“A data-driven approach is a valuable built-in asset you can add to a company. Almost by definition it improves the valuation of that company, hence producing alpha for the PE firm.” - Greg Brown, Business Intelligence Consultant, Blue Margin 

Most investment strategies include a goal of generating alpha, or investment returns in excess of the benchmark rate. Because of this, alpha is known by many as the holy grail of investing (Chen, 2022). However, “since the 2007-2008 financial crisis, it has become increasingly difficult to generate alpha based on traditional investment styles and strategies” (Dow Jones, 2021).  

Using Business Intelligence to Find Alpha 

In this episode of The Dashboard Effect, CEO Brick Thompson and consultant Greg Brown discuss how business intelligence (BI) can help private equity firms find and generate alpha in their portfolio company investments and obtain an edge over the competition. Dow Jones agrees with such data strategies indicating, “Technology and advanced business intelligence tools have created new opportunities for organizations to rapidly gain insight and create opportunities” (Dow Jones, 2021).  

Three ways BI helps generate alpha: 

  • Supporting the planning and execution of new innovations. With BI, real-time insights make innovative approaches easier to monitor and more likely to succeed. 
  • Improving visibility into finance and operations (e.g., activity-based costing, order to cash, or labor utilization), so leaders can address issues based on real-time insight to increase profit margins.  
  • Raising the valuation multiple upon exit. In addition to illustrating the story of future revenue growth for investors, data-driven companies receive higher valuation because they are easier to manage.
Per Adam Coffey, 3-time PE-backed CEO, "A company that has good visibility into data and analytics is going to be perceived to be a better company than one that does not. It's going to trade for higher in the multiple range." (Read or watch our full interview with Adam.)

To learn more, listen to the full episode, How to Find Alpha, or read the transcript below. 


Resources 

Chen, James. (2022, March 19). Alpha: What it means in investing, with examples. Investopedia. https://www.investopedia.com/terms/a/alpha.asp 

Dow Jones. (2021, May 7). Generating alpha with unstructured data. Dow Jones. https://www.dowjones.com/professional/resources/blog/generating-alpha-with-unstructured-data 


Full Transcript 

Brick Thompson: 0:03 

Welcome to The Dashboard Effect Podcast. I'm Brick Thompson. And today I have back with me Greg Brown, one of Blue Margin's consultants. How's it going, Greg? 

Greg Brown: 0:13 

Hey, Brick, I'm doing well. How are you doing? 

Brick Thompson: 0:15 

I'm doing great. So I think we've got an interesting topic today. Why don't you start us off? 

Greg Brown: 0:21 

Yeah. Thanks, Brick. So I wanted to talk today about how PE firms can find alpha or discover alpha, through data driven approaches. And so to keep it short, we just have a couple of different ways that we've seen that play out that we wanted to share with everyone today. 

Brick Thompson: 0:38 

Okay, great. And maybe the best place to start is just to find alpha for those of our listeners who may not be private equity managers. 

Greg Brown: 0:48 

Absolutely. And I should say, we're not experts on internal rates of return or anything like that in the private equity world. But in today's conversation, alpha would represent a return above the normal return that a PE firm, for example, would expect in any of their companies or investments. And of course, that would kind of mean an above average internal rate of return or IRR. That's a complex topic, and we're not going to focus on the details today. There's plenty of good resources out on that. But essentially, it's saying "How could you use a data driven approach to realize alpha or an above average internal rate of return for that investment?" 

Brick Thompson: 1:25 

Yeah, okay. Almost a secret sauce. Not business as usual, but getting a better return by doing something a little different. Okay. Great. So, I think you said there were two or three ways that we think we could do this through the use of data and becoming data driven. What's the first one? 

Greg Brown: 1:43 

Yeah, so the first one is that a data driven approach enables companies to successfully plan and execute innovation in their world in their industry. Yeah, I should start by saying that I think what comes to mind for me with this is that innovation is exciting. That's something that teams can rally around naturally, versus other ways that you could look at maybe improving the margin at a company or cutting costs or different methods or strategies that you can employ. Innovation is really exciting. So, what I focus on here is, how can a data driven approach make innovation even more exciting and more successful for companies? 

