ISA22 Virtual Series: Panel Optimizing the Manufacturer and Distributor Relationship with Data

Sam.

Welcome back, everybody. ISA audience. What a day of sales and marketing knowledge that you guys have all gotten the opportunity to hear. It's been fantastic taking you on a journey, but I've got a fantastic session for you right now. We've been talking throughout ISA 22 and at ISA in general on how we can optimize channel relationships. And we want to bring in a separate angle on this this time. This session is titled Optimizing the Manufacturer and Distributor Relationship with Data. There's tons of data out there in the world and I'm not going to talk too much about this because I've got four or three really smart people that want to talk about it themselves. So, Sean, do me a favor and bring on my panelists. Hey guys, how we doing? Let's get a wave.

Hey.

All right, all right, so what I'd like you guys to do is I'm not going to do too much explanation of what this session is going to be all about because I want to dive into it once we start getting the questions. But before we do that, so let's go around the horn and you guys can introduce yourselves. You do a much better job than I would anyway. So how about we start with you, Kevin?

That's great. Well, thanks for having me here today. Appreciate being part of the panel. So my name is Kevin Brown. I'm the CEO and co founder of Lead Smart Technologies based in Santa Barbara, California. We're the developers of the Channel Cloud, CRM channel collaboration and business intelligence platform. And what we do with that is we help manufacturers, distributors and IMRs all with an industry specific, out of the box, purpose built tool that allows them both to manage data and CRM communications within their business as well as communicating with their channel partners.

Awesome. Awesome. All right, well, thank you for being here, Kevin. How about you, Joe?

Great. Hi everyone. My name is Joe Durante and I lead the industrial channel business for Precision. Precision is a leader in B2B market insight insights and analytics. We specialize in working with manufacturers, wholesalers and distributors to improve business performance. And we take great pride in being true partners with our clients, leveraging our rich B2B experience, unique data sources and proprietary statistical models to help our clients solve complex business problems. And prior to my time at Precision, I spent over 15 years in sales, marketing and account management roles within ISA manufacturers with Stanley, Black and Decker and Dual Brands.

All right, well, welcome to the panel, Joe. I'm glad to have you here. Last but not least, my man Pradeep. Howdy.

Howdy, Howdy everyone. Brendan thank you. Thank you for having me on the ISA session. So I am Pradeep Krishnadevarajan. Don't worry about my last name, first name is fine. Pradeep. I'm the co founder of ActVantage. What ActVantage does is be we provide data driven courage to companies, manufacturers, distributors and everyone out there. Act Manage combines analytics, technology, proven expertise and personalized coaching so you have the confidence to make consistently profitable decisions. In short, we help companies sell better, price better and buy better.

All right, welcome. And for those of you who didn't get the howdy joke, Pradeep and I've talked to a couple of his people who are from Texas. They say it all the time and I just think it's adorable. I love it. So I'm going to start using Howdy all the time now. By the way, we are just in Houston for ISA 22 and it stuck. So I'm going to just keep doing it. All right guys, let's jump right into it because we've got a lot to cover here and I know this audience is keen to get their takeaways here. So first let's level set. Let's talk about what's going on externally. Let's see what's happening out there that's opening the door wide for data. Maybe I'm going to go around and start with you, Pradeep. What is one thing that's happening in the industrial supply channel that is opening up the door for data to take a front seat in the relationship between a manufacturer and distributor and their channel partners?

Absolutely, Brandon. Brandon, the first thing is I want to focus on supply delays. Right. Getting the product available is the key thing that's going on around. So the questions become what which products need to be more diversified suppliers or supply sources? Where should you consolidate suppliers? So there are a lot of decisions and once you talk about supply delays, it automatically goes into a skyrocketing demand. Right. When you don't have the product and then the demand is all time high, you kind of are not able to fulfill the demand. And between that is the price increase. So the supply delays cause the skyrocketing demand and price increases. So what's happening is people were managing fewer exceptions, but now exceptions are the norm. Decisions have become overwhelming and with hundreds of suppliers, thousands of products and thousands of customers, people are overrun with complex decisions. Decision making has become more emotional. So now it's time to really leverage data to make decisions that are more consistent and uniform across the organization.

Absolutely. I mean, supply chain disruption is Top of mind for everybody right now. And it's been that way, but now it seems like every day you're hearing about another industry that's been disrupted or another consumer brand that is struggling. So. Absolutely, that's one. So how about you, Kevin? You got another take on that one?

