Plan Ahead

Sales Forecasts

Improve your demand planning, cash flow management and new product launches with Hypertrade's retail expert machine-learning-driven solutions.

Learn more

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!

Ariane, A Practical Answer to Commercial Challenges

Continuous improvement of the commercial offer

Product Challenge
Global performance management
Piloting ranges’ efficiency
Granularity down to each sku in each store
Business Challenge
Automatic diagnostics and scorecard
Promotion Plan management
Integration of algorithms experts in category management
Efficiency Challenge
Data Collaboration with suppliers & Double segmentation
Automatic generation of supplier analyses
Global performance management
Learn more ➔

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

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.