Case Study: Performance Improvement Strategy
How we helped a technology retailer benchmark store performance to identify underperformers, pinpoint areas of opportunity, and implement strategies to drive revenue growth.
Project Impact
Client Challenges
The client had been attempting to implement performance improvement plans for their retail locations. After months of time and money invested in the effort, they were not seeing any improvements and didn’t know what to do.
Inability to Identify Key Drivers of Underperformance
By misunderstanding the true potential of their stores, the client lacked the capability to establish valuable performance benchmarks. Without them, the client struggled to identify the root causes of underperformance.
Ineffective Improvement Strategies
Trainers were dispatched to low profitability locations to conduct standard sales and process trainings. However, these training structures weren’t informed by data and thus didn’t address the primary causes of underperformance in the client stores.
Misunderstanding of a Store’s True Potential
The client only targeted low profit stores for improvement without realizing external factors can cap a store’s potential regardless of improvement plans. This resulted in wasted investments in locations that were already operating at an optimal level.
Inefficient Field Management Hierarchy
The field management hierarchy was not optimally established with some stores being unreasonable far from district offices. The resulting infrequent field visits from management led to a lack of oversight and poor practices within distant stores.
The Collaborative Solution
During an engagement lasting six months, we worked with client leadership to identify the optimal performance potential of store locations. Improvement efforts were focused on stores with the largest gap between current and optimal performance.
Data Gathering and Multiple Regression Analysis
Our first objective was to define what the optimally performing store looked like in different geographies, store types, and populations. We gathered data from over 1,000 locations and conducted several multiple regression analyses and performance simulations to determine which internal and external store characteristics had the most statistically significant impact on performance. This allowed us to identify critical performance metrics to be used in benchmarking and store evaluations.
Geo-Based Store Benchmarking and Leadership Hierarchy
Store districts and regions were redrawn and the field hierarchy was updated to provide more efficient and effective oversite to all locations. On top of that each store was assigned an optimal performance score based on it’s specific geography and other key characteristics (customer traffic, revenue per unit sold, etc.) identified during the regression analysis. This provided valuable location and market specific insights the client could use to inform their growth strategy going forward.
Identify Specific Performance Improvement Opportunities
Further analysis was conducted on all stores comparing their current performance to the optimal performance score. Improvement plans were put in place for stores that had the biggest gap between the two scenarios. We discovered low profitability stores that were overperforming and should be prioritized for divestment as well as higher profitability stores that were underperforming, creating ample opportunities for profitability improvement. This led to an effective use of resources and significant project ROI.
