Retailer-level recommendations for one of the largest food and beverage company

Our client, one of the world’s largest food and beverage companies, wanted to build a retailer recommendation engine which looks at the historical order placement data and suggests products for the next month.

The objective is to provide retailer-level recommendations to the company by unlocking key patterns in the historical purchase order at channel x retailer x region/district level.

Data Pre-Processing and Feature Engineering

• Optimization for unique hierarchy ID assignment in combined databases.
• Data cleaning and validation.
• Focusing on the top 90% retailers and products based on revenue.
• User-item matrixas implicit feedback derived from transactional data.

Features based clustering.

• Retailer segmentation with comparable purchasing behaviors based on their sales channels.
• Algorithms used such as RFMand KNN.
• Segmentation based on revenue such as:
  • Premium
  • Medium
  • Occasiona

Recommendation Engine

• Implemented product-based recommendations for Retailers through Collaborative Filtering techniques via ML Models like Neural Collaborative Filtering.
• Applied Association Rules to find popular items and co-purchases via Apriori Algorithm.

User Interface

• Updated SQL table with tailored recommendations post model training for consumption over web and mobile app for Distributors and Retailers.

• Projected improvement in revenue by ~3%
• brand consumption improvement from 4 to 6
• Strategically upselling new product variations by increasing product awareness through recommendations.
• Improve Precision: & accuracy by 80%