Focus on Predictive AI: 3 Retail Challenges, 3 Real-Life Solutions

Implementing artificial intelligence effectively may be the most pressing preoccupation facing retailers today. This is perhaps why even though global AI in the retail market is expected to grow by 30 percent, half of all retail executives lack confidence in their company’s ability to implement it effectively, according to Deloitte’s 2024 US Retail Industry Outlook. 

Generative AI is often the first focus, as it enables retailers to create original content, like patterns, images and text. However, shifting their emphasis to predictive AI may offer retailers more solutions to the challenges they already face. Predictive AI leverages data to deliver precise insights that forecast trends, optimize marketing strategies, and drive targeted customer engagement, ultimately leading to increased sales, strengthened customer loyalty, and maximized return on investment.   

As we detail in our recent AI guide, predictive AI helps retailers get the most out of their data, finally realize personalization at scale, and reimagine the loyalty experience. There are examples of retail brands currently using AI that illustrate its potential in each of these applications. 

Retail Challenge No. 1: Getting the Most Out of Data 

Despite having more data than ever to unlock meaningful customer engagement, only 20 percent of retailers are using analytics to its full potential. While predictive AI cannot find new data sources, connect data or create better data strategies, it can help retailers better utilize the data they have at hand to achieve personalization, better customer engagement, and more accurate forecasting. 

Retail Challenge No. 2: Scaling Personalization Efforts 

If there's one simple truth in retail marketing, it’s this: an offer or promotion that resonates with a customer’s individual needs and wants is far more likely to lead to a sale than an anonymized deal, driving incremental revenue more efficiently than traditional mass promotions. Therefore, retailers have long strived to advance their personalization capabilities.  

Improving personalization capabilities and executing them at the scale needed to affect both revenue and the broader customer experience are two different things. For large retailers with customer bases in the millions, even with their resources and access to customer data, scaling personalization has become somewhat of a technological glass ceiling. 

Retail Challenge No. 3: Realizing the Full Power of Loyalty Programs 

With 61 percent of retailers focusing on loyalty program points to drive retention and loyalty, many are missing opportunities for deeper personalization and value for their shoppers. According to McKinsey, 71 percent of consumers expected personalized offers from retailers, and 76 percent said they were frustrated when they didn’t receive them. Retail loyalty programs are much more powerful than simple earn-and-redeem engines; retailers need predictive AI tools to help them harness this power. 

AI Improves Data Usage: Starbucks 

Starbucks has had access to a staggering amount of customer data, due in part to the chain’s high transaction volume, popular mobile app launched in 2011, and geographic footprint, but also because the brand has been committed to data and analytics for years. In 2019, however, Starbucks took its data usage to the next level by launching Deep Brew, the brand’s AI-based platform.   

Deep Brew utilizes all of the data from Starbucks’ enterprise data analytics platform and its data lake to drive the brand’s personalization engine. The results are exemplary: Starbucks has seen a 13 percent year-over-year increase in loyalty program members through its AI-powered Deep Brew tool, adding 4 million new members in Q4 2023 alone. 

AI Achieves Personalization at Scale: Tesco 

One of the most compelling examples of predictive AI’s impact is the significant revenue boost seen by retailers that implement AI-driven personalization. Studies show that retailers using AI to personalize customer experiences see a 40 percent increase in revenue compared to those that don’t. By tailoring product recommendations, offers and content to individual customers, AI increases the likelihood of conversion and encourages repeat purchases. 

Tesco’s Clubcard Challenges is one example of this in action. The initiative uses AI to create bespoke thresholds for each individual participant, drawn from insights into that customer’s past purchase history, preferences and other contextual data points, then analyzed and processed by predictive AI algorithms. The result is a truly personalized, individualized engagement with participants, achieved at scale, and driving results for Tesco. 

AI Enhancing Loyalty: Carrefour 

It’s no coincidence that Tesco’s Clubcard Challenges is a loyalty-integrated initiative; the Clubcard loyalty program provides the data and insights necessary to personalize the challenges. European supermarket giant Carrefour operates a similar program, leveraging predictive AI to encourage more frequent engagement among its loyalty members and using predictive analytics to determine what will trigger the next customer action. Carrefour’s Challenges program then automatically presents that trigger to the customer through the app. Carrefour’s initiative also incentivizes customers to engage with MyClub and Carrefour by turning their shopping experiences into games and making them enjoyable along the way. 

Predictive AI plays an important role in each of these real-life examples of AI in action. With a focus on predictive AI and the right technology partner, retailers worldwide can enjoy the same revenue, engagement and ROI benefits the brands above are experiencing.   

Cédric Chéreau is managing director of EagleAI, a leading SaaS and AI technology company enabling retail, travel and hospitality brands to earn the loyalty of their end customers by powering their real-time, omnichannel and personalized consumer marketing activities, at scale.