Boosting Revenue Per Store with Data-Driven Insights
In an effort to increase average revenue per store, an international salon chain in Mumbai turned to Sherlock AI. Using a mix of our prosperity index, geo-location data, and average spending power of individuals, Sherlock AI identified underperforming locations to consider for closure.
5 Million
Anonymized Users
5
Data Stacks Utilized
2 Weeks
to the first output
The Challenge: The salon chain aimed to increase its average revenue per store. They had various outlets throughout Mumbai, but needed to understand which ones were underperforming and potentially needed to be shut down. This required a deep analysis of not only their own operations, but also the economic status and spending habits of people living near each location.
The Outcome: Sherlock AI layered app usage data, prosperity index metrics, and store location data with the average spending power of people in each salon’s vicinity. With this comprehensive and data-driven approach, Sherlock AI could pinpoint the salons operating in less prosperous areas with low spending power. This led to actionable insights on which locations should be considered for closure, enabling the salon chain to optimize its overall revenue.