Well, I think Pradeep's right on track with this. I couldn't agree more with that. And what's interesting is kind of what Pradeep's talking about with that is the supply chain side of things, which is kind of the opposite side of the track of, of where my experience and my focus is right now. But it makes perfect sense because the challenges that are going on with that are forcing people right now, forcing distributors and manufacturers to really dive in, look at data a lot closer from a standpoint of the supply chain. The other side of that is that customer facing piece. Now, Right. Where we have a customer focus is we have to be able to use that same data and information to evaluate which customers are gonna get product, how we're working with them in that setting. We're in a position right now where the world is changing so fast around us.

Right.

The last two years have thrown us all upside down and the companies that have taken advantage of technology and watching what's going on with that and the availability that we have now of so many technology tools and then deep understanding of the data that's available to us through folks like Pradeep and Joe and so forth as well. The ones that are taking advantage is the people that are getting the competitive advantage. And that's what becomes really key right now.

So. Yeah, I mean, good, good. Add on to what Pradeep said. Obviously there's a problem with supply chain and then there's also the available technology aspect. So those two forces are really opening door for data. Now, Joe, from your perspective and the perspective of what your company really focuses on, what's another thing that is happening out there in the market that, you know, opens the door for companies like yours and our members to start looking harder at data as a way to improve their relationships.

Yeah, thanks, Brandon. You know, they're one of the big challenges that we see affecting manufacturers and distributors right now, driven by the supply channel, is rising costs related to materials, transportation and labor. Both manufacturers and distributors have to place more scrutiny on how they're investing their resources. Your own customer sales data combined with macro market data can really tell a story of which customers to invest in, which product lines to expand or contract, and which distributors or manufacturers you should partner with to maximize your opportunities for growth. Another opportunity related to costs is to study pricing elasticity to understand which categories can be increased without major market disruptions.

Price is another thing that's very much top of mind. And I think that I talk to a lot of people, you know, all the time our members are calling me and it always comes up is what are you hearing from other people on price and how are they reacting? And you know, what should we be expecting in the future? Is this continue to continue to go this way. And by looking at data and by, you know, looking at it that way, you can take some of the guesswork out of it, some of the anecdotal out of it, and really crunch the numbers through it. So glad you brought that up, Joe. So guys, I, I did give you the questions ahead of time, just full disclosure from everybody, but now I'm going to flip it on you a little bit. Everybody ready for a little bit of a curveball? Okay, I'm going to ask this question. What, what data should companies be looking at? Each of you come from a different little bit of a different take on data. But what's one piece of data or one set of data that companies in this channel, remember we have manufacturers, distributors and IMRs on this, on this broadcast today we watching today. What's one piece of or set of data that they can be looking at to help run their business? Let's kick it back to you Joe. And remember, this is about strengthening those relationships.

Sure, sure. Thanks. The first one that I would start with, and there's certainly several to talk about here, but the first one I'd start with is a company's own sales data. This can really tell a story of which of their channel partners are trending in the direction of churn versus growth or help kind of segment their partners according to certain criteria, whether it is growth margin, SKU count, et cetera. Once that analysis is conducted, they can develop customized business plans for each of their partners. And that's really just using their own internal sales data.

Okay, definitely looking internal sales data. Everyone's got it out there. So definitely that's a place, excellent place to start. How about you Pradeep? Where would you go? What was the data?

Brendan? I would agree with what Joe said because I think the sales data or transaction data, if you call, if you will, that is the data that connects your customers, your suppliers and your products. I mean that is what we're looking to add value in the supply chain. So that is where we need to begin with and often Companies are sitting on a mine of transaction data, but then they go out seeking additional information. Absolutely. Additional information is great, but there is so much we can do with existing data. To give you an example, there are two perspectives for the distributors and the IMRs. What they should be looking at is which suppliers add more value to them because not all suppliers are equal. So everybody needs a different strategy. Some of them are strong in terms of performance, some of them are strong in market share. So having that segmentation of suppliers helps them create those specific strategies with all the suppliers and then turn back and then if you look at the supplier lens of it, suppliers want to grow market share but then it feels like often distributors look at that slightly different. And I really want to throw one example here. There's one manufacturer that we work with, they're in the H vac, space heating, ventilating and air conditioning. And what they said is they were able to pull up all the distributors data and look at the volume. This is a simple gap analysis. They said we're going to look at which product category they're strong in and they created a tool that the distributors can then look at and see, put in their customer information and see where they're buying more. And if the vendor wants to grow a particular product line and if the distributor on the other side is not focusing on that line, this kind of points out those opportunities. And in fact the manufacturers are giving them data to upsell and cross sell. I think that is by far the best example I've seen where a manufacturer and a distributor are working closely to achieve a common goal.

Yeah, it's all about really alignment. It's yes, taking what Joe said and then, you know, how do you get transparent with it? How do you work with your channel partners and show that's taking it to the next level. Kevin, how about you? Are you able to take that to the next level?

