How Retailers Can Use AI to Optimize Messaging and Create Relevancy

Today’s e-commerce customers are overwhelmed. Across every part of the shopping journey, retailers are fighting for shoppers’ attention, trying not only to sell them products, but also to convince them to take actions that will drive additional revenue. Whether they’re asking customers to sign up for a credit card, download an app, enroll in a loyalty program, or take advantage of some other offer, e-commerce sites are presenting visitors with numerous calls to action (CTAs) that aren’t directly connected to why they’re on a retail site in the first place — to find and purchase products they want.

Advertisements can sometimes add to this chaos. On some sites, visitors are presented with up to 20 different messages, options and CTAs, which is too much information for most shoppers to sift through. Without any sort of one-to-one personalization or relevancy, the messaging clutter can start to make shopping online feel overwhelming and annoying, something no brand, and no customer, wants.

The good news is that retailers and brands can easily leverage artificial intelligence, including machine learning, to optimize the messages they present to consumers and ensure they deliver a highly relevant, personal experience that makes shopping feel rewarding and enjoyable again.

AI is Delivering Relevancy Efficiently and Cost Effectively

To optimize messaging that drives incremental revenue while delivering a better overall customer experience, strong brands are using AI and machine learning in a variety of ways, including to:

1. Identify which messages are most relevant to each customer and the ideal number to present.

Every e-commerce brand is trying to figure out how best to present the many offers they want customers to act on, whether they’re asking them to opt in to receive emails, join a subscription program, buy an extended warranty or open a credit card. However, simple A/B testing doesn’t provide the answers brands need. Because each customer is unique, companies need to figure out what messages will best appeal to individuals rather than the groups of people that A/B testing targets.

Instead, brands are better served by leveraging AI to optimize both the kinds and number of messages they present. By ensuring customers receive only the messages and ads that are most relevant to them personally, based on their purchase history and many other inputs, AI can help drive the desired actions brands want those customers to take. The technology also automatically edits down the set of potential offerings to identify the optimal number of messages to present at any one time, so as not to overwhelm shoppers. That may be one, a few or even none, in some cases.

In addition, as brands feed more data into machine-learning applications, they become “smarter,” meaning they get better and better at accurately and efficiently identifying the most relevant messages to present to each individual.

2. Personalize the confirmation page.

As the last step in the online shopping journey, the confirmation page is ripe with opportunity to enhance the overall shopping experience and generate incremental revenue. However, it can be challenging to get customers to stay on the page. Typically, shoppers immediately click away once they’ve verified their transaction is complete.

By using AI to tailor the messages presented on the confirmation page based on a shopper’s preferences, purchasing journey and other inputs, e-commerce companies can increase the likelihood that the individual will take the actions the brand desires. An apparel retailer looking to sign up new credit card customers, for example, may use AI to discern which credit card offering would resonate most with a given shopper. A movie theater chain looking to boost revenues by increasing high-margin F&B sales may leverage AI to present the exact popcorn-and-soda offer most likely to entice a customer who just bought movie tickets online. And a rental car site may use AI to entice a customer to pay for a service that guarantees she’ll be able to drive out in the car model she prefers or pick up her rental at the exact time that’s most convenient for her.

3. Generate and optimize multiple versions of marketing messages.

All marketers know that some consumers are best motivated by price-based messaging and others by direct CTAs, exclusive offers or new items. Today, savvy marketing teams are using AI technologies like ChatGPT to generate and tweak messaging quickly to cater to shoppers’ unique preferences and personalities. For example, rather than having to write 80 slightly different versions of the same email subject line announcing a new product or offer, marketers can input different prompts into ChatGPT or another similar tool to generate the copy, which can then be tested easily at scale. Retailers and brands can then learn which customers are responding to which types of messaging most frequently and use that intelligence to continually optimize their overall marketing strategy.

AI and machine learning are enabling retailers and brands to create more enjoyable and memorable e-commerce experiences by delivering relevancy and next-level personalization to customers at every stage of the shopping journey. By using AI to identify which add-on offers and messages are most likely to appeal to an individual shopper, as well as the optimal number of messages that each shopper should see at any given touchpoint, brands are saving their teams time and resources, while easily and efficiently driving incremental revenue.

Jon Humphrey is vice president of solutions and product marketing at Rokt, a provider of e-commerce technology and software solutions that drive more value per transaction with personalized experiences.