Why Technically Correct Pricing Recommendation Can be Strategically Wrong with Sandeep Mathew

Speaker A: Don't take any recommendation at face value. Look at all angles to it. Most importantly from the long term health of the brand. Never compromise that for any short term gains.

Speaker B: Coffee's for closers only.

Speaker A: So we've established my proposal to stand in principle. Now we're just haggling over prize.

Speaker B: Let's see how much we're going for on ebay. It's the same as dunkin donuts. Cost 15 times the price. Every year. The best pricing and commercial strategy minds in the world find each other in one place this November. That place is Amsterdam. Epp is celebrating 20 years by bringing a thousand pricing and RGM leaders together for its Global Summit November 17th and 18th. If pricing is your profession, this is where you belong. Register@pricingplatform.com welcome to Impact Pricing, the podcast where we discuss pricing value and how buyers decide. I'm Marc Stiving. I help companies turn hidden value into willingness to pay. Our guest today is Sandeep Matthew. Here are three things you want to know about Sandeep before we start. He's a sales and marketing leader with 19 years of global CPG experience all around the world. He spent much of his career at Unilever, deep experience building and deploying revenue growth management capabilities. And he believes a technically correct pricing recommendation can be strategically wrong. Welcome, Sandeep.

Speaker A: Thank you so much, Mark. That was a good intro.

Speaker B: Uh, let's start with the big question. How'd you get into pricing?

Speaker A: So marketing is my passion. I did my B school fundamentally to get into marketing and I've been dabbling in marketing since a very long time. From theater plays that he used to put up, you know, magazines that I came up back in college, things like that, trying to build a brand all the way to 200, $300 million brands that I worked with later in life. So marketing was my core passion and throughout my time in marketing and many people who've been in marketing would probably empathize with this, that we are always at the receiving end of a lot of researchers and analyses and different kinds of documents that we need to ingest and pretty much make the best decisions for our brands. In the time that I spent at marketing and all of these material that I used to be handed out, the ones that I found most useful was always from RGM because it always comes from a very, very clear, empirical data point of view and makes structured observations and gives structured insights that you can take clear action on, which sometimes, you know, many of the other kind of researchers would lack. So that sort of got me interested in rgm. And at a certain point in my career, when I was looking for a change in my career trajectory, a role in RGM came up. And that's when I got into it. And I spent a few years in RGM at Unilever itself. And it really, you know, created a very strong foundation for me. So then when I moved out of Unilever, I started working as a consultant, working also with people like Bain and continuing my work with rgm. So, so that's how I got into it. Long answer.

Speaker B: Nice. In a second I'm going to ask you a specific question about rgm, because what you just told me fascinates me. But one of the reasons we're having you on today is because you're going to be a speaker at the, uh, EPP20 event that's coming up in Amsterdam. Can you give us a quick preview on what it is you're going to be talking about there? And then that's not what we're going to be talking about today.

Speaker A: Okay, perfect. So at my time at Unilever, when I was, I started in Unilever doing RGM for one small part of it. And over the years it kept growing. And by the time I left Unilever, I was managing RGM as a capability for the whole organization globally. And at the time that I left, we basically had one framework that we followed across all our market, all our channels. The only customization that we probably had to it was for the E Commerce channel that we called it back then. And I, uh, realized that E Commerce, yes, it's a big, fast growing channel that needs certain custom ways of looking at it. But that's not the only channel. You, you have many other channels in different markets that are really growing. And a few years back it used to be Value Channel, then there was Omnichannel, and now recently in the last few years there is Quick Commerce. So each of these channels come up with their own nuances and a very standardized RGM framework doesn't suit at all. You need to look at all of them very, very differently. So that's what I've got into. And in the last few years I've been doing custom RGM analytics for different channels. So you always do for different markets, different categories. I've added the third element of channels as well to it and that's what I'm going to be talking at epp. Uh, I'm going to talk about how you can customize RGM for the different channels and I'll give some examples across

Speaker B: Each of these channels sounds fascinating. Absolutely. I'm also going to be at epp, or at least by video maybe. But I'm launching my latest book, Buyer Disconnect How Smart Companies Lose Winnable Deals. And I'm really excited about it. It's going to be a lot of fun. So.

Speaker A: So it'll be great.

Speaker B: Okay, so let's jump back into the topic for today. And so one of the things you just said when you described RGM and your career path was that RGM gave you data that was actionable and other stuff didn't.

Speaker A: Yeah.

Speaker B: What's the other stuff we're comparing it to for a minute. Why was the information different?