Well, I think I'll give a little different take. And I was just kind of giggling to myself because I would have been shocked if we got through this without you throwing a curve ball into it. So the. But great question, right. I think it's interesting as we look at the panel, there's, I think we talk a lot about transaction data on a consistent basis. I like to kind of view things as that. That is historically what we've always done has been focused on transaction data in history and that's where we end up kind of myopic if we're not cautious about this. I think if there's an opportunity to take Data and transaction data and other types of data and actually change that to be customer focused versus transaction focused. And I think that's some direction that we're going to start seeing more and more things move towards. Doesn't change the importance of the transaction data that both Joe and Pradeep talked about. But we also see a number of different types of data that are out there. And Joe and Pradeep both mentioned the traditional ones that we look at. But there's a lot of changes going on in the world right now with the types of data and how we get it and how we use it. So within organizations and what I see commonly with the customers that we work with is I use the term siloed. So we've got data siloed in an ERP system, we've got data siloed maybe in a CRM. It's astounding the number of organizations in our industrial distribution world who haven't embraced the CRM concept where they can aggregate data together, but they might have marketing automation, they have an E commerce tool and they have these in different places, but they're not necessarily taking advantage of bringing all that together yet. So that's a big opportunity for us right now. Most distributors, IMRs and manufacturers also right now have some type of a social media presence. That whole world of data, of third party data is changing dramatically right now about the use of cookies and things like that. We have all of these different areas and we can bring this together within the, the data arrangement there. There's even some great tools out there right now and talking about, and we're going to talk I think a little bit more later about manufacturers and distributors working together and even in IMRs as well and sharing data. There's even tools out there now in organizations. In fact, we're having some discussions with a company right now. They're almost like an escrow service for comparing data together where you're not giving up necessarily proprietary data but you can see crossovers and things. So there's a lot of different things happening right now. But I think we have to look at that concept that I threw out earlier and I'll bring that up probably later in the day as well about the customer focused data versus just historical transaction data.

Absolutely, absolutely. And certainly I think that you bring up a good point. There's data everywhere and it's all about bringing it all together.

And.

You brought up cookies is a great, I mean it's scary how much data is out there as it relates to people's behavior online. And their behavior as they purchase and what they are into and what they're not. And so this is a case where B2B is learning from B2C in a big way. But with all of these opportunities, with all these data that's flying back and forth and sharing between business partners, there's got to be some challenges that people face when you want to share data or be transparent because that is where some magic happens where you can combine two data sets that don't normally sit together. You find something that's actionable, you find something that's unique. But there are challenges getting those two together. Can you guys share some examples of what you've seen as a challenge and then maybe how you overcame it. Remember talking about, you know, two companies that maybe like to transact and have an individual interest that they have to protect. So how about we start with you Kevin? I'll go back to you.

Sure. Well, this is near and dear to my heart because I've been in this industrial distribution channel for 30 years plus now and I started my career working for a national distributor, then was an IMR for 10 years and have also been on the manufacturing side before we developed our software system. But you know, historically there's been that kind of scarcity mindset of this is my data I can't let you know. Now typically the manufacturer has been very open with sharing information but you know, I remember in my days as an IMR being very different than today. It's been 20 plus years since I was in the business. But you know, that was really what as an IMR I looked at as that's proprietary stuff for me is, you know, yes, I would happy to have XYZ manufacturer know the distributor but all of their people and where they're calling on and so forth. That became a little bit more of a challenge because that was an asset of the business. Right. Distributors have historically had a similar mindset to that where they look at things from a standpoint of well, I can't let you know where I'm selling. I bought a pallet from you and I'm selling it in the each's. We're in this new place now where we have to get rid of that mindset. We see some very forward thinking people out there. My assumption is Pradeep has probably worked with a number of companies that are very forward thinking about this, but that's where we need to get. I think we see Brendan, some of the buying groups that do a nice job of that of getting their members to talk Some even one of the largest ones, they even have a national account program that they work with distributors and they're sharing data across platforms and so forth. I don't know that any of them really have the technology side of that figured out yet, but they are getting in that place where they're sharing data. But if we go back to that thought that I brought up a few minutes ago about customer focus, if we all were thinking about making the customer experience best, then that sharing of data becomes much more valuable. I think there is part of the question was challenges is I would suggest it's kind of maybe as an overview, it's twofold. One is the willingness to do so and getting past that and doing it in a way that not just says, I'm sharing some data so I get more leads, or I'm sharing more data so I get a better discount. But how do we interact so we grow our partnership that benefits the customer is. Is key. And then really the second piece of that is solving the challenge that comes with. How do we do that from a technology standpoint?

