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Retail Media Networks (RMNs) benefit multiple parties: Retailers receive supplemental revenue streams, advertisers and CPG companies reach shoppers at the point of purchase, and customers get to see better targeted ads and promotions as they shop. As a result, RMNs have surged in popularity in recent years, with everyone from Walmart to The Home Depot to Kroger getting in on the action.
Still, despite their recent growth, many RMNs remain underutilized – especially in physical retail spaces. By analyzing store traffic data, trade area demographics, and consumer psychographics, RMN stakeholders can gain visibility into various chains’ audiences and identify differences between shoppers across a chain’s locations. These insights allow companies to target the right audiences for their advertisements at the transaction touchpoint.
This report highlights how retailers and advertisers can leverage offline retail insights to unlock the full value of retail media networks. By examining the behavior and preferences of offline shoppers at department, grocery, and convenience stores, this report showcases the advertising opportunities of physical storefronts and their abilities to support effective, data-driven RMNs.
Department stores tend to maintain a nationwide presence and enjoy relatively long average visit durations – 37.3 minutes, compared to 23.4 minutes for grocery stores and 8.9 minutes for c-stores. As such, department stores are uniquely positioned to provide prolonged exposure to third-party advertisements for audiences throughout the country.
At the same time, the broad reach of department stores can also mean that advertisers may inadvertently target too wide an audience. So how can RMN stakeholders capitalize on department stores’ national presence and long visits while still targeting their advertisements to distinct consumer segments?
One possibility is for advertisers to use location analytics tools to evaluate the captured trade areas of the stores in a chain’s network to surface locations that attract certain coveted demographics.
For example, evaluating Macy’s store fleet using the Experian: Mosaic dataset reveals that around 20% of the chain’s locations attract shoppers from trade areas that are significantly over-indexed for “singles and starters.” This segment is defined as “young singles starting out and some starter families living a city lifestyle” – so advertisers looking to reach younger audiences can focus their campaigns on these locations and receive the best returns on their RMN investments.
The variety of geospatial datasets available mean that location analytics can also uncover specific consumer preferences. Retailers and advertisers can layer digital insights onto offline audience segments – revealing granular consumer preferences and helping RMN stakeholders further refine their targeting.
Spatial.ai’s FollowGraph dataset, for example, displays brand and influencer preferences within a property’s trade area. Using this dataset to analyze the luxury brand preferences of Saks Fifth Avenue visitors reveals that – unsurprisingly – the chain’s shoppers exhibit higher affinities for luxury brands than the nationwide average.
But diving deeper into the data for four Saks locations and comparing visitor preferences to the statewide averages also reveals differences among Saks visitors. For example, visitors to the Vienna, VA Saks store display a much higher affinity for Tory Burch relative to the statewide average compared to visitors to the Saks store in Boston, MA. Meanwhile, Boston Saks shoppers seem to have stronger preferences for Saint Laurent.
Luxury retailers – along with other advertisers – can use these insights to fine-tune their RMN promotional strategies and target the brick-and-mortar locations attracting shoppers most likely to respond positively to their campaigns.
Grocery stores represent another key category in the emerging RMN space. Despite the rise in online grocery shopping, most groceries are still purchased in-store – and since everyone needs to eat, grocery stores may be the category that reaches the widest range of consumers. This means that offline or partially offline RMNs operated by grocery chains have the potential to reach almost every household in the country.
But what if companies want to maximize their spend on targeting a specific segment?
One way to reach specific audiences is to advertise on RMNs operated by grocery chains whose merchandising mix appeals to the intended segment. Hy-Vee, for example, is known for its fresh produce and for its focus on health and wellness – so companies looking to advertise to health-conscious shoppers at the point of purchase may choose to leverage the Iowa-based grocer’s RMN.
