5 Ways Retail Marketers Can Use AI to Generate Revenue

Have you jumped on the AI hype train, or do you feel like you’re falling behind? 

With the ability to streamline existing processes, personalize customer experiences, speed up manual labor, and enable better decision-making, it’s no wonder artificial intelligence is getting marketers excited. In fact, 68% of marketers claimed they had a fully defined AI strategy in 2022, up a staggering 134% from 2019. 

Adoption of AI by marketers (Salesforce)

So, where should you start? “Marketers should think about generating value from their unstructured data—a large collection of data not yet in a structured database format,” explains Spencer Stirling, Director of Machine Learning & Artificial Intelligence at Wunderkind. “If brands can turn this into actionable, structured data, then they’re tapping into a gold mine.”

While companies effectively using AI report a 30% increase in revenue, it may be unclear how exactly to use AI to drive measurable results. So, we consulted our internal experts and determined five ways marketers can use AI to drive efficiency and revenue.

Types of AI for marketers 

#1. Generative AI

Generative AI has the power to create (and market) winning products for retailers. It can help retailers generate innovative designs that customers will love, reduce time-to-market, and improve supply chain management. 

Generative AI analyzes the vast amount of data generated by customers and businesses to help retailers make informed decisions about what products to create, how to market them, and when to release them.

How can retailers use generative AI?

• Generative AI can assist in content creation. For example, it could create an idea for you by generating new text based on a prompt, write an email from scratch, or even create a product landing page. 
• Predictive text uses language model inputs to predict the next output. For example, it can assist marketers in channel optimization and delivering tailored content.

• 93% of CMOs report a positive or very positive improvement in how they organize their work using generative AI, and 91% report a positive or very positive impact on their efficiency. For example, WunderAI for Text automates text conversations between your brand and the customer. It uses real-time streaming data to enable innovative, intelligent text experiences to be delivered by brands at scale. 

#2. Propensity Models

Knowing what your customers want and expect is crucial for eCommerce success. Propensity models are algorithms that provide retailers with insight into customers’ future behavior and purchasing patterns. Retailers can use these models to create personalized advertising campaigns, product recommendations, and inventory management. 

How can retailers use propensity models?

• Understand which customers are more or less likely to buy your products or perform an action. This allows you to target the correct customers with the right products and offers, reducing wasted marketing dollars. 
• Calculate customer lifetime value (CLV) to help inform where to allocate your marketing investments. 
• Calculate propensity to churn, which allows you to identify which leads and customers aren’t happy with your products or experiences and may turn to a competitor. This means you can attempt a re-engagement campaign to retain the customer. 
• Evaluate the propensity of your leads and customers. This may show which website visitors are expected to click on an ad.

#3. Recommendation Engines

Recommendation engines are one of the most commonly used AI technologies in the retail industry. These engines analyze data about customers’ buying behavior to make personalized product recommendations. This results in increased efficiency, improved customer engagement, and higher revenue. In fact, 72% of marketers believe that AI-driven personalization improves engagement and conversion rates. 

How can retailers use recommendation engines?

• Send email and text message marketing to provide customers with tailored recommendations. For example, SMS and MMS marketing is a highly effective way to reach customers directly in their inboxes. In fact, conversational commerce is expected to grow 29.5% to $1.7 billion in 2023.
• Improve customer satisfaction through product discovery. For example, Discover is Wunderkind’s recurring, machine learning-powered email series that drives customer engagement with relevant products, using learnings from previous behaviors. The multi-part campaign series layers in smart, highly personalized recommendations that help customers achieve one-to-one discovery.
• Leverage chatbots or virtual assistants to interact with customers and provide them with product suggestions based on what they’re looking for. 

#4. Customer Similarity

Customer similarity is another AI tool that helps retailers enhance their customer engagement. By analyzing data about customers’ preferences, behaviors, and demographics, retailers can segment their customers into groups with shared characteristics. 

This analysis allows retailers to understand their customers better and offer more tailored and relevant products. Retailers can match their products to the customer’s preferences, therefore improving customer engagement, loyalty, and customer lifetime value.

How can retailers use customer similarity? 

• Find new customers similar to current shoppers, and target them with tailored marketing campaigns. 
• Increase sales by providing timely recommendations based on similar customer preferences and past behaviors.

• Reduce customer churn by quickly finding new products that fit their preferences.

#5. Product Similarity and Complementarity

By analyzing the data, retailers can identify and recommend similar products to those that a customer is browsing. This leads to increased revenue, improved customer loyalty, and higher customer engagement.

How can retailers use product similarity and complementarity? 

• Increase conversions, as abandoned carts and out-of-stock items can be translated into sales. 
• Lower cart abandonment rates as suggested products potentially match shopper preferences better.
• Target customers in a more personalized manner by offering tailored product recommendations.

“The highest impact businesses are those that are really practical about AI. Concentrate on the products and use cases, and the AI will either fit or it won’t,” Stirling says. “When a new hammer comes out, everything looks like a nail. There are some interesting new use cases, but don’t forget the strategies that we already know work.”

To understand how using AI can benefit your business, read our comprehensive guide: Revolutionizing Retail: How To Navigate the AI Landscape to Drive Performance.