Case study

Doodle's build vs buy decision

Doodle is the simplest way to schedule meetings with clients, colleagues, or teams. Doodle is the leader in scheduling technology since 2007 with more than 30 million users every month. With a truly global operation, providing both platform and powerful API, it is critical for Doodle to understand how users navigate their products and have a clear understanding of their data across their teams.

Like most data-driven organizations, Doodle had faced data silos, inconsistent standards, and tooling discrepancies. 

“Historically Doodle data was managed by individual PMs and isolated product teams, which worked when we were small but became impossible when you wanted to get a full view of the user, because everyone had different tracking and different tools.” - Ethan Brown

Doodle’s build-vs-buy decision

Ethan Brown, Head of Data and Analytics at Doodle, has been hard at work building a data culture and system that allows for true collaboration and organization-wide data literacy. Doodle is working to get as much first-party, homegrown tracking into the product. Like many product-centric organizations, using as few third-party services and applications as possible is important at Doodle, given ever-stringent privacy requirements and engineering-heavy cultures. 

“We want internal tracking for the most part. Right now, we send tracking data into an S3 data lake from Kafka. Then we use Airflow to have a routine batch to Redshift.”

However, when it came to taxonomy management and governance, Doodle didn’t have the resources available to build out a suitable UI that all team members could easily collaborate on. With that in mind, Ethan and his team began to evaluate the tools available on the market. “Free to paid users is a major KPI for Doodle, so it was important that we could track both types of users with the same library, ” Ethan explains.

“We have a world class engineering team, but our strengths were definitely more backend focused. Avo allowed us to have a very front-end friendly tool, with a great UX that allowed non-technical people to collaborate with our engineering and technical teams.”

Better experimentation, fewer errors

With Avo in place, Ethan’s team is currently transitioning their taxonomy and tracking to Avo, something they’re working on gradually with Inspector. A primary indicator of success is the decrease in number of failed A|B experiments. Due to irregularities and errors in their previous tracking, Doodle couldn’t trust their data and wasn’t able to experiment as much as they’d like. 

Closing words of wisdom

“Working with the Avo team has been excellent. Working with Thora [Avo’s Head of Customer Success] has been amazing, and I know it’s hard to maintain that level of care as you grow, but it is one of the main reasons we chose to work with Avo.”



Company size



Zurich, Atlanta, Belgrade, Berlin, NYC

Tech stack

Javascript, iOS, Android, AWS, Kafka, Amplitude

Key takeaways

Avo was the simplest way to clearer, more collaborative analytics--even in a build-heavy engineering organization.