Identified drivers of high performing stores to support a technology retailer's expansion plan
Case Studies:Mu Sigma
Published On: 18 October 2015
What We Did: Mu Sigma's decision science workbench muRxTM enabled the client to set accurate sales goals at store level and set benchmarks for other activities such as staff scheduling, resource management, etc.
The Impact We Made: The decision science workbench - muRxTM reduced the analysis time by 35-40%, which in turn resulted in better efficiency and the business could accelerate their decision making.
Summary – Smart store expansion strategy
The client wanted to design a smart expansion strategy as well as ensure that the upcoming stores generate high revenue. Hence, understanding key attributes of high performing stores was required. However, the analytics division of the business did not have insights/ recommendation for the same, as store level performance was not holistically measured. The client reached out to us to design a user friendly scalable dashboard to track stores' performance and understand key drivers of sales within high performing stores. These insights in turn would be inputs to devising a smart expansion strategy and predicting sales performance of future stores for better decision making.
About The Client - Large technology company
The client is one of the largest technology company in US and its retail wing is a leader in terms of sales per unit area. It has its own flagship stores, franchise and online stores as well.
The Challenge - Lack of insights into store performance
The analytics division of the client was not equipped to provide actionable insights into the store-level performance of the company. As a result, the client was not able to make data driven decisions. The existing dashboard did not provide a holistic view of store performance and was not user friendly.
The Approach - Decision science workbench
Mu Sigma team used its decision science workbench muRxTM, to solve this business problem. The workbench serves as an analytics application with intelligence and translation layer through an interactive workflow environment:
Stores were primarily grouped into growing and non-growing stores based on factors like sales, operational characteristics, macro-economic conditions, demographics, etc.
The decision sciences workbench - muRxTM, was used directly to create 450 iterations of the model in less than two days. All the iterations were documented (details like model stats and variables added / removed in each iteration).
Characteristics of growing and non-growing stores segments were used to plan for future stores and predict their sales as well
Some of the key insights: the higher the employment rate within the area, higher the sales; stores in higher competition areas performed better; stores with optimum working hours generated more revenue, etc.
The tool enabled the client with an interactive dashboard and allowed the team to also focus on the consumption of insights along with the analysis
The Outcome - Reduced time for store level analysis
Strong visualization capability and easy data exploration helped the team identify sales drivers quickly, which were inputs to the smart expansion strategy of the client
The client was able to set accurate sales goals at store level. Benchmarks for other activities such as staff scheduling, resource management, etc. were also created
At large, with the help of muRxTM , the analysis time was reduced by 35-40% and helped the business stakeholder consume the results in an effective way to make better decisions.