Growth in modern retail hinges on knowing how to serve your customers. With high expectations for personalized omnichannel experiences, it’s the difference between being chosen and being ignored.

Fortunately, AI can accelerate the path towards understanding consumer demand and behavior if implemented accurately into business processes.

Real-time data analysis and predictive personalization have been around for decades but now AI provides retailers with more tools to help their customers feel more engaged with their brand and receive a tailored service and experience across different channels.

Customers demand AI assisted shopping for better experience: nearly three in five shoppers expect AI-powered help while browsing and buying.

While most companies have begun using AI across their business, a recent study found that 74% of companies struggle to achieve and scale value from their AI implementations. The retailers seeing the biggest success are those who are rethinking how every AI interaction can add to a better customer experience.

Below we will dive deeper into the trends of AI in retail that help brands serve their customers better. 

Detecting signal from the noise 

Retailers have never had more data at their fingertips. Customers’ transaction history, site behavior, product and service reviews, social messaging, loyalty program activity, and more.

The reality is that this enormous amount of data is turning into a problem for many retailers that are unaware of how to use it to structure it accurately and drive actionable insights.

More than 7 in 10 businesses are struggling to make use of their data, leading to poor decisions and customer complaints.

Successful retailers are leveraging AI-powered data platforms, which provide AI models that surface patterns and anomalies in real time, connecting dots across multiple data sources to uncover what customers want, even when they’re not searching for it outright.

For example, AI-powered data platforms can analyze customers’ site behavior alongside their purchase history and social sentiment to predict when a customer is likely to be in buying mode, what they’re shopping for, and which nudges are likely to tip them into conversion.

Digital commerce platforms have been integrating AI capabilities to help their customers identify key customer insights and build AI Agents to improve customer service, provide assisted shopping, identify areas of optimization, and even enable merchandisers to interact with the platform using AI chatbots.

Salesforce provides a robust framework that enables retailers and their partners to build AI Agents across the Salesforce stack. Salesforce Data Cloud is the new underlying foundations of all data models across the Salesforce ecosystem, enabling merchants to leverage dynamic personalization across channels.

Shopify supplements its own suite of AI-native features on Shopify Magic with Sidekick: a powerful assistant which is becoming a valuable business partner, helping merchants orchestrate experiences faster and, most importantly, visualize important AI-powered analytics drawn from their own data. 

Hyper-personalization

Hyper-personalization is not just a buzzword. Consumers favor brands that give them one-to-one experiences across touchpoints.

That’s because they don’t want to waste time cutting through generic experiences; they want tailored journeys that resonate with their own preferences and are consistent from the storefront and app to email and social. 

And it pays off. According to one study, brands can generate 40% more revenue from personalization activities. 

By using machine learning, brands can continuously learn from behavior patterns to build individual customer profiles – rather than segments. 

Experiences that no one else will have. The journey from the homepage, the offers, the loyalty incentives, all built for individual customers.

Scaling the voice of the customer 

For large retailers, sorting through huge piles of valuable customer feedback is a time-consuming task. What’s more, it’s often trapped in silos – scattered across online forums, social media, surveys, and customer service tickets. 

AI helps to unify the voice of customer by parsing through huge volumes of feedback, helping to uncover themes, feelings, and intent, helping to spot friction in the customer journey before it becomes a problem. 

Examples include tools like Qualtrics XM, Sprinklr, and Salesforce’s Service AI, which flag patterns and identify insights for product, CX, and service teams.

Predictive behavior

AI helps brands analyze previous patterns to predict future behavior, moving from “what happened” to “what’s next”.

Whether it’s forecasting when a high-value customer is about to churn, identifying which segment is ready to re-engage, or suggesting the next-best product to upsell, platform that use AI, like Bloomreach and Adobe Experience, can identify anticipation in the customer experience.

Predictive AI is also being used in inventory, fulfillment, and promotions. For example, it can detect spikes in a region and push notifications to fulfilment systems to reallocate stock preemptively.

AI enhances the returns process

Brands are incorporating AI capabilities to enhance their returns processes, using automation to reduce friction for customers while cutting operational costs for their teams.

For example, the global women’s activewear brand Sweaty Betty integrated AWS AI Stack (Lookout for Vision Service) as platform a platform-agnostic AI interface into its tech stack. This uses image recognition to carry out automated visual inspections of products, training it in helping to identify defects.

This lets Sweaty Betty customers start a return by uploading images of the product; the AWS AI interface then analyzes the images and initiates the return and refund processes if a fault is found. 

Empowering teams beyond customer service

AI-powered chatbots have become the default approach for frontline customer service. Using machine learning, chatbots help to answer common questions and resolve basic issues directly with customers – 24/7.

According to one study, AI-powered customer service can reduce handling time by up to 70%.

In-store, customer service agents can use devices to provide personalized recommendations as well as upsell by showing popular items bought with the product, or suggestions based on the individual’s previous buys.

Beyond customer-facing teams, AI-driven insights into the customer journey can help CRO teams identify friction points and optimize flows faster.

Marketing and merchandizing teams will also benefit, using AI to personalize messaging, push notifications, and product offers as well as carry out A/B testing to refine campaigns.

This means AI isn’t just saving time for support teams; it’s quickly becoming part of the central nervous system of retail. 

AI in retail

Facing sky-high expectations and a constant need to differentiate, retailers are using AI to turn overwhelming amounts of data into actionable insights to better serve their customers.

With greater understanding, retailers can deliver greater customer-first experiences that are personalized, predictive, and consistent across every touchpoint.

That’s how customers will choose you over your competitors. And that’s how they’ll become loyal customers.