How Retail Media Networks Can Adopt AI Tools

Brands and agencies right now are eager and increasingly willing to take advantage of rapidly maturing artificial intelligence. Obviously the use of AI and machine learning is nothing new in business. However, advances such as generative AI tools have opened up new applications and use cases for the technology. AI is now being used in digital media for everything from modeling to messaging. Generative AI and other cutting-edge AI tools are enabling experimentation among brands and agencies seeking a competitive advantage. As retail media networks (RMNs) are, at the same time, proliferating and expanding in the market, those media networks need to explore how they can take a page from the playbooks of businesses that have already leaned into emerging AI tech.

First, let’s look closer at how marketers are using generative AI now. Half the agencies surveyed in a recent study reported they’re using generative AI for marketing — e.g., writing copy, researching, content creation, etc. And generative AI has delivered great value for brands and agencies behind the scenes. It’s enabled more efficient testing for a far broader set of scenarios. This leads to less wasted spend and more relevant (and thereby effective) targeting. That, in turn, makes the inventory more valuable. 

These are all very promising benefits for not only traditional marketing avenues, but emerging avenues like RMNs. At this stage in the development of RMNs, retailers and their brand partners must learn quickly to catch up with peers across the digital industry. Advertisers buying into RMNs will demand a certain standard of maturity, and emerging AI tools can accelerate the necessary learnings and insight gathering like nothing before. 

Among e-commerce sites, AI has long been an important part of businesses’ strategies for thriving in a crowded marketplace. Today, developments in AI tech are helping democratize access to advanced tools for a broader range of e-commerce businesses, beyond the likes of Amazon.com. They’re using AI to collect and analyze insights on consumer behavior and interest at scale. Generative AI chat automates large volumes of replies to common customer service queries, and can deliver answers to customers faster than traditional search. AI can also drive inventory forecasting and management, visual search and product recommendations, price optimization, and fraud detection. 

The endgame for retailers’ use of AI — or one particularly valuable endgame — is predictive analysis. However, retailers can’t get there immediately after jumping in. They first need data insights on the performance of advertising in all online and offline channels of the RMN. And they’ll need to identify metrics to best measure performance, per the goals of their advertisers and any individual campaigns. 

With RMNs specifically, AI brings great value for enhancing both the consumer’s digital experience and the performance of campaigns. As generative AI chat continues to evolve, we can imagine chatbots offering product recommendations more efficiently than search. Meanwhile, businesses are exploring how to incorporate sponsored products into AI chat results to help advertisers retain the reach and engagement that search advertising has always provided. AI can be used to build customer profiles and segment audiences based on factors including interests, position in the funnel, and potential to upsell. AI powers automated creative optimization — as some innovative marketers are already seeing — and nuanced and continually updated personalization based on location, weather, trends and other factors. By placing consumers into buckets in real time, based on recognizable patterns of behavior, AI tools can become much like that intuitive, but not aggressive, salesperson in a physical store who can understand by observation what you’re looking for and guide you toward the best product for your needs. 

On the back end, AI is being used to forecast available inventory and placements, and deliver insights on consumer behavior in physical stores far more efficiently than point-of-sale data. All of these AI applications can make RMNs not merely a valuable place in the digital landscape to reach users, but a truly unique one. RMNs can maximize the potential of consumers’ direct relationships with retailers and brands.

As individual RMNs establish their positions in the marketplace and gather more data, AI will draw more and more insights from that data, and increasingly quickly, to elevate that data to its full predictive potential. This will be central in drawing new categories of advertisers to RMNs. Standardization across RMNs will also encourage more advertisers to enter this space, and AI will help retailers understand sooner what standardization should look like. RMNs may be introducing retailers to a game they’ve never played before in business, but others in the industry have been playing. AI will accelerate retailers’ education.

Fred Marthoz is vice president, revenue and global partnerships at Lotame, a global technology company that makes customer data smarter, faster and easier to use.