Absolutely. So what I'm taking away is that you need some sort of unifying alignment. And the customer is a beautiful one. Obviously, everybody wants their product and their company in front of a customer, and they want it in front of a customer in the best possible way. So that's a great way to combine and align. But I want, I want to give some of our other panelists an opportunity because I think that this is probably one of the biggest hurdles that data has is that how do we get entity A to work with entity B so that their data sets can. Can create something that. Where two plus two equals five, you know. So, Joe, do you want to respond?

Sure, sure, Brendan. You know, one of the big things we see is that data, especially transactional sales data, is. It's obviously very valuable, but it's very sensitive. Confidentiality concerns are often roadblocks to that true transparency related to data. One solution that we've seen work is utilizing a trusted third party. Bring them into the fold to manage that flow of information between the two partners. Rather than sharing all raw data directly, a third party who has significant analytics capabilities can mine and transform that data into actionable insights that can then be delivered and back to the two partners and be used to execute on their joint objectives instead of sharing that data directly in its raw form.

Yeah, I mean, a third party definitely can remove some of that ownership over the what's sensitive and create this level of objectivity as you're looking at it. So great idea. Jody, do you know of any companies that do such a thing? Never mind, never mind. We'll get to that at the end. How about you, Pradeep? What's your take on this, these challenges, and more importantly, what's another way that we can get through the challenges and move beyond this? That what Kevin called that sensitivity and the close to the bestness that people have with their sensitive proprietary data?

Sure, Brandon. Brendan, what I would like to do is I would like to address companies that are in different stages. One is companies who are just starting the data journey. They're looking at, do I need to use this or I'm doing really well, what do I need to do different? Then there are some companies who made the decision to really start using data to make progress in the process. That's where the sensitivity comes in. I want to take a step back and make sure that we address all the different types of companies in various stages of their data journey. First thing that we see is companies who are going down the path is we don't have complete data. We share this over and over. And that is a roadblock that companies, I don't know whether it's the hesitancy to do it or they don't trust the data. But if you're basing a lot of your pricing decisions and your sales decisions on data, transaction data, then that is something that you already trust. So not having complete data, I feel it's an excuse that companies just keep doing to put away the data exercise. And no company, I mean, believe me, no company is ever going to have perfect data because there is nothing called the perfect data. Everything is changing. It's highly dynamic. And if you're capturing all the transaction data that is sufficient to begin with. And to Kevin's earlier point, there are so many different types of data. And the fundamental, if you look at a pyramid, the bottom is the transaction data. Then you start laying all the other information on top of it because that is how it should work. The other thing that we see is companies say we are already successful. We see growth in sales and profitability. Why do we need to use data? That is a very common roadblock. And the last year price increases, companies have seen price increases anywhere from 2 to 40%. If you look at an average, there's about an 18% price increases across various lines of trade. But H vac building, metals, they've seen 18% price increase. So what companies are looking at is they're seeing growth, but that growth is a result of price increase. If you separate the organic growth, it's probably about 5%, 95% of the growth is purely price increase and that is not going to last forever. So we really need to move away from the waiting for things to happen mindset. And as Kevin pointed earlier, the growth minded companies are already far ahead. We want to catch up and just waiting there. It's not going to do anybody any benefit. And there are a couple more things I just want to briefly talk about is we don't have the bandwidth. We have so many things we're doing and we don't have the bandwidth to do it. And this is where companies need to look at data as not an IT project. If you look at data as an IT project, we are not going anywhere, nothing will happen. You have to really look at data as a strategic capability that your organization needs to invest and build. It's not done in one day, it's a journey, it's a marathon. One of the, the key specialist in data would say it's a marathon, it's not a sprint. So you need to find someone in the company that can wake up and think about data on a day to day basis. And often about 80% of the time that person tends to be sitting in the finance department. And that's where the data team originates in small companies and big companies have a separate analytics department. But that seems to be a nice starting point. And the last thing is we have data but no one is using them. So this is companies, they've done all the analytics and they have data but nobody's using them. So that is when companies should think about two aspects of it. One is the creation, the other one is consumption. If I spend all the time creating all the reports and dashboards and everything, it's great, I'm progressing. But what if nobody's using it? So at the end of the day somebody should be thinking about if my sales team is going to use something, what is the pain point? How is this data going to address it? If my inventory team is going to use this, what questions do they need to get answered and how are they going to use it? So spending more time on the consumption is what companies are not doing. They spend about 80% of the time in creation and very less time in consumption. And that is where a lot of companies get stuck in and technology comes to help.

Yeah, it looked like Kevin, you wanted to jump in there or did he just nail it?

