Richard Lichtenstein of Bain & Company joins us this week to talk about advanced analytics. What is it actually and how can companies and private equity firms use this to make better business decisions? He also shares some B2C and B2B examples and use cases. Also, what are some common barriers for companies to incorporate advanced analytics to their toolset?
Richard Lichtenstein is an expert partner at Bain & Company in New York. He has been at Bain for 17 years and he leads their efforts around advanced analytics and private equity. To get in touch with Richard, please email him at Richard.Lichtenstein@bain.com.
Subscribe to our Youtube Channel.
💌 Subscribe to CI Newsletter and gain AI-driven intelligence.
📊 Forward-looking companies become more profitable with Complete Intelligence. The only fully automated and globally integrated AI platform for smarter cost and revenue planning. Book a demo here.
📈 Check out the CI Futures platform to forecast currencies, commodities, and equity indices
This QuickHit episode was recorded on September 10, 2021.
The views and opinions expressed in this Here’s how Bain uses alternative data and AI to solve businesses’ biggest problems QuickHit episode are those of the guest and do not necessarily reflect the official policy or position of Complete Intelligence. Any contents provided by our guest are of their opinion and are not intended to malign any political party, religion, ethnic group, club, organization, company, individual or anyone or anything.
TS: For people that might not be familiar with advanced analytics or what that entails, can you kind of give us an overview of what this encompasses?
RL: At Bain & Company, we have a team of over 50 people that thinks about just how can we use advanced analytics to serve private equity? And so what do all these people do?
Well, we’ve got a bunch of people scouring the world trying to find the latest and greatest and interesting data sources that we can use. And those could be B2B or B2C. And I can talk more about what those are. Right.
Then we have teams of data cleaners, because sometimes those data sources are really messy on credit card data, and they specialize in cleaning them and making them usable for analysis.
Then we have a group of data scientists who are building Python libraries that they can use to take that data and run fairly sophisticated analysis on these over and over again. So these might be looking at retention by cohort or customer lifetime value or understanding switching behavior and things of that nature.
Then we have a group that takes that output and builds ways to automatically turn that in slides or into tableau so that we can get that in front of clients quickly in a form that brings out the insights.
And then lastly, we have some people who just help other people at Bain figure out how to use all this stuff. We get about over a thousand requests a year from teams trying to figure out which tools should I use? Which data source should I use? Et cetera. And so we just have to help them figure out how to do it.
TS: Can you give us some of your use cases, maybe go into a little bit more detail?
RL: Yeah, of course. So in a way, it’s quite different for B2B and B2C, but both of them have a lot of good advanced analytics examples. If we start with B2C, in that environment, there’s a number of interesting alternative data sets that we leverage, things like credit card data, things like e-receipt data. These show us what people are buying online. Sometimes what people are buying in store, where they go. But it goes beyond some of the traditional data sources, like Nielsen and IRI, and actually shows you what customers are doing. What happens at the customer level. And that allows you to learn some really interesting things.
So, for example, we’ve done some work recently with a fast food restaurant chain. And they’re trying to figure out why are we losing share? We were able to see well among people who are going to your restaurant less often or stopped going, a lot of them were going to Chick-Fil-A. And this isn’t a restaurant that sells chicken. So they hadn’t really thought of them as a competitor. But they are. And that was news to them. Or we did similar work for a coffee chain, and they thought they were losing to McDonald’s on the low-end for coffee. But it turned out, actually, that Starbucks was also a threat to them on the high-end. That help them to figure out strategy. But for private equity investor in these companies, it tells them a lot about the business and where to go.
TS: You wrote a piece last year on like Wayfair and how they used advanced analytics to understand that it was like on the precipice of rapid growth. So what kind of other data or companies using to better understand their market?
RL: Yeah. The Wayfair analysis was really quite interesting. So it’s a great example here. So that’s. And in that case, it was understanding the customer behavior that we were seeing. This was early in COVID, right before the huge spike that we all know now happened. And we were just seeing people coming to Wayfair for the first time. We had never been there before, buying stuff. We were seeing people coming back with great retention. And we were able to observe these kinds of customer metrics at completely outside in.
And that gave our client confidence to make an investment there. One of the other ways we can use analytics there. So we’re working with, you know, another company that’s in a similar space. And so one of the things you can see with this data is because you can see what people are actually buying, you can see what they’re buying from the competition.
So, for example, you could see what are customers who like to shop on Wayfair buying at Overstock or buying a Target or IKEA. And then you could say, Well, if you’re Wayfairer, you then say, well, maybe we need to stock those products. Right. So maybe we should think of adding them. Or maybe we had a stock out on that product for a little bit. And that cost us a business. And so we need to think about our inventory.
And so you can quickly. You can quickly think about your customers differently. At the same time if you’re a brand, obviously, you can use this data to get much better analytics than you ever could about who’s buying your products. Because previously, if you’re a brand and you’re selling online, you don’t know anything about your customers. And now you can start to understand loyalty and things like that.
TS: Have you found any big issues for companies using advanced analytics like it’s hard to access data. It seems fairly sophisticated. So is there a barrier to understanding this kind of data and how it’s presented?
RL: Yeah. I mean, I would say it’s not really for the faint of heart in terms of diving into advanced analytics. If you’re an individual company or an individual private equity firm, it’s hard to really dive in to the degree that we have for a few reasons.
