Optimizing Payment Products with Data Analytics

by Dan Shannon 0

Banks stand to rapidly lose revenue traditionally generated from retail checking products due to factors such as Reg. E, the Durbin Amendment of Dodd-Frank and innovative competitors with compelling consumer-value propositions. To restore and grow this revenue stream, banks must re-think their approach to offering retail payment products. Winning institutions align product packages with their existing customer base and optimize their offerings to appeal to target segments of their customer and prospect base.

Sophisticated data collection and analytics are at the heart of this kind of payment product optimization effort. Analytic tools allow a bank to secure a comprehensive, detailed view of the customer, his or her current banking habits and how those are likely to evolve in the future. These habits are then overlaid with demographic data to create profiles that are aligned to retail product packages.

The overlay of demographic data and payment-propensity findings creates new insights that allow for more actionable segmentation. Extensive use of third-party external data, from firms like Nielsen-Claritas, forms the basis for life-stage grouping within a bank’s customer base. Payment transaction data is mined from the core banking data warehouse to more precisely profile buying and product/channel usage. The understanding of customer transaction data includes a payment clustering analysis that segments customers based on the mix and volume of payment options including: checks, POS, ATM, bill payment, ACH and branch/electronic deposits. These findings are then organized into payment propensity segments.

The Nielsen data is then overlaid with the segmentation to provide an even richer view of payment propensities and potential opportunities to enhance revenues and/or optimize delivery channels. The high-level process to create the insight from the payment analysis process is depicted in the following graphic.

Actional Payment Segmentation

After the overlay process, the next step is to systematically analyze the customer/household base and segment them by product and usage components that “tend” to drive product profitability. With data-intensive views of customer segments, value and product usage, a bank can better derive a variety of revenue enhancement and customer retention strategies.

Recommendations can then be made that focus on creating payment/deposit product offerings that optimize a bank’s current product offerings, align with its target segments and position the bank competitively within their marketplace.

The primary steps in processing the payment segmentation data include:

· Refine product, customer and household financial information

· Develop segment/client value propositions

· Create offering profiles

· Align payment customer value propositions with client experience

· Prioritize segments and outline key offerings

· Develop set of proposed product structures

The outcome: A variety of revenue recapture and customer growth/retention opportunities.

Equipped with a data-intensive view of customer segments, customer value and product usage, a payment product optimization creates new avenues for revenue growth. New account acquisitions are optimized through redefining and effective targeting the “best” checking account prospects. Improvement strategies can be more reliably identified, articulated and prioritized. If a customer whose value to the bank has always been his or her propensity to incur overdraft fees – and regulation is reducing the potential for that source of revenue – an alternative product mix that appeals to that customer can be developed and in that way, the customer is “protected”.

The breakdown of how customers are grouped by their potential value as a client is shown in the following graphic.

actional items 2

To summarize, optimizing payment products within the retail banking function helps the financial institution by:

· Recapturing a significant portion of revenue loss due to recent and current regulation

· Gaining a deeper insight into its retail banking customers and products

· Understanding customer product- and payment-usage characteristics and propensity

· Positioning retail checking accounts to be more customer responsive and more competitive, resulting in higher customer retention and acquisition

· Realizing greater effectiveness in targeting customers for the more profitable payment and delivery channel options

· Achieving a much deeper awareness of fast-changing market dynamics around retail deposit services

· Saving time in plan development and execution

· Learning optimal ways of delivering new retail products.

Payment optimization strategies developed with insightful data analytics can help banks preserve and grow fee income, even in tough regulatory times.

Dan Shannon is Senior Vice President of Consulting Services at FIS (NYSE: FIS). He can be reached at 414.357.3551 or Dan.Shannon@fisglobal.com

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