Dear Analyst #120: Marketing attribution, sensitivity models, and building data infrastructure from the ground up with Zach Wilner

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)

Again, they started with the data. They looked at a distribution of their order values. As expected, they saw a normal distribution of orders and could see the average order value across all customers. Using this data, they could figure out what the order minimum customers were reaching for. Then came the sensitivity model to find the tradeoff between a lower conversion % and higher order value.

Hiring the right people for your data team

The sequencing of how Zach went about hiring members to join his data team might sound familiar to folks. The first hire was an analytics engineer, the Swiss army knife of the data world. The analytics engineer can help build the tech stack and do analysis. This breakdown of data engineer, analytics engineer, and data analyst is always good to know:

Source: LearnSQL

Once the data infrastructure is in place, Zach then hired the data analysts who do the more traditional exploratory analysis and dashboarding. From there, Zach built out a consumer insights team. The analytics team is now doing full-stack stuff which goes beyond Excel and Tableau. They are diving into dbt and machine learning as well.

Zach talked about encouraging data analysts to be generalists. One reason people leave their current job or employer is simply being bored with the work. If an analyst is a generalist, they can grow and learn and be excited about other aspects of their role. They will have the opportunity to touch multiple departments. More importantly, they can approach company problems from multiple angles.

What keeps Zach up at night: building in-house vs. managed services

Build vs. buy. No matter how trite this debate may seem to some of you, I think it’s always interesting to hear how different companies view this problem. There’s always a new set of constraints, contexts, and tools to consider this tradeoff. What doesn’t change, however, is that there is never a clear answer. Even when you think if you’d made the right decision, that all can change next quarter.

Source: Customer Success Memes

One of the things that keeps Zach up. at night is whether a certain task should be delegated to a managed service like Stitch or Fivetran. These tools make it easy to tap into APIs. They also allow teams to move quicker and get to impact faster. The problem is that it opens up your company to more risk. If one of the APIs or providers happens to go down, you’re at the mercy of the provider. Zach talked about an issue that Stitch had with the Shopify API and that there was nothing his team could do about it.

The other side is you build in-house and everything is under your control. This requires more resources and you move slower. According to Zach, this tradeoff is something. he revisits often and the work is never quite done even when you think it’s done.

Other Podcasts & Blog Posts

No other podcasts or blog posts mentioned in this episode!