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Recently, a new innovation has taken the advertising world by storm: retail media networks (RMNs). These advertising platforms allow third-party brands to market to retailers’ customers through the retailers’ various sales and marketing channels. And while companies like Amazon that pioneered the trend focused on offering their e-commerce channels to advertisers, a growing number of businesses are discovering the vast untapped potential of their brick-and-mortar fleets.
Indeed, the growing embrace of omnichannel marketing has contributed to a blurring of the line between on- and offline shopping experiences. And many large retail chains still receive most of their visits offline, which means that retail media networks that don’t leverage the potential of the retailer’s store fleet are missing out on a massive advertising opportunity.
And like any other advertising platform, a retail media network needs good data to function. Retailers’ own first-party data can provide rich and valuable insights. But to truly unlock the potential of media networks, retailers and advertisers also need comprehensive location intelligence metrics, which offer greater visibility into shoppers’ offline habits and behavior. Keep reading to learn some of the ways that retailers and advertising partners can optimize retail media strategies by tapping into location intelligence on brick-and-mortar retail consumers.
For advertisers, one of the primary benefits of retail media networks is the potential to reach customers so close to the point of purchase. And with huge fleets covering vast areas of the country, convenience stores are extremely well-positioned to meet this need. When customers pop into their local 7-Eleven, Wawa, or Casey’s to satisfy a craving, they may also be particularly open to trying a new candy bar or energy drink.
To leverage this opportunity effectively, both the c-stores and their advertising partners need tools that can assess the reach of each store and of the brand as a whole, all while understanding the differences between local markets.
In October 2022, 7-Eleven announced the creation of Gulp Media, an advertising network that allows partners to create highly targeted ad campaigns using the chain’s first-party data. While the network initially launched online, the company has already indicated plans to explore in-store partnerships. And with over 9,400 stores in 38 states, America’s largest c-store has tremendous physical reach.
In December 2022, Iowa-based Casey’s followed suit, announcing plans to launch a retail media network in early 2023. With over 2,400 stores across 16 Midwestern states – and famously delicious pizza – Casey’s also boasts a huge offline footprint. The network – dubbed Casey’s Access – will allow partners to advertise across its various channels, including in its stores, at the pump, and online.
For retail media networks operating in the world of brick-and-mortar retail, unique visitors are akin to ad impressions, since each new visitor is one more person exposed to the retailer’s in-store advertisement. And foot traffic data shows that at both 7-Eleven and Casey’s, visitors to the chains are on a consistent upward trajectory, meaning both brands are also growing their advertising reach.
As we’ve noted before, in the face of rising food costs, consumers flock to c-stores both for reasonably-priced essentials and to splurge on cheap meals and treats that don’t break the bank. And with visits soaring, both chains’ retail media networks offer CPG companies an invaluable advertising platform.
To optimize advertising investments, marketers also need to match their campaigns to the unique markets of particular regions and venues. And foot traffic analytics, combined with other datasets, can help advertisers choose wisely between the growing number of retail media opportunities. For example, a restaurant chain looking to attract families with children can use these metrics to identify stores that cater to its target demographic – and direct its advertising spend accordingly.
So, to better understand the potential of location intelligence for targeted offline campaigns , we analyzed the demographics of nationwide and statewide visitors to 7-Eleven and Casey’s using the STI: Popstats dataset. We then compared the share of households with children in each chain’s True Trade Areas to statewide averages to see which media network offers a better fit for marketers looking to reach families.
As shown in the graph above, the appeal of both 7-Eleven and Casey’s to parental households varies significantly across states. In Ohio, for example, where both chains have a significant presence, 7-Eleven’s trade areas have 6.0% fewer families with kids than the statewide average, while Casey’s trade areas have 6.0% more. On the flip side, 7-Eleven’s Tennessee trade areas have 2.6% more families with children than the Tennessee statewide average, while Casey’s trade areas tend to have fewer such households.
So for marketers promoting a kid-friendly restaurant concept or even products in the store that resonate with the audience, Casey’s locations in Ohio may be a better fit while 7-Eleven would be more suitable in Tennessee. And by the same token, companies marketing more gourmet offerings targeted at adults may choose to focus their efforts on 7-Eleven in Ohio and on Casey’s in Tennessee.
While statewide visitation data can inform macro strategy, zooming in on the data for individual stores can help companies create even more highly-targeted campaigns that focus on particular venues.
