Pricing & Price Strategies w/ Ben Reich Ep. 64

Foreign. Welcome, everybody, to this edition of CPG Insiders. I'm your host, Mark Young with my co host, Justin Gerard. And Justin, we're going to. We're going to talk about pricing and price strategies today, which is a great topic because a lot of people really need help with that.

Well, and especially right now, with everything going on with the economy, this is constantly something that we're. A question that we're getting, conversations that we're having. What is the optimal pricing strategy for our products? And of course, there is no one good answer. Right. So this is going to be, I think, a really great show.

Yeah. And something I always try to explain to people, and that is especially when we're dealing with inflation, which we are right now, we have to keep in mind that your nail polish isn't just competing with the other nail polishes on the shelf, it's competing with spaghetti sauce and a gallon of gasoline. Now, because consumers are having to now make trading decisions, they're having to decide, do I get X or do I get Y?

Right.

And not only is your advertising and marketing and your communication to the client important, but your pricing strategy is equally as important because you need to have, you need to have pricing for profitability, but you need to have pricing that will still present a bigger value to the consumer than the price you're asking. And it's something we've always said, which is your ask needs to be smaller or your, your offer needs to be bigger than your ask.

Right.

Whatever it is you're offering people, it needs to have the perception of having more value than what you're asking for. And in the case of a CPG product, that's the number of dollars that you're asking for the product.

Mm, absolutely.

And by the way, this, these pricing. Pricing strategy is dynamic. So for any of you that are already in retail, you already know that Walgreens could have eight different prices around the country or 10 different prices around the country, because they know that certain communities can pay more. They have different real estate costs in different areas. They know that the turn rate of, let's say, a premium product is higher in certain stores than it is in other stores, and they adjust the pricing accordingly. The masters of this stuff is, is the airlines, because the airlines really operate on an incredible dynamic pricing system where literally by the second, they're adjusting the price of airline tickets based on the demand and the velocity that the seats are being booked. And some retailers are moving into that. We're going to learn all about this stuff today. So our guest Today is the CEO and co founder of Datasembly. His name is Ben Reich. I gotta get that right, Ben Reich. And Ben is a, an engineer turned entrepreneur. I used to be one of those too, but, and when I was an engineer, Ben, people would ask me what I did and I would always tell people I stop exciting things from happening which, which is why I stopped being an engineer. But anyways, Ben's company racks pricing on products across 200 different retailers and 150000 different stores. In fact, his company is the company that, that actually called out the baby formula problem. I think prior to the public knowing there was a baby formula problem, you guys actually found it in the data. And there is so much that we can learn from the data. So. So Ben, welcome to the show. And why don't you start by telling us, you know, a little bit about yourself so our audience can get to know you.

Yeah, thanks so much for, for having me today, Mark Justin, really appreciate it. Excited to catch up talk pricing and hyper localization and all the other incredible trends we're seeing dominate cpg. Yeah, by, by way of background, like you said, I'm a software engineer. I like to think that I didn't just stop things, some interesting things, but also built some, some cool technology. Got my career started at a retail analytics firm called Applied Predictive Technologies which is, was then later folded into, to MasterCard but started really kind of delving into the space of price promotion, assortment optimization and big data. And, and I'm excited to, to catch up and tell you all about it.

So tell us how, tell us what datasembly does. Let's, let's get people tuned into what does data assembly do and how does, and how does that, how does that work in the CPG world?

Yeah, great. You know, you spoke earlier about how a place like, like walgreens might have eight or 10 different prices for, for a single product. You said the truth is that we're seeing the most sophisticated retailers forget eight or 10 price zones. The price zone is shrinking down to the size of a single store location.

And that's with dynamic pricing. Right.

You know, dynamic pricing or even just, you know, even if they're doing weekly or you know, monthly price changes, it just, you know, the, the level of sophistication that they're able to bring to bear using price optimization technology, recommendation algorithms and bringing in novel data sets that allow them to be responsive to the dynamics that really governing shopper behavior are bringing us to a new market where that store level, item level Pricing is so dynamic and difficult to track that that was really when talking to the founding of data assembly, we started to see this real emergence of frustration about the inability for many CPGs to track, forget even their competitors sometimes, but even their own items in market as these things just started getting manipulated in order to maximize specific shopper behaviors. The other trend I just will call out to explain kind of what data assembly does is the incredible and almost head spinning speed with which the the rise of Omni channel, click and collect pickup in store, in store shopping mobile companion apps and this whole ecosystem of solutions that allows shoppers to interact with with specific items at specific store locations with that seamless online offline experience that we all as shoppers have come to expect, means that so much of this critical information around localized up to date price promotion and assortment is actually just available online. And so data assembly at our core we bridge that gap. We collect that sort of localized up to date price promotion, assortment and availability data from across the public web to build a normalized unified data stream describing an unprecedented view into, like you said earlier, billions of product observations a day from hundreds of thousands of different store locations across hundreds of different retailers to build a real line of sight into these dynamics that's never been available before.

