77% of Merchants Are Ready to Share Data for Smarter Checkout

Payments platforms have plenty of transaction data. Smart checkout is raising the value of the merchant information they do not have.

The technology can draw on payment history, customer identity, product information, rewards, previous purchases and transaction performance to determine what a shopper sees at checkout. Those records don’t reside in one place. Merchants, issuers, processors and, increasingly, artificial intelligence (AI) platforms each possess different pieces. Data access is rapidly becoming part of the economics of the transaction itself.

Seventy-seven percent of merchants are very or extremely willing to share transaction signals and performance data with payments platforms, according to PYMNTS Intelligence research. Businesses are prepared to give payment providers access to information that can be used for purposes beyond completing an individual transaction.

That willingness arrives as payment providers are finding more uses for the information moving through their networks.

In one example, Stripe’s dynamic payment methods use more than 100 signals to decide which eligible payment methods to present and how to order them for an individual checkout session. Stripe says the models consider factors including real-time payment-method availability and patterns among similar customers and businesses.

Adyen’s Personalize uses payments data and shopper recognition to tailor checkout. Its acquisition of Talon.One added another dataset and another function to that model. As PYMNTS reported, Talon.One’s real-time decisioning capabilities, added to Adyen’s global payments infrastructure, aim to enable merchants to establish a consistent customer identity across channels and apply it directly in the shopper’s car

Checkout is consequently drawing together functions that previously operated in separate systems. Payment acceptance, identity, loyalty and promotions can all inform the same purchase.

The underlying advantage for a large payments platform is scale. A merchant can see how its own customers behave. A processor operating across many merchants can observe payment performance across a much larger population. Merchant data can add context to those network signals, including information about products, customers and commercial objectives.

AI Adds Another Claim on Commerce Data

The arrangement only works if merchants permit that information to travel. Things get a bit more complicated as AI platforms enter shopping.

Consumers can research and compare products without visiting the merchant’s digital properties. Only 23% of merchants can clearly identify both traffic and purchases generated by AI agents, according to recent PYMNTS Intelligence research.

An AI service may observe the customer’s initial intent and product comparisons. The merchant knows its customer, inventory, pricing and margins. The issuer knows the cardholder and rewards relationship. The payments provider sees transaction performance across its network.

Smart checkout creates commercial value by joining selected pieces of that information at the point where the customer is ready to buy. Anthropic’s announcement this week highlights some of that connectivity, launching AI agent blueprints that retailers can use to build shopping and merchant agents. The release lands ahead of the holiday season as retailers consider how conversational search and shopping agents fit alongside their websites, apps and existing commerce systems.

For smaller businesses, accessing those capabilities through a payments provider can be more practical than building the underlying data infrastructure. PYMNTS Intelligence’s analysis of SMB sales channels found that 57% sell through their own websites and 61% through physical stores, with other digital channels also becoming part of the sales mix. Better personalization can require more information to cross organizational boundaries. Each additional connection creates questions about who can use the data, for which purpose and under whose rules.

Merchant trust therefore has measurable commercial value.

Payments providers can build models from transaction activity across their networks. Merchants can provide the customer and commercial context those transactions lack. Issuers can add payment credentials and rewards. AI platforms can contribute information generated during product discovery.

The technical challenge is connecting those signals, but opportunity lies in obtaining merchant permission to do so.

At PYMNTS Intelligence, we work with businesses to uncover insights that fuel intelligent, data-driven discussions on changing customer expectations, a more connected economy and the strategic shifts necessary to achieve outcomes. With rigorous research methodologies and unwavering commitment to objective quality, we offer trusted data to grow your business. As our partner, you’ll have access to our diverse team of PhDs, researchers, data analysts, number crunchers, subject matter veterans and editorial experts.

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