No, I mean, wow, that was awesome. Pradeep just like bullet pointed through some really key great points that I think about constantly. And you know, one of the things if I just. Brent, I think this side question is kind of taking us over today here. But Pradeep made this great, great comment and I can't speak for Joe, but I'm going to assume Joe sees this as well. Is that. And I've had a challenge kind of bringing this to the technology industry. Veteran partners in my company, we are in a very mature market with some long. The startup concept is not really in the industrial distribution world. We've got generationally owned companies that have been highly successful. We have PE owned companies and publicly traded companies in our space. And one of the challenges I think that faces a lot of organizations is is that willingness to take the leap. And then oftentimes we'll see people that are ready to take the leap and then they just start buying tools but they don't have a plan in place. And I'm sure we'll get to more of that later because it's something I'm passionate about, about having a technology plan in place. But Pradeep really kind of nailed it with his. I think his words where I wrote him down was organizations that look at this say, well, I'm doing fine, right? I'm doing fine today. I've got 8, 10, 15% year over year organic growth. That's now that is not what's going to be the world is going to look like in the next few years. Things are going to be changing pretty quickly. The only other thing I wanted to add to that, you know, Pradeep was talking about that falling into the finance world. I think when we talk about that, you know, creation versus consumption, that organizations. We're working with a company right now that's doing a great job of this is they've promoted someone who's getting a lot of training outside of their organization in sales operations. So if you take that finance component that's driving data and getting information available together out of systems that Pradeep was talking about and you marry that with someone maybe in a sales operations role that can drive that down through the organization. So we get the consumption side of what Pradeep was talking about versus just the creation. So I think Pradeep really nailed some great stuff there.

Absolutely. Yeah. I've got, I've got a follow up and it tails into the next thing. I wanted to give Joe an opportunity to weigh back in because I think we are bubbling up a pretty good topic here.

Thanks, Brandon. Yeah. You know, I would just echo one of the points that really stuck out to me that Pradeep brought up. And that's in terms of consumption of, of data. Right. Everyone has data, but not everyone uses it. So really, the key for what we've seen is creating that actionable data that's used to gain insights into the, the business and, you know, reach the goals that you have set forth.

Yeah, I mean, I think that what we're, what we're all saying is don't make data a reference point. You know, don't put it. Don't say, here's this great report that I did. I'm going to log it with last week's report and the week before that report and then put it up on the shelf so that if I have to answer one question in a board meeting next week, I'll just refer to my binder. And I think that what you want to do is you want to have a culture in your business that understands the value of data and one that is there to help you drive data in your decision making. So, Kevin, great suggestion on incorporating sales and marketing in there. You want your sales and marketing people to just have a rough enough understanding of the data that they have available so they can connect the dots and put two and two together. Yes. Maybe the CFO needs to be in charge of gathering it because the skill sets might align a little bit better over there or they have access to the different parts of the data. But really you need every part of your business to be looking at data differently. Why can't hr? Why can't procurement? Why can't sales and marketing? Why can't all the other operations? Obviously? So there's all of these things that data can play a big role in. So we also talked about creating a role or giving somebody this position of being responsible for the data. All that work, getting data together, if you're not utilizing an outside partner or somebody to help you, it's a lot of work. So the question I have is we have companies that are watching this that say, Brendan, I've got seven people that work for my company. Is this data game just for big companies that can hire somebody to do this, or can I still play? So I think I know the answer here, but I'd love to hear how you guys can say the data is for everybody. But let's start with you, Pradeep.

Okay, perfect. And Brandon, before to answer that question, I want to throw some statistics there, just some numbers, so that we can put some color to this discussion. So what we did is when one of the surveys that we did, again this is both distributors and suppliers. So most of them are small companies. So the question was what kind of distributors will we see in 2022? And there are five categories and it adds up to 100%. The first thing is 20% of the companies will not do anything different. They said status quo, we're not going to do anything different. The next 15% of the company said we will add more people for growth. We're just going to focus on people. We're just going to add more people and then focus on growth. And then this is a big, big thing because that's what I think. It's going to separate the small companies from the larger companies in terms of resources. The third option was they will use a big bang approach, which means they will use technology hoping that it will make all the change. That was 35% of the companies. That was by far the longest. The fourth step was some companies said they'll focus on analytics creation, which is 20%. And the last thing was only 10% said that they'll focus on data consumption. I think even though we talk about data, we've been talking about data for a long time, it is still in stage of infancy when it comes to distribution and small companies. So what you said makes perfect sense. And when you look at small companies, I think it is more important for the small companies or in fact the companies that have limited resources. Because as a small company you want to focus your time on what matters most. And most of the decisions that we make every day revolves around suppliers, customers and product. So if we can use data, I mean simple data that you have on a day to day basis, the transaction, I keep going back to transaction data because that is something that everybody agrees on in the organization. There's no dispute whether sales look at it, marketing looks at it, or pricing looks at it. They have the same listing because there is no disagreement there. So if we can use data to segment suppliers, customers and products, in essence what you're trying to do is you're trying to identify which business is core and which business is non core. It's a classical 8020 rule for small companies. I think using data to separate the value and noise is very important because once you do the segmentation of customer supplies and products, you can easily establish clear relationships that is based more on data than emotion, experience or gut. And as human beings we are very consistent in being inconsistent. So what data will help us, especially the small companies, be consistent, specific and relevant in all their decisions?

