Predictive AI isn’t just smart, it’s strategic. This take on retail’s future dives into what matters: precision, pragmatism, and tech that actually works.
Cédric, with your extensive experience in retail analytics, can you share what initially drew you to the world of predictive AI and retail marketing?
I spent the bulk of my career in data analytics for the retail sector, and as machine learning and AI models began to proliferate, it was a natural progression for me to immerse myself in those possibilities. The work we’re doing now at EagleAI is tremendously exciting, not just from a technological perspective, but also in terms of the impact we’re making for retailers and consumers across the globe.
In your view, what are the most crucial components that retailers need to grasp to maximize their return on investment from AI-driven marketing strategies?
I’d say the first thing retailers must grasp is a pragmatic approach to AI. The twin temptations to take a big swing or implement the latest most exciting AI application are real, so retailers should commit to defining proper use cases and pursuing projects that have a definable path to ROI. Looking for ways to implement AI that are scalable and quick to market will serve retailers’ aims better than overly large, complex initiatives.
The second component must be data. AI thrives on data, so retailers must have access to high-quality, structured data, and as we describe in our eBook, AI & the Current State of Retail Marketing, just 5% of companies fully utilize the data available to them. Implementing an AI model’s outputs requires a strategy for making the most of that data, which may involve adapting existing tools or maintaining some manual oversight.
Third, retailers must have the proper technology infrastructure in place to support AI. The right AI tools can deliver real-time individualized offers based on thousands of metrics, but it must be able to connect to systems that can deliver the data necessary to inform those metrics.
The final component is finding the right technology partner with AI expertise to help accelerate an AI transformation. According to Deloitte’s 2024 US Retail Industry Outlook, half of all retail executives lack confidence in their company’s ability to use AI effectively – the right partner can close that gap.
What are some of the most common mistakes retailers make when integrating predictive AI into their marketing efforts, and how can these be avoided?
For retailers, the first mistake is often not seeking a retail-specific solution that can be easily integrated into their tech stacks. Not every AI tool is created equal, and a solution built for the unique needs and use cases of retailers will certainly generate more positive outcomes. Apart from choosing the right solution, some of the most prevalent barriers we’ve seen related to data, such as incomplete, inconsistent, or siloed data across systems and difficulty in integrating legacy loyalty systems or existing retail infrastructure such as POS, supply chain tools, and digital platforms with modern AI solutions. There are also learning curves and potential organizational resistance to change due to a lack of skill set and training, as well as difficulty aligning AI adoption to existing workflows and processes. These are all addressable through a considered, strategic approach to AI adoption, and working with the right AI partner.
Could you elaborate on the concept of hyper-personalization in retail? How does predictive AI facilitate more targeted and meaningful customer engagements?
In the past, retailers have built targeted marketing strategies by creating broad segments of similar consumers and targeting them with specific campaigns – many still do. Predictive AI turns those audiences of thousands of customers into segments of one, achieving that long sought-after pinnacle of customer-brand experiences: true one-to-one engagement.
We call that hyper-personalization, and predictive AI enables retailers to achieve it at scale. For large retailers with customer bases in the millions, even with their resources and access to customer data, scaling personalization has been somewhat of a technological glass ceiling. Predictive AI makes one-to-one personalization a reality.
Predictive AI identifies opportunities for cross-selling and upselling by analyzing past purchases and customer preferences. It can then generate personalized product bundles or recommendations for complementary items. AI enables retailers to generate these offers from a near-infinite offer pool and do it at the precise moment that will influence a sale or specific action, combining existing data with contextual triggers to unlock new revenue development opportunities.
For retailers at the beginning of their AI journey, what foundational steps would you recommend to ensure a smooth and effective implementation?
Again, taking the time to develop well-defined use cases and identifying scalable AI initiatives that can deliver ROI is an important foundational step. We would also recommend that retailers prioritize integrating AI into existing retail operations, like a current loyalty program, at first. Prior to implementation, they should evaluate current workflows and processes, acquire stakeholder buy-in, and train teams on how to use and get the most out of the AI tools they’re considering. And perhaps most importantly, work with partners that understand industry-specific nuances, can streamline the adoption process, and help establish a sustainable competitive advantage.
