Podcast title card: Easy ways to use POS data to sell more and avoid inventory problems

EP12: Easy Ways to Use POS Data to Sell More and Avoid Inventory Problems

With the help of Manfred Reiche we delve into the intricacies of point-of-sale data and its transformative impact on the consumer goods industry. Learn easy ways to use POS data to solve sales and supply chain challenges in the CPG space.

Transcript

[00:00:00] Logan Ensign: From Alloy AI, this is Shelf Life.

[00:00:20] Joel Beal: What are some of the common mistakes brands make when it comes to data?

[00:00:24] Logan Ensign: How can you be using point of sale data to power other parts of your business like planning, promotions, and supply chain decisions?

[00:00:33] Joel Beal: How can you use data to better collaborate with retailers?

[00:00:37] Logan Ensign: On every episode of Shelf Life, we answer questions like these and more with the help of leaders across the consumer goods industry.

[00:00:45] Joel Beal: Today, we welcome our colleague Manfred Reiche, Alloy's subject matter expert on all things related to retail data and product. He spent his time at Alloy being hands on with some of our most sophisticated brands in the consumer goods industry. Prior to joining us at Alloy, Manfred started his career as a technical consultant at Deloitte, focused on SAP implementations. I'm your co host, Joel Beal, CEO of Alloy AI.

[00:01:10] Logan Ensign: And I'm your co host, Logan Ensign, Chief Customer Officer at Alloy AI.

[00:01:14] Joel Beal: We'll be back with Manfred right after this.

 Manfred, welcome to Shelf Life. Really good to have you.

[00:01:57] Manfred Reiche: to be here.

[00:01:57] Logan Ensign: I'm surprised we've done as many episodes as we have and haven't had you yet. really excited to dive into one of my favorite topics.

[00:02:04] Manfred Reiche: A lot to talk about data.

[00:02:05] Logan Ensign: That's right. That's right. let's dive in. I think, between the 3 of us, we have spoken to a ton of companies, enterprise, mid market, SMB that many think have this idea of point of sale data solved already. and then when we peel back the curtain a little bit. We realize that maybe that's not the case.

and so in our experience, I don't typically we do find that a lot of companies feel like they're pretty good at data. but actually a lot of them aren't. so Manny, I am interested in hearing just some specific example stories of, kind of this experience that often we have.

[00:02:41] Manfred Reiche: of my favorite initial stories about data. And have this concept in Alloy we call language policing ourselves, right? Like words mean different things. And when I talk to people either internally with customers here at Alloy or to my friends about what I do, and I talk about sales data, just the word sales can be very confusing to many people.

I was blown away when I found out that most big organizations. Operate their business off of shipments, what they sold in the retail, every decision is made when the truck leaves the facility without knowing what the customers did on the store level. and I've been in so many customer meetings when I come up and I say, Hey, Alloy does sales data.

And someone on the call says like, Oh, we already have that. And then I started asking like, okay, do you actually know what you sold at this Walmart store in California? Sales means shipments. And I was like, no, no, no sales should really mean register. Like what happens at the registered store. And it's one of those light bulb moments.

I often see customers faces light up when they say like, wait, you can do that. It's not possible. It's not in my ERP system. And, just the word sales can be confusing between selling shipments or point of sale, registered movements. And people should really be running their business off of registered movements.

[00:03:54] Joel Beal: So Manny, why do you say that people should run their business off register movements? Because they are paid based on obviously what's ordered to them by a retailer or distributor, assuming we're talking wholesale here. So why is that point of sale so important?

[00:04:07] Manfred Reiche: Well, customers like the end consumer is the foundation. That leads to any retailer ordering product, right? If you don't stock the right shelves, if you don't have the right product mixes at the stores, if you don't understand the tendencies, you might be producing the wrong thing. You might be, forecasting the wrong thing on your end, and it's just going to deviate yourself so far away from what the consumer is doing.

And it's such a competitive landscape out there between brands, trying to fight for shelf space, trying to keep all consumers, we have all these new generations coming up and you have to really understand what people are buying and if you're just stuck on your old behavior, what you were shipping in last year and what you're doing, you don't understand what the customer is doing.

It'll just ripple all the way throughout your supply chain and produce massive problems. and what you're producing and selling and shipping.

[00:04:53] Joel Beal: my experience is maybe a little bit different. I feel as though I've seen with companies, I think it's evolved over the last couple of years, most companies recognize. understanding what's happening with the end consumer of their product is important. they run their business off what they sell.

