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Retail: Customer AnalyticsTo increase wallet share and drive more product category purchases, this leading U.S. department store chain used Datameer to provide a 360-degree view of customer behavior.

About Datameer

Datameer’s end-to-end Big Data Discovery solution enables business users to discover insights in any data via wizard-based data integration, iterative point-and-click analytics, and drag-and-drop visualizations, regardless of the data type, size, or source. Optimized for big data analytical workloads, Datameer uses Hadoop for both storage and compute and can integrate with existing DW or BI solutions.

This leading U. S. department store chain offers a wide range of men’s, women’s and children’s apparel, fashion accessories, cosmetics and home furnishings as well as a store-branded credit card.

The company focuses on understanding their customer’s needs as a means to maintain and increase wallet share. They strive to have a 360-degree view of customer behavior and were looking to quickly integrate new and disparate sources of structured and semi-structured data, including ad hoc correlation of private catalogue data with public, industry standard product category codes and other information. Their existing data warehouse was inadequate in that it didn’t include some readily available 3rd party transaction web interaction and public data.

The project used Datameer to integrate point-of-sale data from a mainframe, external credit card transaction data, web logs, and public data sets to determine how their highest value customer segment used the store-branded credit card at other retailers. The company chose Datameer because the spreadsheet interface provided a low barrier to entry for business uses to start exploring and running ad-hoc analyses on raw data to test hypotheses and then operationalize the most relevant analytics when needed.

Solution Brief

Self-Service Analyticsand Dashboards Cleansing/Aggregation

IBM EnterpriseData Warehouse

SPSSPredictive Model

Hadoop DistributedFile System (HDFS)

Web Log Files

MainframePOS Data

Public Data