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So Much, for So Little

The continued preference for consumers to pay all things, including their utility bills, via credit cards, debit cards, or online account transfer continues. And recent regulatory changes enabling consumers to authorize recurring direct debits holds promise for a true win-win; consumers can utilize more convenient and preferred payment methods and utilities can aspire to realize the promise of reducing costs associated with billing. Indeed, the card associations haven't overlooked the opportunity in utility billing; MasterCard even offers a specific Utility Industry Program to promote acceptance of MasterCard credit and debit cards.

According to a 2008 presentation by the Federal Reserve Bank of Boston, more than 47 percent of all consumer non-cash transactions are now with credit and debit cards. Debit cards have actually seen the greatest growth in usage growing at 25 percent a year since 2003, and are projected to continue strong growth at 15-20 percent annual growth. Eighty percent of consumers have debit cards and debit represents a very welcome alternative for billers compared to high cost, but ubiquitous, credit cards. Even more advantageous for billers is PIN-less debit; through consumer opt-in, utility billers can take advantage of lower interchange fees. However, managing these payment types introduces its own set of challenges and best practices; apart from payment data security, perhaps no aspect should be overlooked more than monitoring authorization decline rates of these payments.

Authorizations on recurring billing transactions have an insistent habit of returning decline response codes with cryptic explanations such as: Do Not Honor, Card Data Does Not Match, General Decline, Issuing Bank Not Available, Credit Floor, Insufficient Funds, Expiry Date or dozen other arcane explanations. In general, credit/debit card decline rates for recurring billing are around 7 percent on the low end and upwards of 15 percent. Declines may be "hard" (card is not valid) or "soft" and authorization recovery efforts focus on soft declines. In our research, 50-60 percent declines are attributed to soft declines. The variation in authorization success rates is a function of many things, but much of the explanation can be traced to the mix of specific payment types and the vagaries of issuers' authorization acceptance algorithms.

In our research we discovered that, all else being equal -- cancellation rates, customer attrition etc. -- a 1 percent increase in the automatic billing authorization rate results in 6 percent additional annual revenue, collected through this channel, when considering a 12-month horizon for a monthly billing business model. Though many billers or their payment service providers employ some authorization re-try efforts on declined transactions, few apply rigorous analysis or intelligent strategies to increase their automatic authorization rates.

Think about it -- if you're a regional utility with 500,000 monthly debit/credit accounts at an average bill of $19.95, a meager 40 basis point (.40 percent) increase in your authorization rate will result in $2,259,000 a year in additional automated revenue and save 113,200 transactions from going into expensive collections, increasing your Days Sales Outstanding (DSO), or otherwise disrupting your relationship with your customers and your revenue flow.

Surprisingly, the implementation of a set of best practices on managing recurring payment is not as prevalent as one might expect. We're going to briefly discuss key metrics essential for running a healthy recurring billing business and how an authorization recycle revenue model can highlight the benefits on improvements to your payment operations.

Identifying Key Metrics for Recurring Billing

As a starting point, it's worth discussing some critical measurements for monitoring the health of your recurring payments. Fundamentally, billers need to know their acceptance rates, authorization re-try (recycle) success rates and decline rates. Depending upon the recycle strategies employed, you may want to measure recycle success rates -- on first attempt, per recycling event, and on an aggregate level (net recycle rate) after all recycling attempts have been completed. Some organizations also will factor in back billing (or "soft collections") and derive an overall recovery rate of failed transactions. More detailed analysis of decline rates -- by source, BIN, institution, reason code, and other authorization response data -- can yield valuable information on patterns affecting your authorization success rates. For billers employing a significant mix of alternative payments, it will be useful to assess declines by credit/debit cards, ACH, and additional payment types offered.

Metric Category Comments
Authorization Rates (Net) 1st Attempt, Post Recycle
Decline Rate (Multiple parameters) BIN, Institution, Source, Product, Reason Code and combinatorial analysis
Lifetime Customer Value Calculate what a lost customer is worth
Recycle Success Rate(s) Successful auto-authorization attempts
Back Billing / Soft Collection Rates Combine with Recycle Success Rate for an aggregate "Recovery Rate"

One additional thought: by combining the payment decline analysis with "non" payment data, such as source of the account, age of account, and product type, organizations may gain further insight into product offerings and customer acquisition strategies leading to adverse decline activity.

