Get assortment optimized to local demand in minutes

How collaboration between human and AI gets things done fast, effectively, profitably – and above all – satisfying the local shoppers

How to satisfy the local demand with an optimal assortment


A growing number of SKUs to choose from, limited shelf space, heterogeneous store characteristics, and the increasingly fragmented customer demand put pressure on retailers to satisfy the local demand.

With real-time access to all mediums, consumers have become more specific and more demanding as far as the products they want to consume, their qualities and prices.

Consumers of all ages and backgrounds exhibit brand recall and top-of-mind instead of merely reaching out for the lowest price or what their local stores happen to stock.

Success can no longer be achieved from mere value chain efficiency. Size class-based or cluster assortments aren't enough to keep shoppers loyal.

Demand-driven assortment optimization guides you to carry products that people want to buy

Retailers can satisfy the local demand by finding an assortment for each store that is optimal from the point of view of demand in its trading area.

Demand-driven retailing forecasts each location’s demand potential guiding you to manage assortments.

That's the demand side, the full potential of each location to which the optimization engine is optimizing each assortment: Delisting long-tail SKUs, identifying and listing new SKUs that shoppers want, optimizing macro space  for both shoppers' convenience and the optimal category allocation.

Retailers applying demand-driven assortment optimization can enjoy over 20% improved profits, simultaneously gaining significant time-saving in every step of the process from planning to store implementation.


Increase your margins with demand-driven assortment optimization


An efficient assortment means fewer missed sales opportunities, less capital tied to over-stocking your products, and improved customer satisfaction.

Assortment optimization algorithm calculates the optimal assortment to each individual location. 

Assortment optimization 

Store space sets the boundaries for assortment.

With the macro space data, factual category space data, your tactics and business rules place you gain the capability to efficiently manage your stores.

All the key data elements synchronized to an entity allows an efficient approach through the entire process of strategy planning, analyses and in-store implementation.

The benefits of moving from manual processes to data-driven, coherent and transparent workflow will empower everyone in your team to focus on more value-added things.

Benefits of Assortment Optimization


Short Term: sales and/or margin uplift by optimizing assortment to local demand delivering the optimal performance.

Long Term: new process and tools that minimize the time needed to assortment work, freeing up time for innovative category development. 


Long Term: macro-level space planning to optimize store space to further maximize sales and customer satisfaction.


Short Term: optimizing Net Working Capital via narrowing the long tail of SKUs.

Long Term: better view of store coverage and utilization of customer demand improves the negotiation power.

Download the case study

How to set prices to address local demand and tackle competition


View Pricing optimization

Frequently Asked Questions  


What is demand-driven assortment optimization?

Demand-driven assortment optimization focuses on the demand potential by trading area, guiding the optimization towards localized assortment according to retailer’s tactics and business rules.


How demand-driven assortment optimization differs from supply-driven assortment optimization?

Traditionally retail chains have focused on the supply side, aiming to increase value chain efficiency whilst maintaining low, steady margins. Supply-driven retail analytics has its root in logistics analytics, aiming to optimize the flow-of-goods based on historical averages.

Demand-driven retail analytics on the other hand focuses on the demand side, the shoppers by trading area, aiming to optimize assortment to meet the full local demand potential.

What is Assortment optimization?

Assortment optimization is a state-of-the-art, service to optimize your assortment.

It offers an End-to-End process that takes you all the way from sales predictions to executing your assortment scenarios in stores.

A Modular process is designed to manage and execute at an individual store level as well as clusters and chains of stores.

What data is needed for Assortment optimization?

Assortment optimization leverages data from different data sources. The data typically includes a store, shelf, module, loyalty card, shopper, POS, product, etc. information which will be synchronized and enriched for the Assortment optimization data model.

What does true optimization mean in Assortment optimization?

Assortment optimization leverages Artificial Intelligence, Machine Learning, and state-of-the-art mathematical optimization model and predictive analytics - ensuring you meet your local, store specific, shopper demand according to your strategy. You do not need to react to the market – you’ll start to drive it.

How does Assortment optimization work?

It enables you to execute optimize your store-specific assortment based on each store’s local demand. Secondly, it enables you to combine optimized store-specific assortment with each store’s true shelf space and optimize the space for each SKU based on your space management tactics and rules.


Which technologies have been used as part of the Assortment in Space solution?

A mix of commercially available and open source technologies has been used. IBM Decision Optimization Studio (CPLEX) has been used for optimizations and IBM SPSS Modeler and IBM SPSS Collaboration & Deployment Services for predictive analytics. As the data warehouse, IBM DB2 Enterprise Server has been used. Alternatively, Oracle Database or Microsoft Database can be used.