Episode Summary
Our broad topic was the expanding use of alternative data, such as credit card transactions and geolocation data, by corporations and consultancies.
While “alt data” has been widely used by hedge funds for many years, its use in the corporate world is still relatively nascent.
Michael went over a number of specific examples, such as cohort analysis, promotion effectiveness, and Customer Lifetime Value (CLV) calculations.
We also covered data delivery and usage: while historically only hedge funds could afford sophisticated database analysis and API expertise, alt data providers have made great strides in creating “consumer app level” interfaces for broad use.
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Guest-at-a-Glance
💡Name: Michael Maloof
💡What he does: Michael is the director of Marketing at Earnest Analytics, a leading alternative data provider for hedge funds, asset managers, consultants, and corporations.
💡Company: Earnest Analytics
💡Noteworthy: Michael started his career as an equity analyst at Goldman Sachs, covering tech and telecom, and this is where he started to appreciate alternative data sets
💡Where to find them: LinkedIn｜Insights Blog｜Free Dashboards｜Twitter
Key Insights
⚡The growth of alt data mirrors the growth of the tech industry. But to understand alt data’s impact, let’s first determine what it represents. ”Alternative data, or alt data, is data not derived from a company’s filings or easily available public information. […] So during the digital revolution of the ‘90s to the ‘00s, companies began storing more and more business records and increasingly easy-to-access databases. And these data warehouses were originally used for reporting to inform internal decision-making. But over time, the owners of this data realized it had commercial value outside of their orgs. So they began partnering with organizations like hedge funds, and data aggregators like Earnest Analytics, that found secondary uses for their data in that investment process.”
⚡Consumer-facing corporations relied on legacy alt data sets, including syndicated data. But, as Michael explains, these come with numerous issues, not including DTC brand sales or the ability to create custom cohorts. ”If Nordstrom wants to know its share of shoe sales in Seattle, it’s only going to see the share of sales against the larger department stores and traditional retailers. Meanwhile, the share of shoe sales by those retailers is eroding over time due to DTC and the social media selling revolution we saw in the last ten years. Nordstrom also would not be able to track the share of wallet or customer retention because there are no customer cohorts and syndicated data.”
⚡Corporate clients want to interact with alt data in many different ways. However, it all depends on the user and their business objectives. Still, a company like Earnest Analytics caters to everyone’s needs, freeing the data user from the time-consuming and complex process of sorting, cleaning, and structuring the data. ”Our mission is to make sure that every user has access to the type of data they need to answer their business questions. The fastest adoption we’re seeing now is with that higher-level, busy data user. […] They’re turning to ready-made tools like Earnest Dash to answer those high-level questions, like ‘sales growth by income cohort across my industry’ or ‘what’s my market share.’ Our […] users can do their work online in the portal, save it, share it with the org, and download it into Excel for further manipulation. Then, [we have] sophisticated data teams. Their executives tasked them with finding untapped sales opportunities and exploring the share of wallet among specific loyalty cohorts. And those users are turning to what we call row-level data. In this case, we’re delivering files with billions of rows of actual transaction and household-level data to users to analyze every day, pretty much as they wish.”
Episode Highlights
Being in the Alt Data Industry Has Been Fulfilling, Both Professionally and Personally
”I love the interesting thought leaders I meet in the role, from university professors to some top corporate strategists and star investors. And I’ve also become involved in several tangential causes here in New York, where I moved for the role.
So I serve on the Young Leaders Council at the LGBT Center here in New York City — along with several folks I met through the alt data industry — where we work towards advancing physical and mental health causes in the LGBT community, including financial literacy and career guidance.”
Users of Alt Data
”I heard an interesting stat at a recent BattleFin Conference in Miami this year, where hedge funds were the first, and still the largest users, of alt data, but only 65% of them currently use it in some capacity. So even among those original use cases, there’s still room to grow.
But in recent years, we’ve been seeing surging demand from corporate marketing and strategy teams and consultants. So companies, especially consumer-facing corporations, have for years relied on a mix of legacy alt data sets, including syndicated data surveys, internal data to answer questions like market share changes, sales growth versus competitors, and customer satisfaction.”
