Ivar Pruijn

Starting on instrumenting analytics tracking doesn't mean you’ve stopped developing your product. Any data you track may change over time, possibly unintentionally. As a result, your data team needs to update their models, or find out where & when they broke unexpectedly.

In this release we’ve made it easy to build end-to-end testing of your tracking instrumentation, in order to catch any instrumentation changes early, before data is even collected.

Ivar Pruijn

In this release we added a Retention Matrix model to the open model hub, that enables you to run retention analysis directly on your data store, with just one operation. No need for data cleaning or building complex SQL queries. Next to overall retention analysis, it also enables you to drill down directly; you can quickly segment each cohort and stack ready-to-use models on top, like which top features are being used.

Use the new model to drive product adoption by understanding to what extent users return to your product, whether that improves over time, and how each cohort behaves in detail.

Ivar Pruijn

Objectiv lets you model raw analytics data straight out of the box. This is partly enabled by early data validation against the open analytics taxonomy (also making instrumentation unambiguous and future-proof), and partly by helping developers set up tracking instrumentation with helpful tooling.

In this release we focused on the second part: we added links to relevant documentation pages in the Tracker SDK's Validation messages, to help you debug your tracking instrumentation.