Product Recommendation System for a Retail Chain

Product Recommendation System for a Retail Chain

Objective

The client was looking for an automated solution to provide recommendations for replenishing their stores with various lines of products according to consumer’s buying behavior. A major cost-effective solution was achieved to eliminate the manual intervention in suggesting replenishment orders for their stores.

Approach

The challenge was addressed by following the below steps:

  • Understanding the data flow streams for various product lines and exploring the data statistically.
  • Analyzing the exploratory analysis for the data and bringing out meaningful insights to identify significant variables to show a considerable effect on the product lines.
  • Development of various statistical and machine learning models to understand the various transactions containing the products.
  • Shaping out the methodologies to sort out categories of product lines for the stores.
  • Generation of priority list of product lines to optimize the product distribution systems by understanding various evaluation metrics.

Benefits

  • Improvement of efficiency in the process by reducing manual effort to track down customer behavior for product consumption.
  • The client was able to understand the ground-level metrics to optimize the process of product recommendations based on scoring techniques.
  • The insights from the analysis helped the client to troubleshoot specific blockers in their supply chain system.
  • The analysis helped to improve the inventory requirements and optimize the marketing campaigns for the featured products.

Results

  • The automated product recommendation system showed 20% more efficiency in the process than the traditional manual system.
  • The solution helped to reduce the man hours in the distribution system by providing accurate recommendations for the products.
  • The client was able to optimize the availability of the products by carefully understanding the consumer’s buying behavior with an improvement of 7%.