Let’s start by stating the obvious: during the last 3 months, Customer behaviors have drastically changed. While each market is different unique, there are some common traits: shopping frequency has decreased, basket value has increased, and the components of the basket has shifted towards more essential products.For retailers, it means that the ranges and assortment they previously had do not deliver the same results today. Some items sell much less, some items selling more than before don’t have enough shelf-space, inventories grow and, margin decrease.Our client was also facing such trends. And they reacted quickly, taking the risk of disrupting stores during a short period to rebuild a more adapted and more profitable offer to their Customers. They decided to “Rationalise” the assortment of all their stores and formats.
An assortment rationalization can deliver many things. So before starting, we had several working sessions with the Client to precisely scope what were the outcome.
In our case, it was a decrease in the number of SKUs to:
Once the Project Objectives were final, the second round of questions was focusing on whether we should focus on strategic categories or englobe the complete store. There are pros and cons to each scenario.
In our case, the decision was to review the all store. Yes, there was a risk of potentially having to review the space allocation afterward, but we agree with our Client that it was an excellent opportunity to establish a solid basis for further improvements.
In other cases, other rationalization objectives could also have been set up, For example:
Before starting with the work, itself, we hold it a bit longer to establish with the Client what should be the business rules the rationalization should follow.
The objectives of the Business Rules go beyond the rationalization: they would also impact how Buyers and Category managers would manage the assortment after the rationalization. Some of the critical rules that were decided are, for example:
It was now time to work with the data. Thanks to the Cleary defined project objectives and the translation of the business rules in metrics, it was easier to identify which data to extract.
To save time, we organized the data to the selected level of the Merchandise hierarchy where the item selection metrics would apply.
In addition to the development of the supporting formulas, we also ensured to build in filters to guarantee that:
After having pressed the “Run” Button, following the programmed business rules, the program delivered its suggestions of items to keep and items to deleted (at sub-category level in the case of our client).
A thorough reviews of the formulas, business rules (with a specific focus on strategy items belonging to the Housewife Baskets and Big Spender Baskets) as well as the use of common merchant sense (if there is only 1 item left in a sub-category whose sales are minimal, should we keep the category?), we were ready to send the Assortment proposal to the Client
When we sent the working file to our Client, we ensured that we built in a validation function that informed when the Category Managers final choices were over the maximum number of items per bay.
In that way, the Client’s Teams were totally independent in their choice with clear visibility of what their decisions would entail.
The one-shot exercise was necessary, and it was successful only because of the wonderful execution done by the Operation Teams.
Such a complete exercise can not be repeated too often, and still, assortment rationalization needs to be performed regularly as Customers Shopping Behaviors keep on changing.
So, we set up a simple monthly schedule, articulating what we called Super Core Categories (that represent more than 50% of the sales) and Core Categories.
And for each category, the work is done across all formats and store size: not only can the Category Manager have a consistent vision and tactics across formats, but suppliers can also follow and achieve the same level of consistency for their Brands’ ranges.
As the tool is built, 80% of the work will be automated.
We marry retail expertise with people, processes, and data to support Leaders and their Teams in their Optimization Journey, working on the 4 Components of their Retail Operations
Hypertrade is a retail tech company in Category Management and Shopper Behaviors Analytics. Manufacturers use our platform to manage their multi-retailer data sets and to automate their category management and sales analytics. Retailers use our platform to improve their Commercial offering, collaborate with their suppliers and Engage their Shoppers. Building on Sales, POS & Loyalty data, some of the things we fully automate are:
• Brand& Category Score card
• Brand automatic Diagnostics
• Range Optimization
• Promotion Planning & Forecasting
• Shoppers baskets analysis