So let me ask you a question. Is it possible that I might go to a major retailer and a product could be on the shelf for 41.99 but it's on the same retailer's website for 38.99? Could I literally pick up my phone while I'm standing in that store, click on the four dollar cheaper price and mark pickup and walk over to the pickup area of that store and buy that product for $4 cheaper than it's sitting on the shelf?

Yeah, it's a great question. I mean really, the, the, the, the kind of complexity in the omnichannel shopping experience is hard to overstate. Forget just 22 price points like you're saying that same item at that same store might be available on Instacart or shipped. There might be a different pickup in store versus drive up and go price. There might be a delivery to home price. That's different still, you know, and this kind of tapestry of different prices and availability metrics has become impossibly difficult to collect without specialized data and tools. I will say though, we are seeing because of the exact experience you just laid out and how many shoppers are using their phones in store to track products and find them even in the store or do comparison shopping that the retailers really have move that, that, that distance between what you can see online and what's happening in store. That Delta continues to shrink and we're increasingly seeing that seamless online offline experience with that same as in store price guarantee. So that in reality, most frequently you are going to see that store level price. But like I said, the complexity here is, is hard to overstate. And there's every iteration of that with the online one being cheaper or more expensive, the delivery one, including different sizes and items and different competitive factors and time frames that all make it really a challenge to respond to these dynamics.

Now let me give you a real world problem that our clients see. We see this dynamic pricing and let's say we have a client, we have a lot of clients with prices, we have a lot of premium clients. So we're talking CPG product that sells for, we'll say 37 or let's go, we'll cover the 27 to 29 product. Then the dynamic pricing takes some of those clients or some of those stores all of a sudden push to 31, 32, 34, where they cross that magic Rubicon of 29.95. And then when we drill down at store level, we see that some of the stores that have jumped into the 32, 34 range all of a sudden aren't moving product like the stores with the 12, $29 price range. So not all these retail algorithms are, are working well to start with, are they? How do we fix a problem like that?

Yes, it's, it's a great observation. We're still really in the infancy of the adoption and development of these core technologies. You know, you're talking about passing some magic threshold where the CPG or manufacturer here is really aware of their shoppers and what that threshold is. But from the retailer's perspective, there's so many inputs now that go into something like a pricing decision. Right? Of course they're trying to find that elasticity curve and understand where that magic threshold is. But there's so many other factors, like what is the store across the street charging for this? Sometimes the same product. What is the private label alternative that's available here? What items are we promoting that are in the same category this month? What's our category strategy? Are there any supply chain issues? What are the evolution of local demographics in this area? Perhaps they're running a test to discover that, that elasticity point that we're discussing that maybe they don't have the same conviction about. So there's so many inputs that go into pricing Specifically that really the only way to be responsive is to track these things with granularity and recency. You know, the more granular and recent the data, the more actionable the, the insight is. Right. And having that information at your fingertips is now critical to drive this sort of activity at retail. That, that, that helps you maximize the, the shopper behavior that you're looking for.

Now let's say we're launching a new SKU and we know what our cost is. We have a, we have an idea on what kind of multiple from cost as far as we need for margin to run our business. But can we use stuff like data assembly? Is there a way for us to use data like that for us to determine what price, what the MSRP on a product should be when we roll it out for the first time?

