Data analytics and business analytics are still relatively new areas of study (in terms of academics). The subject borders business and computer science. When I went to school, the only data analytics classes available were special electives offered through our school's continuing education department. In this episode, I spoke with Zach Wilner who currently leads data and analytics at Pair Eyewear. Zach is a "classically trained" in data analytics (if one can call it such) since he studied business analytics at Boston College. He has worked at various DTC (direct-to-consumer) companies like Wayfair and Bombas before landing at Pair (also a DTC company). In addition to discussing marketing attribution and pricing projects, Zach also talks about building Pair Eyewear's data infrastructure from 0 and how to build the team around it. Scaling a data stack in a step-wise approach When Zach joined Pair, there wasn't really much of a data infrastructure in place. People wanted to analyze and visualize data but didn't know where to pull the data from. The classic multiple data silos problem. The easy thing to do would've been to take the data stack at Bombas or Wayfair and try to implement it at Pair. Instead, Zach asked what if we started with a blank slate? With the help of a consultant, Zach spent 6 months building out a data warehouse with dbt, Stitch, and other ETL tools. After the foundation was placed, he then focused on BI and implemented Looker and Heap. The goal is to make analytics as self-service as possible. Today, 60%-70% of the company use Looker actively. From a marketing analytics perspective, most DTC companies have similar marketing channels (e.g. Shopify, Facebook, TikTok). This means Zach could set up similar telemetry for tracking all of Pair's marketing initiatives. One area the team spent some time on is health data and they decided that they wouldn't be HIPPA compliant or deal with PHI data. Customer centric vs. marketing attribution model Marketing attribution. A never-ending battle between marketing channels and data to figure out which channel gives your company the best bang for your buck. The reason I know this problem hasn't been solved yet is because new marketing attribution vendors pop up every year claiming to be the end-all-be-all omnichannel tracking tool. If you work in martech, we've seen the industry evolve from last-click to multi-touch models. Source: WordStream Zach worked with Pair's head of marketing to figure out what model would work for the company. Surprise surprise, they started with the data. Using the data, they answered questions like how many sessions does it take before a customer makes a purchase? How many ads does the customer need to see before they make a purchase? The team decided to build out a home-grown attribution model and called it a customer-centric attribution model. They basically looked at how individual customers viewed Pair's different marketing messages and optimized spend based on the customer. They were able to properly attribute conversions by comparing their results with lift studies from Facebook. Using a sensitivity model to experiment with pricing Pair's business model is doing limited-edition drops. This means a lot of one-unit orders when the drops happen. With the longevity of the business in mind, the team asked what would happen if they encouraged customers to to purchase two items with less frequency between them instead of just these one-time higher-priced drops? Source: SoundCloud (Mokos)