But advertisers can even further refine their targeting by parsing the grocery chain’s trade areas to find regional variations in consumer preferences. For example, layering the AGS: Behaviors & Attitude dataset onto the potential market of Hy-Vee locations throughout the midwest suggests that Hy-Vee is particularly popular among organic shoppers in Wisconsin, Iowa, and Kansas. Organic-focused brands looking to gain traction in these states can use this insight to optimize their ad spend by leveraging Hy-Vee’s RMN and running campaigns that target their customer base at their point of purchase.
Convenience stores are also a strategic choice for offline retail media networks thanks to their high-traffic locations and broad customer base. C-store shoppers are also particularly prone to impulse purchasing, which could make advertising at the point of purchase even more valuable in the c-store space.
So how can advertisers leverage location intelligence to identify the best c-store-based RMNs for their campaign?
One way of deciding on a RMN partner is to compare shoppers at different retailers to find the audience that best corresponds to the advertiser’s target market. Another way is to analyze the shopping behavior at a variety of chains operating RMNs and choose the chain where the visitation patterns best align with the types of products being advertised.
Cumberland Farms and 7-Eleven are two c-store chains with a significant presence in the Boston metro area. But while the lion’s share of Cumberland Farms visits occur in the morning and afternoon – less than a quarter of visits take place after 6 PM – In contrast, 7-Eleven sees relatively few morning visits and receives almost a third of its traffic between 6 PM and 12 AM.
So companies looking to promote coffee or lunch items may have more success advertising with Cumberland Farms – while businesses marketing dinner fare or alcoholic beverages may be better off advertising with 7-Eleven.
Advertisers that want to take their targeting to the next level can then layer additional insights on top of the visitation data to pinpoint the locations within the chain offering the most effective advertising space.
The STI: Spending Patterns dataset, for example, reveals which Boston-area Cumberland Farm locations have trade areas that over-index for spending on take-out lunch compared to the statewide average. Companies advertising lunch options on Cumberland Farms’ RMN can use this data to target specific locations within the RMN network where lunch ads would be the most effective.
Each RMN category enjoys unique advantages – from department stores offering prolonged exposure to ads to grocery stores reaching a broad range of consumers to convenience stores advertising to shoppers primed for impulse purchases.
By incorporating location intelligence and layering datasets to identify the most strategic locations, advertisers can maximize the impact of their campaigns, optimize spending, and unlock the untapped potential of RMNs. As the retail landscape continues to evolve, those who embrace the capabilities of RMNs stand to gain a competitive edge in reaching and engaging today’s dynamic consumer base.
Analyzing offline shopping data empowers advertisers to optimize campaigns on Retail Media Networks (RMNs). Advertisers can evaluate store traffic and psychographics to refine their offline campaigns and target audiences with relevant campaigns at the point of purchase.
Location analytics can help advertisers identify stores attracting key audience segments: Advertisers can use location analytics to surface stores within a chain’s retail media network that draw particular audience segments, such as young professionals at specific Macy’s locations or fans of specific luxury brands at Saks Fifth Avenue.
Offline RMNs allow advertisers to reach a broad demographic with opportunities for niche targeting: Grocery stores, like Hy-Vee, appeal to nearly all households, with many chains also catering to specific sub-segments – either on a chainwide or regional basis. Hy-Vee, for example, seems particularly popular among organic shoppers in three midwestern states, and advertisers can refine these insights to connect with niche audiences in specific regions.
Advertisers can use visitation patterns to align their campaigns with in-store shopping behavior: C-stores like Cumberland Farms and 7-Eleven have distinct visitation patterns, with the former seeing morning and afternoon traffic and the latter dominating evening hours. These insights can help advertisers align campaigns with shopper behavior, such as coffee ads for Cumberland Farms or dinner promotions for 7-Eleven.
Data-driven insights from offline shopping can enhance RMN campaign effectiveness: Layering datasets such as spending patterns and brand affinities allows advertisers to identify relevant locations within a chain’s network, empowering advertisers to align ads with consumer behaviors and preferences and maximize their marketing ROI.
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