Why Australian retailers are losing sales to fragmented data and what AI personalisation can do about it
Most retailers know what their customers bought. Far fewer know why, what they looked at first, what they almost bought, or what brought them back. That gap between transactional data and behavioural insight is the central problem that AI-powered personalisation is starting to address, and the gap between retailers who are solving it and those who are not is widening quickly.
Global commerce platform Nayax this week launched new AI-powered product discovery and personalisation capabilities for retailers, bringing together shopper data from points of sale, eCommerce platforms, and marketing systems into a single view. The launch is part of a broader industry shift toward unified commerce, where online and in-store data feed the same intelligence rather than sitting in separate systems that never speak to each other.
Yael Kochman, General Manager at Nayax, describes the problem the technology is designed to solve. “Shoppers do not think in channels; they just shop. Yet for years, the technology behind retail has been built in silos, forcing retailers to stitch together fragmented tools that never quite tell the full story,” she says. That fragmentation is not a new problem. But the cost of not solving it has become more visible as consumer expectations for personalised, seamless shopping experiences have risen sharply.
The data fragmentation problem
The core issue for most retailers, particularly smaller ones, is that their customer data lives in multiple disconnected places. The point-of-sale system captures in-store transactions. The eCommerce platform captures online behaviour. The email marketing tool captures engagement data. Each system works within its own boundaries and none of them automatically shares insights with the others. The result is that a customer who browses a product online, visits the store to look at it, and then purchases online represents three separate data events that most retailers cannot connect into a single customer story.
That fragmentation has real commercial consequences. McKinsey’s analysis reveals that leading companies generate 40% more revenue specifically from their personalisation efforts compared to average performers. The differential stems from the ability to deliver relevant experiences at every touchpoint, from initial discovery through to purchase and post-purchase engagement. Retailers who cannot connect those touchpoints cannot deliver those experiences regardless of how good their products are.
The visual search dimension adds another layer. Research shows that AI visual search increases session duration by 33%, with longer sessions correlating with higher conversion probability and larger basket sizes. 62% of Gen Z and Millennials want visual search capabilities as standard functionality, meaning for retailers targeting younger demographics the expectation is already set even if the technology is not yet in place.
What personalisation actually delivers
The business case for personalisation is well established at the enterprise level. The question for smaller retailers is whether the returns justify the investment and complexity of implementation. The data suggests they do, even at smaller scale. AI helps retailers predict intent, reduce friction, personalise content delivery, and guide shoppers through faster, more confident purchase decisions. Each of those outcomes translates directly into revenue for a retailer of any size.
The eCommerce personalisation market is growing at a 24.8% compound annual growth rate, reflecting both increasing adoption and the deepening sophistication of implementations across the industry. What was enterprise-only technology three years ago is becoming accessible to mid-market and smaller retailers as platforms integrate these capabilities natively rather than requiring custom builds.
For product-based businesses specifically, the recommendation engine dynamic matters. Shoppers who click on personalised product recommendations are significantly more likely to purchase than those who browse without them, and the average order value from recommendation-influenced purchases tends to be higher. For a small retailer with a deep catalogue but limited floor space or screen real estate to surface it, intelligent recommendations can effectively act as a digital sales assistant that knows the customer’s history and taste.
Where Australian retailers stand
Australian retailers are moving on this faster than many comparable markets. A 2025 Salesforce report found that 77% of ANZ retailers believe AI agents will be essential for competition within a year, with 74% planning to increase their AI spending. 91% of ANZ retailers are now investing in generative AI to create virtual showrooms, automated product photography, and interactive demonstrations.
More than 17 million Australians now shop online regularly, reflecting a structural shift toward digital-first consumption that has accelerated since the pandemic. Online channels account for approximately 25 to 30% of total retail sales, with projections pointing toward 30 to 35% and beyond. For retailers who still think of online and in-store as separate businesses, that trajectory makes the case for unified data management more urgent every year.
The challenge for smaller retailers is that the investment in AI-powered personalisation has historically been front-loaded. Enterprise platforms required significant technical integration, ongoing maintenance, and data science capability that most small retailers do not have in-house. The shift toward natively integrated solutions, where the personalisation capability is built into the payments and commerce platform rather than added on top of it, changes that calculus.
What smaller retailers should do
For small business owners in retail, the practical starting point is not technology selection. It is data audit. Understanding what customer data you currently collect, where it lives, and how much of it is siloed from your other systems is the foundational step before any personalisation investment makes sense. A retailer with clean, connected data across online and in-store channels is in a meaningfully better position to benefit from AI personalisation than one adding new tools on top of fragmented existing data.
The second step is understanding which part of the customer journey represents the biggest opportunity. For businesses with high browse-to-purchase drop-off rates, product discovery and recommendation improvements will have the most direct impact. For businesses with strong first purchase rates but low repeat purchase rates, post-purchase personalisation and loyalty integration matters more. The tools being built into modern retail platforms increasingly address both, but knowing which problem you are solving first makes implementation significantly more manageable.
The competitive dynamic is moving faster than most small retailers recognise. Nearly 90% of retailers either actively use AI in their operations or are assessing AI projects, with retail executives expecting AI spending outside of traditional IT to surge by 52% in the next year.
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