Most recently I worked on ad fraud detection using browser data. Prior to that, I was responsible for Epsilon’s Analytical Data Assets team. In this role I was tasked with analytic product development based on both Epsilon and 3rd party data. While at Epsilon, from 2000-2016, my primary use of data has been to create analyses and predictive models for client applications.
I’ve worked with the following types of data:
- JSON Browser Data – Developed python code base to read and analyze browser session data containing indicators of potential human or ad fraud.
- Compiled Consumer Data – Household characteristics such as age, income, home ownership, and purchase transactions primarily on Epsilon’s TotalSource Plus file containing thousands of data points on 160MM US households.
- Consumer Self-Reported Data – Self-reported warranty and survey data relating to interests, purchases, and ailments (for example, interest in gardening, shops at Walmart, and suffers from diabetes), primarily from Epsilon’s Target Source database of over 40MM US households.
- Consumer Transactional Data – Bank data relating to credit and debit card line items summarized by merchant and merchant category by month, primarily Epsilon’s Market View data.
- Credit Data – Consumer credit line and credit inquiry data from Credit Bureaus
- Web Data – Email and digital data streams for web analytic analysis
- Syndicated Market Research Data – Census-balanced self-reported consumer research data, primarily the MRI syndicated survey research file.
- Business Firmagraphic Data – Characteristics and contacts associated with business entities
- Aggregated Credit Data – Credit bureau information aggregated at a zip+4 level
- US Census Data – Typically compiled and resold by companies such as, Epsilon and others
- Customer Relationship Management Data – Client specific transactional data in various industries (publishing, travel, financial).