A starting point for shopper understanding.
The many changes in our competitive Canadian landscape have driven a need to adapt the category management approach to become even more collaborative, flexible, forward looking and shopper centric. A first and necessary step towards shopper understanding is for retailers to group their stores beyond store size and volume and expand to consider shared demographics and customer purchasing patterns.
From here, the opportunity is to create cluster-specific assortments, shelving, promotions and pricing strategies that meet the different shopper needs. Retailers who try to move to shopper-focused solutions will be limited in their approach until they cluster their stores to reflect their shoppers and their needs.
Store clustering and geodemographic data analysis is really the starting point for understanding shopper marketing, or shopper insights. Shopper marketing refers to all manners of influencing the shopper from when the consumer perceives a need and is motivated to start the path to purchase, to creating a shopping list or initiating a shopping mission, to researching a prospective purchase, to considering which retail avenues to shop and choosing a store or web site to shop and ultimately looking at the product choices they present, to making a purchase decision. It also includes all research associated with understanding the shopper path to purchase.
Retailers need to build a new set of strategies for store clusters (groups of stores that have similar shoppers, performance, and traits) or even for unique stores, and then assemble the right resources with the right ideas and competencies to take advantage of the different opportunities. They need to consider the types of consumers that they are trying to get, and how they will meet their needs. Are their target consumers large families with young children; seniors; the environmentally conscious; ethnic consumers; single parents; health-conscious consumers; or consumers with lower income levels?
Store clustering can take on new value as retailers look to localization to differentiate themselves and improve performance. It also helps them to define their target consumer within different store clusters, based on the differences in who is shopping in their stores in each cluster.
When deciding to move to store clustering beyond store groupings, you need to identify and implement a clustering approach that is right for your business — without the need for new systems or major organizational changes. This will limit your investment, add less complexity and give you flexibility to make changes and
adjustments as your store clustering approach evolves. Leave the huge investment for later.
There are different types of store clustering, starting with the least and moving to the most sophisticated approaches.1.Store Grading:
Store grading 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.
Usually, stores were ranked in terms of sales and grouped, usually by a per cent 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. Retailers can also take their store grading approach one level deeper; retailers can create category-specific segmentation. Different categories get their own clusters of stores, using the same “A”, “B”, “C” methodology, but at
lower classification within the store.
This is an antiquated and outdated approach that does not consider the shopper and thereby limits the retailer’s ability to focus on shopper. But there are still many retailers who continue to cluster their stores in this limited way. 2.Multiple Attribute Clusters:
The next level of clustering gets retailers looking beyond volume and store size. Some attributes may be more physical — like store size, geographic locations and climate conditions. Other attributes are based on the consumer — based on consumer purchase behaviour like loyalty and conversion. Consumer demographics can also be used when store clustering, for example based on income levels, age or ethnicity with a focus on the most loyal or heaviest buying consumers.
Once these attributes are defined and are measurable for each store, store clustering can be done based on these multiple attributes. Because each of these attributes can be significantly different at a category level, store clustering can also be done by category for the most important categories for the retailer. This
drills one level deeper to understanding that all important consumer. Attribute clustering 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.