Over time, Edenred China accumulated vast amounts of transactional data from all of the solutions and programs run on behalf of clients. Demand for customer insights from large enterprise clients, especially in the fast-moving consumer goods (FMCG) industry, was growing. This propelled Edenred to rethink services provided to clients. Edenred seized the opportunity to find a solution to create a niche in loyalty programs.
"Working with Avanade has enabled us to leverage tried and tested methodologies and world-class Microsoft expertise. Avanade went above and beyond outstanding delivery to drive project and change management across Edenred"
Based on consumer transaction data collected in Edenred’s loyalty platforms, Avanade mapped new business processes to define segments, prepare data extraction, enhance data discovery and help move access of analytical insights to Edenred clients. Avanade then designed, built, ran and scaled a Recommendation Engine to segment loyalty programs members by their profiles and generate automated recommendations for other products and services relevant to those consumers.
The Recommendation Engine leverages Microsoft SQL Server 2012 and SharePoint 2013 including a self-service BI platform (PowerView), Data Quality and Predictive Analytics (Analysis Services).
The Recommendation Engine unlocked valuable member insights to help Edenred clients provide enhanced customer service. This allowed Edenred to deliver new value to its existing clients, while expanding an additional revenue stream. Since the successful initial pilot in China brought positive ROI for Edenred and its clients, the newly developed analytics capability is being scaled and replicated across a range of industries globally, including hospitality, airline and cosmetics clients to accelerate sales.
In addition, Edenred’s data analytics solution has reduced the cost and time-to-market of marketing campaigns for clients, removing high costs of in-person focus groups to target loyal members with automated online self-selected preferences and persona insights