I'm going to steal the very consistent of Being inconsistent one, by the way. It's great. I don't know if you stole it from somebody else, but I like it.

I was inspired by somebody else. Put it that way.

Well, it couldn't be more true. So I'll ask the same question to you, Joe, though. Big companies, small companies, can they optimize their relationship with channel partners with data?

Yeah. So I'll throw this out there. You know, actionable data used to gain valuable insights by a $10 million company is infinitely, infinitely more valuable than data that is merely used as a historical data point by a $3 billion company. Right. Kind of what you were saying before we got into this question. The largest resource constraint that we've seen has been related to data is time. Large or small companies usually have competing interests internally, which definitely creates a constraint on time. So finding yourself a partner, whether it's internal or external, to aid in pulling those insights from your data is a great opportunity, really, to maximize the value of your data and use it to enhance your channel partnerships. When it comes to small companies, they often have advantages, actually. There can be less competing interests internally. Decision makers can often cut through process and make decisions more rapidly. And usually there's increased alignment on goals that increased focus can. Can allow a smaller company to allocate their available resources rapidly toward a specific goal with a specific budget and create a clear output that they can share with their channel partners to demonstrate their investment in that partnership.

So, I mean, great answer. I mean, I feel like there's two things that I took away from that is data capabilities can scale up and down, but it's all good is one thing. And then two, smaller organizations are a lot of times more nimble, which is a big roadblock for some people implementing a data strategy is that they have to go through a lot of red tape in order to get the silos opened up. Where it's a small company, one person can just say, how about it? So, great point. So, Kevin, anything to add there that Pradeep or Joe didn't touch? With big company, small company, everyone's invited to the party.

I think just a couple things to go with that. First is, I'd always. I've used that Pradeep's comment for years about consistent and being inconsistent as associated with my golf game. But now I'm adding it to life in general. But I do agree with what Pradeep and Joe are saying here. I think the reality of it is that, and I think Joe kind of started on this, is that for a smaller organization, this can Absolutely be a competitive advantage for them. You know what I have experienced quite a bit of and I think you've probably all seen is large organizations that decide to sometimes jump into working with data and so forth, start buying tools, different types of software tools, maybe they've got some consultants involved without really setting any type of plan. You do not have to start working with data by saying I've gone out and spent another $100,000 a year on software and I've gone out and, and brought in a large group of people. If you take siloed data within a small organization, this could be as simple for a, let's just say even an IMR agency or maybe a 3 to 5 million dollar a year distributor even, you know, they've probably got data either obviously in their erp, but maybe you've even got somebody that's small and using QuickBooks, still using Mailchimp versus a marketing automation component, has a Shopify store. All of those are areas of data that you do not have to have huge AI systems that describe what you need to do next and things. You don't need that to get started. In this, you can get started by taking each of those areas of data, pulling those together to make small business decisions. I would suggest the comment I made earlier about focusing on the customer versus transactions, but using transaction data to get how we help that customer is going to help our businesses grow. And I would almost suggest that in some instances because of hierarchies and spend and so forth that small organizations might actually be able to do this better. I think the key is that you aggregate your data. You have a clearly defined plan of what you want to accomplish from it. You have a tech stack that goes with it. Right. What are, what's the technology that I have, how am I using that properly and do I need to add to that? But by doing it by a plan versus a, you know, a shoot ready, aim idea. And then the other side of it is, and I think Pradeep actually mentioned this a little bit earlier, is think about crawl, walk, run. You do not have to boil the ocean of data when you start. You can start by doing some very generic things.

All right, we're starting to get into the advice portion of this guy. So I hope everyone in the audience is paying attention that this is a game for everybody. That's what I'm taking away. And it's a game if you're not playing it. If you're not looking at your data, you're missing out on a part of your business. That helps you make better decisions, helps you identify opportunities, lots of stuff. So we touched on this a little bit before where we talked about sharing data and looking at data and certainly we've talked a lot about the investments that, that you might need to make in order to get into this data game, as I keep calling it. What about roi? Say you're year two of your data strategy. Can you quantify the ROI of a data initiative both internally and externally? And remember, the external is really important because if you're convincing a channel partner to share their data with you, you want to say that was worth it because of this. So I'd love to hear your thoughts on that. Can it be quantified and really how? So let's go right to you, Joe.