I mean, that's going to drive their own personal P& L, but I think, the reason, as you said, is you need to understand that kind of leading indicator. The retailer is not going to order more product from you, if they can't sell it. So you better understand that consumer. And look, there's a lot of effort put into understanding that.

What I feel like I often see is a recognition that it's like, yes, there is some data out there, whether you're getting it directly from the retailer, whether you're purchasing it, you know, syndicated data from like a Nielsen or Sarkana, to kind of understand what consumers are buying and what those patterns look like.

But it's really more of a research motion. I guess I would say people are thinking of it more to understand broader trends to be able to talk internally about why is skew a doing or is skew a doing well or not. And should we expect, more rather than this kind of how do we operate our business more at The modern speed of business, because that to me is what's changing is the speed at which, we are turning over skews and introducing new products and sun setting products, et cetera, has just massively accelerated.

It's your comment, Manny. There's just so much competition out there. We're in a global market. and it's kind of that recognition that for a long time, I use this to kind of. Run my weekly sales reports and get a high level pulse of how my business is performing and what businesses are going to take that next level to start saying, I am going to use this on a daily basis and it's going to drive how I execute.

And I'm going to be getting ahead of, consumer demand, or at least as responsive as I possibly can versus that kind of delayed, you know, lagged feedback loop that exists right now, where it's like, you know, the order comes in, I fill it, Watch it from afar and then the next order comes in and, oh shoot, it's lower than I expected.

I better start adjusting.

[00:07:02] Manfred Reiche: Yeah, I hear what you're saying. And to your point, I think using that point of sale, to drive product mix decisions or production decisions is kind of, That next step, right? It really is. based on what I've seen with customers, what people should be doing, but I can actually tell you, I almost have these cautionary tales of companies that are fully operating on sell in and the risks that can happen.

Like you said, most companies care about shipments because that is how they're They make money. That is where the transaction happens. And we had this one customer, really exciting, brand. I personally really liked the products and I found that they used to pay commissions to their sales teams off of shipments, They only care about what made it to the store. They were not looking under the hood that their return rate was like 85%. Right. So people were buying stuff on the shelf, but returning it, they were looking at gross sales, not net sales, but they were actually only looking at shipments. And what we started seeing in the data was These retailers were stuffed. They were not keeping track of returns. Three months later, they went bankrupt, there was just a complete squeeze on their finances because they were paying people off of sell in. They had no clue what was happening at the store. And I think it opened my eyes. If I ever ran a consumer products company, The first thing I do is make sure that I know what's happening at the stores and have tight control on this because it just ripples everywhere.

[00:08:28] Joel Beal: you're certainly reminding me. We have had a couple, I think, customers or sales prospects and, you know, we can get this data live very quickly and you look at it and you're like, oof, that does not look like a healthy balance between what you're shipping in and what you're selling out. and you can anticipate a lot of problems that I know in some of those cases, those are not companies that made it longterm.

So. always a little painful, not news you want to show up you want to share when you first sign somebody up or you're trying to close a new deal,

[00:08:59] Manfred Reiche: Yeah, and we'll probably get to that right in a little later in the podcast, but what you just mentioned, comparing shipments and point of sale can sound trivial, but most people have that data. If they have point of sale, it is in a completely different system, right? Point of sale, to be clear, does not live in SAP.

It does not live in your ERP systems. Your ERP covers how much inventory you have and what you are shipping. The orders that you have go into retail. Point of sale is reported by retailers in different portals and different Excel files. And if you don't have the right tools to put them in the same place and to leverage, that ship to consumption that you just mentioned is a super important, dashboard that is actually non tributed build, just because systems do not talk to each other.

And this data is usually in a completely different data silo.

[00:09:45] Logan Ensign: Well, Manfred would love to kind of unpack this concept a little bit more because again, I know, yeah, you in particular have a ton of experience in this space. And I think as we kind of work with our customers and talking about the role of point of sale data, you have lots of different philosophies and perspectives, but I think in prepping 1 of the most common mistakes we've highlighted is, organizations not talking enough.

About what they want to do with point of sale, and therefore, maybe making some poor design decisions about how to integrate and kind of take advantage of that data. So, I'd be curious in you kind of helping unpack this concept and how organizations may should be thinking about that role of point of sale data and sort of the architecture of how it flows into other systems and how to take best advantage of it.

