- The founders had an idea to use credit card data to help investors make better investment decisions.
- They realized that many hedge funds do not have coders in-house and saw an opportunity to provide an analytics platform for investors.
- The founders come from a background in video games and had experience building data pipelines and analytics teams.
- They recognized that the same tools they used in the video game industry could be useful in the investment space.
- They discovered that investors were interested in transactional data, specifically credit card transaction data, but struggled with analyzing it.
- Investors were particularly interested in how companies like Chipotle were performing after a food poisoning incident.

- The video discusses how the company provides data on consumer spending and its impact on revenue.
- Previously, surveys were used to gather this information, but it was time-consuming and expensive.
- The company's direct observations of millions of US consumers allow them to provide immediate answers and tools for customers to find the answers themselves.
- The company gained its first customers through connections made during the Y Combinator program.
- The company's data is primarily used for due diligence by venture capitalists, providing visibility into companies and their competitors.
- The company can provide metrics such as market share, customer retention, and lifetime value.
- The company does not proactively conduct research but works with the press to provide information for articles.
- The company frequently publishes updates on topics such as Uber and Lyft.

- The company has a dedicated editorial team that comes up with questions and topics to investigate based on what's happening in the news and interesting companies.
- They conducted a study on Stitch Fix to determine if it was impacting department store sales and found that Stitch Fix had no impact, but rather encouraged people to spend more on clothes overall.
- They also analyzed the rise of Peloton memberships compared to SoulCycle and found that Peloton had surpassed SoulCycle in terms of active members.
- The company did research on Amazon Prime members and found that Amazon is increasingly reliant on Prime customers for revenue, even if they lapse in their subscription.
- When it comes to product development, the company incorporates data from internal research, customer feedback, and their own editorial work to improve their product.
- The company is currently hiring data scientists and is looking for individuals with strong quantitative backgrounds and a deep understanding of the problems they are trying to solve.

- The company values a strong foundation in statistics for their data scientists
- They prioritize hiring employees who can approach open-ended problems and think critically
- The company screens for these skills during the hiring process
- One common mistake is assuming the data is perfect and not looking for potential problems
- Good employees are able to identify assumptions and understand the foundation of their work
- Credit card data is not normalized because different humans input the transaction descriptions in different ways
- The company deals with a high number of unique transaction descriptions and works to map them back to the correct merchant

- The main problems with data classification are human error and processing chain perturbations.
- Humans often give up on correcting data due to frustration and lack of incentive.
- The classification process is complicated due to variations in data.
- The company uses machine-based approaches to clean and classify data.
- They have built a pipeline to ingest raw transactional data and an analytics platform to analyze the cleaned data.
- The company initially focused on helping investors but expanded to serve corporations for competitive analysis.
- The company has experienced virality and all clients have come through inbound sales.
- The company recently raised a Series A funding round led by Bessemer, co-led by Goldman Sachs, and with participation from Citi.
- Goldman Sachs and Citi were interested in the company because it falls into the category of alternative data companies, which Goldman Sachs has been focusing on.

- The company has not invested in any credit card data companies, making them unique in the market
- Their seed round was led by Jefferies, an investment bank, and Goldman Sachs, which has helped them connect with investors and companies in New York City and the East Coast
- Citi has a lot of transactional data and sees the value in the company's product
- The company's approach to transactional data is different from others, as they focus on a specific set of companies and prioritize accuracy
- The company has discovered unexpected applications for their product and is concerned about maintaining their competitive edge as more people start using their product
- They have focused on building a tool to help people ask better questions with transactional data, rather than selling insights to hedge funds
- The company sees potential for their product in both the investment community and the corporate world, where it could be used for competitive analysis

- Founders with a background in video games created a platform to use credit card data for investment decisions.
- The company provides data on consumer spending and its impact on revenue, replacing time-consuming surveys.
- They gained customers through the Y Combinator program and primarily serve venture capitalists for due diligence.
- The company conducts research on various topics and publishes updates on companies like Uber and Lyft.
- They analyze data to provide metrics such as market share and customer retention.
- The company is hiring data scientists with strong quantitative backgrounds and critical thinking skills.
- They use machine-based approaches to clean and classify data, dealing with challenges like human error.
- The company initially focused on investors but expanded to serve corporations for competitive analysis.
- They recently raised a Series A funding round led by Bessemer and Goldman Sachs.
- The company sees potential in both the investment community and the corporate world for their product.

