E-commerce has spent years getting better at predicting what customers are likely to do, but the next challenge is deciding what to do about it while the customer is still in the store, according to industry research. This shift is beginning to change the role of e-commerce technology, moving from historical behavior and predefined rules to systems that interpret what is happening now and determine what action makes sense next.
Adobe's 2025 retail research found that 45% of retailers were using generative AI for next-best-action recommendations and 46% were using AI to personalize experiences based on real-time customer behavior. Yet 41% said fragmented data was preventing them from delivering real-time personalization. Adobe's 2026 research points to the same tension at a broader level. Fifty-six percent of organizations identified more personalized customer experiences as a top AI investment goal, while 80% said highly personalized experiences that anticipate customer needs in real time will define breakthrough customer experience over the next two to three years. At the same time, only 44% said their data quality and accessibility were adequate for AI, and 75% identified data integration and quality as a major challenge.
"The industry has spent years getting better at predicting customer behavior," said Ersan Erturk, founder of Peloran. "The harder question is what to do with that prediction when the customer, the product and the commercial context are changing at the same time."
A predictive model can estimate that a shopper is likely to purchase, hesitate or leave, but that prediction alone does not determine the best response. A customer may show strong purchase intent while also being sensitive to price, and the product may have changed in availability or sales performance. The decision therefore depends on more than predicted customer intent; it can depend on what the customer is doing, what the product is doing, what is happening commercially and what has already happened during the customer journey.
Peloran addresses this with its commerce intelligence platform, which brings customer behavior, product activity, sales performance and pricing context into a common intelligence layer. The platform generates behavioral intelligence including purchase intent, hesitation, value orientation, discount sensitivity and customer value, then connects those signals to customer journeys and automated actions. A returning visitor who repeatedly views a product can be treated differently from a first-time visitor arriving through a promotion, and a high-value customer showing hesitation can be handled differently from a price-sensitive shopper.
For merchants evaluating a marketing automation tool, this approach allows automation to respond to a broader set of customer and commercial signals rather than relying only on predefined triggers. The move toward real-time decisioning also creates a fundamental data problem, as retailers have accumulated customer, product, transaction, marketing and operational data across systems that were often designed to work independently. Adobe's 2025 research found that 41% of retailers said fragmented data was preventing real-time personalization, while 35% said it was creating inconsistent experiences across channels. The 2026 research found that only 39% of organizations have a shared customer data platform capable of supporting widespread agentic AI adoption.
Peloran connects its intelligence layer to customer journeys and marketing automation, allowing merchants to act across storefront experiences, email, WhatsApp, web push, advertising and product experiences. A high-intent visitor showing hesitation can enter a different journey from a price-sensitive shopper, and product and pricing context can influence whether an intervention makes sense. A customer who does not need an intervention can be left alone. For merchants considering shopify marketing automation, the approach connects automated marketing decisions with the behavioral and commercial signals generated by the store.
Real-time action also creates a measurement challenge, as a customer may purchase after receiving an offer, but that does not necessarily mean the offer caused the purchase. Peloran uses exposed and holdout groups to compare outcomes and estimate incremental revenue from interventions, changing the question from "Did the customer buy?" to "Did the intervention create additional revenue?"
The move toward real-time decisioning is taking place alongside a broader transformation in e-commerce. McKinsey's 2026 research describes AI reshaping the retail value chain and calls for retailers to embed AI into daily trading decisions, including continuous optimization of pricing, promotions and assortment. On the consumer side, McKinsey reports that 84% of European consumers now use AI in their daily lives and 38% actively rely on AI to research products and inform purchase decisions.
Peloran has launched its commerce intelligence platform to put this approach into practice, connecting customer intelligence, product analytics, marketing automation and revenue measurement around a common decision layer. The platform is available for hands-on evaluation through Peloran's live demo environment, giving merchants and industry professionals an opportunity to explore the product directly. For merchants using customer journey mapping tools, this approach allows journeys to incorporate changing customer and commercial context rather than functioning only as fixed sequences of predefined steps. The broader shift is not from prediction to no prediction, but from prediction alone to prediction-informed decisioning — systems designed to continuously determine what should happen next.
