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 in?uencing 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 pro?tability from OG customers, with purchases 27% more than similar in-store-only customers
                          • About 20% of store customers now have tried OG

                          538porm