Michael Babineau is cofounder and CEO of Second Measure. Second Measure analyzes billions of credit card transactions to answer real-time questions on consumer behavior. They were in the Summer 2015 batch of YC and you can check them out at SecondMeasure.com.

https://twitter.com/mikebabineau
https://secondmeasure.com/

Kevin Hale is a Partner at YC. Before working at YC he cofounded Wufoo.

https://twitter.com/ilikevests

The YC Podcast is hosted by Craig Cannon.

https://twitter.com/craigcannon

Y Combinator invests a small amount of money ($150k) in a large number of startups (recently 200), twice a year. 

Learn more about YC and apply for funding here: https://www.ycombinator.com/apply/

***

Topics

00:00 - What idea did Mike apply to YC with?

00:45 - Where did the idea come from?

4:00 - From project to company

9:45 - What info did investors want to know that Second Measure could provide?

11:30 - Their first customers

14:00 - The primary use case of Second Measure for VCs

16:45 - What questions are they trying to answer?

19:00 - Data examples from their blog

20:30 - Post: Fashion retailers have nothing to fear (yet) from the rise of Stitch Fix [https://secondmeasure.com/datapoints/fashion-retailers-have-nothing-to-fear-yet-from-the-rise-of-stitch-fix/]

23:00 - Post: Holiday sales rocket Peloton memberships ahead of SoulCycle active riders [https://secondmeasure.com/datapoints/peloton-subscriptions-doubled-over-past-year/]

24:30 - Post: Prime members deliver for Amazon every day [https://secondmeasure.com/datapoints/prime-members-deliver-for-amazon-every-day/]

27:00 - Second Measure's product development process

29:00 - Finding good data scientists who work from first principles

36:30 - Why is credit card data so messy?

41:30 - Cleaning data

43:45 - Using their product for competitive analysis

47:00 - Their sales process

48:30 - Raising money from Goldman Sachs and Citi

51:30 - Focusing on a specific problem

53:30 - Keeping the product compelling when it's table stakes

Mike, Kevin was your group partner when you did YC in the summer 2015 batch.

So our basic idea at the time was, was really to use, to use credit card data to help investors make better investment decisions.

And I think like 1 thing that, and that is actually not really far from what we do today.

The only, like the main evolution is that now we work with companies as well, not just investors.

But I think a big part of the idea, though, is not just to look at credit card data and try to find interesting things and then tell investors about it, but instead to build an analytics platform, throw that in front of investors and then let them answer their own questions.

And what led you to coming up with that idea?

So I did not, like I don't come from an investing background or I don't come from finance at all.

I actually worked in video games in the same industry with my co-founder, Lillian.

We worked together there and then at another gaming startup.

We're both software engineers, we've always been in the tech world, but we've got plenty of friends in finance.

1 of those friends just out of the blue called me 1 day and was like, Mike, I need your help.

I've got 2 terabytes of data on a hard drive.

And that was like, it was 1 of those moments where, again, as a software engineer, I get this question, I'm like, oh God, why?

He's in New York, I'm in the Bay Area, it's the middle of the afternoon.

And I wasn't feeling particularly helpful.

I was like, what did your engineer, did you ask your dev team, did you ask your engineering team?

And then, Mike, what are you talking about?

And that blew my mind, because he was at a $30 billion hedge fund.

And I just assumed that all hedge funds looked like 2 Sigma or Rentech or these places that have hundreds of quants and hundreds of engineers.

But in reality, most hedge funds have a handful of analysts and just some back office support, right?

I think that's when we realized there was this huge opportunity because investors are, like, you know, investors, They make money off of having an information edge, right?

Off of knowing things that other people don't.

And a lot of people who work at Hedge Funds are very, very clever, right?

They're looking for this edge, like wherever they can find it.

And over recent years, increasingly, they've been looking at things like Google Trends to see, oh, is there some leading indicator in search terms that would indicate some bigger shift in consumer sentiment about some company.

Yeah, but at the same time, a clever idea and it oftentimes works.

You've got investors subscribing to things like Comscore, looking at how many visits to a website are happening because that is roughly correlated with actual sales.

And it's also this nice leading indicator in the sense that public companies only come out with, they only report metrics once a quarter, and it's not right at the end of the quarter, it's actually sometime afterwards.

So you can actually look at how many people visit, if you can see how many people visited Amazon.com over the past quarter, then you can look at the full quarter of information and then you can see how well that correlates with the resulting reported performance.

