightforward: price was the reason consumers chose the illicit brand, so any credible competitor would need to get close to that price point.
We ran a series of qualitative studies to unpack actual motivations for choosing the X format, expecting to validate that hypothesis. Instead, what emerged was almost the opposite. Consumers weren't choosing X primarily to save money. They were choosing it for portion control, and the format carried a lighter, "less guilty" association. Price was a rationalization consumers offered, not the real driver.
That reframing gave the business the confidence to launch at a 60% premium to the illicit incumbent, not price-match it. What it taught me about consumer value is that it's rarely a single number sitting in someone's head waiting to be extracted. It's assembled from whatever job the product is doing for them emotionally as much as functionally. Here, the "value" wasn't cost per unit, it was a sense of control and a lighter conscience, and consumers were willing to pay handsomely for that. If we'd stopped at the stated price objection, we'd have built a cheaper version of a problem nobody actually had. Real value only shows up once you get underneath what people say they want to what they're actually buying.
When business leaders say, "The product is too expensive," do you think they're sometimes trying to solve the wrong problem? In your experience, how do you distinguish between a pricing issue and a value-perception issue?
Yes, honestly, more often than businesses realize. "Too expensive" is the easiest thing for a consumer to say out loud, and it's the easiest thing for a business to hear, because the fix sounds obvious: just lower the price. But in my experience, it's often someone's way of saying "I don't feel this is worth it yet," and that's a very different problem to solve.
What that really points to is the need to understand what value actually means in the mind of the consumer, not what the business assumes it means. Value isn't a fixed number sitting there waiting to be discovered; it's shaped by what the product does for someone functionally and emotionally, and that can be very different from how a business is internally justifying its cost.
Over the years, I've found a fairly reliable way to tell a pricing issue apart from a value-perception issue. When it's a genuine pricing issue, people respond to price the way you'd expect: demand moves up or down fairly predictably as the number changes, and no matter how you talk about the product, the objection stays roughly the same. But when it's really a value-perception issue, something interesting happens: the same person, at the same price, will feel completely differently about it depending on what you tell them about the product first, what you compare it to, or what problem you frame it as solving. The price hasn't moved an inch, but their willingness to pay has swung wildly. That's usually the giveaway.
The question I keep coming back to is quite simple: if you dropped the price tomorrow, would the hesitation actually go away, or would people still feel like something's missing? If the discomfort would still be there, you're not looking at a pricing problem, you're looking at a story the product hasn't told well enough yet. Most of the "pricing emergencies" I've been called into over the years turned out to be exactly that.
Consumer research often tells us what people say they'll pay, while the market ultimately reveals what they actually do. Can you recall a time when those two stories were completely different? What did that experience teach you about measuring willingness to pay and understanding consumer behaviour?
Yes, and it's one of the clearer lessons I've had on why stated intent and actual behavior are not the same currency. This was a branded product test in the foods category. As often happens in markets with a strong tendency toward socially agreeable responses, respondents skewed heavily positive when asked directly whether they'd buy the product, purchase intention came back high across the board. The client had fixated on that one number early on, and every product in the test looked like a winner on paper.
We flagged, before the launch, that purchase intention on its own was the wrong measure to anchor a launch decision on, particularly in a market where there's a strong tendency to answer agreeably rather than critically. But the number was reassuring, and the business went ahead and launched anyway. The product underperformed significantly once it hit the market.
What made the story worth telling wasn't just the miss, it was what happened next. We then ran a full simulated test market, with some design refinements, and this time the results actually predicted market behavior correctly. The gap between the two rounds wasn't the product changing, it was the measurement approach changing. Purchase intention captures whether someone likes the idea of a product in a research setting. STM gets much closer to actual choice behavior, competitive context, repeat likelihood, and the friction of a real purchase decision.
It was an expensive lesson in time and cost, but it reshaped how I think about stated versus revealed preference ever since. Stated intent, or willingness to pay, is useful as a directional signal, but it was never meant to stand alone as the metric. It needs to be read in conjunction with other measures, like perceived value and sense of premiumness, and how those shift alongside price, not in isolation from it. If a number feels too reassuring on its own, especially in markets with a strong social-desirability bias in responses, that's exactly the moment to slow down and triangulate it across a fuller set of measures, not the moment to fast-track a launch.
Having led consumer research across APAC, LATAM, West Africa and the US, how has your perspective on consumer value evolved across different markets? Is there an example where a pricing strategy that worked in one region failed in another because consumers perceived value differently?
My thinking on consumer value has shifted from treating it as something with a common architecture across markets, to realizing that the mechanics of value perception can differ sharply even when the underlying brand and strategy stay identical.
A clear example: we had a premium brand, let's call it B, that was losing share in a European market. The strategy we tested was to launch a super-premium variant, B1, priced meaningfully above B. B1 itself wasn't meant to be a volume or margin driver on its own. Its real job was to sit above B and cast a halo down onto it, lifting B's premium cues by association. It worked well. Premium perception around B improved, and the brand's declining shares steadied.
