Jordan Glazier, Founder and CEO of Wildfire Systems.
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Banks see the swipe, but retail sees the basket. That gap is the difference between a relevant offer and a forgotten one. Retailers have spent the past decade refining how first-party data drives personalization, measurement and revenue growth, often with clear, quantifiable results. As banks face similar but slightly different pressures around customer engagement and digital relevance, all while keeping an eye on privacy concerns, many of those lessons are increasingly applicable to financial services.
While banking and retail operate under different regulatory frameworks, the core objective remains the same: knowing your customer. Consumer behavior data, consent-based value exchange and closed-loop measurement models can inform more effective banking strategies.
But rather than copying retail tactics outright, banks can adapt retail’s proven first-party data principles to fit the banking industry’s regulatory, trust and relationship-driven environments.
Banks and credit card issuers sit on a tremendous amount of transaction data, yet their personalization efforts often get stuck at simply segmenting on basic demographic information (which is better than nothing, but still not ideal). A recent Harris poll showed that 74% of consumers want more personalized banking, and 66% are comfortable with their bank using data to deliver it. However, an IDC study sponsored by SAS revealed that 54% of North American banks admit their data foundations aren't centralized enough to support true AI or personalization across the organization.
The issue is a gap in the type of transaction data banks possess. A credit card network or issuing bank will see a $200 card swipe at a big-box retailer, but they do not know the items that were actually purchased: Was it diapers, bed sheets or a countertop toaster?
This missing piece—SKU-level data—is the secret sauce that can reveal true intelligence about a bank’s customers. Without granular insight into what a customer is actively shopping for or has purchased, banks are forced to rely on generalized, segment-based marketing, rather than responding to actual life events indicated by products purchased.
Retailers, on the other hand, have cracked the code on utilizing SKU-level, first-party data. Many have unified customer purchase data collection across channels. For example, Ulta Beauty deployed AI-driven personalization engines that resulted in an incredible 95% customer repurchase rate. Macy's turned their loyalty audience data into a way to deliver more relevant ads in their retail media network, which resulted in a revenue increase of 12.5% in a single quarter. And Sephora uses data from customers’ browsing, purchase history, participation in quizzes, product reviews and other sources to deliver more personalized incentives through their Beauty Insider program. The result: 80% of transactions come from Sephora’s 34 million Beauty Insiders.
These brands are moving away from relying on static, points-based loyalty program rules, or making broad inferences on a segment a customer may belong to. Instead, they’re leveraging real-time personalization informed by granular customer data. They know exactly what their customers are browsing and buying, allowing them to customize offers based on needs revealed by this data.
However, banks cannot simply start mining granular shopping data without alienating their customers; the line between "helpful" and "creepy" is easily crossed. The key to navigating this safely is the consent-based value exchange.
The richest first-party signal a bank can earn is the one a customer is happy to share: what they are shopping for, right now. A customer who is actively comparing refrigerators will gladly accept a cashback offer that saves them money on the purchase. In return, the bank earns SKU-level intent data, something it cannot buy elsewhere, captured in the moment and with consent.
By embedding rewarding, value-adding services directly into the customer experience, financial institutions can naturally and ethically capture rich, SKU-level shopping data. This approach transforms the bank from a passive payment mechanism into a trusted, daily ally that truly understands (and delivers) what its customers need.
Once banks have access to this SKU-level intelligence, they can shift from those “batch” campaigns, centered around customer segments, seasonality or the bank’s internal product pushes, to highly effective, individually targeted trigger-based marketing based on actual purchase data.
The results of this shift can be profound. According to Vericast, trigger marketing can deliver a 553% return on marketing investment (ROMI) compared to batch-based campaigns. The publication notes that "moment-of-need" engagement informed by purchase behavior optimizes for the customer's actual life rather than the institution's internal promotional schedule.
Imagine a bank's data capture efforts detect that a customer is actively browsing for high-end wine refrigerators and under-counter ice makers. This product-level intent signal strongly suggests the customer is about to start a home improvement project. Based on this data, the bank can trigger a real-time, highly relevant offer for a home equity line of credit (HELOC), a credit limit increase or a co-branded home improvement retail card.
Capturing such data ensures that offers are based on demonstrated shopping intent or actual purchase data, rather than simply inferring or trying to predict a customer’s behavior. As The Financial Brand notes, “Transaction data reveals engagement opportunities.”
To ensure they don’t lose customers to deep-pocketed megabanks and agile fintechs, forward-thinking financial institutions must go on the offensive with their own data. They must take a page from the retail playbook, building data infrastructure that captures a range of customer insights and data, including harnessing the power of SKU-level first-party data, to finally deliver the dynamic personalization their customers expect.
The bank that learns to see the basket, not just the swipe, will own the next decade of customer relevance. The rest will keep batching offers to segments and wondering why fintechs keep peeling off their best customers.
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