Right. Yeah, that's a great, a great use case. You know, like I said, so many of the factors that come into play with how retailers are determining price are really out of, out of control of the cpg. You know, the local competitive environment, the regional pre that they might want to carry this item in the private label items that are available only in those markets. And so absolutely having this broad category level perspective on dynamics that are emerging over time in your category can certainly help inform that sort of decision. Like on our website we have a product called Snapshot where you could view some of these category level trends. It's like a higher level aggregate. But to see how certain categories, especially in light of the big trends that are governing the whole industry right now, like inflation and supply chain issues. But to see how your category has been affected differently than other categories for things like pricing trends. Another factor I'll just call out here, Mark Justin, is we're so focused on pricing. That's so top of mind for everyone, of course. Right. And that's a large part of the value that that data assembly brings to market. But really we're seeing these dynamics emerge outside of pricing as well. Right. Even just which store locations are carrying this item and decide to make it part of their assortment. If you're selling into a big chain retailer, you know, it used to be again they had some regional decisions. Now it's become far more nuanced. And you could have two different store locations that have a different strategy around which items they're carrying or which sizes they're carrying or which private label competitors they're carrying. That again, the CPGs who are launching a new product should be aware of in order to help them maximize their own marketing strategy and Targeted advertising strategy and, and just to be able to better understand and expect and predict the, the dynamics that are going to emerge.

So what would a, if, if, if I was a CPG manufacturer and it came to data assembly, what would be the type, what would be the types of information I would get and what would be the actions that I might look to take with it?

Right, yeah. We work with many of the biggest CPGs in the country, including places like Hershey's, who are leveraging data assembly data to help them track their place in the market and influence the retailer. Ultimately, you know, this transparency at the item level, store level, and in near real time allows them to, to wield influence over the retailer. Whether it's convincing them to change price, whether it's being smarter about how they're deploying their trade dollars, which is something that we're expecting to, you know, really accelerate in this, in this next year as some of the inflationary pressure cools down and the big brands are going to be looking to, to maintain the, the volumes that they've, that they've increased over the last couple years. And so, you know, having that full picture of the competitive dynamics that allows them to go to the retailer and show them the marketplace dynamics that they're trying to maximize, to change price, to carry a certain item, to run a certain promotion. All of this is, you know, used to be handshakes and conversations and now is a data driven conversation. And like I said earlier, the more recent and granular and up to date the information, the more actionable those insights are that you can extract from the data. So cpg, that's trying to convince a retailer to carry a certain item in a certain market, or to hit a certain price point, or to show them why that magic number is the right price point for them, you know, what should be armed with the transparency of what else the shopper is seeing, what are their competitors doing, what promotions are running, what local trends are emerging, what category level trends are emerging. These are all critical pieces of information that we know the retailers are using to make these decisions. And so having that information at your fingertips and the tools, dashboards, analytics, visualizations and insights that help you motivate a specific action are at this point critical to get the retailer to take any of those actions.

So all of us in the CPG world have been faced in the past couple of years with our cost going up, our cost of ingredients, our cost of packaging, our cost of glass jars, everything has gone up. We've all needed to go back to the retailer and try to take price increases and the retailers as a rule of pushing back, you know, which they do, to try to push back and it's making profitability harder and harder. How can we take your data to go into the retailer and say, look, not only do I need to do this because my costs have increased, I can show you that the market will bear what I'm trying to do.

Yeah, spot on. It's Mark, you've honed in on exactly one of the, the critical use cases that are our users have really been leveraging to great ROI over the last couple years with this inflationary pressure. What's important is that, you know, there's one of the, one of the trends I'm sure anyone listening who's had to try to push through that kind of price increase is aware of is this, this first mover problem, right, where every retailer is going to say, well I'll move when the store across the street moves. And it's incumbent on them, the cpg, to, to fight that pushback by showing them these real dynamics. Historically, many of our customers send people physically into stores to walk up and down the aisles with a clipboard to collect this sort of information and even then, you know, showcasing it to the retailers. Sometimes, you know, perhaps it's their prerogative to be unconvinced in this, in this scenario, but that it's, it's not as convincing coming at them with that full picture of what's happening with your competitors as a retailer with, with your, with that retailer carrying your, your competitors products and what they might be doing with it helps motivate that so you can go to them and say, look, you know, we know the market can bear a higher price. Look at what the stores across the streets from your stores are doing. They've already moved on price. Right?

Look at this like a case study of how that's work. Without giving us the names of a brand or retailer. Can you give us like an example where this has been used recently?

