WibiData Invokes Machine Learning to Create Personalization Models

Millennials Expect Brands to Use Their Unique Data for Hyper-Targeted Experiences

At the National Retail Federation 2015 (NRF) conference today, WibiData unveiled a framework for rapidly personalizing data stored in Hadoop that promises to dramatically reduce the amount of time it takes to create a unique customer experience using Big Data.

While one of the primary reasons to invest in Big Data is the ability to identify trends down to the level of each unique customer, implementing personalization models usually takes months because of the time and effort required to explore, extract and test customer data.

Rob Seaman, vice president of product for WibiData, says that as an extension of the WibiRetail platform that enables retailers to create unique customer experiences, WibiData has created its Experiments by Wibi offering based on machine learning software that can analyze all that data in a matter of minutes by eliminating the need to extract and test all data.

The challenge with most personalization platforms is that they take a long time to set up. As a result, as the customer behavior changes, it’s hard for the personalization model to adapt. WibiData’s Experiments eliminates that problem by allowing businesses to create a new personalization model based on all the latest data in just a few minutes, says Seaman.

As a layer of software that resides directly on top of Hadoop, Seaman says that Experimental Lab also eliminates the need to create a separate data repository to support personalization models. Instead, Experiments by Wibi makes use of all the raw customer data an organization already has stored in Hadoop.

As a discipline, machine learning is about using algorithms to build models that can predict future behavior. As advanced as those algorithms are, however, the efficacy of those algorithms is still dependent on the quality of the latest data that can be accessed.