For advertisers and retailers, granular foot traffic data is critical to making the most of the retail media opportunity.
Analyzing the trade areas of several top-performing locations in different urban centers shows how even within specific cities, some locations are more likely to draw in parental households than others. In Los Angeles, CA, for example, some 7-Eleven locations have significantly fewer families with children than the statewide average, while others have significantly more. An analysis of top-performing Casey’s locations in Des Moines, IA also reveals significant divergences. For both advertisers and retailers, this kind of granular foot traffic data is critical to making the most of the retail media opportunity.
For a growing number of retailers, on- and offline shopping experiences are increasingly merged. Customers may use retailers’ apps to help guide in-store purchases or browse the aisles for things they plan to order online, and then pick them up curbside. In an omnichannel world, brick-and-mortar visitation data is key to tailoring retail media advertising efforts to local consumer behavior patterns.
In 2019 The Home Depot launched Retail Media+, which allows partners to advertise on the retailer’s website and on relevant social media platforms to customers who have visited particular Home Depot product pages.
Given Home Depot’s strong physical footprint, the chain’s retail media efforts may expand offline in the future. In the meantime, the home improvement giant’s well-developed omnichannel strategy means that foot traffic insights can also be leveraged to optimize ads within its online retail media network. Home Depot’s mobile app, for example, includes an in-store mode that helps guide customers to the items they need, offering advertisers an opportunity to reach shoppers in specific locations at the point of purchase. And location intelligence metrics can also be used to optimize digital campaigns for customers looking to buy online and pick up in store (BOPIS).
Foot traffic insights can also be leveraged to optimize ads within an online retail media network.
While DIY activity may have cooled slightly since its pandemic heyday, visitation data can be harnessed to identify the specific areas where demand continues to grow. Analysis of quarterly foot traffic data for Home Depot stores in Austin and Houston Texas, for example, reveals a clear divide between suburban and city locations.
In Austin, suburban venues saw positive year-over-three-year (Yo3Y) visit growth for each of the four quarters of 2022, while nearby city-center stores experienced visit gaps throughout most of the same period. In Houston, TX too, suburban stores outperformed their in-city counterparts.
A look at consumer preferences in the trade areas of Home Depot’s different locations can help explain the stores’ very different performance. In both the Houston, TX and Austin, TX regions, Home Depot’s suburban stores boast trade areas that are over-indexed for households interested in DIY home improvement, as compared to the statewide average. The same areas’ city locations are under-indexed for this segment.
With these insights in mind, Texas-based advertisers may seek to focus campaigns geared towards DIYers at suburban Home Depot locations, while targeting city venues with ads aimed at professionals.
Zooming in even further on Houston’s city-based Home Depot locations shows how foot traffic data can be used to create hyper-local segmentations for optimizing advertising within a local market. While the chain’s Houston, TX-proper venues have in common lower proportions of DIYers, the median household incomes (HHI) of their trade areas differ significantly. Ads targeted at shoppers to the State Highway 249 location – with a trade area HHI of $68.5K – would likely be different from those targeted at the Eastex Freeway store.
Foot traffic patterns and demographic data can provide a good sense of the types of people who visit a given venue or retail brand and will therefore be exposed to ads displayed in that store or chain. Analyzing the psychographic characteristics of brick-and-mortar retail consumers can optimize advertising efforts further by revealing retailers within markets that excel at attracting particular audience segments. Retail media network operators can also leverage this information to promote their network to the best-fit partners in each region.
Analyzing the psychographic characteristics of brick-and-mortar consumers reveals retailers that attract specific segments.
Two leading grocery chains have helped pioneer the sector’s foray into the field: Kroger Co. and Albertsons Companies. The two behemoths, each operating its own substantial retail media network, announced plans to merge last year. And together, they’ll have an estimated joint reach of almost 85 million households nationwide, making their combined networks a true force to contend with.
To show how psychographic data reveals venues popular among specific audience segments, we used Spatial.ai Proximity to analyze the trade areas for five Cincinnati, OH Krogers to see how popular these stores are among health conscious shoppers. We used the same dataset to understand whether Albertsons shoppers in California attract a particularly high share of beer enthusiasts.