[00:10:31] Manfred Reiche: one of the hardest parts in, in starting to use the point of sale data is just, I like to talk in front of this as different data languages. Every data system speaks their own language. Right. They're all databases and they're all keyed with a different identifier. So when you talk to target and you talk to Walmart and you talk to Amazon, they have different keys for what they call your products.

the few companies that I've seen are dipping their toe in a point of sale might have, you know, Individual teams that have their own data sets and they have their own spreadsheets. It's all run in Excel because most of these companies share data with Excel and you start running into problems.

If you're a VP of sales who wants to keep a pulse of the system across, you know, a drug channel or a whole retail channel, you can't consolidate it, right? You might think you're ready because you have to go. Yeah, have the Excel reports. they're tracking stuff every week. But then, you might be, asked for a report of, okay, It's April 4th. We just closed March. What was the total point of sale in the drug channel? You might need to wait two weeks for an analyst to put that together because they have to translate between all these different languages to give you that report. And if you're operating your business two or three weeks after the fact, right?

By the time you're done now, April closed and you're on to May and like, it just slows you down. So I think having a system, one of the proudest things think we've built at Alloy is that translation layer where we can speak retail languages and we basically translate that into your own language in one place that allow you to have that insight.

April 1st, you know what happened in March and you can have a completely aggregated view of the point of sale, across all your product mixes, all your retailers. I find that foundational and I'm blown away by how many companies do not have that today.

[00:12:16] Joel Beal: many on the topic of frequency. So there you're using an example of, a month close how to do on the month. What do you see in the market around how frequently people are looking at this data? What level of granularity? what do you see and what should people be doing or what's possible?

[00:12:35] Manfred Reiche: a good question. granularity and frequency are two very important things that I'll probably talk about separately because when you're running in an Excel sheet and you're running with humans, the amount of detail you can go into is limited. Excel can only handle a million rows and then you're out, right?

And it just becomes completely slow. So what I found is when you talk about data granularity, the level that detail at which they run their business. People might just look at things by skew. They don't go down to the store, there's 50 states in the country, right? Walmart has 8, 000 locations you might need to know.

And you're just limited by tools if you're running reports. So, location granularity is important. You talked about time granularity. Most people that tell me they use point of sale data, I find that they run monthly reports because again, they rely on humans to consolidate stuff and you don't have enough time to be able to operate this.

If you're doing it every week, you know, you need someone to do reports, four times a month. and let alone daily level, right? most of these big retailers give you data for yesterday, by 8am Eastern, you know what you sold, you know, inventory levels across the board. But I find that many people don't have the resources that they're relying on humans to utilize that data.

So it's so important if you're trying to run the business. To be able to run more frequently. You can do that with software, right? computers run faster than humans. It is a repeatable process. and when you can unlock going deeper than a skew level, you can analyze stuff by state by warehouse. It's so much more powerful and the insights you can get. At those levels,

[00:14:15] Logan Ensign: Manny, I know you've sort of addressed this already, but I am curious. You're talking about a lot of data and a lot of, depth. okay, every day, every store, could that be perceived as overkill? why should organizations care about doing this at such a high degree of, granularity?

And why are these shortcuts potentially, you know, not the best approach when dealing with point of sale data?

[00:14:38] Manfred Reiche: it can certainly be overkill if you're trying to analyze this as a human, right? Again, millions of data points. Imagine you have, 100 skews that you sell at Walmart, 8000 locations, and you're trying to analyze every day. you're talking massive amounts of data. Semi small company you're not going to be having people read millions of lines of rows to find an insight So the power comes like I see this detailed data as the foundation to then build Insights right?

So once you have all this information and you're out of an Excel sheet You have a database at your disposal What we can do is start using an empowering metrics at the store level at the day level to tell you exactly where you're hurting to surface a problem, right? An exception report that it's actually this particular item regional in California.

That's struggling. One of your biggest markets is low, it's an insight that. If you think you're doing point of sale data at the surface, you wouldn't have because you're not down to that level, right? So we don't ask people to go through and scroll through all 8, 000 stores, but we have that in our back pocket to service that insight, to tell you it's skew a in this region, you're losing a hundred thousand dollars a day.

Take action. Now.

[00:15:56] Joel Beal: you know, something I've seen is Retailers have scorecards for their suppliers so that they can go in and say, okay, this is how much I'm selling. This is how much it's comping, you know, year over year. how fast the inventory is turning over, out of stock rates, et cetera, and how easy it can be for those high level, indicators to mask issues.