We later applied the same playbook in an APAC market, expecting a similar halo effect, and it didn't hold. A few things were different beneath the surface. APAC consumers there were simply more price-sensitive to begin with, and the premium-to-value-for-money index was noticeably higher there, 1.6, compared to 1.2 in the European market, meaning consumers needed to feel meaningfully more value to justify a comparable premium. Retailer economics also worked against us: retailer margins in Europe were higher, giving trade partners a real incentive to stock and push B1 prominently, while in APAC that incentive was weaker, so the product got far less shelf support. Without strong retail backing and with a more price-sensitive consumer base, B1 didn't read as an aspirational halo, it read as just another expensive variant with no obvious extra benefit. The result was downtrade rather than uptrade, and it wasn't something we could correct after the fact.
What that taught me is that a value strategy isn't just a consumer-perception exercise, it's a system that includes trade economics, category price sensitivity, and how "premium" itself is calibrated locally. The same brand, the same architecture, the same intent, can produce opposite outcomes depending on how those pieces line up in a given market. Since then, I've never assumed a pricing or premiumization strategy that worked in one region transfers by default, it has to be re-earned market by market.
Every pricing decision comes with trade-offs it may improve margins but reduce penetration, or strengthen premium perception while limiting scale. Looking back, what's been one of the toughest commercial trade-offs you've had to navigate, and how did consumer insights help shape that decision?
One of the toughest calls I've navigated was around a flagship brand, let's call it D, priced at Rs 20. A competitor launched a different flavor at Rs 16 and was gaining real traction, and the business decided to launch a competing variant. Once the product, pack, and promotion were locked, the real fight was over price. One school of thought said any variant of D had to stay at Rs 20, because that price point was inseparable from D's equity, and pricing a variant lower would quietly dilute what the flagship stood for. The other school said Rs 16 was simply the accepted price in that fight, and matching the competitor was the only way to compete for share. Both arguments were defensible. There wasn't an obviously correct answer sitting in the data waiting to be found.
Where research helped wasn't in producing a clean number, it was in exposing what consumers could and couldn't actually tell us. The research found that Rs 16 was perfectly acceptable to consumers, and of course it was, a cheaper variant is rarely something people object to on the surface. But what consumers can't articulate, almost by definition, is the slower, second-order effect on how they'd perceive the parent brand once the price association became inconsistent. That's not a question people can answer about themselves with any reliability, because it isn't a conscious calculation, it plays out over time through repeated exposure.
We went with Rs 20, protecting D's price equity rather than chasing the competitor's number. The new variant not only held its own, it gained real traction over time and today leads its category, despite being priced above where the "accepted" logic said it needed to be. It reinforced something I keep coming back to across my career: consumer research is very good at telling you what people will accept in the moment, but protecting long-term brand equity often requires reading past what consumers say, into what they can't say, because it isn't the kind of thing anyone consciously tracks about their own perception.
You've experienced both sides of the table as a research partner advising businesses and as an in-house consumer insights leader responsible for business outcomes. How did that transition change the way you think about pricing, consumer intelligence and decision-making?
The biggest shift was realizing that a research finding is really just the beginning of the story, not the end of it. As a research partner, your job is largely done once you've delivered a rigorous, well-defended finding. You hand it over, and from the outside, you rarely get to see what happens to it next. Moving in-house gave me a ringside view of that next part, and it changed how I think about the whole exercise.
What struck me most was that no single study is ever the whole picture internally. On the client side, my research was just one input arriving alongside sales data, trade feedback, competitive intelligence, and plain instinct from people who'd been in the category for years. Decisions got made by triangulating across all of that, not by treating any one source, including mine, as the final word. That was a humbling but useful realization. It meant a study could be methodologically flawless and still be the wrong basis for a decision if it wasn't read alongside everything else the business already knew.
The other thing you don't fully appreciate from the outside is how many stakeholders a single decision has to pass through, and how differently each of them reads the same finding. Marketing, sales, finance, and leadership can look at identical numbers and walk away with different conclusions, depending on what they're each accountable for. Getting everyone genuinely aligned, not just individually convinced, turned out to be as much a part of the job as generating the insight itself.
It changed how I now think about pricing and consumer intelligence work, even back on the partner side. I stopped asking "is this finding correct" and started asking "how will this finding need to sit alongside everything else in the room, and across everyone in that room, when the decision actually gets made." That's a different design question, and it usually means building research to be triangulated and communicated, not just defended.
You've introduced AI-led workflows that significantly accelerated insight generation. Beyond improving speed, did AI change the kinds of pricing or innovation decisions business leaders felt confident making, or was it primarily helping them make the same decisions faster?
It was both, but the more interesting shift was the second one. We built an agent trained on quarterly business and sales reports for the category, essentially a living memory of everything that had happened commercially over multiple years. On the surface, it did the obvious thing well: instead of someone digging through old decks to answer "what happened the last time we tried X," they'd get an answer in minutes.