First we analyzed the foot traffic data to determine the difference between residents in the captured market and potential market of each Kroger location. Both captured market and potential market analyzes the population of a store’s trade area – but captured market share weighs the population according to the actual visit share from each census block making up the trade area, while potential market share is weighted according to the population size of each census block. We then looked at the difference in the share of health conscious consumers in Kroger’s captured market, and the share of beer enthusiasts in Albertsons' captured market, compared with the share of wellness buffs and beer enthusiasts in each chain’s potential market.
The analysis revealed that each of the five Kroger locations attracts a larger-than-expected share of health-conscious consumers. The six California-based Albertsons locations were also particularly successful at drawing in beer enthusiasts. This means that Kroger locations in Cincinnati, OH are likely a better fit than other local platforms for businesses seeking to convert wellness buffs, while craft beer companies may want to focus their advertising on Albertsons stores in California.
Foot traffic data also empowers retail media networks to keep their fingers on the pulse of their venues’ most popular visit times. Just like television networks, which charge more for commercials during prime time, in-store media networks can adapt their pricing strategies to the changing visitation patterns to their stores.
A look at daily and hourly visits to Hy-Vee stores, for example – the popular employee-owned grocery chain with over 285 venues across eight Midwestern states and a media network established in 2021 – reveals a very clear “prime time”.
In 2022, Hy-Vee’s “prime time” fell on the weekends, between the hours of 11:00 A.M. to 6:00 P.M – peaking on Sunday afternoons. Midweek, on the other hand, foot traffic to Hy-Vee picked up between 4:00 P.M. and 6:00 P.M., with the mornings and early afternoons decidedly less busy.
At BJ’s Wholesale Club, on the other hand, weekday visits are more evenly distributed throughout the day, with a higher concentration of visitors midday on the weekends. BJ’s, which operates some 230 membership clubs and more than 150 gas stations across 18 states, announced its foray into retail media in December 2022.
Retail media network strategies can also be informed by data on consumers’ cross-shopping habits. The other chains and stores frequented by consumers offer visibility into their broader interests and shopping habits beyond that provided by a retailer’s own loyalty and sales data.
Both Walmart and Target have invested heavily in retail media capabilities in recent years. And a look at cross-shopping patterns for visitors to the two chains’ Phoenix, AZ locations shows that they appeal to somewhat different audiences. Walmart shoppers are more likely to frequent dollar stores like Dollar General, but Target shoppers are more likely to visit specialty grocery stores like AJ’s Fine Foods.
So while Walmart may wish to provide its Phoenix, AZ customers with ads for particularly low-cost items, Target may wish to serve up ads for organic or other specialty food items.
In-store and omnichannel media networks offer both retailers and advertisers a win-win proposition. In-store advertising offers retailers the opportunity to monetize their physical footprints and increase their margins, while giving advertisers the ability to reach customers when they’re shopping and primed to buy.
Like any advertising platform, retail media networks thrive on data. And while retailers’ own first-party data provides important insights, more comprehensive location intelligence can help all players in the retail media game gain a deeper understanding of consumer behavior across markets and industries.
In-store and omnichannel retail media appears poised to usher in a new era in advertising. While digital retail media networks will continue to play a significant role in the advertising space, physical stores represent a tremendous untapped opportunity for retailers and advertisers alike. Physical retail media networks enable brands to reach customers during the discovery process, while intent is high and the consumer is close to the point of purchase. Location intelligence plays a critical role for brick-and-mortar retail media becoming the equivalent of what website analytics mean for online advertising.
Foot traffic data lets marketers analyze advertising opportunities across and within markets. Comprehensive visibility into the specific audiences that frequent different chains and venues allows advertisers to hone in on the best places to market their products. The alignment enables a potential maximization of impact by combining these perspectives jointly to merchandising and promotions.
In an increasingly omnichannel world, combining multiple datasets can unlock new opportunities. Demographic and psychographic indicators remain critical elements, but the ability to align these traits with behavioral elements like cross visitation can unlock significant improvements in reach and targeting.
Location intelligence can highlight retailers’ competitive advantages within smaller markets to help advertisers identify high-converting markets and channels. By comparing their captured and potential markets of key demographics, retailers can attract the right partners to their networks. They can also leverage the same perspective to help partners maximize efforts.
Location intelligence can help retailers optimize their retail media offerings. Hourly and daily breakdowns of visit share and shoppers’ cross-shopping data are two examples of how foot traffic data can offer chains additional layers of insight – above and beyond their own loyalty and sales data.
