Using Assortment Recommender Results

The assortment recommendations provided within AA aim to achieve the maximum possible value for the chosen target sales measure, such as profit or revenue. Assortment planners may use the results of this process while preparing for the next selling season or fiscal period in order to adjust their current assortments based on their business strategy. Planners may choose to accept the recommendations as-is, or choose a subset of changes in order to see how that will affect their financial targets and assortment plan. This modified assortment can then be provided to AA and reprocessed in the future to refine the recommendations.

The following example describes the workflow of an assortment planner (Anne) working to improve sales in the Coffee category.

  1. Anne starts with the existing assortment of coffee items available in stores, along with current performance measures describing how each item is selling. She knows that certain items are performing poorly and wants to mark them for removal from the assortment. She also has a list of new items that can be added to the assortment. All of this information is provided as inputs to AA prior to performing the optimization process.

  2. After AA has been provided with the assortments and lists of possible changes that Anne wants to make, the system will schedule and execute the optimization process using pre-defined business rules (such as the maximum number of items that can be added or dropped by the system).

  3. Knowing the process executed over the weekend, Anne logs into AA on Monday morning to review the results. She selects a location and her Coffee assortment in the UI prompts, and then begins to analyze the recommendations.

  4. AA has selected three items in the current assortment to be dropped and another four items that should be added. Anne first looks at the dropped items and compares them to the other items she was thinking about removing. She notes that the dropped items are not the worst-selling ones she had chosen, but AA shows them as contributing very little to halo effects in other areas, so they may have very weak market basket affinities that contribute to their removal.

  5. Anne next reviews the recommendation for items to add to the assortment. Of the four items that AA has chosen, three of them have significant halo effects driven by the market basket affinities. Anne is not as sure about the last item, so she selects it and clicks on the Cannibalization button. She notes that the item has relatively low substitutable demand from other items, suggesting it is not very similar to anything in her current assortment. This makes it a good candidate for addition, as it can bring in new demand that the assortment may not have today.

  6. Anne decides to accept all of the recommendations made by AA for this assortment, exports the results to Excel for later reference, and then exits the system to make the necessary changes to her assortment plan.