Yeah, it's been used, you know, across the board. Exactly like the sort of scenario I'm laying out where you know, they have to push through a price increase. A big CPG manufacturer told us that his last time they pushed through a price increase, it took them 20 weeks to get 75% or something of the store locations to push through the price increase because they were able to take this data from data assembly and that hyperlocal near real time price promotion and availability information from their competitors to the retailers every day and show them what's happening in market. This time it took them three weeks. Right. And so that was 17 weeks delta of being on strategy because they were able to go to, you know, retailer A and say, hey, retailer B moved yesterday. Here's where they moved. We want, we think it's time for you to follow suit. And because they're coming at them with shareable, up to date item level, store level information, none of it is obfuscated at this kind of category level Trends or an 8 week rolling average where these dynamics can be argued back and forth. It's like this is what's happening at the store across the street. It's time for you to respond. And that pushback that they're trying to wield, like you were saying, denying the trend or arguing that the market can't bear it is just, you know, it drowns under a tidal wave of evidence when you're able to bring that sort of information to them every day or every week and show them it in a localized, up to date way.

So using your service, I'm going to use Walgreens as an example. Walgreens got what, 9,000 stores? Roughly 9,000 stores. Would we go back to Walgreens and actually give them location by location pricing, or would we give them regions? What, what would we deliver to them if we wanted to adjust?

Right.

You know what, in many of these cases, the real, one of the big advantages of having the data assembly data at your fingertips is that because we collect data from public sources, our, our CPG users are able to share the data across retailers. So in that example, forget sharing Walgreens localized pricing data with Walgreens, you might show them one of their competitors in order to motivate a specific action. So yes, you would, so would I.

Literally show the CVS across the street from a Walgreens or would it be that granular?

Yeah. Or you know, I mean you can, yes, you could, you can do that kind of store by store connection saying, hey, for each store, here's the nearest one of your competitors stores, or you know, any store that's within a mile and do that sort of level of analysis or if you know that's not the nature of the relationship, you know, you obviously can view those, those metro level or area trends if that's the level at which, you know, you're operating as well. Because, you know, I know a lot of people are still, like I said earlier, maturing on this front and kind of coming up to speed on just how precise, how surgical, you know, that sort of precision can be.

I'm envisioning, Justin, I'm envisioning going to Walmart.

Right.

And demonstrating to Walmart that, you know, they are $6 under CVS and Walgreens.

Right.

And that being $3 under CVS and Walgreens would cost them nothing.

Yep.

In the way of volume, that they're leaving money on the table because the gap is too big in real time.

Yeah.

Is that, does that happen, Ben? Is that kind of something that goes on?

Yeah, that's, that's spot on. You know, every retailer has their strategy around how their, you know, what they want their price perception in market to be and what they perceive as an acceptable price gap given the other factors of their, you know, locations and store experience. And they all have their strategy and they'll, they'll talk about that strategy with the CPGs and they'll say, hey, we don't like to be within 10% of our nearest competitor. Right. So they might even know that fact. And then you could come to them and say, great, 10%, so you should. And so that's sort of, you know, if you, if you, as a, you know, cpg, can kind of forge that relationship, you know, with the retailer where you understand what their own strategy is, and then you can do that sort of joint business planning by coming at them, by helping them stay, stay tighter to the strategy that they've themselves expressed. You know, there could be a lot of, a lot of really positive impacts for everyone. Right. And, you know, whether it's a margin increase or whether it's carrying another item or when you're launching a new product, convincing them to carry it in certain items, or running a specific promotion, if you're, if you're promoting in an area, all of these factors. Yeah, just like you said, the price increase1 is quite salient right now.

Have you ever been able to validate through your data that increasing the price of a specific product actually increase the sales volume?

Right, that's a, that's a, a great question. You know, look, obviously price changes are one of the few levers that the retailer can really pull. Right. They're choosing their assortment, they're choosing their store experience. But price is the big one, especially when you're selling packaged goods where it's the same product across the street. In our data, you know, we're, we're mostly viewing what the shopper can see, which doesn't include, you know, volume or sales data.

Right.

But when we work with our customers who have their own sales through data that they're receiving from the retailer or through their own, you know, their own systems, they're of course able to, you know, back into their own price elasticity curves by understanding, like you were saying earlier, where that magic number is, right, that one of the advantages in market we're seeing right now to, to a cpg, you know, look, they ultimately can't set the price at the store. But if the retailers are suddenly running all these tests and experiments and manipulating price, it means that you have a clearer than ever vision about what those elasticities are because, you know, you've had that test run with your items in different store locations and you could come back to the retailer with that information and showcase how sales were affected by price and promotion.

Which of the major retailers do you know of that are thoroughly embracing dynamic pricing? Is everyone using dynamic pricing yet?