How do you see the retail AI landscape evolving in the next few years, especially concerning customer loyalty and personalized marketing?
AI will continue to be integrated into more aspects of existing retail operations. We expect AI to eventually help retailers utilize more of the data generated through customer interactions across all digital and physical touchpoints closer to real-time. This will ultimately allow retailers to tailor relevant offers and products to each shopper as an individual, near real-time, and at scale.
AI’s ability to quickly analyze a shopper’s purchase history, loyalty information, account preferences and other relevant data will also help redefine loyalty program experiences. We’re already seeing some retailers create custom experiences and implement more effective gamification elements within loyalty frameworks with the help of AI. For example, UK retailer Tesco introduced its Clubcard Challenges this year, a program that delivers unique challenges for loyalty members to complete and receive personalized rewards. These initiatives will become more widespread in the next few years.
What strategies would you suggest for retailers to bridge the gap between AI adoption and practical implementation to realize tangible business benefits?
We would suggest four best practices to maximize any retailer’s ROI on AI initiatives:
- Prioritize pragmatic, scalable and quick-to-market AI applications that meet specific business objectives.
- Utilize only those AI tools that allow for the dynamic creation of offers and customer interactions based on consumer behavior and preferences.
- Be sure to use AI solutions that easily integrate with existing systems and marketing tech stacks to optimize processes, improve data utilization, and enhance customer experiences without disrupting current operations.
- Partner with AI experts or vendors specializing in retail-focused AI solutions that can provide the necessary expertise and technology to ensure a successful AI implementation, avoiding pitfalls and accelerating adoption.
Can you provide examples of retailers who have effectively used AI to enhance their customer interaction strategies? What outcomes have they achieved?
As we mentioned, Tesco’s Clubcard Challenges uses AI to create bespoke reward thresholds for each participant, drawn from insights into that customer’s past purchase history and preferences, which are then analyzed and processed by predictive AI algorithms. The result is a truly personalized, customized engagement with participants, achieved at a previously impossible scale.
A similar program from French grocery retailer Carrefour employs AI-powered gamified Challenges that encourage customers to modify their behavior to earn their reward by exploring a different product category or achieving buying targets rather than simply waiting for their loyalty points to accrue for their anticipated discounts. This active participation deepens engagement, not just with the loyalty program but with the retail brand itself.
For these loyalty-integrated, AI-powered Challenges initiatives, as well as other similar initiatives implemented by EagleAI, retailers see an average of seven dollars in incremental sales per dollar spent on promotions, a 7:1 ROI.
How can small and medium-sized retailers leverage AI to compete with larger enterprises in offering personalized customer experiences?
AI’s true power in retail comes from leveraging what retailers already know: their data and customer insights. The fundamentals of retail marketing haven’t changed. If a small or mid-sized retailer has an advantage in customer engagement, responsiveness or tailored marketing, AI will help them accelerate that advantage.
AI can also close the resource gap between smaller retailers and enterprises. By integrating retail-specific AI solutions, a small retailer can access the customer insights and data analytics capability that a larger retailer would once have employed a department of analysts to achieve.
Looking ahead, what innovations in predictive AI do you anticipate will revolutionize retail marketing and customer engagement over the next decade?
We predict a new wave of AI integration across multiple retail sales channels to create seamless omnichannel experiences. Retailers will deploy AI in physical stores, mobile apps, websites, social media, and other digital platforms to ensure a consistent and personalized experience across all touchpoints. Dynamic pricing will become a commonplace practice, allowing retailers to use prices to maximize revenue, improve competitiveness, and capture more sales at the best price point for each customer. Inventory and supply chain management will also see positive impacts from AI, where retailers will be able to optimize inventory, reduce costs, increase product availability, and ensure they meet customer demand without tying up excess capital in unsold stock.
But as every iteration of AI has shown us, it may well be the unlooked-for evolution that revolutionizes retail. With this technology, the possibilities are endless.
Cédric Chéreau
Managing Director at EagleAI
Cédric Chéreau has more than 20 years of experience in retail analytics, supporting retailers and FMCG companies from Europe and North America. He holds a Master of Science in Marketing from EDHEC.