"The model tells us which users we should target for WhatsApp.”

“Snowplow has been helping us centralize our data and build the single customer view since day one. As we get closer to pinpointing precisely, for example, the booking value of a particular segment, and more complex data analysis, Snowplow will continue to be what we need.”

“Snowplow has been crucial for Supercell. It gives us deep insights into how players engage with our games and events. This has empowered our teams to build better experiences, especially for Brawl Stars, while driving more collaboration and aligning us closer to our community. It’s been a game-changer for understanding …

"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."

"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.”

“Snowplow’s rich, granular data enabled us to build sophisticated audience intelligence and double the efficiency of our clients’ trailer advertising campaigns.”

“Having access to the event-level data with Snowplow enables us to not only finely segment users, but also provide users with highly personalized adverts and experience.”

“With Snowplow data, we set sales records five months in a row from when we started the ad tests.”

“Many people have this hope that from day one they will instantly have all the answers, but Green Building Supply has invested in a year-long progression of starting by answering one small question, and building on the answers incrementally and getting to the point where you have a meaningful result …

“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 …

"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.”

“It was especially important for us to calculate conversion rates accurately for our clients, so they could compare us to other job boards. This wasn’t possible before Snowplow.”

"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.”