Last updated on July 3, 2026
How AI and Data Analytics Are Transforming Customer Segmentation in E-Commerce and Retail
Summary: E-commerce platforms are leveraging AI-driven segmentation engines to deliver hyper-personalized shopping experiences, shifting from broad demographics to individualized, predictive storefronts. Kantar research indicates a 47% rise in AI shopping, with 58% of consumers utilizing generative AI for recommendations over traditional search, signaling a rapid shift toward the “non-human consumer” model. For more insights, visit Kantar.
E-commerce and retail platforms increasingly present a different storefront to every shopper who visits. Homepage banners highlight products related to what a customer viewed during a previous session, search results reorder themselves around recent clicks, and AI-driven chat assistants follow up on items left behind in an abandoned cart. Each of these moments is generated by a customer segmentation engine working continuously behind the interface, built to understand individual shoppers on e-commerce platforms rather than treat them as members of a broad demographic group.
Consumers have noticed. The leading market research company, Kantar, describes this emerging behaviour as the rise of the non-human consumer, a shopper whose decisions are increasingly delegated to artificial intelligence rather than made independently. The firm’s research found that more than a third of global consumers already use AI to shop, a 47% increase within a single year, and that 58% have replaced traditional search engines with generative AI tools when looking for product recommendations. For shoppers, the experience is beginning to resemble walking into a shop and being attended by someone who already understands their taste, their preferences, and what they are likely to need next, rather than browsing shelves stocked the same way for every visitor.
What is far less visible is how retailers built this capability, a process that required far more than upgrading a single recommendation algorithm.
The quiet death of the customer “bucket”
For decades, customer segmentation on e-commerce and retail platforms meant sorting shoppers into a manageable number of static groups, such as age bands, income brackets, or loyalty tiers, and treating everyone within a group in the same way. Artificial intelligence is dismantling that logic. It predicts an individual’s next likely choice directly from that person’s own browsing and purchase history, rather than inferring it from whichever group they were once assigned to.
This progress is reflected in the platforms operating behind the scenes. Gartner’s 2026 evaluation of customer data platforms describes a market dividing into two models: one where the platform serves as a foundational data layer beneath a broader application suite, and another, increasingly relevant for retail, built around what the firm calls agentification, in which the platform ships with packaged AI agents capable of autonomously deciding and acting on customer data rather than simply exporting a list for a marketer to use manually. The same research notes that these decisions are no longer purely a marketing matter, since buying groups for these platforms now typically span IT, sales, supply chain, finance, and customer service, reflecting how deeply customer data now touches the wider business.
What is actually running underneath
A few specific technologies explain the progression from static customer segments to real-time, individual-level prediction:
- Identity resolution. Before any model can personalize anything, a retailer has to recognize that the app user, the in-store loyalty card, and the abandoned web cart belong to the same person. Vendors are now combining dozens of machine-learning models to stitch these identities together without relying on rigid, manually defined schemas.
- Predictive models paired with generative AI. Machine learning still handles propensity scoring and demand prediction, while generative AI increasingly drives the content and conversation layer on top of it. Market intelligence firm, IDC tracks this as one of the fastest-growing parts of enterprise AI spend.
- Operational embedding, not dashboards. The latest customer experience research describes a gradual move away from static dashboards and quarterly journey maps toward embedding customer segmentation data directly inside the AI models that power live interactions on e-commerce platforms. As a result, segmentation becomes something the system acts on automatically rather than something a team reviews periodically.
What retailers are investing
This is not a side project for retailers. IDC places retail among the three largest industries for AI investment worldwide, alongside software and information services and banking, with the sector spending roughly $25 billion on AI in 2024 alone, as part of a combined top-three-industry AI spend growing at better than a 25% annual rate through 2028.
That investment does not guarantee success on its own. Research cautions that brands risk damaging customer trust when they roll out self-service AI before the underlying data and segmentation logic behind it are actually ready (Forrester). The technology is only as good as the segmentation logic and data quality feeding it.
Where this goes over the next decade
Three developments are likely to shape customer segmentation across e-commerce and retail platforms over the next several years.
First, segmentation keeps moving from periodic to continuous, with customer data living inside the operational models themselves rather than in a report reviewed once a quarter.
Second, ownership keeps broadening. Gartner’s analysis of customer data platforms shows them increasingly being treated as enterprise infrastructure decisions rather than marketing tools alone, pulling IT, finance, and operations into what used to be a purely marketing exercise.
Third, and arguably the most significant change, retailers will need to design for the non-human consumer becoming a larger share of their customer base, shoppers whose purchases are mediated by AI agents acting on their behalf. Product data and brand content will increasingly need to be legible to algorithms as well as appealing to people.
For retailers, the practical takeaway is that the visible personalization shoppers see is the smallest part of the investment. The real work, and the real competitive advantage, lies in the identity resolution, data architecture, and model infrastructure underneath it. That is the layer worth getting right before chasing the next AI feature.
No retail or e-commerce enterprise can (or needs to) build the required IT infrastructure and talent pool needed for advanced AI and data analytics. Reliable partnerships with consulting firms can greatly help them in this regard. These partnerships no longer operate on the periphery of marketing but have grown deeper into the operational core. If you are an enterprise in the retail or e-commerce sector keen on transforming your marketing efforts and customer satisfaction with AI and data-analytics, do let us know your requirements.