Thanks, Brandon. So the answer yes, and I can give an example of both. So internally, as we've discussed a few times now, the supply channel challenges have led to pricing actions nearly everywhere. Right. That data can help your company track how, how much of those pricing actions increases have been realized in the market, with whom and where there are still gaps in terms of executing those actions. If you look externally at roi, you may choose to invest in a data sharing partnership with one of your channel partners. And after gaining insights from the shared data, your sales teams can jointly target new end user opportunities and track growth process. Ultimately, you can use that data to track share gain or share loss. You can track it by vertical, you track it by product category. There's a lot you can do to track that ROI externally with your partners.

Yeah, I mean, I think that the historical and I want to give Joe and Kevin, I'm sorry, I want to give Kevin and Pradeep a chance to respond here. But the historical way is you would tie growth to a new go to market strategy or a new sales strategy or new product or something like this. What you want to do now is say we grew 5% more than we expected because we had better data from which to glean insights. And I think that that's important so that now you can double down on your data, you can double down on looking at it because that can lead to growth. So great point there. All right, Pradeep, same question. How do you quantify ROI of internally and externally utilizing data?

Absolutely. And I think I agree with what Joe was saying. I mean, it's on spot. So if you look at another example you want to start with before jumping into any data exercise, you want to see which metrics it's going to impact. So typically when companies are trying to grow or in the business we try to look at growing, which is growth, which is a growth driver or profitability. We're looking at asset efficiency or cash flow. And all of them of course, connecting to customer experience and customer service. So if any of the analytics initiatives or data initiatives are not tied to growth, profitability, asset efficiency or cash flow, then we are missing the boat to begin with. And any metric that you use to quantify it should tie directly to the income statement of balance sheet. I mean that is something the leadership, at the end of the day, the leadership team is going to pull the plug or at least continue to keep going down the journey. And if you're not able to show that numbers on the balance sheet or the income statement, it is not going anywhere. So let's look at an example. Joe talked about an example on the pricing side of sales side of it. So let me take it down to the inventory side. So this is one company they're looking at using data and analytics to optimize their inventory. And what they looked at was internal metric was slow moving inventory. Okay, we have 15% of slow moving inventory that we're sitting on. It hasn't moved in about eight, nine months. What do we do with it? And this, if you look at this item, if you look at your balance sheet, there's a line there which says inventory. So I can easily look at what portion of that inventory is part of the balance sheet. And what they started doing is they said, how do we work through this? We're going to look at these, we're going to look at items that are bought only by one customer. We're going to look at items that are bought specifically for certain customers. Because to slowly work through it and when you start doing it, what happens is your inventory, the slow moving inventory dollar goes down, which means on the numerator, the gross margin. There's one metric called as gmroii, which is gross margin return on inventory investment. So you've got the gross margin on top, you've got the inventory at the bottom. So as the inventory shrinks, the slow moving inventory shrinks, the ratio gets higher. So that is, is how they quantify internally. Now looking at externally, nobody wants inventory is a great form of investment. So when I say reduce slow moving inventory, what we're doing is we are releasing investment from slow moving inventory and moving into new products or items that sell better. So when I reduce that inventory and move them into new products, from a customer perspective, I know that I'm servicing a new set of customers. These are Customers I do not have business with currently, but with these new investments and new products that they will buy, I can make that investment. So this is one way that this company quantifies. And their goal in 2020 and later part of 2021 was mainly focused on inventory. And we saw that this was. They were able to clearly connect all the metrics. And now the leadership team, look at, looked at this. They were able to reduce the storm inventory by 15%. The new inventory went up as an effect. The growth was higher on new products. Suppliers were happy, sales team was happy, everybody was happy. Now they are starting to establish a larger budget to take this process into sales and pricing as well.

Great example. Yeah, excellent example. And again, it ties, like you said, right. To the income and balance sheet. So I mean, that's income statement and balance sheet, which ultimately, you know, is the bottom line. Right? So, yeah, great example. How about you, Kevin? What is something that we can do to create? Because I think that what we're aiming for is how do we create data evangelists out there in the world that say, yes, I will give you whatever data you want because it helps with business.

Yeah, I think you kind of put that into a nutshell a little bit there. Right. But we didn't talk a lot through this yet about kind of the external side of it. And I think historically there might have been a little bit of an effort of, you know, manufacturer a says, you know, if you share this much information with me, it'll help your pricing, you know, column, it'll help certain programs. Maybe there's, there's marketing funds available, whatever it is. But I think what really people need to do before they even figure this out from an ROI standpoint is understanding really an internal plan that says, okay, if I'm a distributor or an IMR and I'm being asked to share this data, not only what do I want that to do, to be compliant, but how do I turn that into something that's of value to my business versus just I'm an IMR and the factory asked me to send them some more data about customer visits or whatever it might be. Right. That's another just set of data points for us. But I think having a plan in place becomes really critical that says, how is this going to translate into my growth and my partnership solidifying relationships better and then looking across the board.

