ECOMMERCE LEADER Ups Customer Experience with BI Live on Hadoop

Posted by Robert Noakes
Saturday April 1, 2017

This leading digital eCommerce company provides a fast and reliable online experience through which customers can receive digital cashback offers from retailers across the U.S. Working with 3,000+ merchants, they needed to deliver daily transaction + event tracking reports, across 5TB of usage data (growing to 30TB in 2017) and nearly $4M in cashback transactions per year. Retail merchant decision-makers depend on these reports to adjust offers to align with daily fluctuating market demand.

Report creation entailed moving all online-activity data, offer redemption and other data into Hadoop. Next came ETL (extract, transform, load) processes, taking unstructured data into structured within SQL Server. Finally data landed in 3 separate SQL Server Analysis Services (OLAP) cubes (marketing acquisition, shopping, and merchant); there was so much data it all couldn’t fit into just one cube.

Data grew at such pace and volume that delivering the latest day’s data via data and cube refreshes began to take 24+ hours. Not only was 24+ hours outside of reporting SLAs for retail clients, but insights revealed were in some cases no longer relevant; consumer desires and demands fluctuate quickly in the online offer market. To maintain pace with consumer behavior and merchant insight needs, this leading eCommerce leader recognized eliminating multiple data writes and data movement was key to faster analytics and reporting insights.

With AtScale, they capitalized on their Hadoop and BI (Tableau and Excel) investments; both tools and skills. Their analytics professionals are now able to execute Tableau and Excel queries without IT moving data out of Hadoop and into a relational database or cube. AtScale’s single semantic layer adapts aggregates in response to user queries, so Tableau queries and reports respond at an interactive pace that analysis require. For the first time analysts can analyze current and historical usage data (not just data subsets pulled into cubes); and they do it in less time (aka immediately). They are now finding trends, outliers and opportunities they never did before.

By driving faster performing analytics on data that doesn’t have to be moved out of Hadoop, this digital cashback leader supports more offers that better meet end-customer desired cashback demands. They deliver better service to retail clients and a better end-customer experience that truly differentiates them from competition.