So someone may go in and say, well, I've got, 98 percent in stock target. I'm doing just fine. And yet when you roll that back, it doesn't mean that you might not have chronic issues in certain regions or locations, that you're just losing money on, and you might look at that top level number and be like, that looks fine.

And not recognize that there actually are very addressable issues. So that's, that's one of those things where people I think are very used to those numbers. They have line reviews, they look at them, but oftentimes actually masking underlying things that are just kind of glossed over because people assume that the issue is, 

[00:16:56] Logan Ensign: great point. You remind me of, one of our, customers and they were actually quite a small company. I don't know if Manny, if you were thinking along the same vein, where you kind of look at the profile of this company and you can say, but on paper, this isn't a company ready to sift through daily store skew level data because they were very small.

but they had an emerging relationship with Best Buy. and you know, Best Buy had set. in stock targets, like you're describing Joel, and very quickly in alloy, this company was able to see that although they were hitting their on shelf availability targets, there were only 10 stores. That represented an insane amount of their volumes could because they sold a product that would sell in sort of high adventure areas and they were able to come to best buy and say, yeah, we're hitting our in stock rates, but our Honolulu and our San Diego and our, Miami stores go out of stock the day after they replenished.

Let's actually adjust the reorder point for just these 10 stores so that we can stay in stock and have the velocity that we need. And this is a small brand that was really trying to get traction at a particular retailer and you can appreciate. Of course, they're not going to sift through every store every day.

But without that granularity, you're losing opportunity. Right? there's a lot that is unlocked again to your point, Manny, like a database technology perspective that. you're leaving on the table

[00:18:17] Manfred Reiche: I remember that client. Exactly. It was one of my first meetings. When think you and I went on site, And it stuck in my brain, right? Because going back to Joel's question about granularity, you're right. That we were able to detect that the problem was in Honolulu, right? So that's already one level above, we can detect region.

underneath the skew, we're already helping, but we went one level deeper, right? Going back to this weekly versus daily data set. Joel's right. They were running business reviews with Best Buy. They're running them on Fridays, right? And every week, everything in stocks look good. Across the board because weekly in stocks were fine, but we found out that they replenished on Thursday and they were actually stocking out Monday, Tuesday, Wednesdays, the shelves were out of stock.

And, you don't see that if you're only running the business by your week, right? But we could tell them in fact, last week, you were not a hundred percent in stock. At the end of the week, you were 50 percent in stock throughout the week. And we think you should be selling twice this. So we gave them that recommendation.

So it's very early days. They actually told Best Buy, Hey, I think you can order twice as much. That's why I was like, ah, okay, let's see. Right. It's mutually beneficial for them to also order more if they're going to sell more. And it stuck Honolulu started selling twice the volume, and then they applied that across the nation because they realized they were stocking out.

[00:19:39] Joel Beal: Here's an example of a smaller, it sounds like brand relatively new to Best Buy. Do we see a difference between, your large global CPGs, for example, that have thousands of people that are much more sophisticated and. then you got these small brands that are maybe going into retail for the first time.

So do all people benefit from this data? Similarly, do we find that they do different things with it at different sizes? Curious, Manny, what, you've seen on this front?

[00:20:08] Manfred Reiche: So think you're spot on that bigger companies, right, carry more weight behind them and they have big retailers almost one phone call away. they can provide a call they have more influence over buying decisions, right? Just because of the nature, they probably have a longer history.

They probably have a personal relationship there. But I have found that even small companies, when you bring the right data insight, right? No one can dispute a data backed recommendation could take a few emails to get there. this particular example a good one because, it took a few emails.

We had a very specific insight at one location, one data point. And it landed, right? So gave this small brand kind of a reputation of someone who is data driven. the Best Buy team doesn't have enough time to track every single SKU that they have in their portfolio, but there are, again, mutually aligned and mutually beneficial to want to maintain shelves that are stocked.

so yes, there's more sway if you're a bigger company, but that's the power of data. No one can dispute an insight. if you can convince the right people that it's in their interest. 

[00:21:10] Logan Ensign: you're spot on from an execution perspective in that distinction. I think in my experience as well here, Joel, you find these larger organizations, having other systems, other processes, you know, investing a lot in initiatives, investing a lot in. Planning applications, and supply chain optimization.

