AI Adoption in Retail: Challenges and Opportunities

AI Adoption in Retail: Challenges and Opportunities

By Jonathan Reeve (pictured), Vice President APAC, Eagle Eye

With Artificial Intelligence (AI), we’re on the verge of achieving the ‘retail holy grail’: true one-to-one personalisation. This is crucial because recognising 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 personalisation. 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 personalisation. And even more (76%) are frustrated when they don’t receive personalisation. 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 retailers to maximise the potential of AI to overcome the challenges in today’s dynamic landscape – or risk falling behind. AI is set to impact personalisation efforts, the importance of data in building predictive models, and how retailers can optimise AI outputs for maximum results.

Transforming the future of retail with AI

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.

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 behaviour 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 utilising 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 optimisation, 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 optimisations multiply quickly, and the initial performance improvements are only the beginning.

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:

  1. 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 optimise offers and promotions in several contexts, including:
  • Personalised brand or product recommendations
  • Customised discount percentages based on customer data
  • Predictive cross-selling
  • Hyper-personalised loyalty program engagement

Using already available data, retailers can understand customers’ minds. And this translates to knowing what they want, potentially even before the customers know it.

  1. Personalisation for better outcomes: It’s widely accepted that personalisation 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 utilisation to close to 100 per cent data utilisation, 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.
  2. 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, research 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 utilisation. Leveraging AI in this way will also make retailers more efficient in their marketing spend by increasing campaign success rates and reducing wastage.

Powering next-generation retail

As we navigate this new landscape, organisational readiness, strategic planning, and ongoing optimisation will be key to realising 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 personalisation 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.