Making Sense of Customer Transaction Data

Using Big Data technology and data science, Inmar’s Data Science team is able to understand and predict customer behavior. Learn the science used to answer the basic questions “who are your customers” and “what products will meet their needs”.

During this session, attendees will learn about:

A clearly defined process to turn results from models and analysis into data products
Steps to ingest various data sources to make the data accessible and consumable
Customer segmentation based upon predicted spending with overlaid profile and descriptive data

Models presented include Netflix-style collaborative filter for grocery purchases, Amazon-style shopping for retailers, and customer lifetime value, which predicts customer spending.

Shari Wakiyama is Director, Data Science and Product at Inmar. Currently, she manages Inmar's Data Science Team, which solely focuses on driving value for Inmar, its clients and its partners from its data. Prior to this role, she led Inmar's BI, Big Data and advanced analytics initiatives. Shari has more than twenty-five years of experience in business strategy, finance, business development, operations/engineering, technology, and education, working in several industries and function areas.