How Companies and Consultants Use Alt Data in Their Everyday Decision-Making Process
”Chief marketing officers and strategists are coming to providers like Earnest as it’s helped fill the gaps in their data mosaic instead of a corporate data analyst they would’ve had to hire for this. So in most cases, we provide insights into areas of the business they never had; they’re complementing their existing data understanding.
The first and most obvious pain point clients ask us to solve is to help identify their real market share and benchmark sales against competitors, not just the ones that participate in data syndication.
In one case — a DTC clothing brand — a client wanted to know their market share in a competitive metro area and if their targeted marketing strategy was creating positive ROI, which is difficult to say accurately with syndicated data. And the lag with that data set would be too long to make any immediate course corrections.
But with an Earnest Online Dashboard, they can go in with a few clicks, create a full market share cohort using transaction data and see what their share of sales is, how it’s changing in real-time, pretty much down to the day.”
Top quotes:
[02:52] ”I think of myself as our chief storyteller. So I go into the data daily to look for interesting stories that apply to investors, companies, and consultants.”
[07:43] ”Companies and consultants are beginning to turn to these newer alt data sets, like transaction data and web pricing data, to fill in their data mosaic and gain a fuller understanding of their markets.”
[10:01] ”We’re also seeing a big focus on customer acquisition costs and lifetime value on the heels of these well-publicized advances in marketing measures like Professor Dan McCarthy’s customer-based company valuation. These models focus on matching acquisition costs with the value new customer cohorts bring to the company. And they’re huge improvements over the old buy-till-you-die model of customer lifetime valuation. So the main ingredient for this analysis, though, is years of customer spending history, and that’s not available in companies’ internal CRM tools or via syndicated survey data. So the models demand higher accuracy.”
Full Transcript
[00:00:00] Michael Maloof: We’re also seeing a big focus right now on customer acquisition costs and lifetime value on the heels of these well-publicized advances in marketing measures like Professor Dan McCarthy’s Customer-Based Company Valuation. These models focus on matching acquisition costs with the value that new customer cohorts bring to the company,
[00:00:21] and they’re huge improvements over the old buy-till-you-die model of customer lifetime valuation. So, the main, uh, ingredient for this analysis though is years of customer spending history, and that’s just not available in companies internal CRM tools or via syndicated in survey data. So, the models demand higher accuracy than that.
[00:00:40] Nick Mazing: Hello, and welcome. You’re listening to Signals by AlphaSense, and I’m your host, Nick Mazing. Today we’re joined by Michael Maloof, who heads marketing at Earnest Analytics, a leading alternative data provider for hedge funds, asset managers, consultancies, and corporations. Alternative data, and we’ll get into more details in just a little bit, is
[00:01:09] things like credit card transactions, including basket sizes, shopping frequency, and more. It includes dual location data such as full traffic or through trade areas, it includes app downloads data, it includes Glassdoor data, LinkedIn data, and more, and it has been in use by hedge funds for many years. But what we’re seeing now, and this is very consistent with the concept of technology diffusion, is alternative data is utilized more and more by letting corporations around the world.
[00:01:37] So, today we’re going to focus on this, how corporations are using alternative data to make better decision. Michael, welcome, and can you tell us a little bit more about yourself and about Earnest?
[00:01:48] Thanks so much for having me, Nick. I’m a big fan of the show and the work you guys are doing over at AlphaSense. I’m the Head of Marketing at Earnest Analytics, and as you said, we’re a leading provider of consumer and healthcare data for companies and investors.
[00:02:02] Michael Maloof: I started my career, actually, as an equity analyst at Goldman Sachs, covering tech and telecom. That’s where I really gained an appreciation of the role that data plays in decision-making. You hear the phrase “Garbage in, garbage out” all the time with data, but it really was difficult to discern in those days quality signals from an increasingly noisy data space.