Speaker A: Okay, so usually a lot of research are based on customer surveys, consumer, you know, qualitative data. It can be as sound as possible, so you can make it statistically relevant, go to as many respondents and all of those things. But at the end of the day, they're still. I mean, your end customer has not put their money down. They have not. You know, you can say one thing and do something completely different. So a lot of those things, they're good analysis. But typically, as a marketer who's been very close to the brand for a very long time, sometimes you just have this gut feel that, you know what, this recommendation cannot work. But when it comes to rgm, it's very difficult to say something like that. So when I look at an RGM presentation, it's very difficult for me to say, you know what? I have a gut feeling this will not work, because RGM will show me data that it can. So, yeah, that's what sort of got me interested. I figured there are ways to do analysis, and there are ways to look at data that can make it almost irrefutable in logic.

Speaker B: So if I were going to reinterpret what you just said, RGM data is actual purchase data. We get to see people put money out. And most market research data is intention data. Right. So what do people say they're going to do? Yes, and there's a huge difference between the two.

Speaker A: That's absolutely right. Yeah.

Speaker B: Excellent. And so it surprises me because the topic we want to talk about today is how rgm, um, often misleads us in terms of buyer's brand equity, how buyers are perceiving our brand, and it almost feels like it's contradictory to what you just said. Yeah. So let me just let you pontificate on that for a minute, and then I'll see where my confusion lies.

Speaker A: Yeah, yeah, no, absolutely. Which is why I knew this topic is going to be a little difficult to manage because I pretty much went through a full circle. So I started exactly what I told you, you know, that I was not very convinced with the different non RGM analysis that used to come through. And eventually, you know, RGM analysis would end up being a lot more convincing. But after having spent so much time at rgm, I actually have realized that although data can be sound and the recommendations are very, very solid, sometimes you need to go against those recommendations. And the reason for that is as a marketeer or as a brand custodian, if you're working on a brand or working with a company where you are interested in the long term success of that company, sometimes you need to disregard some of the RGM recommendations. Because most RGM recommendations usually do analysis on very recent data. It could be three months, six months like I've seen in a lot of quick commerce data, or at best three years or five years, at best, you know, with some of our more evolved mature categories and channels. But it doesn't go beyond that. But when you're talking about a brand, and at least at Unilever, I've been with brands that have been around for hundreds, you know, more than 100 years. So when you are making decisions at that level for a certain brand, you don't want to only win over the next three months or six months, you want to win for the next five years, 10 years. And that's why you need to sometimes be a little careful about the recommendations that RGM gives you. So that's where I completely flipped. Um, flipped as in I still do a lot of RGM analysis, I still believe in the logic, but I've now reached a certain level where I look at that logic and then I still try to bring in my gut or try to figure what could be the impact if I make this decision on a near term basis against a certain long term strength of the brand. So I don't know if I can answer that.

Speaker B: No, no, no. I think that's absolutely brilliant and I think that's exactly the tension. So before I ask the next question, I want to tell a quick story. A hundred years ago, not quite, I had hired a pricing consultant. They had come into our company and they helped us make some specific changes. And I can tell you that they were phenomenal at short term price increases. Yes, right. At short term impacts. But the long term impact is actually kind of painful. And so consultants tend to get paid for short term where in truth the company wants to manage for the long term. And it's almost exactly what you just said, right. Uh, where RGM is telling us short term. But in truth I want to manage for long term.

Speaker A: Yeah, absolutely. And I think this is a very real gap in the industry because the teams function very differently. You have marketing teams, brand custodians who understand the brand really well. They don't understand rgm. And you have RGM teams that do great rgm. They don't look at the long term picture of the brand. And that's the tension that I feel I'm trying to resolve most of the time in my work right now because I'm trying to bring them both together, trying to bridge the gap and tell them I've been a marketer, I've also been rgm and I know the tension that exists. And therefore this is what the analyst is telling you and this is what the analysis is not telling you. And equally, I mean this is probably the recommendation that you should take. And these are the impact at a short term level, at a long term level. And now you decide, I mean it can be so that certain times short term pressures are huge and you need to make that decision, which is fine, make that decision, but at least know what you're offsetting.

Speaker B: So, okay, so here's a hard question for you. Assuming that I want to know the long term impacts, what are the metrics or what are the studies that you would use? Is it a brand equity study? What is the study I would use to say yes, this price decrease impacted my long term future?