And I think often in the enterprise, we find that that's another area that point of sale data can help deliver a ton of value. Right? I think is probably the most interesting concept right now. You know, a common question is when you have massive data sets, how do you make sense of them? I can play a key role there.

And so as we sort of advise enterprises, 1 of the pieces advice that we have is. Hey, maybe 10 years ago, this granularity we're talking about store level day skew level wasn't as important. but now, as AI sort of advances and you can get value out of that data at scale, even these really big enterprises, you know, that's something we really push on.

So getting that data ready, sanitized, harmonized, at that level of granularity, I think. and the enterprise gets pretty exciting. 

[00:22:15] Joel Beal: Yeah, that's an interesting point, Logan. And one, I mean, when you look at the size of data sets, and if you're looking, as we talked about at the beginning, you know, kind of the traditional way that a wholesale, at least the wholesale side of the business is going to work. Is there going to be like, well, I've got my orders that I make to my factory.

I've got the inventory across however many warehouses, that I'm putting my product at, and then I'm filling orders as they come in. And those are kind of the data points that you have. So maybe you have five different warehouses, you're fulfilling these orders that come in, you know, every week or what have you from various, retailers and distributors, you go to the point of sale and all of a sudden the volume of data, I mean, goes up exponentially.

you're now not looking at five locations and a thousand skews. You're looking at a hundred thousand locations. it can be overwhelming. I think we've certainly seen that. And, how do you distill that down? How do you use, you know, alert people to the things that are going to be the most interesting, so they don't have to sift through it, which nobody would be able to do, but as AI advances, you know, its ability to kind of augment that and say, Hey, the more data that it has, the more powerful it's going to be in terms of getting those insights.

So yeah, size of data sets. I know we alluded to that earlier, but, it definitely goes beyond anything an individual or humans in general can kind of manage You've got to use, you know, tools to take advantage of it. and so then it's probably just a question of, like, how much incremental value is there in all these nuances?

which I think the stories we're telling is there's a lot as long as you can find it.

[00:23:50] Logan Ensign: well, and many shifting up to kind of other functions and organizations and kind of where data plays. I'm curious from an execution perspective. You know, we often don't think about that as a data problem, but, could you speak a little bit to. Where you should be using daily, highly granular point of sale data across your business, right?

When you see kind of best in class, taking advantage, we've talked about collaborative replenishment and kind of collaborating with those, retailers, providing perspectives, but what other ways do you recommend or see being an effective place to deploy? these kind of large detailed data sets.

[00:24:28] Manfred Reiche: a great question because. we've talked a lot about data as that foundational layer for you to run your business, right? And data is only as good as the people who use it, can have the best analytics, you can have the best visualization tools and all these insights, but if you don't have people that can, actually take action, then they're worthless to you as a business.

Right. and one of the most important things You know, I was talking a little earlier about data silos. You have a sales team in an organization that usually has their POS reports in their own thing, and they're completely disconnected from your supply chain team. And supply chain operates in a different way.

They meet once a month. If you have an SNOP process where they kind of talk about production forecasts, things like that, there's no communication throughout the month. And we were just talking about that story, right? Honolulu, we recommended, That they should be ordering twice as much into, a particular skew into that volume where the execution, right?

I call that collaborative replenishment. We brought them a collaborative replenishment insight that the sales team at this company that they can take to the retailer to take action. Imagine if they don't have their own inventory ready to fulfill twice the amount of orders, imagine if that sales team had not collaborated with their supply chain team before they, you know, recommended this be sure, right?

what we found is that an alley, when you can put people on the same platform to collaborate, Right. When you can connect that point of sale insight with your own inventory, you can make sure that you're, breaking on that data silo. You could have sent them an email to ask, Hey, do I have enough inventory and wait a few days to hear back.

If you have the right insight in the right place. You know, immediately. So I think collaborative replenishment is a great example of when you execute to sell more, but it's super important that you coordinate internally between all your teams to make sure that you are able to deliver on what you're promising.

[00:26:20] Logan Ensign: that I think is a fascinating example to kind of round out that part of the story. so, we've talked about even improving that collaborative replenishment, avoiding mistakes, uncomfortable situations. but I also am curious, because I know again, we touched lots of different, or you get to touch lots of different functions and teams, other examples of maybe how this can be deployed and kind of helping deliver value for the organization.

[00:26:43] Manfred Reiche: Absolutely. so this example we talked about, right, was a sales team going into their supply chain team to ask for help, but in my experience, it could start the opposite way. It is very rare. Yeah. Even if an organization is using point of sale data, it is extremely rare for the supply chain team to be acting or to have visibility to that data.

