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Jonathan Reeve, Eagle Eye

Generative to Predictive: AI’s One-to-One Personalisation Revolution

Retail marketing is on the verge of achieving true one-to-one personalisation. Customers no longer hope for a customised shopping experience, they expect it. In order to remain competitive, brands must recognise the individuality of each shopper and deliver a tailored retail experience that best reflects their needs and desires, particularly when up against eCommerce pure-plays like Amazon. 

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 per cent) 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. 

Now, the onus is on retailer marketers to leverage the power of AI to overcome the challenges in our modern dynamic and digital 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.

Generative versus Predictive AI in retail

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.

Three considerations for retail marketers implementing AI:

  1. AI works when data quality is clean: 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. Striking a balance 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. AI will only improve over time: 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 can help brands to become trailblazers in the retail industry

AI can be used in several impactful ways:

  1. A healthy mix of generative and predictive AI: 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

  2. Personalisation for every shopper: 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 five 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.
  3. Astronomical ROI on promotions and loyalty programs: Retailers face continuing challenges in providing value to consumers via loyalty programs, promotions, and sales. Consider this, research demonstrates that:

    36 per cent of customers failed to renew their loyalty program memberships due to a lack of engagement
    31 per cent 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.

AI still needs a helping human hand

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 organisational buy-in, training team members and having a viable long-term strategy. The promise of AI is efficiency and optimisation, but before that promise can be realised, there must be preparation.

AI in action: How a leading retailer is maximising the potential of AI

The personalised challenges that Carrefour, one of the world’s largest grocery chains, is running, together with its suppliers, is probably the most advanced, personalised 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.

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 incentivising customers and members to engage with Carrefour, its promotions and its loyalty program.

Unlocking AI’s potential begins with planning and preparation

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

Jonathan Reeve
Author: Jonathan Reeve

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