Plan Ahead

Sales Forecasts

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

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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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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

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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 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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