零售媒体网络战略：利用生成式人工智能解锁高利润收入

Accelerating retail’s high-marginrevenue opportunity with gen AI Making the businesscase for retail medianetworks—the high-margin revenueopportunity retailersmay be overlooking. Think back to the early days ofsocial media. Some companies,including retailers, didn’t realize thevalue of their own first-party data.Now, asretail media networks(RMNs)—platforms that leverageretailers’ rich customer dataand owned channels to deliverhighly targeted ads—begin toinfiltrate the marketing mix, someorganizations may, once again, beunderestimating the value of theirdata and the opportunity of thisasset as a whole. Retail media networks offer a high-margin revenueopportunity for an industry that typically operates ona razor’s edge. These networks deliver personalizeddigital experiences to high-intent customers, aligningperfectly with brands seeking to expand theiradvertising strategies beyond traditional media andestablished marketplaces. They also enable retailersto track and measure the customer journey from theinitial point of exposure all the way through to the finalpurchase, providing greater visibility into customerbehavior and offering new ways to optimize outcomesfor both the retailer and its brand partners. With retail media network ad spend expected to reachnearly $110 billion by 2027 in the U.S. alone, retailersmust act now to capture their share of the market bydelivering highly targeted ads supported by reliableproduct availability. In this POV, we explore how retailers can create awinning RMN strategy, bolstered by strong datacapabilities and enabling technologies, to turn thisoften overlooked revenue opportunity into a high-margin revenue stream. Margin stats: RMN market at a glance $109.4BU.S. RMN ad forecastspend by 20271 $31BEurope RMN marketby 20283 20% Estimated YOY channelgrowth of RMN through 20272 Overcoming datachallenges in RMNs One of the biggest challenges retailers struggle withwhen launching an RMN revolves around data—access,analysis, integration, insights generation, governanceand so much more. customer profiles, offering valuable insights intoboth purchasing and browsing behaviors that drivecompetitive advantage and enable high levels ofpersonalization. To scale an RMN, retailers must establish a strongdata strategy and processes to ensure data is properlyprepared, formatted and integrated. Withoutthis, companies may face issues such as incorrectsegmentation, flawed insights, and difficulties inintegrating data sources. 2. Leverage data to drive segmentation Once retailers have consolidated their data estate, theycan use resulting insights to create customer segmentsbased on behaviors, demographics, or preferences.This will allow retailers to deliver highly targeted adsthat resonate with specific groups, which will help drivebetter engagement and increase conversion rates.This data-driven approach allows retailers to optimizead spend and create more personalized experiences,increasing the overall effectiveness of their campaigns. Beyond that, many companies will need to overcomelegacy technology limitations that hamper integrationefforts as well as the adoption of more advancedtechnologies, like AI and generative AI. This is a criticalcomponent to enabling continuous innovation anddrawing the maximum value from the RMN investment. 3. Use segmentation to enable personalization An effective segmentation strategy also allowsretailers to personalize ad campaigns. These segmentshelp marketers tailor their messaging and offers tomeet the specific needs and interests of each group,improving the relevance of ads. By targeting smaller,more focused segments, businesses can boostengagement, increase conversion rates, and enhancecustomer satisfaction. This approach also helps allocatemarketing resources more efficiently by concentratingefforts on high-potential customer groups. Finally, data-driven insights must be tied to operationaloutcomes. Simply knowing what customers want ismeaningless without the ability to deliver. For instance,if the data reveals that consumers want a specificproduct, the ad will only be effective if the retailerhas the product in stock. In fact, advertising a productthat can’t be delivered will only reduce the return oninvestment by the consumer brand, which lessens thevalue of the retailer’s service. Here we look at the steps retailers must take to definethe data strategy and build a flexible and scalablearchitecture, setting the organization up for long-termRMN success. 4. Unlock a predictive capability Just as data can be used to recommend products byanalyzing past actions and patterns, so can it be used todevelop personalized ads that feature those products.For example, if a customer browses products relatedto bathroom renovations, such as flooring or faucets,predictive models can infer that they may soon beinterested in purchasing vanities or lighting. Retailerscan use this insight to serve targeted ads or productrecommendations, increasing the lik