Brick Thompson: 2:21 

Okay yeah, that makes sense. Do you have an example? 

Greg Brown: 2:23 

Yeah. So, you know, and before I go to an example, a couple stats that are interesting to me around innovation, I read these recently. New product innovations are not as common as existing products and innovating on top of those. 75% of innovations are really based on existing products that companies already have in the market. Only 10%, really, of product innovation entails new products, and for new products for new markets. And then this is kind of key to me in terms of connecting this to a data driven approach is that 40% of new product initiatives fail to make a profit. And that's kind of the double-edged sword of innovation. It's exciting, and it can be a pathway to create value, and realize alpha, and above-average rate of return. But it can also end up wasting a lot of time and money. And so that's even more of a reason to make sure that you have a data driven approach. And modern BI and reporting tied to innovation efforts that a company might pursue. And so, one example that comes to mind for me with this is that I spoke with a company a little while ago, and this is a large distribution company. They had a number of products, and of course, a large customer base. But they had absolutely no visibility into their customer level and product level P&Ls. And so, their executive team was really trying to innovate their go-to market strategy and find a way to drive a greater return and greater profitability there. But they had absolutely no visibility into those customer level and product level P&L. So, there you have a company that's saying, well, we want to innovate that go-to market strategy, so that we're more successful. So that we expand our margins, and that we can even develop new products or packages for customers. But we can't do that without the actual visibility through data into what those margins and what those P&Ls and those successes and failures are in our current products and current customers. And so really, without that, they were unable to innovate and realize returns based on that. 

Brick Thompson: 4:27 

So not having data, made it so that it was difficult for them to know where to focus and where to dig in and how to approach it. 

Greg Brown: 4:34 

Exactly. And I will say that it was great for them to realize that we don't want to fail in changing or innovating our go-to market strategy. We want to make sure that that's successful so that we don't waste that time and that effort in that innovation without first understanding where do we stand now? What are those benchmarks and how do we track that progress as we start that innovation? So that was very smart of them obviously to say we don't want to fail and sync those resources and innovation, we want to give ourselves the highest probability that we'll succeed as we go along this path of innovation. 

Brick Thompson: 5:07 

Yeah, that makes perfect sense. You know, most companies, well, I would say every mid-market company has a lot of data. They've got business analysts, they're running spreadsheets, they've got reports coming out of an ERP system. They may have some actual BI coming out of a data warehouse or a data lake. But I think what you're talking about in terms of finding alpha is truly taking it to the next level and becoming data driven. So that's not sort of, okay, we're getting some reports a few weeks after we close the period. And looking at what we did, and maybe adjusting, probably too late to do anything about what's happened over the last few weeks. But rather, having data be part of your daily, what you're doing, you know. It's that that old Drucker quote, "What gets measured gets managed." That just seems so applicable here. And so in order to innovate and find alpha, get a greater than average return, you've got to be data driven. Probably, I think, (obviously, I'm biased that way because that's what we do). But you've got to have that to really sort of know where to attack and know how it's going and know where to pivot and adjust. 

Greg Brown: 6:18 

Yeah, you need a system in place to have that real time monitoring, especially when you're innovating. Because, of course, everyone has plans. It's the famous Mike Tyson, quote, I won't even go into it, but you have a plan until you actually start implementing that plan is another way to look at it. 

Brick Thompson: 6:31 

Or get punched in the face. Makes sense. So being data driven, helps you to be able to 

Greg Brown: 6:32 

Yes. Or get punched in the face. And, and so you have to monitor that you have to say, "Okay, we're launching this initiative, we're innovating something, we're launching it, we're taking it to the market." It's not going to be good enough to just run some reports every week and see how it's doing; we really need a pulse on that. And that means you need that data architecture and that reporting structure in place to deliver those insights in real time, because that's how quickly you might have to adjust. Lest you actually waste money or time on that innovation. engage and innovate better and more successfully. Exactly. And in so much as you'll be more successful that way, that produces alpha for a PE firm and an above average return on that company when they go to exit. 