Certainly not everyone. And you know, it's even the phrase dynamic pricing, I think it actually encompasses such a spectrum of, of different technologies and tools and, and mindsets, right? Is it changing prices intraday, intra week? Is it, you know, local, how far your localization goes? Is it whether you have different prices online and in store and in your other parts of the omnichannel experience? So, you know, I'll say every big grocer and retailer in the country now is moving along that spectrum, right, and becoming more dynamic, whatever that means for, for that retailer. Whether it's, you know, changing price more frequently or for a higher percentage of your items, or whether it's, you know, changing the, the online versus in store experience. All these kind of different versions of what it means to be smarter. They're all just waking up to the fact that they have this lever that they can pull with far more precision than they have been able to in the past. And they're all trying to mature on that front.

Who's, who's, who's best at it right now? Who's, who's, let's not say best. Who's farther down the road, right?

Well, certainly a place like Walmart prides itself on being a pricing leader and being really smart about pricing. And I think that's borne out by viewing a lot of their dynamic pricing strategies. But it is across the spectrum right now, a lot of the kind of really intelligent power regional grocers are getting quite smart about this. And you know, of course, no surprise, places like Amazon are farther along on that spectrum as well.

So to help me out and help our listeners out when we're talking about dynamic pricing and fill in the blanks where I'm missing it, here we're we're measuring for a certain category. Let's use Walmart as an example. We're measuring the category sales. We're measuring the full mix or full array of products in the category. But we're also measuring the velocity that they're going through the cash register. So my understanding is Walmart is looking at how many minutes go by between the sale of product X and the next unit X of the same SKU that goes through the cash register and is using the velocity as part of the, one of the data points to determine the price of the product. Is that an accurate statement?

Right, but it goes far, far beyond that as well. I mean, yeah, please expand on it for people. Yeah, you know, the grocers and retailers are able to now not just incorporate their own velocity and volume data into their pricing strategy, but a far broader array of different data sets, including their competitors prices. So they're all tracking what's happening at the store across the street in order to be smarter about how they themselves are affecting their prices. Issues like local demographic changes, local weather patterns, the new stores that are opening in the area that might influence just kind of a corporate strategy to try to squeeze out the competition or influence price perception for the local shopper. You know, the rise of third party solutions and where they're available.

Right.

If Instacart moves into a new area that it wasn't in previously or whatever the newest iteration of those sorts of buckets of technology are, all of these things are now incorporated into the sort of dynamic pricing strategies that retailers are able to bring to bear. And so what you weather data is weather data? Absolutely. Yeah, absolutely.

Without naming any names, if a grocery store saw that there was an incumbent blizzard coming, could the dynamic pricing system move the price of bottled water, milk and Pop Tarts up before the, the storm? Which is one of the things that always blows my mind because whenever you see a weather emergence, I don't, you know this Justin. But if you look, if you look at stories or data on these weather emergency Pop Tarts sell out. Have you ever seen that bad of your Pop Tarts?

It's just I never noticed that one. But we could, I'm telling you, look into our data and I'll, I'll let you know what, what that looks like.

I'm telling you, if you look at your data, you will find that when you have these, these weather disasters coming up, Pop Tarts just, just burn the whole building down. Because I'm assuming it's because Pop Tarts will survive a nuclear holocaust.

I mean.

They'Ll probably live a hundred years. They never, they never go bad. I guess people figure if we buy cases of Pop Tarts and, and Snowmageddon doesn't come, we can still be eating them for three years from now.

Right, right.

But can the store respond fast enough that a weather alert comes out and the store can actually move that cross quickly, knowing that they're going to clear the shelves on certain products?

Look, they have the capability to do so. We find many of them are sufficiently, you know, especially on grocery, which is where our biggest focus has, has always been, you know, this is a repeat customer experience and they want to win your loyalty. I think there's a perception of price gouging that everyone's very concerned about.

Absolutely. I'm thinking mild and mild increases, not radical increases.

Right, right.

Yeah. I mean, I, I, they all have that capability or not all. I should say that capability certainly exists now where they might mitigate a promotion that they were planning because they find that it's unnecessary and they could stop it, you know, before it launches. You know, they might also, by the way, on the flip side of this, the kind of positive aspect of it, which is that they're also able to move much faster on some of their supply chain issues than they have been in the past and get, you know, if they're concerned about a stock out to, to get additional inventory more quickly than they have been in the past because of those same factors.

