Treselle Systems

BUSINESS GOALS: Reduce overall head count of mechanical turk force by 80%. Reduce error rate of manual checks by automating many mundane tasks. Speed up the scraping process of 3500 store locators from 45 days to few days. Reduce dependencies among Data, Quality Control, and Research teams. Improve the accuracy of data collection and geo-location of retail stores by comparing with multiple geo-coding software. CHALLENGES: Treselle had to take over from the original team that had 47 members and reduce the head count to 10. Treselle received only 3 1-hour knowledge transfer sessions from the original team. 70% of the store locators are done manually with different frequencies based on the importance of the store locators. Complex store locators that are map, search, infinity scroll-based were taking majority of the scraping team’s time due to their complexity. Most of the store locators become outdated for following scraping frequency due to layout changes of the store locator page. Only 30% of the store locators were processed monthly and the remaining in different frequencies such as 3-months or 6-months due to teams inefficiency. 70% of the team was focused on the data quality checks of the scraped store locators and further mapping of stores to their respective banners. Identifying stores that are opened, closed, relocated, etc is a tedious process and error-prone due to manual comparison of historical scraped files with new scraped data.