The Recycling Revenue Model

With these recurring billing metrics in hand, organizations can use some straightforward modeling to highlight the impact of improving authorization rate success and identify realistic objectives against which to manage their performance. For starters, we can suggest that you focus solely on the impacts to revenue with respect to payment declines (at a later stage you may want to layer in customer lifecycle, cancellation rates, recovery rates on back billing, and other factors that impact your recurring revenue).

One relatively straightforward analysis to isolate the impact of improving authorization rates can be approached by using the following formula (it's not as complicated as it looks; essentially it's a present value of future cash flow calculation -- a straight shot on a spreadsheet):

A = Annual revenue in $
x = Recurring Base Transactions in month 1
y= Monthly New Adds
z= Net Decline Rate
i = month in cycle
j = month in cycle - 1
$ = monthly billing amount

This analysis projects revenue based on today's customer base and monthly new customer adds holding steady over the time period in question. For example, let's further analyze the regional utility with 500,000 customers. Today, this utility's recurring base is 500,000 monthly accounts with an average invoice amount of $19.95 with an initial authorization success rate of 92 percent; they add roughly 1,500 new accounts a month; after several repeated authorization re-try attempts, it increases its net authorization rate to 95.20 percent. In 12 months, everything holding constant, this company would have $90.1M in revenue. Change the authorization rate to 94.60 percent, and revenue increases $92.3M. That's a three percent change in revenue -- an additional $2,259,000 in convenient, automatically collected revenue to our regional utility, on a 40 basis point (.40 percent) increase in authorization rate!

More sophisticated (and realistic) recycling revenue models incorporate full costs associated with non-captured billing and factor in collection costs, and DSO costs. Other factors to consider: differentiation between new customer decline and recycling rates and existing base as well as payment type.

Putting it All to Work -- What Actions You Can Take

Monitoring performance and determining realistic objectives provide the navigational tools by which to direct management activities of your payment environment. But what are some of the day-to-day actions you can take on your recurring billing operations?

Focus on Auto-Billing Recycling Strategy -- get more sophisticated about your recycling approach, whether it is done in-house or with a third party. You need to conduct thorough, periodic analysis of your decline data and re-try success rates in order to identify meaningful patterns, combinatorial factors, and individual variables. As the title of this article implies (and hopefully we have demonstrated), a little bit of effort to improve your recycle rate returns tremendous results; this effort should be commensurate with "found" revenue, and require minimal, if any, resource outlays. We'd also recommend you evaluate the card association Account Updater services.

Review Back Billing & Soft Collections -- understand the costs of account recovery and collection efforts, as well as the internal process. Knowing these aspects of your business will help your organization make rational decisions about how much to pursue auto-billing recovery, and which strategies are appropriate such as the time window in which to execute your re-try efforts.

Contact Issuers -- it may be hard to believe, but there is a growing recognition in the Issuing bank community to work more proactively and cooperatively with billers or their service providers. Calling them directly to review decline patterns or ask for clarification on prevalence of certain response codes, may initiate some action to evaluate their systems and/or authorization acceptance criteria.

Conduct an Authorization Payment Audit -- what you provide in your authorization requests can significantly impact the responses you receive from issuers. Specific fields need data populated to solicit the optimal responses. If you're not an IT person, get to know one of them, or ask you're payment processing provider or a third party to audit your authorization request data and order flow architecture.

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Great Article. Unfortunately, most payment processing merchant banks don't offer much in the way of reporting, so it may be difficult to compile the information you need to make intelligent timely adjustments. Fortunately there are third party payment providers whose 'gateways' offer much more robust reporting capabilites, making statistical analysis realtively easy and much quicker. Some also offer turnkey recurring payment managers and some even offer integration into major billing systems to provide for automated payment posting.

B Jones Electronic Payments Consultant Billing Tree

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The equation listed for "A" contains multiple errors. There are many, but two specific ones include: (1) the term in the first summation (X+Y) should be indexed to the monthly counter "i" so that the term should read [X(i) + Y(I)], (2) The second summation does not define the term "i" unless it is a nested summation within the first sumation; but in this case, it is missint the order of operations brackets. There are other errors such as the multiplication and power terms in the second summation. Basically, this equation looks technical and impressive but would crash if someone tried to actually use it as written.

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