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AI in Retail: How Retailers Can Drive Unprecedented Personalization and ROI

The onus is on retailer marketers to maximize the potential of AI in retail to overcome the challenges in today’s dynamic landscape – or risk falling behind.

With AI, we’re on the verge of achieving retail marketing’s holy grail: true one-to-one personalization. This is crucial because recognizing the individuality of each shopper and delivering a custom retail experience that reflects their unique needs and desires is a must to remain competitive, especially with the growing competition from eCommerce pure-plays like Amazon. Consumers today don’t just want personalization. They expect it. 

Eagle Eye’s recent eBook, AI and the Current State of Retail Marketing, quotes research demonstrating that 71 per cent of consumers expect personalization. And even more (76%) are frustrated when they don’t receive personalization. It comes as no surprise then that AI adoption in retail is expected to surpass 80 per cent in the next three years. 

The onus is on retailer marketers to maximize the potential of AI in retail to overcome the challenges in today’s dynamic landscape – or risk falling behind.

AI is set to impact personalization efforts, the importance of data in building predictive models, and how retailers can optimize AI outputs for maximum results.

The transformative potential of AI in retail

The role of AI in business and society is still finding its place. Since the emergence of ChatGPT in 2022, the world’s eyes have been transfixed by generative AI without fully understanding how it will be applied or where it should be positioned.

There is a difference between generative AI – the term on everybody’s lips – and predictive AI. Generative AI engines rely on existing data patterns to create something new. In contrast, predictive AI uses patterns in historical data to project future outcomes. In other words, it can support strategy formulation and decision-making. Retailers already make data-driven decisions, but predictive AI’s emergence can take it to the next level.

Retail has already experimented with generative AI for language-based applications in areas like customer support, but predictive AI also delivers results. Critical functions like promotion spending, offer permutation and big-data-based consumer trend forecasting are already possible because of the retail industry’s primacy of numbers (specifically, UPCs). Generative AI has its uses, but predictive AI is transformative for an industry built on barcodes.

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3 critical points for retailers:

  1. The need for data quantity and quality: Predictive AI is an exciting development in retail, but it remains in its early stages. Just as future customer behavior cannot be predicted from a single data point, usable retail AI outputs (like measuring a shopper’s brand affinity) need sufficient data to be effective. Similarly, AI models trained on poor-quality data will generate subpar outputs. Therefore, pre-processing data, from that perspective, is of paramount importance.
  2. Optimal integration of AI outputs: When implementing an AI model’s outputs, there is a trade-off between full automation (AI outputs trigger events such as emails, promotion offers sent to clients, generated images used for real-time ads, etc.) and systematic manual review. Sometimes, the choice is obvious. However, finding the right implementation balance often requires adapting existing tools (or utilizing purpose-built monitoring dashboards), putting common-sense guardrails in place, and enforcing manual review when AI predictions are uncertain.
  3. An AI-driven virtuous circle: A significant driver of the relevance of AI outputs (prediction/content) is the ability to observe whether predictions are correct – or not. This allows for the next round of AI system optimization, driving performance upwards. This continuous improvement cycle can end up being a solid competitive advantage. The first step of the journey to AI integration might seem high, but retailers should understand that optimizations multiply quickly, and the initial performance improvements are only the beginning.

Retailer marketing challenges: How AI helps brands break new ground

Like the transition ancient humans made when they moved from stone arrowheads to copper and bronze, AI is a tool designed to help us overcome the same challenges and achieve the same goals. In other words, AI is a state-of-the-art arrowhead. But it’s still just an arrowhead.

That being said, AI can be used in a few impactful ways:

  • From generative to predictive: Generative AI can provide retailers with tools for addressing engagement through creating promotional materials; predictive AI can dig further into retailer data to optimize offers and promotions in several contexts, including:
  • Personalized brand or product recommendations
  • Customized discount percentages based on customer data
  • Predictive cross-selling
  • Hyper-personalized loyalty program engagement
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Using already available data, retailers can understand customers’ minds. And this translates to knowing what they want, potentially even before the customers know it.

  • Personalization for better outcomes: It’s widely accepted that personalization is the next frontier of the retail marketing landscape. But to achieve it, retailers need to leverage all of the data at their disposal. And that’s where AI comes in, allowing retailers to move from 5 per cent data utilization to close to 100 per cent data utilization, pumping up the value of this coveted asset brands already have. Forget eight offer variations for 10-million customers. With AI, we’re looking at the potential of 10-million variations for 10-million customers.
  • Explosive ROIs on promotions, loyalty programs, and more: Retailers face continuing challenges in providing value to consumers via loyalty programs, promotions, and sales. Consider this, demonstrates that:
  • 36% of customers failed to renew their loyalty program memberships because of a lack of engagement

  • 31% of customers failed to renew their loyalty program memberships because of too little perceived value

AI can boost ROI in all these areas by moving away from mass promotions that apply to everyone to intelligent promotions based on individual customers. This is already possible, but AI can drill far deeper than ever due to superior data utilization. Leveraging AI in this way will also make retailers more efficient in their marketing spend by increasing campaign success rates and reducing wastage.

AI still needs a copilot

AI, including the buzzy generative AI and traditional AI and machine learning tools, can accomplish what retailers previously dreamed of. But, purchasing an AI platform and pressing a button isn’t enough. And it certainly won’t guarantee that retailers will be printing money until the end of time.

Implementing AI in retail operations is nothing less than a business transformation. As such, it requires rethinking processes, getting organizational buy-in, training team members and having a viable long-term strategy. The promise of AI is efficiency and optimization, but before that promise can be realized, there must be preparation.

AI isn’t a magic bullet. It’s a tool that must be purposed and repurposed if retailers are to turn this emerging technology into more customers, more engagement, and more profits in the short term. But where it will take retail and how it will take us there five years from now is still a massive work in progress.

AI in action: Leading retailer’s AI-powered gamification

The personalized challenges that Carrefour, one of the world’s largest grocery chains, is running, together with its suppliers, is probably the most advanced, personalized loyalty/promotional program being implemented at scale today. It’s powered in part by AI and machine learning algorithms. And it’s something Australian retailers can take inspiration from.

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Carrefour’s Challenges, built and run by Untie Nots (part of the Eagle Eye group), uses AI to create custom thresholds and goals for loyalty program members based on user purchase history, offer frameworks from suppliers, and predictive analysis of what will trigger the next desired action.

The gamification of the shopping experience through the Challenges initiative provides “the nudge” that is very effective at incentivizing customers and members to engage with Carrefour, its promotions and its loyalty program.

Powering next-generation retail 

As we navigate this new landscape, organizational readiness, strategic planning, and ongoing optimization will be key to realizing AI’s full potential. With each advancement, retailers move closer to unlocking new dimensions of customer engagement and profitability, setting the stage for a future where AI-driven personalization becomes not just an expectation, but a cornerstone of retail excellence. 

Find out more about AI and the current state of retail marketing in Eagle Eye’s latest eBook.

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