All Signal, No Noise: How Item-Level Data Brings Retail and Banking Intelligence Together

There’s plenty of competition in the retail and banking sectors. But there’s no good reason for them to compete with each other, as both face mounting pressure to enhance customer engagement, streamline operations and deliver new media solutions. This evolving landscape is ushering in a new trend in data usage and analytics, driven by the strategic use of item-level data and collaborative efforts between the two sectors.

Unification is the key to unlocking maximum potential, according to Banyan Chief Commercial Officer Mike Minelli. In a recent interview with PYMNTS, he detailed a vision in which retailers and banks focus on enhancing consumer experiences and uncovering new revenue streams through the strategic use of item-level receipt data. This collaboration      allows for more precise customer engagement and refined loyalty programs, delivering personalized rewards and targeted promotions that meet the expectations of digitally astute consumers.

“Unifying data views starts with recognizing that both banks and merchants are dealing with the same customer, but from different perspectives,” Minelli told PYMNTS in a discussion for the series “What’s Next in Payments: The Halftime Report.” “The key is that they’re starting to realize they can help each other by providing signals.”

Historically, the retail and banking sectors have operated in isolation, missing opportunities to partner to drive mutually beneficial solutions for their shared customers. Increasingly, both industries are recognizing the benefits of collaboration: retailers see banks as key partners in customer acquisition and marketing optimization, while banks find item-level data valuable for improving their offerings and engagement.

“Retailers are trying to bring new people into the door,” Minelli said. Banks can provide them with a more precise ability to target lapsed customers or net new customers than is possible with transaction-level-only data today. By targeting new or lapsed customers with tailored offers, he said, banks can boost sales for retailers while simultaneously addressing their own challenges in creating relevant promotions.

One of the significant hurdles that banks face is the saturation of generic offers in their marketing efforts.

“If you go into any banking app and look at certain offers, they pretty much look very similar,” Minelli said.

These offers often lack the specificity and personalization that can make them more relevant and compelling. By integrating detailed item-level data from retailers, banks can craft more targeted and effective promotions, moving beyond the standard 10% discounts on generic categories. And, importantly, they can more accurately connect offers to the sales retailers are seeing.

The crux of this transformation lies in the granularity of data. Traditional credit card transactions are categorized into three levels: Level 1 data includes basic information like merchant name and transaction amount; Level 2 adds another layer of data such as tax and location details; and Level 3 encompasses detailed item-level data. While Level 3 data is available for commercial transactions, most consumer and small business transactions do not capture this detailed information. This gap has been a barrier to more personalized and effective marketing.

“The big unlock here is usability,” Minelli said.

Banyan’s platform is addressing this issue by facilitating seamless integration of item-level data into various applications.

“What happens is retailers spend more money on search and marketing channels, but they struggle to measure their effectiveness,” Minelli said. Creating connections between banks and retailers enables a more nuanced view of customer spending, allowing for better-targeted promotions, more effective media investment and improved marketing strategies.

For instance, if a do-it-yourself store promotes a specific product line, such as a specific       drill brand, using item-level data, it can create more targeted and effective marketing campaigns, even drawing in increased marketing media ad dollars from the manufacturer brand. This, in turn, uncovers new opportunities for banks to enhance the relevance and differentiation of their reward programs and offers.

The collaboration between banks and retailers also introduces new possibilities for artificial intelligence (AI) applications. Minelli pointed to the role of AI in fraud detection, noting that understanding the context of transactions — such as distinguishing between gift card purchases and grocery spending — can significantly impact AI model accuracy.

“Responsible AI is the hot topic of the year,” he said, highlighting the importance of ethical and collaborative approaches in leveraging AI for better outcomes.

Productivity is another area poised for improvement through this data integration. Banyan’s team is exploring how AI can accelerate content development, such as dynamically generating offers based on SKU data. This capability promises to streamline the process of creating relevant promotions and offers, enhancing the overall efficiency of marketing efforts.

At the heart of this shift is the concept of unifying the customer view.

“The companies that will win will unify the view of the customer,” Minelli said.

By integrating data from both retail transactions and banking activities, businesses can gain a holistic understanding of their customers’ behaviors and preferences. This unified view allows for more personalized and effective engagement strategies, benefiting both consumers and businesses. Minelli envisions a future where collaboration and data integration become standard practice.

“It’s not just about merging all data,” he said. “It’s about sending and sharing signals in a responsible way that benefits customers.”

Focusing on making item-level data collaborations seamless and secure is a step toward realizing this vision, paving the way for more effective and customer-centric strategies in both retail and banking.

The post All Signal, No Noise: How Item-Level Data Brings Retail and Banking Intelligence Together appeared first on PYMNTS.com.