How well do you know your customers? You may have figured out their age, income level, and household type, but do you know what they tweet about? Can you segment them based on what they share on Instagram? Can you tell the behavioural differences between two middle-aged, affluent females that look quite similar demographically, but whose lifestyles are quite different?
As consumer behaviours become increasingly communicated online, retail real estate decision-makers have a wealth of information that has become available. Brands who capitalize on understanding what their customers are doing, thinking, and posting about online uncover new opportunities for optimizing their site selection strategy.
A critical part of site selection comes from a foundational understanding of your target market. As such, customer profiles are frequently used to help with growth and marketing strategies for retailers. One of the go-to methods for customer profile creation is segmentation. The great thing about segmentation data is that it provokes an image that most people find familiar enough to say yes/no, my customer fits (or doesn’t fit) that type of person.
ADDING COLOUR TO THE PICTURE
While traditionally segmentation has been based on consumer surveys and demographics, now retailers, brokers, leasing teams, and marketers alike have another tool in their toolbox; segmentation based on location. It’s called geosocial data.
Geosocial data combines social media data with geolocations. This gives companies deeper insights into a community’s social DNA: how do people live, work, and play in a given location? Using Spatial.ai’s leading-edge technology, you can draw insights from public conversations happening on social platforms like Twitter, Facebook and Instagram. Those insights can be leveraged in PiinPoint to give you a fuller picture of your customers.
With the changing retail landscape today, it doesn’t hurt to evolve along with it by diversifying your toolkit and incorporating new insights for site selection. Especially as experiences that are shared online, and social-media-worthy moments can help to put a brand on the map.
Drawing on the preferences, ideas, and positions of consumers via geosocial segments offer a powerful way to further illustrate the characteristics of a community, beyond what survey data might be telling you.
GEOSOCIAL FOR SITE SELECTION
One of the most straightforward ways to apply geosocial data for retail is in site selection. Let’s look at how geosocial segmentation helps us better understand the relationship between Freshii’s Canadian store locations and geosocial data.
Example 1: Finding Patterns Across Networks
A big part of site selection is understanding what factors to optimize around. There are lots of statistical and technological tools like machine learning and AI to help retailers understand what their factors are for success.
Using Geosocial segments offer a whole new perspective on location-based factors that can be used for assessment. Even on small networks, you can identify trends based on consumer preferences and behaviour.
Let’s look at Freshii as an example. We took a look at 282 Canadian Freshii locations, to see which segments were top indexing for the areas their stores are located. Looking at just the no.1 highest indexing segment (which average at 93% higher than other geographies) there were 6 that stood out as being most significant.
Depending on how well you know the brand, some of these may come as a surprise. Others, not so much. Here are some insights we can pull from this:
LGBTQ Culture: LGBTQ culture tends to index highly with urban areas, so it doesn’t surprise us that there is a definite correlation here between the segment and Freshii’s store locations, mostly in urban markets. Freshii is also known to have supported and sponsored LGBTQ initiatives, such as the San Diego pride parade.
Coffee Connoisseurs: The same type of people who appreciate fine coffee, also appreciate Freshii’s menu of food and drink. Some Freshii stores have actually already taken advantage of this, with locations like the one in Uptown Waterloo serving a local roasters product, Smile Tiger Coffee. Offering local brews is a great tool for drawing in the customer. Another consideration here from an operational perspective could be to consider keeping stores open earlier in order to cater to a coffee-based clientele.
Film Lovers: Who would have thought that film buffs tend to favour food from Freshii? This segment is particularly correlated with millennial-aged consumers.
Pieces of History: This behaviour is particularly associated with areas where there are high levels of people in a student age range. These people are commonly in close proximity to the Freshii network.
Fitness Obsession and Fitness Fashion: Not only one, but two fitness-related segments link strongly to the Freshii network - which makes sense given a health-conscious menu. These people take fitness to the next level, making sure their personal style and brand communicates their fitness goals, and by setting aggressive PRs for their physical workouts.
By analyzing a retail portfolio in this way, you can add to your intuitive understanding of what segments correlate with your store’s physical network. These concentrations can be viewed across any geography on the map in PiinPoint, or you can use PiinPoint to identify at the property by property basis, which segments index highly with an area.
You can take this one step further by using geosocial in predictive modelling, to understand how these segments impact your store success. Contact email@example.com for more information.
Example 2: Augmenting Existing Site Criteria
Geosocial Data can also augment existing criteria being applied for site selection, offering a new perspective or insight into dynamics that previously were not visible.
Let’s look at these two sites in the Greater Vancouver area, for example. Most retailers like Freshii will have set requirements by market for their growth, meaning that stores require a certain number of people around each location, they must be a minimum distance away from other Freshii locations, they may try to lease near complementary brands, and cater toward a certain demographic, etc. Based on all these criteria, we can assume the Candidate site fits.
Judging by the demographics of our Existing Freshii and Candidate Freshii, the existing site matches our existing locations’ demographic make-up very well; with Total Population, Median Age, and Median Income values all closely aligned.
While the sites may appear the same on the outside, they have distinct community traits that are only visible using Geosocial data. In this case, our Coffee Connoisseur segment is actually far less concentrated at the Candidate location than our existing Surrey location. If it’s an important variable for the network, a Medium concentration (52%) may not be high enough to understand how many customers could be captured:
Example Three: Discovering Segments that Drive the Area’s Geosocial Profile
While Coffee Connoisseurs may not dominate the community profile, you can create detailed trade area reports to quickly understand what other segments are occurring nearby that could influence a store’s viability.
In this report here, we see the top segments are “Smoke Culture”, “Gratitude”, and “Artistic Appreciation”. Albeit interesting, they don’t match the segments we know to be most common for Freshii locations.
With the amount of data available to retailers today, accessing a variety of perspectives and data sources has become standard to uncovering truths about one’s business.
When we add geosocial data to the mix, we’re able to see beyond the demographics and get to know the people that hangout, live, work, and play around any location. We learn that these communities have unique social behaviours, and can make decisions for the store’s location, operations, marketing, and sales as a result.