The Case of a non-performing Modern Format Retail Outlet
Today’s marketers have the scope of looking at large amounts of data while diagnosing a problem or taking a marketing-related decision. We present a Case here, for a modern format retail outlet, where the poor performance of one outlet needs to be diagnosed with respect to a high-performing store by looking at data from several sources. The sources are as follows:
- Monthly footfalls and number of bills generated
- % of Loyalty Card users on a quarterly basis
- Demographic profile of customers using loyalty cards
- A sample of actual transactions (bill details) for 100 customers of the outlets
- Feedback of mystery shoppers from the outlet
This Case is just a simple demonstration of how data from different sources need to be assimilated to build a story. In reality, the complexity of the data will be much higher as there will be millions of billing transactions for the outlets (unlike 100 transactions given in this Case) and one would need to use analytics tools and techniques to work on that data.
The Case Facts:
- Your company is a well-established one having several modern format retail outlets across different geographies of India
- You have several large format stores in the metros, which stock products ranging from Satples / FMCGs to apparels and Electronic goods
- In one of the metros, a particular large format store of yours (named Store 2 in this Case) is not performing well. Its Revenue and EBITA compared to another similar sized store in the same metro (named Store 1 in this Case) for the financial year 2015-16 is as follows:
Rs 29.8 cr
Rs 13.1 cr
Rs 3 cr
– Rs 0.5 cr
Prof. PD Purkayastha
Prof. Srinivas Govindrajan
- Both the stores have been existing in this market for more than 3 years… yet, Store 2 is a loss-making store
- The data provided in this Case are from different sources, for Store 2 (the non-performing store) and Store 1 (to be treated as a benchmark store).
The data from different sources have been given in different sheets of the spreadsheet
- Sheet 1: Month-wise footfalls and the number of bills generated for Store 1 and Store 2
- Sheet 2: Proportion of transactions by loyalty card holders. This has been given as % by “no. of bills” and by “value of bills” for Q1 to Q3 and for Q4 separately
- Sheet 3: Demographic profiles of loyalty card users for Store 1 vs Store 2 vs Avg across all Stores. The profiles have been given by Gender, Age and Occupation
- Sheet 4a: A sample of 100 actual transactions (bills) has been given for Store 1. The total value of each transaction has been broken up by value of FMCG/Staples, Apparel/Electronic goods or other items bought. The catchment area where the customer resides is also given
- Sheet 4b: A sample of 100 actual transactions (bills) has been given for Store 2. The total value of each transaction has been broken up by value of FMCG/Staples, Apparel/Electronic goods or other items bought. The catchment area where the customer resides is also given.
- Sheet 5: Mystery Shoppers’ feedback on some parameters have been given for Store 1, Store 2 and an Avg Store
Based on the above background and the data given, one needs to
Understand the key issues concerning Store 2… In other words: What could be the main reasons for the poor performance of Store 2?
Suggest some possible corrective actions for Store 2