Assortment Optimization

Build Robust In-Store Merchandise Assortments

Celect Assortment Optimization helps retailers build robust assortments specifically optimized for the foot traffic in each individual store, resulting in improved merchandise forecasting, rapid category growth, and increased inventory turns. Filter, analyze and make sense of an overwhelming amount of data from disparate sources.

The result is an accurate model of future buying patterns and behavior, helping you make better decisions while leveraging your honed intuition - across the merchandise planning process.

 

SOLUTION OVERVIEW (pdf)    CASE STUDY (pdf)

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    Colored treemap boxes indicate recommended level of investment for each store.
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    Hovering over the "Orland Park" store, Celect recommends increasing receipts to 2.64M.
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    Clicking on the Orland Park treemap box pops up a menu so we can Split By: "Department".
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    Now we can see all Departments at the Orland Park store.
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    In this case, we are interested in seeing which "Salon Shoes" brands we need to address.
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    Only Michael Kors and Coach brands have been offered so far. Celect recommends Pliner as a new brand. 
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    Clicking on the "Details" tab reveals any change in Receipts and Revenues. Receipt constraints can be adjusted in the left column.

Improved Merchandise Planning

Accurately predict the optimal vendor or product matrix for the current or upcoming seasons. Ensure your merchandise buys exceed expectations, to acheive maximum sell-through.

Increased Inventory Turns

Allocate the right product to your stores, based on what your customers want (and prefer), and move more inventory. Identify store locations where product quantities should be increased or decreased, based on projected demand.

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Rapid Category Growth

Easily see which categories are primed for growth, and which are being overallocated. Make changes dynamically based on your own constraints, limitations, or goals. 

Enhanced Customer Loyalty

Offer a localized product assortment, based on your customer's preferences, for happier customers and higher in-store revenues. Move beyond traditional clustering methods to truly understand what your customers want.

Ready for more?

Request a Demo