So how'd you go from helping someone with a 2 terabyte Excel problem and working on video games to being like, okay, this is now time for us to quit our jobs and solve this problem.

Yeah, so we are not video game programmers.

We were working at a video game company, but my specialty was building high scale infrastructure.

And Lillian's specialty is building data pipelines and analytics teams.

And What you know, when you look at when you look at the video game space, you know, like what, like how does, how does like a company like, like Zynga, I think Zynga epitomizes this right there.

They're like very metrics driven, very data driven, right?

They, they were 1 of the things they did very well is like optimize that, they optimized the hell out of their games.

So when you think about an online game and you think about what you want to optimize for, right, you want to, actually let's just, let's talk about this in terms of fun.

If your game is too hard, no one's going to play it.

And the game is too easy, no one's gonna play it.

So you have to find this balance where the game is not too hard and not too easy.

And if you have an online game, then you have this amazing leg up over games that are pressed to disk and then shipped out because you can update them.

And the best way to tell if your game is too hard or too easy, it's to simply look at where, like how far people make it into the game.

And so for instance, you could look at how many players make it, you know, from level 1 to level 2.

And if there's like a severe drop off, right?

If like not enough people are doing it, then it's a signal that, hey, you know, it may be, you know, maybe we need to tweak this.

Of course, the person who needs to answer that question is a game designer.

And usually game designers aren't writing SQL.

And so you've got all these metrics, you're tracking all these events of a player, they passed level 1 or a player died or whatever.

All of these events are being tracked and this is like a standard analytics pipeline.

You instrument your application, you have all these events streaming out, you store them somewhere, you do some sort of processing on them, and then you dump them into some place where you can query.

Then you've got these people who typically aren't coders, like a game designer or a product manager, they want to answer questions about how people are behaving in the game.

And you basically have 2 paths at this point.

If a game designer says, how many people made it to level 2, then as somebody on the data team, you can say, okay, let me run that report for you.

And then you go and you query it and you put together the results and you send it back.

And then they look and they say, this is great.

And you roll your eyes and you're like, I see where this is going.

And at that point you're like, okay, I have a choice.

I can either play this go-between and fetching data over and over again, or I can build tools.

And if I build a tool and hand that tool to the designer and say, here, answer this yourself, then I can focus on doing much cooler things and much more interesting things.

I'm no longer in the way of this person answering their own questions.

This is exactly what we did in the video game space, And this is a pattern that we recognize could be really useful in the investment space, right?

You've got all these all these investment analysts and they like they know so much about the companies that they are making investment decisions on.

And they know what questions they want to answer.

And so you can either put yourself in a position where you're trying to guess at the questions and pre-writing reports and trying to sell them reports, or you can just give them some sort of tool that actually empowers them to answer all the crazy questions that they thought they couldn't answer.

Rimas- So this is the thing that's fascinating.

You guys are building tools for understanding how to improve video games.

How does that become all of a sudden the skill set needed to sell financial and analytics software and insights to people who run hedge funds and investment firms or even to do corporate competitive tracking.

Because to me it's just like, I imagine they're going to ask about what's your background, how do I know?

What made you realize that we could probably do this?

Walker, Ph.D.: Yeah, I think it comes down to what is the fundamental problem being solved?

The fundamental problem is that you've got somebody who probably isn't a coder and they want to answer a question of behavioral data.

Edith Leonard You write video games, and then how you decide what was the first product going to be.

Brian Dyer I think for us, it was really digging in further to understand what types of data investors were most interested in.

And what we found is that is that transactional data, like specifically credit card transaction data is 1 of the things that they were really excited about, but they were banging their heads against it.

This is fundamentally like credit card transaction data.

It's a messy data set with unstructured data problems baked into it.

And the skill sets of investors, even the more technical ones, those tend to lean more towards time series analysis as opposed to dealing with large, messy data sets.

What kind of questions were the investors interested in from that data set?

Yeah, I think 1 of the main things is just how is Chipotle doing?

So they famously had a food poisoning incident a couple of years ago.

Actually, I think they had several, but eager investors wanted to know what is the impact to their actual revenue.

How come there was no way to answer this before you guys came onto the scene?

Yeah, so this is 1 of the interesting things.

So there actually was a way to answer it, it was just a terrible path.

The way to answer this before was with a survey.

So you go to some market research company and you say, hey, there's this Chipotle, the whole food poisoning thing, Can you help me understand how many people stopped going to Chipotle?

