Pillar 6 – Decision Intelligence: Converting Payments Data into Action

Payments generate some of the richest, most actionable data in the business. Every transaction contains insights into customer behavior, operational efficiency, cost, and platform performance. Yet in many organizations, these insights are fragmented across acquirer reports, gateway dashboards, CRM systems, and other tools and environments, making it nearly impossible to form a unified and holistic view of what’s happening in the business.

You can’t optimize what you don’t measure.

Many companies measure payments performance and optimization narrowly, if at all.  Some track metrics like processing costs, chargeback rates, and basic authorization rates. But few leverage their data to build a complete, connected picture of how payments impact customer experience, authorization performance, or overall business growth.

Because of this lack of visibility, many retailers tend to base decisions on assumptions, vendor advice, or legacy practices rather than evidence. They simply default to what’s easiest to quantify: cost. Meanwhile, other critical indicators go undermeasured or unmeasured entirely – conversion rate, false decline rate, retry success, time to implement a new payment method, repeat sales/customer retention rate, customer engagement, or impact of a change – metrics that could point to the biggest issues or opportunities in the business.

Without a structured approach to these types of measurements, organizations struggle to understand what is working and what isn’t. Trends or leading indicators that signal risk or opportunity often go undetected. By the time a problem surfaces, like a drop in approval rates, spike in chargebacks, or increased latency in checkout resulting in failed conversions, the business is already feeling the impact.  Further, companies can’t see where investments – such as in new technology partners, redundancy, or fraud prevention – can drive the greatest value or improve business outcomes the most.

For example, consider this subscription merchant for whom retention is critical to the business model. In this case, the merchant was operating on a legacy, homegrown billing platform and managed its own retry logic but lacked visibility into payments performance, particularly renewal approval and decline recovery rates. The data required to understand failed renewals was fragmented across multiple systems, including the internal billing platform, gateway, acquirer, fraud provider, and customer communications tools. As a result, decision-makers had no baseline view of what was driving failed renewals or where recovery efforts were breaking down.

The business addressed this by implementing a structured payments data strategy. This included joining previously disconnected datasets, rebuilding the renewal funnel, evaluating token lifecycle management, analyzing account updater timing, reviewing retry logic, and assessing pre-renewal and post-decline customer messaging. With a unified view, the team could clearly identify where customers dropped off, how often account updater succeeded, and which retry patterns were most effective.

The impact was immediate. Recovery rates improved, retention increased, and customer lifetime value rose, even as overall provider costs increased. The analysis also reinforced a broader truth: payments optimization is not a one-time exercise. The ability to see, measure, and respond to payment performance enables continuous improvement, better technical decision-making, and stronger customer experiences over time. Without these capabilities, optimization efforts tend to default to periodic, cost-driven decisions rather than an ongoing practice aligned to sustainable growth.

Data and intelligence must sit at the center of every payments strategy.

The most successful organizations treat data as a strategic asset, not a reporting function. It’s the proof that tells whether any other payment optimization strategy – customer strategy, technology alignment, risk mitigation, redundancy, and future flexibility – is working to deliver the outcomes it was designed to deliver. Or conversely, that something isn’t working, has stalled, and that it’s time to pivot to something new.

Within these organizations, connected data environments, shared dashboards, and recurring review processes make performance visible and actionable across finance,

technology, operations, and customer experience teams, ensuring everyone is working from the same insights and aligned on where to adjust, improve, and invest next.

A KPI for insight-driven optimization

A best-in-class optimization approach begins by defining what success looks like for each pillar and establishing clear, quantifiable KPIs that teams track consistently over time to measure progress, helping to prevent biased measurements. Such a framework might look like this:

By defining KPIs upfront and reviewing them regularly, teams can detect friction earlier and take corrective action more quickly, before issues escalate. Decisions and adjustments are based on what’s really happening in the business, not speculation.

From passive reporting to proactive payments optimization

A connected intelligence framework doesn’t just report performance – it predicts where issues or opportunities are emerging. With the right data, retailers can see where authorization rates are slipping by issuer or tender type, whether latency is reducing conversions, or where customer preferences are shifting toward a new payment method or network. These signals are essential for moving optimization from a one-time initiative to an ongoing practice that adapts as the business evolve to sustain profitable growth.

« Pillar 5: Future Flexibility

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