Background
Point-of-sales performance is a key revenue driver for the retail business of a leading telecoms provider—due to the heterogeneous store portfolio, it is a challenge for the omnichannel analytics team to predict an appropriate revenue potential per shop.
Task
Our task was to first categorize the shops based on external and non-financial data to uncover hidden potential. This was done in four (4) consecutive steps.
Approach
Data Categorization
Activity: Mapping data and analyzing over 500 retail shops to improve clustering.
Result: 17% better grouping of similar shops based on clustering algorithms.
Team & Time: A team of 6 experts carried out this task over a period of 5 months.
Peer Comparison
Activity: Uncovering improvement potential in shops by comparing them with their nearest neighbours. Identification of hidden potential through comparative analysis.
Result: Hidden potential identified in 32% of shops based on metrics such as footfall, sales conversion rates or customer demographics.
Team & Time: A team of 6 experts completed this task in 6 months.
Predictive modelling of future performance
Activity: Forecasting future business performance.
Result: 14% accuracy improvement in forecasting future performance of shops to enable proactive decision-making.
Team & Time: A team of 8 experts worked on this for 8 months.
Strategy Recommendation Engine
Activity: Deriving targeted improvement strategies for each shop based on successful interventions in other shops.
Result: The success rate of implemented strategies was increased by 18% through collaborative filtering to suggest targeted improvement strategies.
Team & Time: An agile team of 9 professionals carried out this task for over 7 months.
Result
The data-driven insights enabled the omnichannel marketing team to make informed decisions, uncover hidden potential, and implement targeted strategies. This led to a significant improvement in retail performance.
Customer feedback
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