Developed with insights from industry leaders like Paul Brenner at Vibenomics, the IAB and its partners designed the 2024 Digital Out-of-Home (DOOH) & In-Store Retail Media Playbook to enhance the execution of DOOH and in-store retail media strategies. The playbook serves as a guide for retailers and brands to navigate and implement these media channels effectively. It focuses on practical applications, advanced targeting methods, and leveraging retailer data to optimize shopper engagement and campaign success.

Brenner tapped into Vibenomics’ tailored approach to retail media to shape his insights for the Playbook. As a leader in providing in-store digital advertising technology and services, Vibenomics focuses on aligning with retail media owners’ business models, providing technology and infrastructure that respect ownership and operation dynamics. 

We chatted with Brenner about the roles of retail media, shopper marketing, and category teams in modern merchandising. Our conversation also explored advanced data targeting methods to understand shopper behavior and outline strategies for measuring the success of DOOH and in-store campaigns. Brenner provides insights into leveraging retailer data and in-store technologies to create a cohesive, dynamic shopping environment that bridges traditional merchandising with innovative retail media strategies.

Andrew Byrd: What is the primary focus of the 2024 Digital Out-of-Home (DOOH) & In-Store Retail Media Playbook?

Paul Brenner: At a high level, it’s crucial to understand the distinction between digital out-of-home (DOOH) and in-store media. The IAB faced difficulties because many mistakenly believed that DOOH included in-store media. 

After a thorough discussion, we concluded that DOOH ends at the venue’s entrance. The retailer and brand can achieve the most attributable results inside the venue. This is because in-store media is owned and operated, allowing for more direct control and measurement of impact. 

In contrast, DOOH is a third-party solution that operates independently of the venue’s internal media strategies. This fundamental difference in operation and attribution is why these two types of media are categorized separately and viewed through different lenses.

AB: What role did Vibenommics take in working on the Playbook and what perspective do you bring to the retail media space? 

PB: Vibenomics is designed explicitly for retail media, ensuring that our partnerships with various retail media merchants respect their ownership and operation of the media. We provide the technology and physical infrastructure, aligning our strategy with their business model for venue operations. 

This approach differs from digital out-of-home advertising, where the focus is on investing in signage and seeking foot traffic. Instead, we follow the lead of retail media owners, ensuring our financial models, operational methods, approval processes, and creative control align with their rules of engagement.

As part of Vibenomics, I focus on ad tech and advertising within Mood Media. I leverage my experience working with numerous retail media networks to understand the diverse approaches to building retail media spaces. I bring insights from our current brand partnerships to refine our playbook and advance the industry.

AB: How does the playbook envision using disruptive in-store technology to enhance the shopping experience?

PB: Over the past five years, I’ve observed the evolution of retail media from within the stores, especially on the periphery of the retail media landscape. This shift has seen omnichannel strategies integrate on-site and off-site elements as retail media companies have the freedom to design webpages and leverage data and shopper insights as they see fit. Shopper behavior has predominantly been a digital experience, whether through apps or online interactions. Now, there’s a need to merge this digital experience with the physical world, considering new approaches to privacy and delivering what consumers truly need for better preparation.

The challenge lies in transforming traditional, static signage—like cardboard stands and paper shelf tags—into more cohesive and engaging elements that offer consumers a seamless digital-to-physical experience. Instead of simply navigating around static signs, consumers should encounter dynamic, noticeable, helpful promotions that drive their behavior and enhance their in-store discovery and exploration process.

AB: What roles do retail media, shopper marketing, and category teams play in the context of merchandising within the retail sector?

PB: Retail merchandising professionals must now incorporate insights from Retail Media Networks. This shift means traditional trade deals and merchandising strategies, like shelf placement and promotional value, can no longer be considered in isolation. 

Instead, retail media, shopper marketing, and category planning merge into a single, integrated conversation. Brands increasingly need to allocate more of their budgets to retail media, drawing funds from traditional merchandising investments. This necessitates closer collaboration between teams, a focus we specialize in. 

To create a cohesive strategy, we aim to bridge the gap between traditional merchandising and retail media by controlling the environment, creative aspects, context, and store mobility.

AB: What types of data are emphasized for advanced targeting in the playbook, and how can they be used to understand shopper behavior?

PB: The current playbook focuses on execution, providing insights for brands, retailers, and service providers leveraging retailer data and in-store technologies for maximum shopper benefit. The initial version mainly covered retail media standards with a brief in-store section, reflecting the evolving nature of in-store experiences. 

This playbook now addresses utilizing retailer data and technologies to enhance the shopper experience. It differentiates between online (one-to-one audience) and in-store (one-to-many audience) advertising, highlighting the challenges of demographic variability in stores. 

By analyzing shopper transaction data, retailers can adjust in-store strategies to improve spending and category share, integrating first-party data into the broader shopping experience. For instance, mature retailers can evaluate how creative impacts spending per trip, household, and category share across both digital and in-store environments, aiming to translate online insights into in-store successes.

AB: What methods does the playbook suggest for measuring the success of digital out-of-home and in-store retail media campaigns?

