With the average person encountering thousands of ads daily, consumers cannot possibly process the volume of messages they receive. Brands and advertisers must invest in the most effective advertising channels available to cut through the clutter.
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.
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.