This release includes two updates to the Tracking SDK for React that enable you to:

1. Track all form inputs: Use Tracked elements for checked-based inputs such as radio, checkbox, and multiselect, next to text input elements in forms.
2. Predefine & later customize Contexts for Hooks: Set up a Hook with a predefined Location Stack and/or set of Global Contexts, and then customize the tracking once you use it later on, e.g. to capture additional Contexts.

In this release, we've added a Funnel Discovery model to the open model hub that enables you to uncover which paths in all user journeys impact your product goals, and analyze opportunities for improvements to your product.

Objectiv now supports Deepnote: collaborate on notebooks that are powered by Objectiv’s high quality product analytics data and pre-built models.

We're very excited to announce the release of Google BigQuery support for storage and modeling with Objectiv.

Next to the ability to validate & store Objectiv data in BigQuery, we’ve enabled working with the open model hub and modeling library Bach (an SQL abstraction layer with pandas-like syntax) directly on your full BQ dataset.

This is an important step in our mission to enable data models to run across data stores, so data teams can take and run what someone else made, or quickly build their own with pre-built models and operations. All current models and operations now work with both PostgreSQL and Google BigQuery, with Amazon Athena next, and more data stores coming.

This release makes three additions to the open analytics taxonomy and open model hub:

• Identity resolution of users across sessions/devices in a new IdentityContext, e.g. using your own internal User IDs, or simply using consent-based cookies;
• Store the user’s locale/language in a LocaleContext to analyze multilingual applications; and
• Capture the value of input fields like a search field in an InputValueContext to analyze searches, funnels, etc.

Objectiv now supports Hex: collaborate on notebooks and build beautiful data apps that are powered by Objectiv’s high quality product analytics data and pre-built models.

In early 2021, our team of long-time data geeks started on the development of an open analytics taxonomy. The goal: to come up with a generic way to structure analytics data, so models built on one data set can be deployed and run on another.

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.

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.

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.