Young And they just have to try to find a bunch of people that match a demographic and then hope these people will answer.

It costs tens or hundreds of thousands of dollars, you end up with this tiny sample of, like, oh good, we got a hundred respondents and they said that from this pool of 100, like 20 of them said that they have considered stopping their Chipotle dining altogether.

Paul A So for us, because we have direct observations of millions of US consumers, We see all the purchases.

In fact, we don't even need to look it up.

We can just give you a tool and then you can find the

And so you got your first customers during YC, correct?

Paul Matzkoff Yeah, that's a good question.

Our very first customer was a VC who also ended up investing in us.

And I think 1 of the things that was interesting is that, actually, this is 1 of the things where I feel like we got to cheat a little bit because we were in YC.

Because we were in YC, VCs are always excited to talk to YC companies.

trying to figure out who is in the batch and then try to invest before demo day.

And so we had a whole bunch of these funny meetings where we're trying to get in front of them to pitch them on a product, and they're happy to take the meeting because they want to hear about what we do.

And so it ends up being this dual-purpose thing where they're like, okay, show me the product, now tell me your business model.

And you're like, well, would you like to buy the product?

And fortunately, a lot of times the answer did end up being yes.

And now most of the VCs here in the Bay Area, they are our customers.

But it was really interesting navigating those early conversations.

Because with credit card data, there's some things that it's really good at showing and identifying, and some things that are not so good.

So for example, it tends to be great for predicting consumer trends, but.

Peter Graham-Harris Yeah, I mean, it's basically, I think you just have to keep in mind what is it we're actually seeing, and what we're seeing is spending for a large proportion of US consumers.

And so if you want to understand a company that doesn't target consumers, if it doesn't target specifically US consumers, and more specifically, if it doesn't sell things directly to them.

We're not going to see General Mills that's all sold through grocery stores.

But if it's something that you might see in your credit card statement, then those are the things that we can help with.

Leonard All the meal, like Gobble, et cetera.

And then what you're not going to see is B2B, enterprise companies, et cetera.

But it tends to be lots of people are interested in consumer stuff because they're the fastest growing, most interesting segment.

And so are they using it as a market sizing tool?

Because if you're investing in a seed stage company.

Yeah, so probably the primary use case among VCs is actually diligence.

And when you think about, put yourself in the shoes of a venture capitalist.

So, you know, some company walks in and they say, you know, they throw some numbers on some slides, they show it to you and you're like, okay, great.

Like is this, I have like lots of follow on questions.

Do I, you know, do I try to get these, the numbers from you?

And then additionally, there's a whole bunch of questions I have about your market, which you may not even know the answers to.

So a good example of this would be if you are, like so as a VC, somebody comes in and pitches you, they're in, actually, let's just talk about Bird and Lime.

Right, so imagine you're a VC, Bird comes in and pitches you, and they're like, they show you this chart, and it's like, it's the perfect hockey stick chart, and you're like, this is amazing.

You know, like, I've never seen growth like this before.

And at the same time, though, you've heard of other companies.

And also fundamentally are the like, if you know, if Bird is showing good unit economics, like is that is that best in class, you know, or could it be even better?

And this is an area like this is 1 of the key areas where we help VCs is in giving them visibility, not just into the company they're talking to, but into into their competitors, right into each of the like every company in that space in relation to 1 another.

So we can say like, oh yeah, Bird Lime, like here's where Bird's winning, here's where Lime's winning, right?

Here are where the differences are in how well those customers perform, like how much they spend, and so on and so forth.

When you say unit economics, how do you uncover that data?

So obviously, we don't see the cost side.

So you could say an average bird customer spends $40 a week versus a lime customer that might be 20.

And I think generally, again, if you're a VC, you have your own ideas for how to estimate the cost side of the equation.

Edith Leonard What other metrics are you able to show?

I was always impressed when looking to the dashboard instead of second measure about, wow, I can not just see how much revenue is being pulled, but also things like cohorts, lifetime value, et cetera.

And so what metrics get investors super excited when you guys share them?

Peter Schiffmann I guess taking a step back, let's think about what are the main problems that we're trying to solve?

So 1 is generally focused on company performance.

And this includes things like competitive intelligence and benchmarking.

Like show me what is I don't know, like what what is the relative market share of the various meal kit players?

How long do their customers stick around?

What are the lifetime sales after 12 months?

And again, if we split those into different cohorts, are newer cohorts performing better or worse than older cohorts?