Speaker A: Yeah. So in a couple of my studies I've actually tried using long term data. See, brand equity does not change in two, three years. A lot of typical portfolio product performance, brand attributes, they, they don't fluctuate in very near term. You need to look at significantly long term, at least 10 years, 15 years. So a couple of projects that I've worked with, I've actually looked at pretty long term data. And once you look at that kind of data, you, you are able to extrapolate and project for the future. But not a lot of brands or companies have the luxury of access to that kind of data. So in those cases you sort of need to make judgment calls, which is basically a combination of what you can do with consumer surveys, which is the analysis that I told you that you know, always comes with a lot of question marks combined with, with sound near term rgm, uh, studies and then try to put in as much of brand equity measures as you can in those studies. So even if it is Just two, three years. Use those data points. Don't just leave out those brand equity measures, use them and. Yeah, so basically to summarize, use long term data whenever possible and when that's not possible, go with consumer surveys and then combine it with sound RGM analytics.

Speaker B: Okay, so first off, understand that I'm 100% behind your thought process and desires. I'm not 100% behind how we're getting

Speaker A: there yet, to be very honest, it's not like I've solved it. Absolutely

Speaker B: right. Because once you go to 10 years, there's 100 things that happened that could have been causality other than the RGM decisions we're making. Yes, right. And so that becomes challenging. It would be really nice if there was a metric, if there was a consumer survey we could run that was a precursor, a predictor of future brand equity.

Speaker A: Mhm. Yeah, true. Exactly. I mean you can design a lot of these studies to give you that indication. You can never, I mean it'll be very difficult to justify the, the accuracy of it, but it can be designed in that manner and then supplement that with RGB analytics. So that's the only way I know to go about it right now. You try to use long term data and where you don't have it, you try to make the best of your near term RGM analytics and surveys and make the best decision. So it's difficult to navigate it. I don't have a permanent solution. I'm still working with a lot of clients and trying to figure out the best way to go about it. But I think the more important question is to just make sure you're asking that question. Don't forget that there are long term impacts on the brand. That's the most important thing.

Speaker B: So can you think of any great examples? I'm going to give you the one that I could think of and you could tell me, hey, this is a great example or not, but I remember when I was growing up Izod shirts were like all the rage. They were super expensive. They had the little alligator on the side of the polo shirt and suddenly they became almost worthless. They lost that complete brand. And I think it was because they started selling at discount stores and started selling at prices really low because as they were trying to push volume. First off, is that a reasonable example? And do you have any other examples like that?

Speaker A: Quite a few. I wouldn't want to name the brands because I've worked with any of them, but I think they all tell the exact story that you said because you get into A certain actually, you know, if I could draw one parallel across all these brands, all of them had had actually one issue which actually boiled down to something to do with the brand equity. And because you couldn't address it at that point in time, you, you try to solve it with other near term solutions. So there was a certain brand equity problem and you try to fix it by addressing volume. Like you just said, you give some promos, try to make it up, and then every time you do promos, that's a downward spiral. You have to keep giving promos and then promos keep increasing and keeps eroding profitability. So it just keeps going that way. And I think all the brands at some point in time that sort of eroded equity and went down that funnel and lost volume M and some of them even died, pretty much started with some issue in their equity. And probably if you went back in time and could address that issue, and a lot of times the equity issue can be as fundamental as saying, I'm not standing or I don't stand for anything my end consumer does not. If I were to ask them what do I represent, or summarize me in one word or two words, three words, they're not able to do it. So what do I really stand for? What do I bring meaningful to them? What, how am I differentiated to them and how am I salient? So those are the three parameters that you typically check with consumers. You need to ask that question and really answer that, find where your gap is and then solve for that. So in all the brands that I have seen, they have failed to solve for that and try to sort of just fix the symptoms and not address the real problem and ended up going down that spiral.

Speaker B: Yeah. Uh, so let's assume that everything we're talking about is absolutely right and that is, uh, I can make really bad pricing decisions to impact my brain brand. Yeah. How do you know ahead of time and can you give examples or can you think of, hey, this is where my gut says don't do this.

Speaker A: Yeah. So one of the things, in fact, I've noticed that a lot of brands that end up making these mistakes end up mostly making it in promotions. You know, they start going down that route and therefore whenever I'm doing certain promotion optimization work with the brands, I always try to create a framework first. So to say that what is it, you know, what is your brand job to be done? And you may have many of them and for each of those jobs to be done, what is the corresponding promotional strategy? So what is it that you really want to get out of. What action do you want to get out of your consumers and promote for that only? And once you have that clear promotional framework, then it becomes an easy filter. Then when you make these RGM recommendations that can come up with say 20 different promotional options, you can actually look at it and say, you know what, 15 out of these don't relate to my original strategy in my original job to be done, and therefore let me look at only these other five. So I think that's usually the way I approach it. Before you get into this framework, start from the brand, start from your basic job to be done. Where is your source of growth? And translate that to promotional objectives and then do your study. So that's one year.

