MULTI-BILLION DOLLAR SPECIALTY RETAILER:

Senior management needed to free up cash tied to slower moving inventory and defined a goal of $200M in cuts. They needed process to ensure transparency and gain organizational buy-in to determine necessary cuts without jeopardizing sales. Previous efforts, primarily focused on GMROI measurements, had stalled and results were spotty.

Understanding item selling information paired with customer information revealed insights they hadn’t seen before—specifically the impact on different customer cohorts. This analysis married customer data and localization opportunities to help bring key customer insights to the decision table. Previously, the typical answer had been to ‘cut the tail’ and move lowest volume SKUs from stores to web. This time, they looked at items through a customer lens to understand which items meant the most to their best in-
store shoppers. This helped ensure big-spending customers remained satisfied.

They involved stores teams in the conversation to get feedback and their buy- in. This proved itself to be imperative to get the organization to adopt a relatively large amount of inventory change. Transparency through the process was key for adoption of the findings both in the home office and in the field.

A common data lake was created using Alteryx and SEQL servers to wrangle data. Structured data like loyalty, credit card, product selling information, store productivity and margin per linear foot were all added. And then unstructured data like IRI and Neilson were added too. The pivot point was in how the data was standardized based on how the customer looked at the product. For example, leashes might have had basic attributes like red and nylon. This new process added attributes that were important to their customers like large dog versus small dog. Customer data had been infused into previous efforts, but using this data was inconsistent and sporadic—company wide adoption was what created consistent usage for the future.

Attribution was created as a dynamic model so future changes to customer behavior could be quickly adopted. Different factors were each weighted differently depending on the business. Flexibility was built-in so as the process generated results, the planner could adjust weighting for future line reviews.

With a more flexible and agile process as well as insights to better help see around corners—the results were outstanding. Over $250M in poor selling inventory was dialed back, which helped grow the topline by 10%. Line reviews also shifted from a seasonal approach to one based on overseas versus domestic lead times which enabled all businesses to move at their own speed.

The project started with one area and quickly spread to all non-services areas in the store. The change model was the three- step approach that allowed for negotiation and art to blend with the science. Plus,
there were conversations started to defend the new POV

MORE INSIGHTS

THE OPPORTUNITY Create a planning function for assortment and inventory..

OPPORTUNITY Create a planning function for assortment and inventory that..