So there's all of these things in and around company performance.

And then separately, there's stuff around consumer behavior.

And these are things like, where else do my customers shop?

Things intended to help you get a better picture of who your customers are, and really help you hone in on who your best customer is.

And I'm saying you, but really it could be you, it could be your competitor, it could be a company you're doing diligence on, some target company.

Because your blog is basically just this, right?

Because our core product is really about empowerment and saying, hey, you as a user, you can answer whatever questions you want within the space of US consumer spending.

So you don't answer questions for people directly.

So we'll do it on a case by, on like a project by project basis, but we're not the ones coming up with the questions, right?

If somebody comes to us and says, I have this specific question, I tried this in your application, I can't quite answer it yet, I have this more specific question, could it be answered?

Those are cases where we can do a one-off research project.

And those are paid projects, but we don't publish those.

The thing we don't do is we don't proactively do research and go out and call up 10 of our clients and try to sell it to them.

What's some stuff that you guys have put on the blog recently that's your favorite?

Yeah, so 1 thing we've started doing is, actually, if we talk about our blog, we also need to talk about our press mentions.

We actually work with the press a whole ton.

We keep getting quoted in Wall Street Journal, Financial Times, et cetera.

It's great for the reporters, too, because, you know, they're trying to write about like the upcoming like potential Lyft IPO or, you know, whatever.

And they want to support their reporting with more information and we can help provide them with that information, we're happy to do so.

The Uber Lyft thing is a recurring topic and so in our blog we've decided, you know what, we're just going to keep publishing periodically, publishing updates on that.

when you choose a question you want to ask about the Uber versus Lyft, do you guys have come up with the initial questions and now you listen to what the press are asking you that they want to verify, or is it always you guys are coming up with?

Johnson So I'd say it is us always coming up with it.

We actually have a dedicated editorial team.

So we literally have a team of data scientists and writers who just pay attention to what's going on in the news, what's going on with companies that could potentially be interesting to others.

The person who runs it, she has a journalistic background.

Is find interesting things to write about and then write about them.

So before we started recording, 1 you mentioned was Stitch Fix and where the customers of Stitch Fix do and do not maybe spend.

Yeah, So specifically we had, so this is a really interesting thing right?

Because 1 thing, so part of understanding what questions people are asking is just going out and talking to people.

And 1 recurring question we heard about Stitch Fix was, is Stitch Fix cannibalizing department store sales?

Are they competitive with department stores?

We had no idea what the answer was, but we decided to dig in and We attacked the problem by basically saying, let's look at people spending at department stores before and after they become a Stitch Fix customer.

What we found is that Stitch Fix had no impact on department store spend.

People just started spending more on clothes, period.

And in fact, the people who Stitch Fix, Stitch Fix's best customers actually spent even more on clothes before becoming a Stitch Fix customer than after.

Peng Oh, like Stitch Fix inspired them to go out and find more clothes,

Yeah, I think 1 way to characterize it is that it piques their interest in fashion, and so They don't spend any less, they just.

They're like, oh, I'm introduced to a variety of stuff that I never would have considered beforehand.

And now it's like, oh, now when I'm out there in the real world looking at stuff, I'm like, oh, there's more things that might appeal to me because I've been exposed to them.

Peter Graham-Harris Yeah, the key thing is that it's not displacing the spend.

And also, it's also a really important question to answer because if you're at a department store and you're trying to figure out, is this Stitch Fix, friend or foe?

So do you actively track like the rise and fall of brands?

Because I'm wondering, there must be instances of certain things being swapped out.

A recent post was Peloton memberships going up ahead of SoulCycle.

Are there trades happening that you can follow?

So sorry, when you say trades, do you mean people sign up for Peloton instead of SoulCycle?

Yeah, so I mean really we, again, this is something we will attack from an editorial perspective, but again, it's like our core business is about putting a product in front of our clients through which they can answer their own questions.

Now on the blog side, yeah, I mean the Peloton and SoulCycle story is super interesting.

And SoulCycle has some interesting, like actually, so after we came out with this article, SoulCycle, basically they had a nice non-denial denial where they basically said, we don't know what they're talking about.

Our numbers are great, but didn't actually dispute the metrics.

To give some context, what did your blog post say and then what was it that SoulCycle was nervous about?

See all Y Combinator transcripts on Youtube

Analyzing Billions of Transactions to Understand Consumer Behavior - Michael Babineau and Kevin Hale