Speaker B: So when you say where's your source of growth? Here's what's going through my mind and tell me if I'm totally off. And that is, am I pulling future purchases today? Am I stealing business? My competitor would have won. Am I generating more demand than would have existed before the promotion? Is that what you're thinking when you say where's my growth going to come from?

Speaker A: Yeah, 100%. I mean, is my growth going to come from new users? So is it penetration? Is it going to come from competition? Is it going to come from category switches? Is it going to come from future buyers to now, like you said, there are different ways you can articulate your source of growth. So get that clear. And that can change not just at a brand level, but brand variant, brand variant sku, brand variant SKU market, you know, all of those things. So articulate that very clearly. And then for every single brand jtbd, you can clearly have a promotional jdbd. Like for example, sampling will only work if you want to do penetration, don't do sampling if you want to switch from your lead competitor, for instance, I mean, there is a reason why they are the biggest clear in the market. You can't sample and expect consumers to just switch. So that's just one example. But for every single job to be done, you can clearly map it to what promotional objectives you can have and then do your analysis accordingly.

Speaker B: Yeah, and I think the interesting thing is once you have these hypotheses, you say, this is what we're going to go do. Now we can find ways to go test it, Right? We can go find ways to go collect data to say is this actually what happened or not what happened? And. And what do people think?

Speaker A: Correct. Exactly. And a lot of times RJM analysis don't work that way they always work bottom up. You know, you start with all your data, just do a bunch of analysis and then you can come up with a lot of recommendations that give you amazing volume, amazing growth. But then, you know, you end up missing the big picture when you do that. So it needs to start the other way around or at some level link the two.

Speaker B: Yeah, it'd be really interesting to know who's buying the product. Right? Is it a first time buyer? Is it someone who buys from competition? This is hard to get when we're talking about consumer data.

Speaker A: It's. Yeah, there could be certain cases where it's hard to get, but it's not that difficult because sometimes if you work with, you can, you know, just work with a certain customer, you can have a certain relationship with them where you can agree to certain ways of working, where you have access to data from them and then scale it up to your entire market. That's one way of doing it. That's how we usually work with the quick commerce, for instance, because quick commerce as a channel, I mean, it's too evolving. So we work with a few partners, few customers, try to work with their data and then make reasonable assumptions to how it would work with other quick commerce customers as well. So that's one way of doing it. So you can scale data, you can do surveys, of course. So there are different. And now with AI, there's a lot of new things coming in. So you have these things called synthetic data where you can, you know, pretty much like the data doesn't exist, but you can make reasonable assumptions to what that data could be and synthetic data could fit in and fill in gaps. So you have data to a certain level, but it lacks certain areas and that you can bring in with synthetic data. And once you add synthetic to your overall data, it makes the whole data a lot more robust and you can do better analysis that way. So I think with AI there's a lot, you know, there's more better ways of doing it, but essentially it comes down to the same thing. So try to answer those original questions. Even if it's difficult to do. There could be partnership, there could be ways of getting there and try to answer them.

Speaker B: Nice. So, Sandeep, I find this fascinating. I've been, my world is focused on how buyers make decisions because that's, you know, how I get to figure out how much I want to charge them. And your world is more about how do I aggregate all of that, look at all this aggregated data and then make inferences about how buyers are making decisions.

Speaker A: M. Yeah.

Speaker B: Which is really cool. Really cool. Okay, we're running out of time, but let me ask you the final question. What is one piece of pricing advice you'd give our listeners that you think could have a big impact on their business?

Speaker A: I think pretty much exactly what we discussed. Don't take any recommendation at face value. Look at all angles to it. Most importantly from the long term health of the brand. Never compromise that for any short term gains.

Speaker B: Awesome. And Sandeep, thank you so much for your time today. If anybody wants to contact you, how

Speaker A: can they do that on my LinkedIn? I'm available at SandeepJohnMathew on LinkedIn and you can always message me.

Speaker B: Perfect. And we'll have that link in the show notes. And to our listeners, thank you for your time. If you enjoyed this, would you please leave us a rating and a review? And if you have any questions or comments about the podcast, or if you want to see value through your buyer's eyes, email me marking.com now go make an impact.