[00:02:24] That’s why I, I was really drawn to Earnest approach, the, the founder, Kevin Carson, believed that noisy data that the folks were using would be more commercial with dedicated business analysts and data scientists normalizing, interpreting it on a micro level. So, about six years ago, I joined the Earnest team when it was just about 20 folks in a pretty cramped office, and Flatiron,
[00:02:46] and I started to learn sequel, how to work in AWS, eventually made my way into a marketing role, and now I think of myself as our, our chief storyteller. So, I go into the data daily to look for interesting stories that apply to investors, companies, and consultants. So, growing up with the alt data industry has been really fulfilling for me, both professionally and personally.
[00:03:08] I love the interesting thought leaders I get to meet in the role, from university professors to some of the top corporate strategists and star investors. And I’ve also become involved in several tangential causes here in New York, where I moved for the role, actually. So, I serve on the Young Leaders Council at the LGBT Center here in New York City, along with several folks I actually met through the alt data industry where we work towards advancing physical and mental health causes in the LGBT community, including financial literacy and career guidance, actually.
[00:03:39] Nick Mazing: That is great. Now, so, let’s start at the beginning for somebody completely new to the space. What is alternative data, and how did it come about?
[00:03:48] Michael Maloof: That is a great question. I’m sure my counterparts across the industry have somewhat varying answers here, but alternative data or alt data, as most of us call it, is data not derived from a company’s filings are easily available public information. That growth of alt data really mirrors the growth of the tech industry in general.
[00:04:09] So, during the digital revolution of the nineties, the two thousands, companies began storing more and more business records and increasingly easy-to-access databases. And these data warehouses were originally used for reporting to inform internal decision-making things like that. But over time, the owners of this data realized it had commercial value outside of their own orgs.
[00:04:31] So, they began partnering with organizations like hedge funds and data aggregators like Earnest Analytics that found secondary uses for their data in that investment process. Today, companies, entire business focuses on monetizing their data while providing the underlying service or product for consumers for free, actually.
[00:04:52] Other data originators do count data sales as a substantial part of the revenue, although maybe not all of it, and a few major examples of that alt data, you mentioned a few. But they include consumer spending transactions, product-level data from consumer packaged goods, inventories, web scrape data, healthcare claims, mobile locations, and a ton of others.
[00:05:14] Nick Mazing: Now, investors, especially hedge funds, you know, fundamentally focused investors, were the first adopters of alt data, but that’s changing. So, like, gather data historically, it’s getting more democratized. So, can you speak about what’s going on with the adoption by corporate and consulting users?
[00:05:31] Michael Maloof: Yeah, that’s right, Nick. I actually heard an interesting stat at a recent BattleFin Conference in Miami this year where hedge funds were the first and still the largest users of alt data, but only 65% of them currently use it in some capacity. So, even among those original use cases, there’s still room to grow.
[00:05:50] But yeah, like you mentioned, in recent years, we’ve been seeing surging demand from corporate marketing and strategy teams as well as consultants. So, companies, especially consumer-facing corporations, have for years relied on a mix of, like, what we call legacy alt data sets, including syndicated data surveys, internal data to answer questions like market share changes, sales growth versus competitors, customer satisfaction.
[00:06:18] Michael Maloof: The issue is that all of these data sets fall short in some major way. So, syndicated data, which is a process in which retailers give their sales data to essential aggregator like an NPD in order to get back their own market share has glaring issues. It doesn’t include smaller brands sales or the ability to create custom cohorts.
[00:06:39] So, this means if Nordstrom wants to know its share of shoe sales in Seattle, it’s only going to see the share of sales against the larger department stores and traditional retailers. Meanwhile, the share of actual shoe sales by those retailers are eroding over time due to DTC and social media selling revolution we saw in the last 10 years.
[00:06:59] Michael Maloof: So, Nordstrom also would not be able to track the share of wallet or customer retention ’cause there’s no customer cohorting and syndicated data. So, retailers in the past would fill these gaps with very expensive surveys, net promoter score studies. But those are also imperfect because they have pretty small sample sizes and they’re unable to measure if buyers actually followed through on those buying intentions.
