Find tenants that match the community's social interests.
As a retail property owner or operator, how can you drive success?
Ultimately, it all comes down to attracting the right tenants. If your tenants succeed, you succeed. Tenants succeed when they are in the right location to draw their ideal customers. As the operator, there are only so many variables you can control. One you can control is recruiting optimal tenants. To do this you need to be able to:
Everyday people share their personalities and activities openly on social media, much of it with location information included (aka Geosocial data). In aggregate, this data paints a dynamic picture of the personas and behaviors that make up a community. Tapping into this information allows businesses to understand and differentiate trade areas more effectively than simple demographic facts.
Providing and analyzing this data is the singular focus of Spatial.ai. The retail real estate industry is becoming more and more data-driven. There is a consistent frustration that traditional datasets can’t explain why some stores or restaurants perform better than others in similar demographic locations. The difference between those two locations is in the behaviors of the community and captured in social media data.
Identifying the social qualities that drive success or signal failure is helping retailers select sites where they match the community. There’s no better business in the position to play matchmaker between community and retailer than the property operator.
Further reading: The Essential Guide to Geosocial Data
A void analysis can be a useful tool to identify which retailers or restaurants appear to be missing from a trade area to aid tenant recruitment strategy. A void analysis is created by comparing how many types of any business could be supported by the population to how many are actually in the area to identify retail gaps.
Knowing that matching the right tenant to the right community is key to success, how can void analyses be improved?
Certain types of people create demand for specific business types. Whether it’s active moms looking for daycare, empty nesters enjoying weekday activities, or athletic fashionistas shopping for activewear. Combining analysis of the types of businesses that thrive in certain social atmospheres with data on the competitive landscape transforms a traditional void analysis into what we call a Demand Index.
Demand Indexing provides a quantitative understanding of the retailers and restaurants that are underrepresented in a trade area, and if there is sufficient demand in the community for them.
Here’s an example of how it works.
Analyzing Newport Pavilion in northern Kentucky, we first measure the social activities in the trade area (usually 1 to 3 mile radius). We find the highest social activities to fall into the following personas or behaviors:
Notably, there is a strong lack of the following behaviors:
Update: These are legacy segments. To view the latest generation of segments, Explore the Taxonomy.
We then run a nationwide scan of social data to identify areas with similar social profiles and identify the types of retailers that tend to be successful in those neighborhoods. For the Newport Pavilion, we found similar neighborhoods near Chicago, Houston, Denver, Nashville, and Seattle.
From there, we measure the likely demand for any type of business compared to the number of those businesses present and score potential tenant demand on an index from 0 (oversaturated in this market) to 100 (maximum demand/opportunity). A score of 50 means the market has a balanced supply of that type of retailer or restaurant.
If you were considering opening up a tattoo parlor in or near this shopping center, a competitive analysis combined with demographic data would tell you that there is no competition (closest one is over 2 miles away) and the right demographics to signal you should move forward. Our demand index using Geosocial data reaches the opposite conclusion. A lack of the right segments, like Addictive Personality (data shows segment to be a driver of tattoo shop success), causes the demand index to report a 57 demand score. 0 tattoo shops is perfect for this area.
Alternatively, the people in this specific community demand more salons and Mexican restaurants (demand indexes over 70).
Using the demand index, you may know exactly which tenants will be successful in your property. Sharing with them the results of the demand index can be a highly compelling approach to attract data focused retailers. Geosocial data also presents an opportunity to appeal to tenants that have specific customer profiles they prefer to focus on. There is an art and a science to retailers’ site selection process. Provide them with reports on the personas they find most appealing, like this one:
Many of our segments, like Trendy, are highly appealing to certain tenants. Sharing reports like this makes operators appear to be skilled local experts and helpful consultants.
Looking to include Geosocial data within your strategy? Download our sample data package or simply contact us to get started.