A Fortune 500 American multinational company best known for its footwear, apparel, and sports equipment wanted to understand its customers’ buying patterns to scale up sales at its retail outlets.

Client wanted to analyze:

  • Impact of campaigns, promotions, and discounts on performance of its company owned and company operated factory outlets.
  • Products which are complimentary to each other and could be sold together.


TransOrg conducted an exploratory data analysis at multiple levels:

  • Popularity of the offered discounts

We analysed the sales in different discount categories and assessed ways to prevent cannibalization. We also analyzed the “discount elasticity” of a product in different price buckets by offering differential discount on the same product. TransOrg merged inventory and sales data for better discount management.

  • Sales of product variants

Product variant analysis was done to understand the demand of different variant across products which helps in better positioning of less selling variants and offer differential discounting on different variant of the same products.

  • Market basket analysis

TransOrg used the Apriori association method to find frequently bought product item sets and derived association rules to uncover meaningful correlations between different products according to their co-occurrence in a data set. The following measures are used to evaluate the strength of the association:

  • Support for the rule indicates its outcome in terms of overall size
  • Confidence determines the operational usefulness of a rule
  • Lift ratio indicates how efficient is the rule in finding consequences, compared to random selection of transaction

Table 1: Association rule. For Illustration only

Product 1 Product 2 Support Confidence Lift
Short sleeve top Shoes 0.065 0.95 2.1
Length tight Short sleeve top 0.018 0.75 3.8
Sleeveless top Short sleeve top 0.0024 0.70 3.5

The rule suggests that if customers buy short sleeve top, then there is a higher chance of cross selling shoes as well.

TransOrg also performed separate market basket analysis for male vs. female customers and across different factory outlets stores based on location.


  • 70% discount had a negative impact on sales of products in the 30% discount category.
  • Identified products which are sold together despite low to no discounts offered on either one of them.
  • ‘1+1’ offer is the most prominent offer for product X in price range of 4k-6k.
  • Identified product variants with the highest and least sales.
  • Offer differential discounts on high vs. low selling variants
Want to learn more about TransOrg’s value proposition, solution methodology and implementation approach?