Moving Beyond the Transaction: How Data Intelligence and Aggregated Insights Drive Strategic Merchant Value

At a focused session of the FF News Virtual Arena, industry professionals explored how data intelligence is transforming the relationship between commercial merchants, payment providers, and banks. The conversation moved past basic transaction processing costs to focus on data interpretation and strategic positioning.

The discussion featured:

• Ian Horne, Host at FF News

• Breno Alves De Oliveira, Chief Product Officer at payabl.

• Kirill Lisitsyn, Co-Founder and CEO at Torus

• Mariia Komissarova, Data and AI Retail Business Lead at Raiffeisen Bank International

The panel highlighted that the payments market has moved past the question of how to store big data. Today, success depends on an organization’s capacity to interpret data flows, build segment-specific insights, and create collaborative ecosystem partnerships.

The Paradigm Shift: From Pricing Spreads to In-Depth Data Conversations

The evaluation of transaction value by modern merchants has undergone a fundamental shift. For years, interactions between merchants and financial institutions were treated almost exclusively as pricing conversations. Discussion topics focused on basic transaction metrics, card rates, and whether a business should be billed on a blended fee model or an Interchange Plus Plus (IC++) structure.

Driven by widespread digital infrastructure and automated onboarding tools, processing capabilities have become largely commoditized. Consequently, the industry has transitioned into a data conversation. Merchants no longer evaluate value by comparing a few basis points at the isolated transaction line level; instead, they analyze the data value across the entire consumer journey.

This structural shift requires banks, orchestrators, and acquirers to step up their capabilities. Financial institutions must ensure that the operational data they expose to merchants is highly accurate. Providing flawed, un-reconciled, or incorrect information can actively harm a merchant’s downstream operations, making clean data delivery a critical priority.

Capitalizing on the Pareto Principle in Payment Flows

The payments industry has successfully navigated the early, infrastructure-focused hurdles of the “big data” era. Financial institutions and enterprise merchants understand the baseline mechanics of scaling servers, maintaining uptime, and deploying data engineering languages like Python to process large datasets.

The modern challenge lies in the active interpretation of that information. Rather than analyzing a transaction as an isolated event, companies look to map the direction and behavior of that transaction value within the broader payment flow.

This tracking requires breaking down core transaction flows across key variables:

• Card Type Rates: Evaluating processing fees and approval performance across different card categories.

• Issuer Performance: Mapping transaction success across specific issuing banks.

• Geographic Variations: Isolating regional and cross-border behavioral trends.

By tracking work across these granular data points, companies can apply the Pareto principle (the 80/20 rule) to their payment workflows. Instead of deploying massive, resource-heavy overhauls, optimization teams isolate the critical 20% of operational adjustments that deliver 80% of the positive business impact.

This data-driven focus explains how lean, modern digital native businesses can scale into multi-billion-dollar unicorn valuations with as few as 20 employees. By ignoring broad process noise and focusing entirely on high-impact wins, software-first operators utilize data and transaction systems to drive widespread efficiency across their entire enterprise footprint.

Segment-Specific Data Needs: Enterprise vs. Mid-Market

A major trap for payment providers is assuming that delivering more data automatically creates a better understanding for the client. The utility of transaction data is highly segmented, and its value depends directly on the size and resource capacity of the merchant receiving it:

Large Enterprise Merchants

Large organizations typically maintain dedicated, internal payment or data intelligence units. These specialist groups can ingest raw, highly detailed data payloads directly from their acquirer and translate them into custom optimizations.

Small and Mid-Market Merchants

Mid-market businesses rarely possess the specialized engineering headcounts or operational resources required to extract value from complex datasets. Flooding these merchants with data dumps creates operational confusion rather than clarity.

To bridge this operational gap, financial institutions must move beyond standard daily or monthly reports. Merchants value partners who can deliver pre-interpreted, actionable insights directly on top of traditional statements. Translating raw numbers into clear recommendations helps payment players deepen relationships and protect margins.

Building Tri-Party Win-Win Ecosystems via Market Benchmarking

The primary limitation a merchant faces when analyzing data independently is visibility: a business can only look at its own internal transaction history. A merchant cannot determine whether an underperforming conversion rate or a spike in fraud is an isolated internal issue or part of a broader macro-market trend. This is where traditional banks can deliver significant ecosystem value.

While an individual merchant operates in a silo, an international bank like Raiffeisen Bank International possesses an aggregated, market-wide view of consumer behavior across similar industry categories. By leveraging this broad perspective while maintaining strict data compliance under GDPR frameworks, banks can provide powerful comparative statistics to merchants.

This shared data framework creates highly successful, tri-party “win-win-win” scenarios across the retail network:

1. The Merchant Advantage: Merchants gain clear, aggregated data showing how consumers interact within their vertical compared to market competitors. Seeing this positioning motivates businesses to participate in and fund the bank’s centralized consumer loyalty programs.

2. The Bank Advantage: Because the merchant sponsors the rewards matrix, the bank can deliver a premium loyalty ecosystem to its clients without absorbing the direct marketing costs.

3. The Consumer Advantage: The end customer receives targeted bonuses, rewards, and cashbacks on their daily transactions.

This collaborative flywheel drives higher transaction volumes back through the bank’s network, generating more data to produce deeper commercial insights. Ultimately, this framework shows that data is more than a technical tool; it serves as a foundational layer for building innovative, multi-party business models that drive sustainable corporate growth.

Key Highlights from the Virtual Arena Panel:

• The Evolution of Value: Merchants have transitioned away from narrow, transaction-level fee discussions to analyze data value across the entire consumer journey.

• The Interpretation Hurdle: Modern payments have evolved past basic data storage; the current priority is extracting actionable business value from existing data assets.

• Targeted 80/20 Optimization: Financial teams use card types, issuers, and geographic data to identify the 20% of operational changes that deliver 80% of business results.

• The Danger of Data Dumping: Because mid-market companies lack specialized data teams, payment providers must deliver clear, actionable insights rather than raw reports.

• The Aggregated Bank Advantage: Banks utilize their broad market visibility to provide secure, aggregated benchmarks that help merchants track competitor performance safely.

• Tri-Party Flywheel Creation: How combining compliant market data with co-sponsored loyalty programs creates a self-sustaining growth loop for banks, merchants, and consumers.

The post Moving Beyond the Transaction: How Data Intelligence and Aggregated Insights Drive Strategic Merchant Value appeared first on FF News | Fintech Finance.