Ep. 84: Jason Krantz - Data and Analytics | Driving Business Strategy
Jason Krantz, Founder and CEO of Strategy Titan, joins Count Me In to talk about data, analytics, strategy, and risk mitigation. Jason has over 10 years of business analytics, data science, and strategic leadership experience in public and private equity owned business. With like-minded data scientists and strategists, he formed Strategy Titan, a data analytics company that helps organizations grow their top lines, bottom lines, and valuations by "weaponizing" data and transforming it into a competitive advantage. In this episode, you'll hear Jason talk about the difference between "data and analytics" and "data analytics", how these analytics strategies need to drive the overall business strategy, and how aligning your organization's strategies will help mitigate risk. Download and listen now!
- "Data & Analytics in the Boardroom: Raising Your Digital Quotient" Video Series: https://www.strategytitan.com/blog/data-analytics-in-the-boardroom-raising-your-digital-quotient-video-series
- Build out your “business and finance” centric data and analytical skills with our practice dataset. Perfect for accounting and finance pros looking to develop their data skills: https://www.strategytitan.com/blog/titanized-real-world-dataset-to-develop-your-analytics-muscle
Additional Work: Using data and analytics to make business decisions with confidence during times of uncertainty: https://www.forbes.com/sites/brentdykes/2020/04/29/why-your-business-must-double-down-on-data/#6e180f597a68
FULL EPISODE TRANSCRIPT
Welcome back to Count Me In, IMA's podcast about all things affecting the accounting and finance world. I'm your host, Adam Larson. I'm here to bring you episode 84 of our series. Today's feature presenter is Founder and CEO of Strategy Titan, Jason Krantz. Strategy Titan is a strategic management data and analytics advisory firm, and Jason is a business professional with over 10 years of business analytics, data science, and strategic leadership experience. In this episode, he shares his perspective on data, analytics and strategy and explains how these functions of the organization need to ultimately drive your overall business strategy. Jason talks about the products that have great opportunity to increase your firm's revenue by connecting these strategies, and how aligning your businesses strategies helps mitigate risk. Keep listening and will now head over to a very insightful and analytical conversation.
Alright, so Jason we just were having a brief conversation before we started here and, you know, we were talking a lot about the importance and the value of this conversation. So to start us off, why is a data and analytics strategy so important for innovation today?
Yeah, in my mind, really what it does is it gets down to identifying opportunities. They're going to move the needle strategically and financially ever. All of us have financial goals, revenue, goals, growth goals, whatever it may be. I'll just give a real example. I like to use examples to illustrate these concepts, to move them from fuzzy to concrete. I used to work for a company $2 billion company. We are an industry consolidators. We acquired numerous companies. We had everybody on different ERP platforms, something I'm sure a lot of people can relate to. Each company really had a different behavior profile on the way that they did things. So, me being in analytics for practicing this company, one of my first jobs was to consolidate all this information and start looking at it, look for opportunities. Now we had pretty big, EBITDA growth goals. We were tightly focused on EBITDA growth as, our strategic objectives. And so as we're looking through this, we find out that we have well over $10 million in uncollected freight. Now this was freight that we're owed. You know, we just had not gone out and collected it. And as we looked at it, started asking questions saying, Hey, why is it that we do this? Like, Oh, well, you know, it's in our contract that we can do this, but we typically let it go. Or, you know, it's just, we we've done it this way for so long. So every company was doing it a different way, but the common theme was this freight recovery. So as we looked at it, we're like, you know, we can actually make about $14 million if we just collect the freight that we're owed. Not only, let's not even account for freight increases for gas, whatever it may be surcharges. And as we brought to some management team, they're like, how could this be? And this is, we got everybody aligned behind it, they said, this was a serious opportunity. This is a process improvement opportunity. This is real money that will impact the bottom line and help us meet our objectives. Now, the reason I share that story is analytics by itself didn't do anything. We didn't actually collect that money or drive that force forward. But what it did is it served as a tool to identify the opportunity that if we never went, I would have gone through that process. We never would have identified that opportunity. We would not have socialized it and gotten everybody to see, wow, this is something very significant and a lot of times, growth comes from, you know, looking outside for new initiatives or whatever it may be, but a lot of times now you've seen this time and time. Again, a lot of times there's opportunities sitting right underneath our noses. I call these piles of money, just sitting around, waiting to be picked up. And it's, you know, it's one of these things where this insight it wasn't anything revolutionary, but it was very significant in that it could help us move the financial needle. And that's what gets me excited about analytics ant data is its ability to identify concrete opportunities like that, to help improve business results. You know, it's not all ponies and unicorns. This is real stuff.
