By Philip Plambeck, Managing Director, Computop UK
In 2021, McKinsey reported that 71% of consumers expected companies to deliver personalised interactions and 76% became frustrated when this didn’t happen. Since then, personalisation has only become more central to online retail strategy, with businesses investing heavily in data collection, targeted promotions, curated content, and advanced measurement tools.
But where does payment fit into this blueprint for personalisation? Increasingly, near the very top.
The Visa Global Digital Index 2025 highlights a clear shift, suggesting that retailers must think beyond omnichannel commerce and build unified strategies that embed digital features, especially preferred payment methods, directly into the customer journey.
Payment personalisation goes far deeper than simply offering multiple payment options. Two approaches are generating the strongest results: geographical personalisation and behavioural personalisation.
Geographical personalisation, or localisation, prioritises payment methods based on where a customer is shopping from. A Polish shopper, for example, expects BLIK; a Dutch consumer expects iDEAL; Brazilian customers increasingly default to Pix. When these expectations are met immediately at checkout, conversion rates rise sharply. We have seen that enabling the right local methods in the right markets has produced conversion lifts of 39–46% for some retailers. The explosive rise of Pix in Brazil—growing to 150 million users in under four years—illustrates how quickly local payment habits can form when the experience is fast, familiar, and frictionless.
When it comes to behavioural personalisation the focus is on recognising the customer and optimising for mobile experiences. When a returning shopper reaches the checkout, their previously used payment method should appear first. This creates a “fast lane” effect that is especially powerful on mobile devices, where more than 75% of online shopping in the UK now takes place. Reducing unnecessary scrolling or decision-making lowers cognitive load and leads to higher completion rates, like the simple, near-instant checkout experiences pioneered by one-click flows and mobile wallets.
Both forms of personalisation are already delivering significant commercial impact. Retailers implementing them typically see a lift in conversion increases of 20–40%, in our experience. This matters in a landscape where cart abandonment averages over 70% and where consumers will abandon a purchase if their preferred payment method isn’t available. Beyond conversion, the loyalty effect is substantial: around 80% of consumers are more likely to buy from brands that offer personalised experiences, and frictionless payments make customers more inclined to return and spend more frequently.
The scale of opportunity becomes even clearer through the growth of real-time payment systems. Pix processed 42 billion transactions in 2023 alone, worth $3.5 trillion USD—a 75% year-over-year increase. It succeeded because it solved real customer needs with instant, free, always-available payments, and its dominance shows how quickly habits can shift when payment feels personal and effortless.
How generative AI will elevate payment personalisation
Generative AI is set to amplify these benefits dramatically. Today, most personalisation is driven by predefined rules such as geolocation, device type, or past behaviour. GenAI, however, enables retailers to shift from rules-based to predictive and adaptive personalisation. It can analyse behavioural signals, session data, basket composition, and historical patterns to anticipate the payment method a customer is most likely to choose—even for first-time visitors.
This intelligence also enables continuous optimisation of the checkout experience. Instead of relying on static payment ordering, AI can learn from each interaction and dynamically adjust which methods appear first based on conversion probability, time of day, traffic source, and customer segment. It can also create far more nuanced micro-segmentation, identifying groups such as “price-sensitive mobile shoppers who prefer instant bank transfers” and tailoring the checkout flow accordingly.
GenAI has the potential to balance between fraud prevention and friction reduction. By analysing risk in real time, it can fine-tune authentication flows so trusted customers face fewer unnecessary hurdles while higher-risk scenarios trigger stronger verification. At the same time, AI can personalise the micro-copy that appears at checkout, highlighting instalment plans, rewards, or instant bank options based on what’s most likely to drive completion for everyone.
What retailers should consider next
To move beyond basic payment acceptance and into true personalisation, retailers should start with a clear view of their customer journey. Understanding where customers shop from, how they navigate a website, which devices they use, and which payment methods they choose by segment is essential.
From there, they can implement dynamic payment method ordering powered by geolocation, customer history, and, before long, AI-driven prediction models. If they treat these changes as revenue optimisation, and not just improvements to the user experience, in markets where products and prices are similar, the checkout will become a decisive differentiator.
If in doubt, quantify the opportunity. How much of your current abandonment stems from payment friction? And what would a 20–40% conversion lift represent in annual revenue?