Right.

What, based upon that, what do I want to accomplish? Is it growth? Maybe it's only growth? Is it growth in margin? Something we haven't talked about today at all is customer retention. Customer retention could be huge when you're using data correctly. Right. And that flows all the way across from a manufacturer, distributor and imr. Customer retention is critical with that. Is it inventory optimization? Some of the things that Pradeep was talking about, having a plan going in, make it so much easier to get ROI at the end. Right. Because you know what you want to see, what you want to accomplish.

Absolutely. So the ROI is there, out there. We just need to. We need to find the way to package it up and tell those that you know are looking at us internally. So if you're going up to your board and saying, we need to keep doing this data thing because of this, you need some firepower there. It's there. And if you need to go externally to get the data sharing going, it's there too. You just need to understand how to package that up in a way that makes sense from all perspectives. So I've got one last question. This is a quick one because we're running out of time here, guys. I want you guys to leave some parting advice for this audience. There's a whole spectrum of where people are on their data journey right now, but I want to address those that are data neophytes right now that are a little bit scared, that are saying, I've been running my business this way. I don't necessarily know that I need to do this. But you're saying some things that say that it might be easier. Where do I start? What should I do? As the first thing, Kevin, why don't you start that off? What's the first thing that they should do?

So I'm going to shock you and I'm going to go really fast. I have literally four, four points to consider. Set a plan with goals. How will this help the growth of my company, my people, my customers and our partners? Set that plan in place right off the bat. Clearly identify where all of our data sources are within our business. Most businesses have many of them and they're typically siloed, as we talked about. And then before taking steps forward, identify what the right tech stack is so that you're going to use these pieces not just internally with finance, but your customer facing your consumption, as Pradeep called it earlier. Right. Have an identified, clearly identified tech stack. It doesn't need to be overly sophisticated. And then think about potentially doing a crawl, walk, run scenario. Pradeep made a great comment earlier. Right. This is a marathon. I like to always say it's a journey, not an event. So think about it in the Long term of the business. Not that I want to dive in and just have this big bang happen. And now I'm a data company. It's a process, not an event.

Absolutely. All right, Joe, you're up. What's the one piece of advice?

Start internal. Before you can start sharing data and insights with your channel partners, you have to understand your own data first. Your own data can tell you so many things. You can use it to find margin enhancement opportunities, portfolio management strategy, customer churn, prediction, product segmentation, customer segmentation, product product category, white space. There's so much that you can, that you can find just from really understanding your own data first.

So dig into your own data. Like it. All right, Pradeep, you get the last word here. What's the one piece of advice for the audience?

So what I'm going to do is I'm going to get inspired by what Kevin said and what Joe said. I'm going to put that information together and just kind of sum it up in quick five steps. It's again, I'm just going to reinforce what they said. The first thing is what is your data aspiration? Don't use data just because your competition is using it or other companies want to use it. Clearly figure out why you need to use the data for. The second thing is which area are you going to apply the data for? Is it pricing, sales, marketing, inventory, etc. Decide that third thing. By using data, how do we add value? Are we going to make our sales decisions better? Are we going to improve the lives of our people working on inventory? The fourth thing is what capabilities must be in place? The technology stack or the resources. And the last thing is once you have this, how will you enable and implement, how will you enable your people to become better data users and how will you implement it? So I'm not saying anything new, but pretty much what Joe and Kevin said, as an engineer, I have to put that in steps. So Kevin said it in four steps. I said I'm going to add another step and make it five steps. So that's what happened.

That doesn't sound like an engineer. It sounds like a salesman or somebody that needs to one up somebody else. So I didn't, you know, it's fine. You win the number of steps awardee. All right, guys, this was a phenomenal session. Obviously we could talk about data all day long. There's a whole world of it out there and so many applications. We touched on a lot of them here today, but there's so much more and guys in the audience or out there in the audience. These are three really smart people. If you have follow up questions for them, reach out to them. Their contact information is below and their companies are below. They're great companies. Member companies from isa. Please do reach out to them if you have questions. If you did ask any questions over here in the chat, I'll share them back with Kevin and Pradeep and Joe and we'll find a way to respond. We've got your name associated with the question. Maybe we'll write a joint blog or something like that together. We just had so much to cover in this short amount of panel space. But guys, thank you so much for being here. Really, really appreciate your insights. This was a phenomenal session. I walked away feeling a little bit more confident about data and I have to go internally and now look at my data. So we'll see what we're going to do there. All right, thanks so much, everybody. Enjoy a little break here and we'll see you soon.

Bye. Bye, Sam.