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.
In this release we introduce new models to easily predict and analyze user behavior directly on data collected using the open analytics taxonomy, and seamlessly switch between the full data set or a sample. No manual cleaning, transformations, exporting samples or complex tooling required.
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The modern data stack is on the rise. Many companies use raw data from their SaaS analytics tools as input for their data warehouse, but this introduces problems downstream. Are there better ways?
In this release we add new functionality to modeling library Bach and the open model hub that enable User Intent analysis on your raw analytics data. It comes with an example notebook to get started with your own analysis.
The tracking plan. Every company that’s serious about analytics has one, but they are notoriously difficult to properly set up & execute. What if you didn’t need one?
With this release we now provide a Tracker SDK for React Native, so you can use Objectiv's advanced modeling on your mobile app's user behavior data as well.
Analytics tracking instrumentation shouldn't cost you a lot of time. So we've put a lot of effort into ensuring that it's incredibly simple and quick.
With this release we've added several example notebooks that help you get started with the open model hub to analyze and model data. We've also released the Objectiv libraries to PyPI.
We've released Objectiv v0.0.15, which enables validation of data at the first step of the pipeline: the Tracker.
We've released v0.0.14. This release adds several new pandas-like date & time operations to the Bach modeling library that provide more granular control when wrangling/exploring time-related data.