This is a graphical representation of a project that was my first with Mu Sigma. The solution addresses the problems faced by the Data Innovations team of one of the largest consumer banks in the world in developing a merchant recommendation engine for its credit card customers. The solution aimed at developing a new, improved algorithm which generated highly personalized recommendations in significantly lesser time.
Number of records processed: 1.2 Billion credit card transactions
Platform used: Hadoop
Processing languages: Java, Hive
Note: To ensure confidentiality, dummy client names and numbers have been shown in this document.
While user-experience research is traditionally driven by qualitative insights, this analysis carried out by my team at Mu Sigma enabled quantitative, data-driven decisions in the user-experience domain.
To showcase the results of our exploratory analysis to the stakeholders, who were always short on time, I created this infographic independently, converting a 30 slide powerpoint presentation into a single image.