But the more meaningful change was in confidence, not speed. Business leaders could now ask "has this been tried before, and what happened" before committing to a pricing or innovation move, rather than relying on institutional memory sitting in a few people's heads or not asking at all because digging it up wasn't worth the effort. That changed the nature of some decisions. Leaders were more willing to greenlight moves once they could quickly verify whether a similar bet had backfired before, or find evidence that de-risked an instinct they already had.
It also became something we didn't originally design it for, a learning tool for new joiners. People coming into the category could get up to speed on years of pricing history and market context in days rather than months, which meant they were contributing informed points of view much earlier than they otherwise would have.
So my honest answer is: it didn't just make the same decisions faster, it lowered the bar for leaders to actually ask "have we been here before" before deciding, which is a question that often used to get skipped simply because answering it was inconvenient.
Consumers rarely make purchase decisions as rationally as businesses expect. Looking back, what's one aspect of consumer behaviour that organisations consistently underestimate when making pricing decisions and why do you think it continues to catch them by surprise?
If I had to pick one thing, it's how much consumers rationalize decisions they've already made emotionally. Businesses tend to build pricing models assuming people weigh cost against benefit in some orderly way, price it, feature it, compare it, decide. In reality, a lot of the decision happens before that logic ever kicks in, often driven by identity, mood, social context, or a need the person can't fully articulate. The "reasoning" consumers offer, including the reasoning they offer in research, is frequently a justification stitched on afterward, not the actual driver.
I think it keeps catching organizations by surprise because it's genuinely counterintuitive from the inside. Businesses live inside spreadsheets, cost structures, and competitive benchmarking, so it's natural to assume consumers are running a similarly rational calculation on the other side of the transaction. They're not. And because consumers themselves aren't always aware of what's actually driving their choice, they'll answer research questions with the most logical-sounding explanation available, usually something about price or features, even when the real driver is something else entirely, a need for reassurance, a fear of being judged for the choice, or simply the comfort of picking something familiar under pressure.
The businesses that get this right stop asking consumers to explain themselves and start observing what consumers actually do, and where their explanations and their behavior diverge. That gap is usually where the real insight is sitting.
Looking back across the many pricing, innovation and consumer insight initiatives you've led, what's one business decision you're most proud of influencing not simply because it improved commercial performance, but because it fundamentally changed how the organisation understood its consumers?
The one I'm most proud of wasn't a single pricing call, it was building a measurement capability that didn't exist before, in a category where it mattered enormously. This was a restricted category in an APAC market, a genuine dark market where advertising was banned across every conventional channel, television, internet, print, all of it. The only legal touchpoint left was in-shop, at point of sale.
The problem was that while every other channel in the industry had decades of measurement built around it, POS didn't. Businesses were investing in point-of-sale activity essentially on faith, with no reliable way to know whether it was actually changing consumer behavior, retaining buyers, or just occupying shelf space. We developed tools specifically to measure the efficacy and efficiency of POS activity, and over time that grew into a proper measurement suite: how well consumers actually understood and recalled the ad at shelf, how well it retained them as buyers, what formats worked versus what didn't, and even where in the shop the ad needed to be placed to have real impact.
It also opened up something that simply wasn't possible before: we could now test multiple POS ad executions against each other before committing to one, rather than picking a design on instinct and finding out months later whether it worked. That alone changed the discipline from a single guess to an ongoing experiment.
What made this decision matter beyond the numbers was that it gave the organization a language and a lens it never had for its most important, and only, lever in that market. Before this, in-shop spend was allocated essentially uniformly, a cost of doing business because there was nothing else available. Afterward, budgets started shifting toward what the data showed was actually working, and away from placements and formats that weren't earning their keep. It fundamentally changed how the business understood the one moment where it could still reach consumers directly, from an act of faith to a measurable, improvable discipline.
Over the years, you've helped businesses make countless decisions about pricing, innovation and growth. When you look back now, do you think the real value of consumer insights lies in finding answers or in helping organisations ask better questions? Why?
If I'm honest, I've come around to believing it's the latter, even though early in my career I would have argued the opposite. When you're starting out, insights feels like a search for the right answer: what price, what feature, what claim. And there's real value in that. But the projects I look back on with the most pride weren't the ones where we found a clean answer, they were the ones where the research changed the question the business was asking in the first place. More than once, a business has walked in asking "how low does the price need to go," and the real value of the work was in getting them to ask "what job is this product actually doing for someone" instead. The pricing decision that followed was almost always easier once the question itself had changed.
Answers have a short shelf life. A price point, a claim, a segment definition, all of that ages as markets move. But an organization that gets better at asking sharper questions carries that skill into the next decision, and the one after that. I think that's the real, compounding value of consumer insights: not the specific number you hand back in a deck, but whether the business walks away thinking differently about how to interrogate itself the next time.
If I had to summarize it in one line: good insights give you an answer for today, good questions give you an advantage for years.
