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Assortment Optimization

Machine learning driven

Over the last 2 years, assortment rationalization has become one of the most important objectives for many retailers.​​ 100's of working hours have been spent in the exercise, with sometimes great results and sometimes less successful results.​​

Over the last 6 months, our retail experts and data scientists have been working to build a unique assortment rationalization algorithm that deliver best optimised results under your constraints and taking each store specific catchment areas in considerations.​​​

Liberate working capital and increase space profitability now!

Download the
​Assortment Optimization Brochure ​

Rationalize Your Brand or Category

in 4 Steps

Build your scope

Define the store, brand and category scope where the rationalization takes place

Define your constraints​

Assign the constraints to the rationalization for the selected brand or category.​


Launch the rationalization engine and build several scenarios​


Select among your favorite assortment scenarios and deploy for execution.​

The Process of Assortment Optimization
Can Be Broken Down Into 4 Steps:

Step 1: Segment the customers based on their shopping pattern

  • The Algorithm searches the common pattern among the customers by considering basket size, penetration, pack size/type, and selling price.
  • Identify behaviors that are similar, also refer to as customer types.

Step 2: Identify the favorite products for each customer type

  • The machine finds the most popular products in each customer type by using shopper’s basket or shopper counts.
  • This step provides an item list and data for sales forecast.
  • Calculate the proportion of customers who purchase each product, ranking them accordingly.

Step 3: Determine the proportion of customer type by store

  • Based on the output from Step 1, the machine can define various customer types based on their shopping patterns.
  • At this step, the machine determines the proportion of each customer type by store.

Step 4: Optimize the assortment

  • Once we get the customer type proportion, algorithm will work based on the favorite product list of each customer type.
  • Various constraints, such as the Number of SKUs, pack size, and variant, could be input at this step.
  • The system will select the combination that optimizes sales value, considering the proportion of each customer type and their favorite items.

The Benefits of Assortment Optimization Include

Optimal assortment planning
  • Adapt your range to the specific buying patterns of each shop's catchment area
  • Automatically manage your space & products constraints
Improved customer experience
  • Provide a personalized and relevant shopping experience.​
  • Higher customer satisfaction and loyalty at each store
​Time-saving and efficiency
  • Leverage machine learning expert algorithm to save 100’s of hours
  • Build several scenarios and choose the optimal one
Drive assortment rationalization as a collaboration project
  • Retailer & Supplier jointly defines their constraints​
  • Retailer & Supplier jointly monitor each new assortment’s performances

Liberate Cash Flow & Grow Sales with Assortment Optimization

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