Shoppers Stop Earns Customer Loyalty with Project "Drishti"
A case study on Data Management in RetailExecutive Summary
CIO 100 Winner: With a quick thinking, ingeniuous mind, Arun Gupta gave Shoppers Stop one view of its customers - across retail formats a feat few have matched in India.
When executives at the 18-year-old Shoppers Stop Group noticed their sales falling into an 80/20 pattern, they knew they were onto something. The chain observed that its loyalty card holders - despite being only 15 percent of its customer base - contributed over 70 percent to Shoppers Stop's revenues, 50 percent to Crossword's, and 35 percent to HyperCity's.
Case Study Highlights
If the chain could use the data it collected from the loyalty cards maybe it could use it to beat back its rivals. "I wanted to understand what the customer buys, when she buys, why she is buying what she buys, and how we can improve her experience thereby increasing the frequency of her visits and targeting a larger part of her overall spend," says Arun Gupta, customer care associate and group CTO, Shoppers Stop.
It was a tall order, especially since the chain wanted a single view of its 1.5 million loyalty customers across all six of its businesses and three loyalty programs. (Most retailers use analytics for specific formats or segments of merchandise.) Project DRISHTI, Shoppers Stop's data warehouse and mining project, was set up to do just that. Conceptualized in 2007 and launched in early 2008, the project combined a never-before-attempted stack of best-of-breed solutions from various technology domains. But like all pioneering IT projects, although the technology was tricky, it wasn't the biggest challenge. "Getting users out of a 'reports mindset' and into thinking analytics was," says Gupta.
Today, however, the chain is focused on results. Project DRISHTI enabled micro-segmentation across Shoppers Stop's multiple retail formats to create offers targeted at its high spenders, resulting in a 3 percent increase. It's also allowed the chain to analyze customers by demography. One six-month study of the Gujarati community, resulted in targeted offers and increased that customer base (within a city) by two percent. In addition, it's allowed the retailer to check whether multiple stores in the same city were cannibalizing each other's customers and measure merchandise availability in a store.
"It also offers us the ability to analyze over three years of data across formats thus tracking the evolution of customers, which is valuable in understanding churn and wallet-share," says Gupta.
The Person Behind It
“Getting users out of a ‘reports mindset'’ and into thinking analytics was the biggest challenge."
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