Now, could a retailer use data like yours to, let's use baby formula as an example. Could I use that data and say I'm going to be out of baby formula, my supply chain isn't going to supply me. I can see that. I'm going to change my baby formula pricing strategy because I'm going to have empty shelves here. So do I move the number up to slow the take rate so that the shelves are not empty? Do I move the number up to maximize my profitability on baby formula? Because I know I'm going to have holes on the shelves for two weeks coming up. How does it work there?

Yeah, yeah. Tracking stock outs and, and general kind of out of stock trends is another really great use case of leveraging data, assembly data. I mean, these are some of the most important moments for, you know, I'm sure any of your listener listeners here, who, who's, if, you know, if your product's out of stock, you know, your sales go to zero. Right. And so making sure that you can track those factors, predict them, understand the competitive forces that might be Affecting that it's all critical to help you respond in real time. Whether that's a price change or perhaps, you know, more often it's just trying to, trying to avoid the out of stock activity and make sure that there's an inventory influx that allows you to ameliorate that situation is the action that many of our customers will desire. But just tracking these out of stocks is totally non trivial as well. And again, it's a really kind of dynamic movement where when there does emerge some supply chain issue or like in the baby formula case that you brought up, when there's some kind of external force that causes a major out of stock crisis, just being able to track these things and help your shoppers and help communicate with the retailer, it's, it's a critical moment for many of these manufacturers. And having that up to date, recent localized data allows you to, to be responsive to all those factors.

How did, how did your company uncover the baby formula issue? What happened there?

Well, you know, we, we collect this sort of item level, store level information at a massive scale. And because, you know, we're constantly collecting almost every item at every store location, we're able to see these category level trends emerge as well. And because we collect the data at such a granular level and so frequently those insights are able to emerge much quicker. And so when we're tracking category level out of stock trends, you know, you could see some trend level data where the out of stocks might be hovering at, you know, 5 to 10% week on week for, you know, half a year or a year or our whole history of data and then suddenly attacks up to 15 or 20, you know, numbers that are prior to it being a true crisis, but still a real deviation from the historical norm. And that can catch, you know, that should catch everyone's attention. So in this case it went, you know, from 10 to 15 to 20 and then it reached, you know, 90 a couple weeks later when it started really capturing the national and the national media and ultimately, you know, congressional hearings and White House hearings discussing these things. Incidentally, I'll mention the FDA did leverage our data to help track and understand that, that emerging situation as well. But you know, it's that sort of ability to track things comprehensively that allows us to see these trends as they're emerging, whether it's a out of stock trend or, or inflation as well. I mean, if you go to our website, we launched a product called the Grocery Price Index which tracks category level pricing trends. Really, you know, you can see inflation in real Time and see, see, you know, the, the trends emerging not just, you know, at the CPI level, where you get this one number once a month, but at a much more granular level at the category level or even regionally. Right. Seeing what's happening in the Northeast versus the southwest or state by state or urban versus rural.

Right.

Where some of these trends are. Have affected different areas very differently. And so that's data that you put.

Up on the website.

Yeah, yeah. I'd encourage everyone to check out the grocery.

What's the URL? So it's Grocery price index.

Yep. It's. If it's on our website. If you go to dataassembly.com under resources, there's the grocery price index.

Yep.

There's also the solution there. I called out earlier the snapshot. If you go to the live data snapshot, where you could see an even more granular picture. If you know, if you're in a specific category and you want to understand how your category is evolving against the national trends, or to break it out by metro area or urban visceral. Like I said, you know, you could view those trends there as well.

Now, I have a question, and I don't know if you could help with this or not. So I'm just trying to solve problems. So here's the next problem.

I want to hope I can help.

Yeah, well, I'm. I'm thinking about all the, all the issues that our clients bring to us.

Yes, absolutely.

Help with. So one of the problems we get is a client looks at their POS data and let's say the POS data shows this week that they have 95% in stock. So a 5% out of stock. Our argument has always been that the 5% out of stock doesn't represent a 5% decrease in sales, but it can in. But it can represent a much greater decrease in sales because the stores with the greater velocity are probably the ones more likely to be out of stock. Is there any way that I could leverage your data to make that argument?

