48 Snowplow Testimonials

"With data you solve marketing, advertising, and editorial questions you improve your digital products."

“With Snowplow data we can show a customer that 95% of actions taken during a video happen between 0:30 and 0:35 seconds. The customer can then learn what works, what doesn’t, and make the needed changes to drive significant increases in KPIs.”

"From the beginning, I was able to see that it was a really flexible platform with the custom JSON schemas you can make that’s one of the primary reasons I went with Snowplow."

“The Snowplow framework has become indispensable to understanding the Picnic app usage and allowing us to focus on the most impactful features.”

"A shopper who browses products on Burberry.com is telling us something about what she really wants. Thanks to Databricks Lakehouse Platform and Snowplow, we can build an AI-Ready Customer 360 so our client advisors have that information from the moment she walks into our store — and can deliver a personalized NextGen customer experience (CX).”

“Snowplow provides all of our event data in a data model we own end-to-end, and can easily shape to fit our organisational needs. Snowplow has really helped accelerate our analytics; now we can quickly answer questions that would have required a tremendous amount of engineering effort with our previous solution.”

“Without Snowplow data, a data-driven approach to systematically improving retention would not be possible.”

“At some point, you will reach a certain performance plateau in terms of what you can optimize if you have batch features. We were thinking, is there value for us if we get these near real-time signals into the search? That's why we started looking into solutions that we can use that don't require us to rebuild the whole infrastructure for ourselves. Because we are aware that this is quite cumbersome. And it takes quite some engineering effort to get this into a production stable state.”

“We would not have achieved our current level of self-serve data without Snowplow. It has enabled us to democratize our data culture, significantly improving our analytics coverage and deepening our insights.”

“Often we think a lot about operations, observability, all the things you need to make that thing work for your specific use case but we didn’t always focus on being able to get the data to analysts, what the shape of those queries would be and what questions we would be asking of that data, and are we going to be able to provide the answers.”

"Instrumenting a feature was something that was difficult and time consuming for our analysts, product engineers, and data engineers. And this limited the appetite for vertical teams to add tracking to their features. We thought that if we could reduce the complexity of event tracking, instrumentation coverage would improve dramatically.”

"With Snowplow data, we were able to measure project success through an A/B test. In our experiment, we hypothesized that our new Route Detail Page will help Strava users feel like they have enough information to take the next step with a route, resulting in increased engagement with the product.”

"With GA, you didn’t know when you’d get your data – it could be 3 or 5 hours. You just didn’t know. Even with 360, you didn’t have everything in minutes.”

"When we attempted to integrate data from Adobe Analytics into our existing platform, we encountered significant processing and performance issues.”

"Auto Trader loves open source technologies. Snowplow is an open source technology—we didn’t see the value of managing it ourselves, but we like the fact that we can contribute code.”