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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.

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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

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.​

Simulate​

Launch the rationalization engine and build several scenarios

Choose​

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 1 Segment the customers based on their shopping pattern
Identify the favorite products for each customer type

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.
  •  
Determine the proportion of customer type by store
Identify the favorite products for each customer type

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

assortment optimal

Optimal assortment planning

- Adapt your range to the specific buying patterns of each shop's catchment area
- Automatically manage your space & products constraints

improve customer

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

Retailer Supplier jointly defines their constraints​

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


Simplify · Automated · Optimize
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