So I just want to pause for one second. You just said analytics and data,and for clarification, my first question I did ask data and analytics. So what I would like to do is for our listeners, if you can offer a distinction and define the difference between data and analytics like we're talking about and data analytics in your perspective
Yeah, sure. Great question. So, you know, I separate data and analytics as I see them as being vastly different topics. Now I will fully admit upfront, these are my biases. These are the ways that I view the world and not saying it's necessarily a correct quote unquote, but for me, data has a much heavier infrastructure and asset management perspective to me. Now I say asset management, because I do look at data as an asset. I know it's not on the balance sheet,as an intangible asset, but it is.Our accounting rules have not evolved it, but I think we will get to that point. So things like master data management, governance, security, database management, and other backend activities, these are really important. But from my perspective, they mean little to the strategic side of the equation, from the perspective of the business. Now, again, that's not to say that it's not important, you know, data breaches things, we've all seen. Those are extremely serious things, but it's a different process, where analytics is more front end and strategy focused to me, the business's extremely interested in that. So that's why I look at these things as two totally separate things. The analogy I use is, you know, and it's very cliche, but data is oil, and then the analytic process, is plastic. Oil serves as the raw material to make a bunch of different things out of plastic, and in the same way, data serves as a raw material to do all sorts of strategic analytic and business analytic things. but in both cases you can't have that end product without the raw material. So the process though of sourcing and transforming that raw material is a process in and of itself. So getting that oil and transforming it is a process, getting the data, storing a transforming, it is a process, but then the design creation, marketing sales and distribution of those end products, you know, that analytics process is totally different. Data analytics, to me, it conveys a heavier focus on the mechanics of analysis. And so you're just for me, I prefer to take a very heavy output in business results perspective, because what I've observed is that we focused on the genetics. It tends to be harder to be effective with kind of the implementation of these findings and drive strategy, drive results, and persuade people to do things different.
All right. Thank you for clarifying that. I think it makes total sense and I appreciate your distinction there, and now we're going to kind of take a step forward with all that in mind. Why does this strategy really need to be an executive or even board level topic, you know, depending on our audience, why is this such a valuable topic to be discussed?
Yeah, so I would say one thing is it depends on your industry, right? As always, it depends. But I would say for most industries, you know, at least the industries, I work in manufacturing, wholesale trade, kind of industrial, what I call dirtier industries in general. And I'll say in general, very broad brush strokes. They tend to be less further along on the data and analytics, maturation curve, you know, manufacturing let's use as an example. Within the four walls, they're incredibly skilled at that year. You got your Kanban six Sigma, continuous improvement, all these different things that within the four walls are very well oiled machines. These are things they've been doing for since World War one, I think. But when you look outside your four walls, that process tends to be far less developed and in a lot of what I've done in my career, in manufacturing and wholesale trade and industrial, a lot of the significant value has been derived from looking at those things outside the four walls. And the reason why I kind of preface my response with that is that if you're used to looking within the four walls, you need to build a new innovation in mindset and strategic muscles to really start looking at what can we do outside of our four walls, and that's a big muscle to build because we're creatures of habit. We do things that we're familiar with. Change is hard. So as we look at this as an executive or board level topic, the reason why it's important is because I personally see this as being one of the most effective approaches for identifying and creating new and innovative strategies. You know, it's the raw materials for understanding your markets better. It's really important for deploying your resources effectively and mitigating risk, and when I say deploying resources effectively, it gets down to that ROI equation. What are the strategic items that you have on your agenda? What is the risk associated with those? What is the potential return, but sometimes, and I've observed this time and time in my career, which is why I'm so passionate about this. Sometimes there'll be strategic opportunities that are very significant, extremely low risk that are right underneath our noses, Aad we might not even be aware of them. And I go back to that freight example. That is a real example that I've seen play out time and time and time again in multiple organizations. It's almost, it's almost like, well, how, you know, you assume that you're doing these things, but if nobody really looks at it or, you know, another great example is our total cost to serve for your customer base. Like, okay, you've got your gross margin, your contribution margin, material margin, but what's your, you know, after you look at all that stuff, what's your total cost of care for your customer base. Most companies will be blown away to observe that most of those customers that they thought they were probably making a lot of money on after you account for all things like freight being a part of that equation, you're gonna actually be losing significant sums of money. And again, I say that just because I personally have observed that that's how I've made a career out of this stuff is finding these things that they're not crazy. They're not revolutionary. And as a result, you know, they're not the sexiest thing in the world, but for a lot of executive, executive teams or boards, these can move the needle in major, major, major ways. And they don't require massive ERP implementations or massive, massive, big bang projects that can be started and piloted quite quickly and easily, but they can develop like unusually huge ROI if, if done appropriately.
