Retail Media Networks Are Here to Stay

Data Basics, Use Cases & Projects Carla Winston

Retail media networks (RMN), the advertising platforms that bring brands up close and personal with shoppers at (or nearly at) the point of purchase, are solid profit centers for retailers with gross margins for onsite retail media ranging from 70% to 90%. Though RMNs are a proven win for participating retailers, retail media advertisers are left trying to piece together a clear picture of total value attached to retail media spend as 42% of advertisers question their investment

Circulating among retail media advertisers are performance questions like: Is my retail media spend worth it? Will I see an impact significant enough to justify the cost of advertising? Across which channels and platforms will I see the greater influence on conversion rates or larger shopper baskets (a marketing attribution question)? And the list doesn’t end there. 

RMNs in A Shifting Landscape

Despite these lingering concerns, RMN advertising is projected to grow 60% by 2027, as advertisers continue to allocate a growing portion of their spend to RMNs for critical access to first-party data insights and stronger strategic partnership with retailers, and also simply out of a perceived expectation across the industry that RMNs will only become more central to retail. But because brands are not immune to economic uncertainty and the financial constraints that come along with it, understanding how their advertising spend influences shoppers and proving the ad led to the desired outcome (e.g. higher conversion rates, increased household penetration, incremental sales) will remain priorities as many companies tighten their marketing budgets and reassess their strategies. 

While RMNs have become increasingly mainstream over the last few years, due in part to the omnichannel boom experienced during the global COVID-19 pandemic and to their enthusiastic adoption by E-Commerce/retail giants, achieving line-of-sight to more in-depth performance metrics beyond the standard like clickthrough rate and impression counts is still a struggle. Providing the predicted impact generated from retail media spend across channels and platforms has not yet become the rule. It is the reality that most advertisers are flying blind while trying to understand the current (and future) attributed influence of their retail media spend. This gap provides an opportunity for RMNs to enhance measurement capabilities and foster better retailer-brand partnerships. And that’s where artificial intelligence (AI) and machine learning come in–to fill the measurement gap by accelerating the path to automation and optimization.

Tipping the Scales With AI and Machine Learning

Tackling the RMN measurement problem with AI means using machine learning algorithms to maximize desired advertising outcomes all while training the algorithms with large sets of transaction and behavior data to anticipate shopper purchase decisions. The role of AI in retail media would allow brands to influence the shopper experience in real-time and make optimized advertising decisions that lead to a higher return on ad spend. AI in the world of advertising and measurement is not a novel concept. In fact, 53% of marketers have used AI and machine learning for targeting, and as we discussed in a previous blog post on Marketing AI, 72% view AI as a “business advantage.” So how can AI and machine learning do the heavy lifting–leverage large volumes of data efficiently and automate the data analytics to uncover insights at-speed resulting in improved marketing effectiveness–and answer the call from advertisers to demonstrate that their retail media spend is worth it? 

The answer lies with a few key measurement types that stem from the questions brands are looking to answer, like: marketing mix modeling (MMM) — an oldie but goodie — which can be used along with the more advanced lens of full-funnel modeling to analyze interactions at each stage of the customer journey (only 15% of brands are getting MMM measurement today from RMNs); uplift modeling to predict how individual shoppers would react to a specific marketing campaign; and attribution modeling for analyzing the paths shoppers take toward conversion and for forecasting the value linked to each marketing component. 

With these measurement types locked in as core retail media capabilities, advertisers can extract much needed (and demanded) granular insights from predictive features; and retailers who scale these capabilities can set themselves apart from the growing number of RMNs. Now’s the time to put into motion in-flight retail media optimization with AI. 

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