Right. It's a. I. Well, I'll say as a theory, it certainly holds water with me. It seems to make a lot of sense where there's out of stocks. It's, you know, you're more likely to be kind of overrepresented and loss there, all things being equal. You know, I think what you're pointing to is, is kind of a way to try to unpack, you know, what, how much an out of stock might be costing you. And there's so many factors, like I said that go into that. One thing that you might also look at in that situation is which of your competitors are out of stock. Right. Kind of the flip side here, where if you're seeing an increase in an area, you might then track that your competitors were out of stock and that you're the replacement item in that situ in that area, or that they're trying a new item, that maybe they had loyalty to the item that's out of stock. And so, you know, more broadly, I think that the ability to unpack the impact of out of stocks, again, like with pricing, requires more than just understanding your own data. Right. You know, if there's out of stocks here, you know, did people just go across the street and get your item? Right. If there's two stores that are very near each other, or did it in fact cause them to go to a replacement item? Or if your competitors are out of stock or if there was a promotion. All these factors I think as a whole, you know, all have major bearing on shopper behavior, need to be considered in totality.

Would I use your data to, to build a case of why certain stores in a chain need to have more pieces per store on hand?

Right.

Because oftentimes the chain might say, we're going to bring in three pieces per store per sku. Now you and I, all three of us know there are some stores who probably don't even need three pieces per SKU per store. And there are other stores that probably ought to have nine pieces.

Yeah.

Per SKU per store. But the big retailers are not always launching and not always managing like that. How could I use this data to fix that problem?

Yeah, absolutely. I mean, first of all, just being able to, again, like I said earlier, go to the retailer and show them, hey, this is how frequently you're out of stock on these items versus my competitors or versus your competitors. Right. At the stores across the street that never go out of stock on these items and they're carrying 10 instead of three.

Right.

So being able to show them that information, real data driven motivation in that joint business, planning to motivate them to carry a specific item or size or a number of SKUs is, is, you know, everyone's trying to be more data driven about that decision making process. And so having the data and the tools that help you express that those trends at your fingertips is, is critical. One other one I'll quickly call out here is just tracking which new items are listed as well. Right. What new items in my category have emerged this week? You know, it's Often some kind of small regional launch or a test that a big retailer might run, you might slip under the radar for you, and you're only finding out about it 8, 12, 16 weeks later and suddenly it's, it's nationally launched. Being able to get ahead of that trend, understand your competition, start building your case for, you know, or your marketing collateral or however else you want to mitigate the competitive forces. Just tracking new items by category that you're, you might be competing with are also critical. Not to mention, depending on the category, private label items.

Right.

Which are increasingly a major piece of the mix for many shoppers. And every CPG needs to measure that to insulate themselves in, in their category.

And this is, and just think about how important this is, because when, when we deal with CPG clients, they're always looking at their POS numbers every week.

Yep.

And you and I have experienced the client getting panicked because my POS numbers, I'm advertising and my POS numbers went down this week.

Yep.

But we need to look at those POS numbers and say, well, wait a minute, you have, you have two competitors that just did a bogo this week.

Yep.

So now you're facing a 50 drop in price from your, from your number one competitor and you've only lost 5% of sales. You're actually winning at this point.

Right.

So stop panicking. Don't, don't panic. It's, it's, this is a temp. This is temporary. What's going on here? Oh, yeah, I can see where this data would be critical to be able to, to look at that and be able to spot where you're winning and where you're losing.

Yeah.

Now that's spot on, Mark. You know, the, the. And sometimes that's kind of an internal panic at a cpg, like you're saying, absolutely, information. And sometimes it's the retailer coming to you and saying, hey, you know, sales are down. What's, you know, what's going on and you're able to go to them and say, and explain it. You know, by having this information available in a shareable, usable format. You know, just. The shopper journey has become quite complicated and there's so many inputs. You know, the kind of core tenet that we follow is that having transparency into those marketplace dynamics allows you to be smarter, more data driven and to influence the retailer and the shopper into, you know, getting your, your great products on their shelves.

And I want our smaller brands to understand something. You're probably thinking, well, Walmart, Walgreens, cvs, they have all this data. Why do I need to take them data? They already have it. Let me explain something to you. You have a buyer, you have a buyer that might have 100 items in his or her category, and chances are you represent, you know, two SKUs or three SKUs out of 100 or 200 SKUs. And that buyer has to manage all of those SKUs. That manager, that product category buyer has a category captain in their ear. You know, one of the big companies that is taking them data and giving them advice and they've got it. They've got to keep their boss happy and they've got to look at new products and they've got to look at their planogram review that's coming up. So what you, what I'm saying with this is just because they have the data doesn't mean they're drilling into it on your sku. And when you're a smaller brand, quite honestly, you're just not that important. I hate to disappoint everybody, but you have two SKUs and, you know, and a Walmart Superstore has148,000 SKUs, and you are two of them, right? So you are two over 148,000. How important are you to Walmart in that, in that grand scheme of things? I'm not saying this is a reality, folks. I'm not saying that you're not important and you're really important to us when you're a client. But, but the reality is your product is one over 148,000.