And supply chain team operates in truckloads, right? They're producing, they're moving stuff between warehouses. They're never understanding what a customer wants. is doing at the store. And I remember we had one customer that was pretty visionary and he said, I want my supply chain team to use point of sale data because I think it's important.

And I think that's, it's a relevant piece for them. And, I remember going to these calls and like, it was a little bit apprehensive, that team, the supply chain team was apprehensive. They, they had enough data points already. Why did they need more data points? But then we started talking about something, every supply chain team dreads.

Which is the a word allocation, right? You produced, you didn't produce enough and you have a thousand units. You've got orders that total 5, 000 units for that item and you have to allocate it. You have to pick who you send it to. That's a supply chain problem, right? Like you're running low, you're at risk.

You have to make a decision that's going to impact the sales team. Many companies today will probably just do FIFO, right? First in, first out, who placed the first order? It goes that way. But we started providing insights that said, hold up, we actually know which retailers need this product because we know what the consumer is doing.

And if I could tell you that Walmart has six weeks of inventory, Target has two, Amazon is at one. You can now start prioritizing your allocation decisions in a way that can try to protect your relationship with the retailer. And even more important prevent last sales at the store, right? Because these a thousand units you have are pretty precious.

and if you're just doing first in first out, going back to data driven decisions, you don't have any data, but when you connect the point of sale all the way up there you can bring that insight to the supply chain team. Now everyone can live more harmoniously you're making a better execution decision.

Cross the board, pretty seamlessly. If you could do it in one system,

[00:28:54] Logan Ensign: I was gonna ask you a question, Joel, if it's a topic that's I think, passionate on your end is this kind of planning process and planning and forecasting process and how that intersects with point of sale data. I know you're working partnerships for us and have a lot of experience in that space.

could you speak a little bit to kind of what you see as best in class and taking advantage of point of sale data in that planning domain?

[00:29:17] Joel Beal: Yeah. I think we're kind of in the midst of a evolution, as we've, talked about a couple of times. Earlier this podcast, historically, brands have used their, shipment data to really forecast out what sales are going to be. I mean, that's been kind of the primary historical data point.

Then they're going to layer in all sorts of other things on top of that. Models have gotten a lot more sophisticated. A lot of people are using machine learning, but if you really break it down, it's like the primary driver of predicting future sales performance. Is going to be prior shipments. I think for a long time, there's been a feeling, well, point of sale should be a better indicator.

and it shouldn't just be point of sale itself. You also need to understand what inventory levels are like at a retailer. if you're selling a lot of product, but there's tons of inventory that's piled up. That's very different than you're selling a lot of product and the retailer's about to run out. But as we talked about earlier, it's just been so hard to bring that in. So I think what we've seen is an interest, I think over the last couple of years, more and more brands are trying to figure out how they bring that into their models. I think there's interesting kind of statistical questions about how they actually incorporate that data.

but I know Logan, you and I, we were talking to a very large company. we're talking to him right now, about this idea. I mean, this is a brand everybody would know, and they are very intent on saying we really think that. Using point of sale is going to be a better predictor than looking at shipments.

And that's something that they want to shift to. And so I think everybody assumes that it's going to switch over at some point. It's a question of when and how quickly that is. And this gets to, again, the core problem we've been talking about is point of sales been out there for a while. It's been getting better.

People are recognizing with technology. You can really incorporate this. It doesn't just need to be kind of a sideshow. but these shifts take time.

[00:31:11] Logan Ensign: Excellent. So, 

really fascinating stuff. Joel Manny, thank you both for your insights perspectives on data. I know this group's quite passionate about it. So great conversation. And, thanks for joining us, Manny.

[00:31:24] Manfred Reiche: Absolutely. one of my favorite things to talk about, right? Data. People think they know what they're doing and they're not right. When they talk about sales or talk about shipments or unit sales, it's a big decision. Even if they are talking about unit sales at the store, is it weekly? Is it daily?

Like, are they actually knowing this are the right teams getting this insider? Is it kind of siloed in their own world? there's so much under the hood that is important love talking about this and more than talking about it. I love all the value we can bring to customers when we can get them on our best practice.

[00:31:53] Logan Ensign: You've been listening to Manfred Reiche, subject matter expert at Alloy.ai. That's all for this week. See you next time on Shelf Life.