Brick Thompson: 7:15 

Okay, what's the second way? 

Greg Brown: 7:17 

So the second one I have on my list is that it can help expand margins being data driven, improving the profitability of a company's value creation and value delivery processes. And I think a lot of people listening will probably recognize the concept of activity-based costing, or in short, really just measuring the cost of activities in the value chain at a very granular level. This has evolved a little bit, and some prefer Lean Accounting, if folks are familiar with that term instead. But really looking at activity-based costing and looking at that and expanding margins. It's really an exercise that's tailor made for data and reporting. And again, that real time, everyday insight into the data and what it's showing you. But if you think about the complexity of most middle market companies, and all the activities that you have to account for, in delivering a product or service, mapping all that out is its own exercise. But then measuring those activities and comparing them against the benchmarks. And what you want to achieve in those activities is kind of its own exercise as well. And that's where the proper data strategy and reporting really comes into play. 

Brick Thompson: 8:18 

Yeah, so I've got an example here, there was a client we've worked with for a number of years, actually. But there was a particular initiative that we embarked on a couple of years ago after we had good data warehouse in place. And that was to increase the utilization of their techs, they had a bunch of field techs, several 100 of them, and turned out that for every percent utilization they could increase, or every percent they can increase utilization added about a million dollars to the bottom line for the company. And so very quickly after we got reporting in place, so that managers could understand, first of all, what utilization was by tech and and by market and to rank, sort them. And then be able to look at, alright, how can we learn from the ones that are getting the best utilization and apply that to the ones that are getting the worst utilization? They were able to very quickly increase utilization by about 4%. So that's a significant drop to the bottom line. And, you know, in terms of creating alpha or finding alpha, I mean, even if your multiple doesn't change on your sale, you've got a much bigger EBITDA for that multiple to act against. 

Greg Brown: 9:30 

Absolutely, I think that's a great example of it. 

Brick Thompson: 9:30 

Actually went backwards. And of course, you know, expansion of margins could look 

Greg Brown: 9:32 

Exactly. And again, you can only imagine having real different in every different industry or vertical. But the key point is that you have to understand where those opportunities in that value creation and delivery chain are to drive margin expansion. And then again, you have to have that system to measure it as you launch that initiative, or you work in that area to know if you're really being successful. One conversation that comes to mind recently was just a conference that I attended. A panel was hosted by PE private equity operating partners. And one of them was giving a great example of a company that he worked with. And they, of course, they were very excited that they had improved top line revenue. But when they went into look at the results, they saw that yes, okay, top line revenue improved, but this new revenue was not very profitable compared to what our benchmark was. And so here you had a company saying, well, you know, we're really proud of this, but in terms of looking at the bottom line, saying, maybe our efforts would have been better placed elsewhere. Because we did increase our top line revenue, but the margin actually shrank. And it's painful to hear those stories to have all that time and energy put into that time reporting, the right kind of BI and data architecture, initiative. But then to realize, well, it didn't really produce what we want it to for the margins. that just would not be allowed to happen. Because every single day, folks would be saying, hey, this margin is not moving the way we want it to. We have to examine and course correct. So again, you know, margin expansion generally, not always related to data. But when you're pursuing that strategy, having the right data driven strategy is going to help you measure it and make sure you're hitting your targets, and that you're not sinking time into efforts that are going to end up being margin neutral, or actually lose track on margin. 

Brick Thompson: 11:25 

Exactly. Okay. What's, what's the third way that data can help you to find alpha? 

Greg Brown: 11:32 

The third one's really interesting. And this is a critical one, in my opinion. So a data driven approach can really improve the valuation multiple upon exit. Data driven companies can more easily show investors the future revenue growth narrative. And they also have a built-in value because a data driven company is easier to run, and easier for anyone to look at the status of the company, how everything's working in value creation and value delivery, to be able to strategize and make improvements in the future. So it's an incredibly valuable built-in asset that you can add to a company that almost by definition improves the valuation of that company, hence producing alpha for the PE firm. 

