Provided a robust framework to predict the roll out of online grocery pickup service in stores, for one of the largest retailers in the US

CASE STUDY

Provided a robust framework to predict the roll out of online grocery pickup service in stores, for one of the largest retailers in the US

SUMMARY

The client wanted to launch online grocery pickup service as a competitive angle to counter the often pricier Online Grocery (OG) delivery services. Their plan was to roll out this initiative in a phased manner, by markets. To make the roll-out successful, they were looking for a solution that could provide intelligent recommendations on physical stores where OG should be rolled out.

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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