And let's talk strategy a little bit here. We've mentioned data and analytics strategy a couple of times. what's actually included in this strategy? Can you walk us through what the process looks like of creating a data and analytic strategy?
Yeah, so, my approach, again, it depends on where company is at in their life cycle. You know, are they in a turnaround mode? Is it a younger company looking for growth? Most of the companies that I have been in have been more mature organization. So I will preface it by saying that I approach it from that lens. But many of the times the companies I've been a part of in most companies, I think have strategic goals related to, revenue, goal, revenue, growth, EBITDA growth, market share game, things like that. So as we're looking at this, I tend to look at it as what are those projects that have probably very, or what of those areas that have low analytical sophistication have significant upside in terms of we can get up the analytics curve quickly. What is, what is going to be those projects that are most likely to get people excited and show the potential of data and analytics, and then most importantly, which projects are most likely to actually physically move the financial needle either directly or indirectly. And I'll give you some examples. One of my favorites, to go after is, sales analytics along with pricing analytics, because these two are extremely closely related. Pricing analytics is something that can directly impact, you know, revenue and EBITDA growth. It's the most powerful profit lever we have, but there's complexity that goes into that as you know, sales incentive plans, comp plans, all these different factors go into effectively deploying a price increase in being successful in that price increase without shedding business. So that one introduces a very significant human as most analytics projects do. We don't want, we don't for the sake of time, we're not going to dig into that, but that's a very significant it's actually probably the most significant challenge in an analytics initiative is, is people sign it the change in the culture related to it. But focusing on the projects themselves right now, the sales analytics is one that I personally always start with. Always, always, always because I found time and time again, that if you can help revenue drivers do better and do their jobs better with less work, everybody benefits from that. So, you know, I'll give you an example. One organization that I went into to start up an analytics capability, sales reps are spending five hours a week putting together various types of report, either for their customers or preparing for meetings or whatever it could be. As we look at that, we're like, wow, we have 80 reps. So, you know, that's a lot of time every single week putting into a no value add activity for a sales rep, right? If your sales rep and you're creating reports thats opportunity costs that you're not out selling. So it was like, okay, well, if you have revenue growth goals and market share growth goals, you're going to have to sell more, sell more, requires more prospect, but there's a finite amount of time. So if one of our objectives is to sell more, what if we could put together this analytics platform to free up the time so that sales reps aren't putting together reports anymore. We're automatically delivering standardized content to them on a regular basis that they can deploy to their customers are used for internal reporting purposes. We create that standard. Not only that a lot of times there'll be errors through no fault of their own, maybe a filter's wrong and a pivot, or, you know, whatever could be. There's a variety of situations. But what we're doing is we're taking that time and this was one of these things that the five hours a week as an illustration happens every single week, let's say, it doesn't go away. And it probably gets worse during strategic planning cycles and your operating plans, the budgets, all that stuff. So what we're doing is you're taking every single week, then that fictional five hours and you're reallocating it to selling, which should help you hit your goals of more sales growth, more revenue, more revenue, whatever it may be. So that's just one example of how it can be used as a tool to free people up to do the things that you hired them to do, right? You hired a sales team to sell, you know, whether it's farmers or hunters, whatever it may be. You hired them to sell, not do reports. And then, you know, by creating the standardized perspective around the numbers and the views that you take, everybody starts to speak the same language and they understand more intuitively what the other party is doing, because everybody's looking at the same thing. And this is one of, this is an example of one of those that sounds extremely simple and common sense, and it is, but I've observed it's remarkably rare in a lot of organizations.
Yeah. You know, that makes total sense once again, and I'm going to keep this conversation moving forward here one more step. We looked at the analytics strategy from, you know, you mentioned different departments, right? The different functions of a business, but how did these different strategies ultimately drive the overall business strategy for the organization then?