[00:07:23] Consultants face similar headwinds, you know, it’s very difficult to assess the health of an entire industry or provide a SWOT analysis to our client if you don’t know who the actual emerging threats are, or can compare customer health across companies. They historically have relied on a patchwork of pretty and complete data sources as well to answer them.
[00:07:43] Michael Maloof: So, companies and consultants, they’re really just beginning to turn to these newer alt data sets, like transaction data, like web pricing data, to fill in their data mosaic, gain a fuller understanding of their markets. But the switch is pretty recent, many companies still haven’t even explored adding new data sources to their market intelligence suite yet.
[00:08:04] Nick Mazing: So, you already mentioned, uh, a retailer example. Can you give us some specific examples how companies and consultants are using alt data in their everyday decision-making process?
[00:08:18] Michael Maloof: Yeah, and, and I think you also need to back up just a little bit to talk about why, why they haven’t until now, you know, the adoption curve was pretty steep due to a mix of hard use interfaces and, and size of the data sets. But what you see in the last three years is that alt data aggregators are popping out of the market to make it much easier for folks without these advanced stats degrees to get value from the data sets.
[00:08:41] So, chief marketing officers and strategists, they’re coming to providers like Earnest, it’s helped fill the gaps, like I mentioned, in their data mosaic instead of a corporate data analyst that they would’ve had to hire for this. So, in most cases, we provide insights in areas of the business they never had.
[00:08:58] They’re complimenting their existing data understanding. The first, most obvious pain point their clients ask us to solve is to help identify their real market share and benchmark sales against competitors, not just the ones that participate in data syndication. In one case, a DTC clothing brand, a client wanted to know what their market share is in a really competitive metro area and if their targeted marketing strategy was actually creating positive ROI,
[00:09:26] which is very difficult to say accurately with syndicated data, and definitely the lag with that data set would be too long to make any sort of immediate course correction. But with something like an Earnest online dashboard, they can go in with a few clicks, create a full market share cohort using transaction data and see
[00:09:46] exactly what their share of sales is, how it’s changing in real-time, pretty much down to the day. So, I can say, okay, this promotion moved the needle, but this advertising didn’t, and in fact, we lost share. So, let’s stick to promos to pick up share. We’re also seeing a big focus right now on customer acquisition costs and lifetime value
[00:10:07] on the heels of these well-publicized advances in marketing measures like Professor Dan McCarthy’s Customer-Based Company Valuation. These models focus on matching acquisition costs with the value that new customer cohorts bring to the company, and they’re huge improvements over the old buy-till-you-die model of customer lifetime valuation.
[00:10:28] So, the main ingredient for this analysis, though, is years of customer spending history, and that’s just not available in companies internal CRM tools or via syndicated in survey data. So, the models demand higher accuracy than that. That’s where we’re seeing more sophisticated clients actually buying the disaggregated row-level data of actual consumers and their transactions so that their own analytics teams can run tests to determine how much they should spend to acquire and keep new customers.
[00:10:57] It’s really interesting because right now, especially, we’re looking back on all the new customers acquired by DTC and subscription brands during the pandemic to see which kept spending and which lapse.
[00:11:09] Nick Mazing: Mm-hmm. So, let’s double click on something you said, and that is usability, or how do people actually get access to the data because historically the data was delivered in, like, you had to be a database expert or an API expert or something like that, and I think selling into the enterprise now essentially requires, people expect consumer level, ease of views, and that kind of what they’re using their personal life that is, built for me,
[00:11:42] ready, easy to use, easy to understand, easy to customize. So, what is your approach there? And, you know, I have looked at your dashboard, I’ll say I like, I like it, I, I cle, I, you know, looked around, but how do you think, how do you think about that?
[00:11:57] Michael Maloof: Yeah, that’s a great question. So, corporate clients wanna interact with alt data in a lot of different ways, and it really depends on the user and their goal. Here at Earnest, our mission is to make sure that every user has access to the type of data they need to answer their business questions.
