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Winning assortment starts from understanding the customer and changes in the demand. By default, it means local customers, local demand – and local competition. And hence local stores.
That’s a far cry from the present cluster approach that’s driven by summarized data and averages.
Our optimization algorithm calculates your next assortment in minutes, taking into account local demand and available space, enabling you to sell what people want to buy in the right volumes.
Conventional demand forecasting is capable of forecasting unit sales of existing assortment, guiding your stores’ replenishment cycles.
However, it’s blind to actual customer demand by location.
Demand-driven retailing takes a completely different approach. Forecasting each location’s demand potential it guides you in managing your assortment. Simply put: it also tells what products you should add to your assortment to better serve the local demand. And thus improve your sales performance.
Assortment optimized to local demand speaks volumes to your customers: it shows that you're listening and understand their needs.
Avoiding out-of-stock situations enhances your customer loyalty. Nothing makes a customer jump ship faster than empty shelf space.
With all the macro space data, factual category space data, your tactics and business rules in one easy-to-access place you gain the capability to efficiently manage your stores.
All the key data elements are stored in one place and visualized as Digital Twin of the stores allowing an efficient approach through the entire process of strategy planning, analyses and in-store implementation with digital planograms and instructions.
The benefits of moving from once siloed and manual processes to data-driven, coherent and transparent workflow will empower everyone in your team to focus their time on innovative scenarios.
An efficient assortment means fewer missed sales opportunities, less capital tied to over-stocking your products, and improved customer satisfaction.
Assortment in Space’s optimization algorithm calculates the optimal assortment to each individual location. Performance improvements have so far been up to 24.6% margin increase.
Assortment in Space is a demand-driven retail analytics solution designed to optimize your assortment to local demand and factual store space.
It offers an end-to-end process that takes you from demand potential forecasts to executing your assortment and space scenarios in stores.
The solution is designed to manage and execute at the individual store level as well as chains of stores.
Looking around us we see that consumer markets can no longer be easily segmented by demography, geography or income-based categorizations.
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 stock.
This fragmentation of consumer demand can only be addressed with solutions that understand demand by trading area.
Category shelf space varies by store making it challenging to optimize the assortment and shelf space in stores. Therefore, having the true space data in digitalized format is essential for advanced store-specific assortment and space optimization.
Assortment in Space leverages Artificial Intelligence, Machine Learning and state-of-the-art mathematical optimization models 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.
Assortment in Space enables you to digitalize your store space and manage both macro- & micro-level assortment and space, create your assortment and space scenarios based on your local shopper needs and your category assortment strategies.
It enables you to execute unique double-loop optimization: First optimizing your store-specific assortment based on each store’s local demand. Secondly, combining optimized store-specific assortment with each store’s true shelf space, optimizing the space for each SKU based on your space management tactics and rules.
As the name implies, it helps you to optimize assortment to local demand and factual store space.
Houston Analytics has a separate solution to find optimal store locations called Store Network Optimizer.
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.