Optimize Product Offerings Using Behavioral Clustering

Consumers now have increased choice, and demand a shopping experience that is personalized to them. This means retailers need to stay ahead of the game and adopt a consumer-centric approach, which is equip to evolve as consumer tastes change.

Many retailers currently define store groupings on a manual basis, either at a national level or through utilizing top-down attributes, such as store, size, region or banner. While these approaches may promote efficiency in business operations, they fail to truly account for localized demand, leading to missed opportunities, consumer dissatisfaction and ultimately causing consumers to shop elsewhere.

Enter behavioral clustering.

Behavioral clustering moves away from traditional store clustering methods, and instead, uses product data to generate category level segmentation from the bottom up.

Stores and categories can be analyzed using granular product performance data such as historical sales, market data and forecast data. Analyzing consumer purchasing patterns across an entire chain enables you to group stores, which show similarities in demand. As a result, retailers are provided with a laser focus to drive targeted assortment, merchandising, promotional and pricing strategies, growing consumer loyalty and maximizing profitability.

Having the tools to conduct detailed cluster analysis in a matter of days means that retailers can efficiently create store groupings for different categories based on customer buying behavior. This proves useful for very fast moving categories such as televisions, washing machines and cooling Systems. The ability to refresh store cluster analysis at scheduled times throughout the year is a key function to behavioral clustering, and helps ensure a store's assortment and space remain in line with consumer demand.

Behavioral clustering gathers key insight into customer behavior at category level across online sales, as well as those made in store.

By understanding customer buying variation across attributes such as price and brand, a retailer is able to quickly realize significant differences in shopper patterns in physical stores vs. online. For click and collect services that source products from the most local store, utilizing shopper insight helps to improve the balance of assortment and inventory in line with demand.

Let's take a look at a category such as ovens and panels. Space allocation for this category, for example, can be standardized across stores by using the following approach:

  • Understanding the current space allocated across the store estate
  • Analyzing the unique sales for the category; looking at the number of unique SKUs sold during the month on average across both Ovens and Panels
  • Analyzing the efficiency of space bands by looking at the percentage of unique sales against the current space allocation

With effective and standardized space bands identified, retailers are able to quickly define the optimal space allocation. Furthermore, they are able to accurately allocate stores to the right cluster in line with demand, unlocking a number of significant benefits:

  • Improved understanding of customer behavior across ovens and panels
  • Improved assortment mix in line with identified customer demand
  • Improved inventory turns and savings across the store estate, as well as for specific categories
  • Improved logistics efficiency with more accurate replenishment
  • Improved customer experience in store

Using behavioral clustering enables retailers to gather key performance analysis on a granular level using a combination of bottom-up and top-down clustering. This key insight allows retailers to make informed decisions based on consumer behavior, instead of pre-set assumptions or constraints. Using this optimized approach leads to better customer experiences and more sales.

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