Yeah.

And you can't expect that buyer to spend the next eight hours drilling down into data to make a good decision about you. So you need to make this easy for the buyer. You need to walk in and say, look, here's the trends on our competitors. Here's why they should probably get less shelf space and I should get more shelf space. This is why I know you're saying that my numbers, you know, I didn't grow 10% as, as your store is saying, but let me show you why I didn't grow 10% and why you still need me. Because, yes, I only grew 5%, but my competitors dropped 11%. Right on what happened over here. And you can go in and you can make some, some terrific arguments as to why you need more shelf space or in some cases, why you should not be delisted if you have the right data. So this is not just about going in and getting a price increase. This is sometimes about getting shelf space or maintaining your shelf Space.

Space.

Ben, it's been great having you here. How can, how can our clients learn more about all the things that you guys can do for them?

Yeah, I mentioned some of the resources on our website, dataassembly.com where you could see the grocery price index or our live data snapshot. You know, I'd encourage anyone to go and look at their category there and learn a little bit more about how dynamic things are, how fast things are changing. And we'd love to be. Be in touch if we can be helpful. You could reach out on our website in the Contact us form at the bottom.

So, folks, go to the Show Notes, go to CPG Insiders and go to the Show Notes, the show with Ben. And in the notes, we'll have links to their website and links to the resources where you can get in, you can get in touch with them. And Ben, it's been great and I hope you'll come back with us sometime as you guys have. I'm sure in the world of big data, you guys are going to have new and new products and newer products and breakthroughs, and there's probably things that you're going to learn how to do that none of us have even thought about doing yet in the future.

I'll be excited to share it when we do, so. Definitely. Thanks for having me and excited to continue the conversation.

I appreciate it. This is good stuff, isn't it, Justin?

This is amazing. This is, this is. I mean, I said I. As you were talking, you kept asking all the questions. So I'm like, I don't have to because I'm, I was going through the same thing you were. I'm going to. I'm thinking about this client and that client in this client situation. And exactly, again, to your point, and this is, this is just to emphasize it even more, is understanding that you have to be a partner with your retailer. Okay? So first and foremost, so constantly ask yourself, how can I be a better partner? And at this point, the more data you have, the better partner that you can be for your retailer. And I assure you, remember, at the end of the day, everybody wants to make more money. And what Data assembly is doing is providing you with the, the information and the ammo to go there and say, here's how we can all make more money together and service and bring value to our customers. So leverage it. Because the consumer journey, as you mentioned, Ben, is so much comp. So much more complex than any of us can truly understand. And this is just providing us even more and deeper and Better insight into how, how to do that.

Something we say on this show. And that is, who do you want to be a hero to?

Yep.

And that's right. You know, it's a Dan Sullivan thing. And I'm asking you to, to our listeners, who do you want to be a hero to? I'm going to tell you. The person you need to be the hero to is the buyer.

Yes.

At the retail stores. You need to be a hero to them. And how can you be a hero to them? Make their job easy. How do you make their job easy? One, have a great sku to have a marketing campaign. Three, provide them all the data they need to make easy, rational decisions.

Yep.

So one, they don't have to spend eight hours researching your product. And two, so that when they make that decision, when a boss looks over their shoulder and says, why did you do this? They can say, here's why. Look at the data I have not. I did it because I really think Justin or Ben are great guys. I did this because look at this data Ben brought into me. Me.

Yes.

And you've now given them the plausible deniability of why I'm making the decision. I'm making. Be the hero to the buyer. Make their job as easy as possible.

Absolutely.

That's it for today on CPG Insiders. As always, if you like the show, send a link to somebody else. And if you would, go to wherever you get your podcast and leave us a five star review. Now, notice I said five star review. Not, not a one star review. You're always welcome to go in and say, I give Justin five stars. I give young one star because he talks too much. But that Justin, I just love that guy. So we're giving him obviously star review.

That's it, folks.

We'll see you on the next episode of CPG Insiders. If you're looking to greatly increase sales on your CPG product, don't hesitate to contact us at Jekyll and Hyde Advertising and Market, by the way, the only advertising agency with a guaranteed result. Just go to jekyllheidagency.com or feel free to give us a call at 800-542 10.