We combine and use all available data sources to guide customer-oriented management of the product range. With this information, we can optimize your range to meet local demand requirements and make the best use of the available store space. The same application can cover cluster, chain and store level design. Clustering stores in terms of selections and placing the stores in the map view provides detailed information on how demand varies across different market geographies.
Optimizing selections gives us detailed information on what products and package sizes should be used to meet local demand. With this information, it is possible to accurately plan the use of shelf and store space. Designs can be carried out either using CAD software that supports 2D and 3D modeling, or the entire space usage process can be transferred to virtual reality design laboratories where customer behavior in the store can be planned and tested in a virtual reality environment.
The customer-oriented selection management process is typically repeated several times a year in order to be able to manage changes such as seasonal variations and changes in the range on offer. In this way, it is possible to maintain up-to-date selections at all times.
The ultimate goal of the customer-oriented selection management process is to make the right selections available for each store. The store-level compliance of the recommendation selections can be implemented and the implementation monitored with the aid of image recognition, for example. This helps guarantee the success of the selection management process.
By taking advantage of the latest innovations in range management applications, a truly customer-centric range management process created, at the same time supporting easy collaboration between different users of the process and third-party suppliers. It enables selection management to be transformed into a strategic differentiation factor and offers a holistic approach to the various stages of the selection management process, such as clustering, creating space-efficient selections, creating shelf images, and other stages of the process.
By entering store clustering data that takes purchasing behavior into account in the selection management process, retailers can optimize store grouping and design more relevant item selections at the level of a particular format, store, department, and product group. Sales, margins and customer loyalty can be increased with selections based on customers’ purchasing behavior, taking into account the use of space. These customer-driven ranges are developed using information on price sensitivity, product equivalence, sales, profitability, order of importance of selections, and many other kinds of information. The result is selections that fit the available space.
We combine and use all available data sources to guide customer-oriented management of the product range. With this information, we can optimize your range to meet local demand requirements and make the best use of the available store space. A single solution can handle cluster, chain and store level control.
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