Use purpose-built model templates to derive customers insights out of the box like affinity or propensity scores for driving customer engagement. Leverage best in class pre-processing and validation techniques while developing custom models.
Rapidly develop models and schedule automatic execution of models by using a flexible and integrated workspace. Make use of visualization and program libraries (R/Python/Scala) along with the capability to run small blocks of script for intermediate results to accelerate iterative model development.
Integrate data from different sources with ease using a rich library of API’s. Create ‘dataframes’ in few clicks to read data from as well as to write analytics output into an inbuilt data store.
Ensure data privacy and security with appropriate controls, quality checks and generation of insights in a walled garden environment. Manage end-to-end insight orchestration process, including API integration and third party access control.