Ivar Pruijn

One of the key ways to improve performance on your product goals is to understand where users drop off and how often they do, so you can optimize user journeys for better conversion.

In this release we enable you to use the open model hub to quickly find which locations/features drive users to drop off on any of your product goals, and how (relatively) often this happens. You can do this for your entire product, or just a subset.

Ivar Pruijn

In this release we enable adding Objectiv tracking to your existing React Components library, making it possible to instrument all your Components just once, and reuse them anywhere in your application with full analytics tracking support.

Any JSX element or custom Element/Component can now be tracked, while automatically capturing all relevant context, plus information about where an event exactly happened in the UI. You also also get the same autocompletion, typing, and validation tools as for Objectiv Components.

Ivar Pruijn

This release adds a 'Marketing Analytics' notebook that shows how you can fully analyze behavior of users who enter your site/app via marketing efforts, in order to understand what really drives your product goals.

Using the open model hub or your own models, you can deep-dive into the raw marketing data from a notebook. Example analyses are conversion, time spent, features/content and journeys that drive conversion or drop-off, predicting if users will convert, etcetera - all split by marketing source/campaign/creative.

Ivar Pruijn

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.

Ivar Pruijn

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.