+ Menu
The client, an international retail industry leader, faced the challenge of designing the future retail store to test innovative concepts and scale them across high-impact locations. A key task was to better understand cross-purchasing behaviors in order to optimize how products should be displayed, ensuring a more seamless and effective customer experience.
Association rule mining algorithms were utilized to find cross purchasing patterns across all global transactions to inform decision on placement strategy.
Designing future retail stores for scalable success
The client needed assistance in designing a future retail store model that would allow them to test innovative concepts and roll them out to high-impact stores globally, aiming to enhance customer experience and increase performance.
Analyzing customer behavior to optimize product presentation
The key task was to analyze cross-purchasing patterns to better understand customer behavior and improve how garments were displayed. Using association rule mining algorithms, insights were derived from global transaction data to uncover these hidden patterns.
Challenging assumptions and piloting new strategies
Contrary to expectations, the analysis revealed that customers were primarily destination shoppers rather than occasion shoppers. This insight led to a revised product presentation strategy, which, when piloted, showed a 4% increase in top-line performance. Following this success, the strategy was rolled out to 200 high-impact stores. The process was built on challenging existing assumptions, testing hypotheses with data, ensuring statistical rigor, and committing to scalable implementation.
A successful implementation of the new store concept led to notable improvements and insights.
A revised store presentation strategy was developed based on data-driven insights
A 4% increase in top-line performance was achieved during the pilot phase
The new strategy was successfully rolled out to 200 high-impact locations