PB: There are two approaches to consider. One is a straightforward control test, which is easier to execute. For instance, we could test a campaign with a major home improvement customer by isolating a test group from a larger control group. We would then analyze pre- and post-campaign effects on shopping behavior using statistically relevant data.

The second approach, announced with Microsoft last year, involves taking a brand’s media plan and extending the control test to in-store activities. This includes examining product listing ads, search strategies, and in-store tactics. We then determine which tactic or combination of tactics drives greater lift or increase.

In essence, we perform both isolated and combined tactic tests. By comparing in-store tactics alone with combined on-site and in-store tactics, we can assess their impact on category share and lift. This dual approach has provided valuable insights into optimizing the integration of online and in-store shopper data.

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High purchase propensities environments such as grocery stores, drug stores, convenience stores, and big box retailers are ideal for targeting consumers. Why? Consumers who enter a physical shopping environment are more receptive to hearing and seeing messages as they navigate the aisles to make a purchase. 

Not only are in-store shoppers more receptive to the advertisements, but they can interact with, purchase, and take home the product within the same trip.

E-commerce and other online channels need to  provide customers with this immediacy,and  other out-of-home channels like billboards or radio ads. This opportunity is unique to in-store environments and may be one reason global retail media spending will reach $101 billion this year, a 15% increase from 2021.

However, for advertisers to truly take advantage of shoppers’ necessity to spend at the point of purchase, they must craft appropriate messages the in-store audiences will value. Real-time optimization powered by data-driven ad placement automation eliminates the risk of broadcasting irrelevant messages. Also called dynamic content generation, real-time optimization automates the curation of ads based on data factors offering the most appropriate times and occasions to send a personalized message to that specific location. 

In other words, by leveraging various data sources, real-time optimization ensures personalized messages reach customers through each channel of the retail media network. Here’s how three key data sources work to provide real-time optimization: 

In-store Inventory Data

We’ve all experienced frustrating out-of-stocks, price increases, and down counts — especially during the current economic state. With supply concerns threatening brand trust and customer enjoyment in stores, real-time optimization guarantees supply synchronization by directly connecting retail media ad channels to store data. 

For example, suppose a grocery location sells out of specific on-shelf product. In that case, the in-store audio network could synchronize with supply data to stop running that ad in the specific store with the depleted product. 

By connecting retail media channels to real-time data about supply, stock keeping units (SKUs), and other first-party factors, in-store channels maximize every impression. Retail media channels like in-store audio are especially advantageous, considering they drive impulse buying: 48% of shoppers state that in-store audio influences their purchase propensity. 

Non-invasive Targeting 

Data sources that don’t rely on information about individual customers, such as geolocation, day of the week, time of day, and general demographic data, enhance optimization at the point of purchase.

Advertisers using non-invasive targeting strengthen relationships with in-store customers and ensure messaging meets their needs in real-time, all without relying on cookies and third-party sources. 

For example, if it rains one day, advertisers can sync in-store display ads with local weather data to show discounts and aisle locations for umbrellas. If the local sports team has a big game that night, advertisements might sync with day-of-the-week data and the team’s schedule to encourage customers to stock up on popular snacks or adult beverages. 

These real-time updates build a relevant connection with the consumer without obtaining personal data from third-party sources, invading shopper privacy. 72% of Americans are reluctant to share information data with businesses over privacy concerns — a percentage likely to increase as consumers continue to become more aware of how data mining invades their privacy. 

In-store audio advertising is a non-addressable channel — advertisements targeting more broadly through demographic trends, hourly impressions, or other in-venue digital techniques. Unlike e-commerce, where advertisements rely on invasive, identity-based cookies, in-store advertising targeting doesn’t threaten privacy. Advertisers need to leverage this advantage over e-commerce. Brands that use non-invasive targeting strategies show consumers that brands value their privacy while still wanting to build personal connections.

Consumer Shopping Patterns

Consumer shopping patterns represent advertisers’ most crucial data source to implement within a real-time optimization infrastructure. The foundational questions asked by advertisers when building  campaigns should be:

•     Does the campaign link the goals across e-commerce and physical store distribution?
•     How can your campaign gain your share of market over your category competitor? 
•     When and where are customers most receptive to promotions, discounts, and special offers? 

Real-time optimization automates the process, allowing these strategic questions to factor into ad placement decisions. 

 For example, shopping pattern data may reveal times of day, days of the week, or seasons in which shoppers choose to “trade down” or pick a more affordable  option over their usual preferred  option. 

Perhaps families opt for cheaper grocery brands to save leading up to the holidays, or college students purchase discounted home essentials leading up to back-to-school. On a smaller level, shoppers may adjust their spending habits leading up to or following their weekly payday. By implementing consumer shopping data, relevant ads for each shift in spending trends automatically run. Advertisers can have the confidence that their messages will reach the targeted audience. 

When shopper attention is at a peak, retailers and advertisers cannot afford to run campaigns irrelevant to consumer needs. Advances in targeting capabilities have allowed advertisers to speak to select consumers using shopping patterns and first-party data. Real-time optimization is no longer a luxury but a necessity with the evolution of in-store retail media.

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