Using Retail Segmentation to Find Your Brand's Best Co-tenants
As retailers continue to search for new ways to evolve with the changing retail landscape, retail segmentation represents an innovative solution not to be overlooked. But what is it and how can it help a brand with site selection and performance forecasting?
In short, the concept of retail segmentation means to group different retail areas based on the types of stores that do business within the area. It is not uncommon for retailers to co-locate with specific brands to form retail clusters and generate a retail synergy. Retail segmentation uses an ensemble of unsupervised clustering algorithms to quantify these retail relationships to better understand which brands locate next to each other in these different retail ecosystems.
How Retail Segmentation Analysis is Used
Retail segmentation’s unique data insights allow a better assessment of a site and the immediate surroundings of the stores when creating real estate models. This co-tenant analysis can be used to identify and score potential locations to help optimize opportunities.
In forecasting models, the results from retail segmentation can be incorporated to give additional pieces of information for the model to learn about the store’s performance. Retail segmentation analyzes the direct co-tenants. This allows data scientists to go beyond understanding only the market quality and lets them focus more on the site attributes to zero in on what’s going on directly next to a brand’s stores.
Co-location insights are used to select brands to track. Whether they become new, potentially indirect competitors or synergy brands, retail segmentation allows you to identify how other retailers affect your brand. From a marketing perspective, retailers can look at favorable co-locators as cross-promotional opportunities, since they are already co-locating together and can share similar customers.
Once retailers identify the general trade area they want to locate in, retail segmentation can help them choose between two retail hubs inside that trade area to maximize the retail mix effect that is ideal for their brand.
You’ve heard of demographics, and maybe even psychographics, but what is ShopoGraphics?
is a proprietary retail segmentation dataset from CBRE | Forum Analytics that uses advanced machine learning to categorize over a million retail locations into 41 distinct retail segments based on consumer shopping habits and co-tenancy. By identifying the segments where brands currently locate, we can help clients understand their most successful co-location profiles and identify new opportunities for expansion. The results can be integrated into a mapping platform or conducted as an independent analytical study.
Identify Ideal Co-tenants
ShopoGraphics helps identify the typical retail clusters where your brand usually locates and what kind of retail is generally present in those clusters. The analysis also shows in what retail segments your stores earn more so you can find which retailers might be more favorable as co-tenants. ShopoGraphics goes beyond the individual brands and helps to identify groups of similar brands that you might want to consider for co-location.
For example, a ShopoGraphics analysis conducted for a consumer services client found the client’s most frequent co-locators are fast food and fast casual. However, according to the analysis the client’s top-performing stores are located more often next to specialty food, real estate, and fast casual industries.
Additionally, retail segmentation analysis can determine how your competitors tend to fit into different retail ecosystems. You can then choose to replicate or avoid similar retail clusters where your competitors tend to locate.
Similar to finding ideal co-tenants, retail segmentation helps to identify retailers around your lower-performing stores. By analyzing retail clusters of your lower-performing stores, you can spot patterns of your possible competitive co-tenants.
In addition to describing retail clusters and understanding how brands co-locate in them, retail segmentation allows analysts to correlate store sales to presence in the same retail zones as various brand categories. This helps provide a scientific analysis of which retailers have a positive or negative effect on sales.
It can also be determined how often you locate next to different retail industries. There may be industries that have a negative correlation to your sales, but you don’t locate next to them very often.
The Evolution of Retail Segmentation
The initial goal of ShopoGraphics was to focus on retailers. As it continues to evolve, this retail-only view will be augmented with other data. Some non-retail traffic generators like universities, schools, public transit and others are included in the retail segmentation. There are other traffic drivers like stadiums, museums, tourist attractions or large office buildings that could have an influence on a retail ecosystem in the area and may be available as ShopoGraphics progresses.
Lastly, most of the segmentation is built on large chain locations. There may be small business retailers in almost every industry that might affect the composition of retail clusters but that may not appear in our analysis. The variability of one of those retailers and lack of consistent information nationally prevents their being included in some of our retail analyses.
Identify and Prioritize Markets to Target
Without heavy analytics, retail segmentation can provide an initial set of insights to identify similar retail clusters that your brand tends to thrive in and how those clusters are spread out in different markets. Retail segmentation allows analysts to quickly summarize what kind of retail is prevalent in a market to help understand how you can fit into it.
Contact us today if you are interested in learning how retail segmentation can be applied to your brand strategy.