Brick Thompson: 12:13 

Yeah, I think sometimes people don't think of their BI platform, their data warehouse and so on, as truly an asset, but it is. I think one of the things (you sort of alluded to this), but when you're going to sell a company, having the ability to really back up what you're saying, and make it really easy for buyers, in their due diligence to confirm what you're saying about the company, by having great bi, makes a huge difference and definitely can ease that negotiation over the multiple. 

Greg Brown: 12:45 

Exactly. And you know, those in the private equity world be very familiar with CIMs or confidential information memorandums, which contain a lot of information about how the company is doing, what that value creation plan or that future revenue growth narrative can be. But you know that pales in comparison to be able to show investors, here's how all the parts of our business run. And here's how all of its measured. And here's exactly how we're doing. And you can explore the data and filter that and drill down into it. I don't think anything builds more confidence in investors. And it's not just about showing them, here's where the company is at now. It's also about saying, this is something that allows anyone, whoever is going to take control this company, to make it easier to run that company, and easier to find ideas on how we can innovate or how we can expand margins. Because again, all the data is already laid out, and it's being fed to in real time. So that's an incredible asset. It's a dated analogy to say this, but in the in the world of iPhones and smart devices that we have. But you know, if you're buying a car without a built in GPS, I mean, you can still use the car, it's still very valuable. A car with a built in GPS inspires confidence because you say, okay, wherever I want to go with this car, it's got the metrics that kind of feed me the data that I need to know where I'm going and how I get there. And that's already built into what I'm purchasing, not something that I'll have to add later. So, I think that can be an overlooked one, I feel like a lot of folks might assume, well, you know, this company has reporting. And to your point earlier, you know, reporting off ERPs and other kind of general reports, are not what we're talking about. When you actually build in a data driven approach to a company that is unique, especially with those companies that are in those more traditional industries, like manufacturing, distribution, commercial services. You don't see that as often as you might assume, and so I think that that's a really overlooked one. Again, can produce alpha for a PE firm, because you can make that argument and you can show folks what's going on with that company and how you can run that company and drive returns in the future. So I think that's a really big one that sticks out. 

Brick Thompson: 14:42 

Yeah, that's great. Did you have a specific example in mind there? 

Greg Brown: 14:46 

I did. It's a company that we partnered with, and really built dashboards to show all those parts of their value creation and value delivery processes. Upon exit, it was incredibly easy to show investors exactly how the company ran, and exactly where the opportunities were and what they could do in the future. That inspired a ton of confidence, and it made the negotiations a little bit more straightforward. It answered questions sooner. And by doing that, I think inspired that confidence that this is a company that's easier to run. It's more valuable. We're more interested in purchasing this company. And so that's a real-world example. Of course, not using the company's name, but of a company where we saw that play out ourselves. 

Brick Thompson: 15:27 

Okay. Yeah, that's great. All right. Well, we're getting near the end of our time here. So, I think we'll wrap up. Any last words you want to add? 

Greg Brown: 15:36 

No, just that it can look different for every company. And of course, it depends on where the company's at, in terms of what reporting they have. But overall, building in a data driven approach is going to help now in terms of managing and running the company. But crucially, and this is where it gets back to alpha for PE firms, it's going to build in value to that company that you can't realize in another way. And so, whether it's through margin expansion or innovation, or just having that feature that this is a data driven company, that approach can really help return alpha to the PE firm. It can make that transition, that exit, a little bit easier as well. 

Brick Thompson: 16:13 

Great. All right. Well, Greg, thank you so much. It's great sitting down with you. I always enjoy it. And I always learn something. So, hope to see you again soon. 

Greg Brown: 16:23 

Yeah, same here, Brick. Thank you. 

Suzanne Rains

Written by Suzanne Rains

Suzanne Rains is a communications specialist at Blue Margin Inc. With a MA in Human Resources and BAs in Marketing and Management, Suzanne unites an understanding of human nature and a keen interest in industry research to author thought leadership articles for today’s business leaders.