Sure. Great, great question. So this is one of those where, you know, let's, let's take a fictional organization and again, they have the, the goal of revenue growth. Okay. So now what happens is by having kind of the standardized framework, there's this understanding that you've got this team quarterbacking, this effort right. In my mind, that's the benefit of having an analytics team or an organization within, or I'm sorry, a group within the organization that is responsible for quarterbacking these efforts, because what they can do then is they can take care of a lot of the stuff, right? Some, some organizations try it out and they put the analytics function in IT. frequently though, at least in the people that I've talked to in my experience, that's when that runs into a hurdle very quickly, because so much communication across the organization in coordination or coordination across your organization has to happen that this team, it really has to be involved in all components of the business, right? Again, using the manufacturing example, if you've got a revenue growth goal that has a sales management team, a management team, internals sales, incentive plans, are you comping people based on revenue, profitability, whatever it is, it takes coordination on that front. On the sales side, it gets people to understand their comp plans and track performance against those goals. If you're men, if you're manufacturing something, it takes coordination with operations to make sure you've got the capacity to make the stuff that you're selling right. With finance, itt gets, it comes into coordinating with finance. You understand, along with operations and others, this is the product mix that we're going to bring to the table. This is the margin profile of all these different pieces that come together, but that, that piece of working together is really what gives the analytical power. You know, it gives it all because that coordination is where it's getting everybody on the same page to march together to address these key strategic agendas. You know, in this example, revenue growth, it takes a lot to get everybody on the same page, and in my mind, data and analytics creates a common language, a common view, a common framework and approach for identifying opportunities, executing against those and tracking success, but it's really, again, I get back to the point of having that one group that is responsible for quarterbacking that is so massively important because if you do not have that, then what you have is, well, you're taking care of this well you're taking care of this, welll you’re taking care of this. Nobody's responsible for it. As a result, nothing gets done, and then your analytics initiatives, they get a bad name. Nobody's nobody, no individual entities fault, but they've got competing priorities. So if you have that entity that is responsible for driving the strategic agenda through analytics, in conjunction with everybody else, I think you've got a winning formula.
All right. So I'm going to jump ahead into our last question here for our conversation, and this really is kind of what prompted, you know, ultimately getting together on this call and, and having this conversation here. You know, we talked about the different strategies and what that means. So how does the marriage of these strategies ultimately mitigate organizational risks and really, you know, enhance the opportunity for the organization to succeed?
This depends on the individual entity, but, I'll give you a one example, using pricing, right. So from a risk perspective, you know, there's a lot of dry powder on the M&A front, sit on the sidelines. Industry consolidation is extremely common across a number of industries, and it's going to probably, as you get more distressed businesses coming forward, it's probably going to be even more. Now, one of the things, especially for larger companies as they're acquiring, is to be cognizant of the pricing exposure. So let's take an example. Let's take customer X is let's call it Walmart. You're selling a widget to Walmart for a dollar. And let's say that there is a customer that Walmart just acquired that you're selling to for that same widget for 90 cents. Now that little company that Walmart just acquired buys one, 1000th of the volume that they've got, but now Walmart buys and says, Hey, wait, you're giving this company a 10 cent better price than us.You know what, we want that price. So now as you're looking at, you have legitimate pricing, exposure, risk, to the tune of ungodly sums of money, due to poor price management, right? And I use the pricing one just because I think that that's extremely common. If you sell something, you have pricing. And while that exact example, it could be unique to manufacturing, I'm sure there's some lesson in there that could be teased out to other industries, but the idea is, is that risk mitigation, there's, there's that type of risk mitigation where you've got an kind of exposure due to process controller or whatever it may be. But it's also that when you're marching into a project, by going through the due diligence, using an analytical framework and data, right, gut feel is very valuable, but gut feels even more valuable. If you supplement it with data and analytics. You can have a better understanding of the risk profile associated with a particular project, then maybe you would, if you didn't do that, and I'll give another example using pricing again, is that, you know, price increases typically are met with fear people, fear that business is going to be put at risk. whatever it may be I'm in, this is a real example that I've done numerous times in my past job. With eCommerce channel becoming more and more prevalent, you know, that's a good thing, but it also brings pricing transparency. So let's say you're going to go do a price increase and you sell the widget for a $100 and you're going to increase it to $120. Let's say again for illustrative purposes. Now you've got a 20% price increase. That's, that is scary. If I'm a sales rep, I'm looking at that petrified that I am virtually guaranteed to lose that business. So now what if we can use analytics to mine our quote data, and also web scraped pricing from, you know, e-commerce sites, our main competitors, or whoever's selling these things. And then you learn through these pricing analytics, using data, you've already got an sourcing, external data that the market will actually support a price of $150, that your nearest competitor for an equivalent product is selling it at 150. You know, all of a sudden at one 20 doesn't look nearly as risky. So while you haven't technically mitigated risk, per se, you've mitigated the perception of risk in the mind of the person that is responsible for selling that price increase. That's the one thing I've learned is that if somebody who doesn't believe that a price increase is justified or that they can sell it, they have an extremely difficult time selling that. So that's an example, those are two examples of how analytics can help mitigate different types of risk, both real and perceived risk.
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