By Shawn Conahan, Chief Revenue Officer, Wildfire Systems
For decades, the banking and payments industry has relied on a reliable, yet increasingly insufficient, proxy for consumer shopping behavior: merchant labels on transactions. So, when a customer swipes their card for $125 at a big-box retailer, the bank or issuer simply sees the where and the how much.
But they are entirely blind to the what.
As with many things, context is everything. Did that customer buy a week’s worth of groceries? Or did they buy diapers and formula? Did they buy lumber and power tools?
Without context, these transactions look identical. But they represent three fundamentally different consumers: a head of household, a new parent, and a home renovator. Without visibility into the items bought, financial institutions are missing lifestyle triggers —such as a new baby, a marriage, or a home renovation —that act as the primary catalysts for high-margin financial products.
Financial institutions must examine how they can turn aggregated transaction totals into actionable consumer intelligence, moving from passive observers to proactive, intent-driven advisors.
The high cost of the data blind spot
Currently, banks rely on broad assumptions for targeting. If a customer frequently uses their card at a home improvement store, a marketing algorithm might assume they are interested in a Home Equity Line of Credit (HELOC). But if that customer is a contractor buying supplies for a job, that’s just irrelevant spam.
However, what if the bank possessed the granular intelligence to see that a customer is buying moving boxes, packing tape, and furniture pads? This indicates a life event: moving, shifting the context. They’ll likely need renters’ insurance, a mortgage, or homeowners’ insurance.
The lack of context forces banks to act reactively rather than proactively. But the secret sauce is access to SKU-level data to see what the customer is buying, and being able to identify opportunities before the customer starts seeking financial products from another provider.
Fueling the agentic economy
This data is becoming necessary, fueled by the rapid rise of AI-assisted shopping via chatbots and, soon, agentic commerce. In the near future, consumers will not just browse the web; they will use AI agents to find products, compare prices, and execute transactions on their behalf.
In this setting, data facilitates better alignment between the human and the agent (that is, delivering the product the consumer wants at the best value). An AI agent cannot effectively serve a user without context. This is where forward-thinking financial institutions have an advantage. By leveraging granular transaction data, banks can create value over time for these AI agents.
When an AI agent initiates a shopping task, it requires contextual data to align with the specific user’s needs. If a bank can provide a tokenized, privacy-compliant layer of data, for example, data points including “this user values sustainable brands, buys premium travel gear, and has two airline loyalty memberships,” the agent can find the most well-suited products for that consumer.
Granular, item-level shopping data enables that valuable agent-customer alignment. It ensures that when the agent executes purchases for a user, it chooses payment methods and merchants that reflect key individual preferences like their travel habits, brand preferences, and loyalty rewards status.
The bank isn’t just processing the payment anymore; it’s actively making the AI agent smarter. By sharing relevant intelligence with the agent, the bank plays a direct role in helping it make better decisions. The result is an agentic shopping experience that actually delivers on its promise: transactions that reflect what the user wants, take less time, and involve far less friction.
The value exchange: privacy and zero-party data
The obvious hurdle to accessing item-level transaction data is user privacy. How does a bank see “inside” the shopping cart without being creepy?
The answer is the value exchange. Consumers are willing to share their data when they receive clear benefits in return, according to a Salesforce survey in which 62% of respondents said it’s acceptable for companies to send personalized offers or discounts based on items they’ve already purchased.
By offering their customers valuable offers or integrating money-saving tools directly into the banking experience in exchange for visibility into their purchases, financial institutions can create an explicitly permissioned environment that provides the necessary zero-party data derived directly from the consumer’s behavior. In effect, the customer uses the bank’s tools to save money or earn rewards; in exchange, the institution “sees” into purchases.
This creates a virtuous cycle. The customer receives more relevant offers (e.g., 10% back on cribs because items purchased indicate a new baby), which drives engagement. The increased engagement generates ever-richer data, which allows the bank to refine each customer profile further and deliver even better, more relevant offers.
Moving from passive observer to proactive advisor
Ultimately, the financial institutions that see beyond the commoditization of payments will view the credit card not as the product, but as the enabler of a deep data ecosystem. Banks that think beyond credit cards as just a traditional payment tender will be able to get to know their customers better and drive deeper engagement as a result.
Thinking this way will enable bank product managers to say, “We noticed you’re buying materials for a nursery. Here’s an offer for a 529 college savings plan and a discount on life insurance.”
This represents the shift from simply facilitating payments, to acting on explicit customer intent. It turns the bank from a utility that processes payments into a lifestyle partner that owns and acts on strategic customer intelligence assets.
About the Author
Shawn Conahan develops strategic partnerships with major finance, banking, and fintech companies to enable the creation of new revenue streams and modernize their customer experience, positioning them competitively for the future of banking and money. He has been an entrepreneur, senior executive and investor in the wireless, technology and Internet industries for over 15 years, having previously built and sold three companies. His industry experience ranges from digital media to wireless technology to big data, with a common thread of building platforms with broad applicability.
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