APPROACH
Predictive analytics was performed to prescribe recommendations.
- Key parameters were chosen from an exhaustive set of attributes such as OG data for existing stores, Point of Sales data, competitor information, market factors, and behavioural segments
- Machine Learning techniques like GLM, Random Forest, and SVM were used to predict OG orders for new stores
- Bootstrapping technique was implemented for model robustness
- The algorithms were tested and validated recursively on 100 random samples
- The model predictions improved over time
KEY BENEFITS
- The solution helped identify factors influencing OG orders such as the client’s grocery share in CMA, percentage of shoppers who fall under primary grocery households, grocery sales over the past 4 months, OG awareness in CBSA, etc.
- Based on model predictions, the client was able to classify stores as super-high, high, and medium, allowing optimal budget allocation for rolling out OG in select stores
RESULTS
- Client has successfully rolled out OG in more than 600 stores
- Client was able to derive more profitability from OG customers, with purchases 27% more than similar in-store-only customers
- About 20% of store customers now have tried OG
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