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.

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