Category Management: Create Strategic Retail Store Clusters

Category Management TrainingThere is an opportunity for Retailers to cluster (or group) their stores together based on consumer-focused attributes, instead of using some of the more traditional clustering approaches like store size and territory.  There are different demographics within each local community, and organizations need to understand the demographics in order to better meet the needs of a larger range of people.  This consumer-focused store cluster approach enables retailers to quickly identify clusters of stores with similar demand patterns, which also allow the retailer to focus on the most important target consumer segments for them.  It is also cited as one of the biggest Category Management opportunities in the industry.   Below are the steps to move to consumer-focused store clusters.

Define target consumer groups. 

Many retail and supplier organizations focus too much on the “average consumer” performance – and really – does the “average consumer” even exist?  How do you combine all these demographic variables ranging from an individual or family’s lifestage, their ethnicity, their generation (such as the Baby Boomers), and/or household income levels, to come up with an average consumer?  The opportunity is to better define the target consumer groups (which we won’t get into in this article).  Once this is done, the next step is for retailers to cluster their stores in with the target consumer that they have defined.

Cluster stores based on target consumer groups.

Category Management Training Many retailers already cluster their stores – but some in a much more traditional way.  Traditional clustering started many years ago, as retailers saw the need to look at their business based on store groupings – particularly for retailers who had hundreds of stores to manage.  Stores were ranked in terms of sales and grouped, usually by a percent of average sales across all stores.  The “A” stores were the stores that performed at a certain % above the “average store”; “B” stores were within a mid-range index of the average store, and so on.   “A” stores were the most important stores, because they represented the largest volume for the retailer.  This is an antiquated way for retailers to be looking at their business, because it fails to appreciate the consumer’s perspective.

The way that Retailers can move away from this more traditional approach is to change their store clusters to focus more on the consumer by considering some of the following variables:

  1. Similar geographic locations and climate conditions can have a big impact on a variety of categories, based on something as simple as the climate within a region.
  2. Store size is another physical attribute that can influence store volume significantly.
  3. Attributes are based on the consumer – things like demographics, for example income, age or ethnicity.
  4. Consumer purchase behavior is another attribute – loyalty can be measured – so a retailer may want to cluster their most loyal or heaviest buying consumers.

Each of these attributes can be significantly different at a category level, which in turn drills much deeper into understanding that all important consumer.  The thought of clustering at a category level can be a daunting task. Approximately 20 percent of a retailer’s categories generate 80 percent of its revenue.  Therefore, retailers should focus on strategic categories that significantly impact business. This can be driven by categories that are assigned a destination role, combined with a determination of which categories are dynamic, variable or static.  For example, a grocery retailer may view a category like “fresh meat” to be dynamic, which would benefit greatly from localized assortments.  A more basic category that may not vary as much on a store-by-store basis may be something like oral care.

  1. A combination of robust consumer data provided by the supplier and a retailer’s store POS data will help to create some of the basic consumer clusters.
  2. Knowledge of how to create different types of clusters using multiple attributes,  calculations like demand indexes, product demand indexes and sales indexes to create store clusters based on demographics, and an understanding of geodemographics and location intelligence.

Store ClustersOnce those clusters have been determined, the new insights and views of the data will help the Retailer to make business decisions based on key performance indicators applied to the different store cluster types.  This approach to store management by a category manager allows for a more focused approach and breaks down the stores in a retail chain by their potential.  Rather than trying to begin this project on a holistic level, it is important to start small, perhaps completing a pilot test in one or two categories, evaluating the results, and evolving from there.

Click here for a complimentary download on running control store tests.

 

“Store clustering” no longer means combining stores with similar volume and square feet – it means focusing on the unique needs of consumers based on unique demographics.  The insights from this type of work are crucial to the future of Category Management.

Category Management Knowledge Group has an accredited course entitled “Store Clustering Through Store Level and Geodemographic Data and Tools” that will give you the knowledge you need to start creating more strategic store clusters.

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