[00:12:12] The fastest adoption we’re seeing right now is actually with that higher level, busy data user, they were not served by the complexity of legacy alt data tools that require dedicated analysts just to interpret it, maybe a stats degree, this is a busy user, they likely have a trusted advisor helping key decision makers, like a chief marketing or product officer, and they’re turning to the ready-made tools like Earnest Dash to answer those high-level questions, like, what sales growth by income cohort across my industry, what’s my market share?
[00:12:45] In that case, our proprietary Earnest Dash platform, users can actually do their work online in the portal, save it, share it with the org, download it into Excels for further manipulation. It’s just got a level of polish that didn’t exist in the industry until pretty recently and is turning on a lot of new users to alt data.
[00:13:05] Then they’re always gonna be those very sophisticated data teams, their executives tasked them with, like, finding untapped sales opportunities, exploring share wallet among specific loyalty cohorts. And those users are turning to what we call row-level data like I mentioned. This is called Earnest Direct product
[00:13:23] Michael Maloof: in our case, a lot of companies have something like this. In this case, we’re actually delivering files with billions of rows of actual transaction and household-level data to users to analyze every day, pretty much as they wish. And this is much better for bigger, and like, really bigger questions that maybe more are organization specific.
[00:13:44] Nick Mazing: And I actually forgot to ask as a part of that question, but it, it was in my mind, like, why doesn’t big box retailer A go and talk to bank B and get a data directly, like, why doesn’t that not happen?
[00:13:58] Michael Maloof: Yeah, that’s a really good question, too. The data is incredibly complicated and messy when it comes to, to us. So, you come to Earnest to do the hard stuff for you, we clean it, we normalize it for regular adoption churn, taking out all those customers that otherwise, over time, use different cards and looks like sales are declining.
[00:14:19] We’re the ones who are gonna make sure that each transaction is attributed to a company, and we also add helpful metadata on top of it, like, where that person lives, we can help figure out exactly how much they make. And, and the data science team is on top of problems. So, your analytics org can focus really on gaining the insights you need and not cleaning and structuring data.
[00:14:42] Nick Mazing: And also touching back on something that, that you said earlier, I was surprised that only 65% of funds actually use alt data, I would have thought it’s closer to 90 or, or, or something like that. So, when we look at the adoption by corporations and consultants, as the technology obviously becomes, becomes more accessible. So, is it already table stakes, or is it, like, first inning?
[00:15:09] Yeah, well, 65%, I agree, it does sound low, but then we forget there are, is a huge universe of one-and-two-person hedge funds out there that, you know, identify, that call themselves hedge funds as similar strategies, but just do not have the budget for data. And so, in some ways, I do think that, that long tail necessarily, is not necessarily
[00:15:29] Michael Maloof: all qualified users. But if we’re at the 65% of hedge funds using alt data benchmark, my guess is that we’re in the low single digits of corporations using this next generation of more user-friendly alt data. And in some industries, this type of data is already table stakes, yes, if you go to the large mass retailers who are very data-hungry, have large teams to handle it, you’ll find that almost all of them are using something like this right now.
[00:15:55] But there are a lot of companies out there looking for help choosing store locations, assessing market share, sizing market opportunities, valuing their customers who are just not yet alt data users. So, we truly have a really long way to go with that corporate adoption.
[00:16:11] Nick Mazing: Michael, thank you for joining us.
[00:16:13] Michael Maloof: Yeah, thanks so much for having me, Nick. For everybody listening, you can actually sign up for a free limited trial of the Dash Portal that I mentioned today. So, if you log into our website, www.earnestanalytics.com, start for free, in a few minutes, you can be sizing up fashion resale growth versus athleisure or seeing which categories are losing to fast fashion, world’s your oyster. So, happy hunting, um, I use that same tool pretty much every day.
[00:16:39] Nick Mazing: Yeah, I, I, I love your emails too, like, how did crocs do with the stimulus spending and things like that. So, I, I’m an email subscriber as well. So, this was Michael Maloof, Director of Marketing at Earnest Analytics. We covered how leading corporations today leverage alternative debt who have all the relevant links in the show notes.
[00:16:56] My name is Nick Mazing. This is Signals by AlphaSense. You can subscribe to us on all the major platforms. Thank you for listening.