One is there’s a lot of data sources out there. If you go to one of these conferences, there are hundreds of these sources out there, and then there’s more even if you don’t even go to these conferences, right. There’s a lot of sources. It’s hard to figure out which ones are good, which ones really have sufficient sample size and data quality. And these sources also come and go.
Sometimes you might have a source that you really like, and sometimes they disappear or the quality degrades. And what have you. And so you need to maintain a rotating stable of sources. And you need to think a lot about sourcing them. And again, we have people whose job is just to figure that out, which is hard for an individual company to do. And then you also need armies of people to figure out how to use the data in productive ways.
Again, at Bain, we’ve set all that up, but there is a high fixed cost associated with it. And so I think it’s a little self-serving. But I think my view would be that if you’re a firm and you want to get your feet wet in this kind of data, you’re better off partnering with a company like us, like Bain & Company or someone else who’s already got all this figured out and see what insights are possible. What can I really learn doing this? How can this help me make smarter business or investing decisions?
And then once you’ve figured that out, then sort of and you got a narrower focus, then figure out how can you get that on a recurring date? Get a feed of that on a recurring basis versus trying to start from scratch.
TS: Right. That absolutely makes sense. Did you have anything else that you wanted to add to give us any broader scope of your company?
RL: Yeah. The one other thing I might mention, it’s easy to get. And I mean, I just fell into this trap. It’s easy to get sucked into the B2C examples because they’re so enticing and easy to under stand. But I do think there is a lot of exciting work and B2B that we see. And so just to give a couple of quick examples.
One, I think is around people analytics. So that’s an area that’s really come a long way in the last few years. And there’s a lot you can do outside and to understand at a company who works there, what those people do, what’s their turnover and how does that change? And that’s actually enabled a lot of interesting insights. Just to give an example that we did a recent diligence on a software company that served, did a complex sort of B2B type of software.
And the company we looked at was cloud native, and there was a legacy software provider in the space who had been there forever and was slowly developing cloud functionality.
And there was a big question of, well, how fast are they going to catch up? At the moment the cloud native company was ahead. But obviously, the question is could they maintain advantage forever? And so we just looked at the people data, and we saw that our target, the cloud company had a hundred people there and software engineers doing R&D, and the legacy company had 200 people doing it. And so I mean, you sort of figure, well, if one company’s got 200 people and one’s got 100, the 200 person, and it’s going to catch up at some point.
RL: And I don’t know if it’s in a year or two years, but certainly within the holding period, you have to worry about them reaching parody. And that was not a super complicated insight, but one that had a big impact on thinking about the investment. And if you bought the company, what kind of investment in R&D is required? Just an example.
TS: I was actually I was looking at your site. What is the founder’s mentality?
RL: So that’s a great question. I mean, I will admit, I’m not the expert on founder’s mentality. That was a book that Jimmy Allen wrote. That’s a great book. And if you can get him on your show, he’s far more articulate on this than I am.
But the idea of the founder’s mentality is that, you know, founders can bring a certain sort of secret sauce to their companies and create a dynamic and innovative culture. And that once they leave, sometimes that dynamism can erode and things can become more bureaucratic and ossified. And it can be harder for companies to innovate.
And I think that that is actually, it’s interesting you mention that because this is actually something that’s come up in some of the work that I’ve been doing. One of the ways you can apply this data is in sourcing. So you can help a fund scan the ocean of companies out there and find, you know, of the millions and millions of companies, here’s a sector that’s interesting. And here’s a sub sector. And then within that here are companies that meet our specific thesis and so forth.
One type of thesis that we see sometimes is they’re interested in companies that are still led by the original founder or sometimes they’re interested in companies where the founders just left very recently. And there is an opportunity to think about the culture in a different way.
And we’ve actually built some tools that allow you to look at which companies have founders that have just recently left right. And that was something that at least the fund that we worked with on that, that was very exciting as they look for opportunities. So anyway, that’s the concept. And that’s at least how it fits into my world.
TS: I got it. It seems very interesting. And did you have anything else? We’re going to wrap this up here in a minute. So did you have anything else you wanted to add?
RL: No. I mean, I think we covered the main point. The main thing I would just say to people who are thinking about this is the world of alternative data is really exciting. And the insights that are possible today that just we’re not possible even a year ago.
So it’s really moving fast. We’re signing a new data source practically every month, at least. So it’s great. But it’s also very complicated and tricky and hard to navigate. And again, it sounds self serving. But we strongly recommend that if you’re waiting into this for the first time, you talk to people like us at Bain & Company to really understand specifically how this stuff can help, because often it’s hard to sort of just talk to a data provider. And then from that conversation, really figure out if they’re going to be the right fit. So anyway, we’re here to help, of course.
TS: If people want to contact you, how would they go about contacting you or.
RL: Sure. I mean, I’m happy to have someone reach out to me. I’m certainly here to talk to anyone who wants to think about this, how they can use alternative data. It’s Richard.Lichtenstein@bain.com is an easy way to get in touch with me. And I’m happy to talk to anyone again who wants to think about this stuff.
So thanks for the time, Tracy. Really appreciate it. And hope somebody out there who sees this gives me a call.
TS: Absolutely. Thanks again, Richard. We really appreciate everything you’ve shared with us today.
And for everyone watching, please don’t forget to subscribe to our YouTube channel, and we look forward to seeing you on the next QuickHit.