WhoBuilt

Analyzing Billions of Transactions to Understand Consumer Behavior

May 29, 2019· 56 min· 8.4K views·Watch on YouTube

Speakers

Michael Babineau, co-founder of Second Measure, talks with Y Combinator partner Kevin Hale about turning credit card transaction data into tools for understanding consumer behavior and company performance.

From game analytics to investor tools

Babineau and co-founder Lillian brought experience building data systems for video games, where designers needed to answer behavioral questions without writing SQL. After a hedge fund contact asked how to load two terabytes of data into Excel, they saw an opportunity to give investors tools for exploring data themselves. They focused on credit card transactions because investors wanted the data but struggled with its messiness.

What transaction data can show

Second Measure uses observed purchases to help users investigate company performance, competitors, customer cohorts, and where customers shop. For example, the team found that Stitch Fix customers did not reduce their department store spending, and that active Peloton memberships had surpassed active SoulCycle riders. The data shows spending, not a company’s costs, so it cannot establish full unit economics.

Cleaning data and checking assumptions

Credit card descriptions were designed for people reading statements, not for machines, so one merchant can appear under many variations. Babineau said Second Measure had more than 50 billion transactions and one billion unique transaction descriptions, and built a pipeline to resolve merchants, estimate missing locations, and correct sample biases. He also looks for careful analysts who investigate data assumptions before drawing conclusions.

Growing through different customer needs

The company expanded from helping investors assess company performance to serving corporations that want competitive analysis. Babineau said all 150 clients at the time had arrived through inbound referrals, including when venture capitalists introduced the product to portfolio companies. He described the product as a way for customers to answer their own questions, with shared access to data making better questions a source of differentiation.

Takeaways

  • Give domain experts tools to answer their own data questions instead of making them wait on analysts.
  • Choose data you can observe directly, and be clear about what it cannot reveal.
  • Investigate messy data and test assumptions before trusting a result.
  • Look for adjacent customers with the same underlying job to be done.
  • When data access becomes common, help users develop better questions.

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