Increasing Cash Flow is a Key Priority for Utility Companies

Posted on March 18, 2013
Posted By: Douglas Picadio
Utility companies are under increased pressure to build new infrastructure that will drive better performance and returns for decades. Recent natural disasters have brought this issue to the forefront but it requires capital. According to Booz & Company, "Capital expenditure requirements across the U.S. utility industry are expected to exceed US$100 billion annually through 2020. This represents an increase of 100 percent over the annual costs of the early 2000s, according to Edison Electric Institute." Couple that with the increasing pressure to minimize bad debt as delinquencies and losses continue to rise and it becomes evident that utility companies will need to be more strategic about how they increase their cash flow or capital. Improving collections is one area utility companies can improve in order to help increase cash flow.

Due to the size of their customer base, utilities have a huge cost of collection. Whether it's the cost of mailing a disconnect notice, running an IVR, using a predictive dialer and making outbound calls, handling an inbound collection call or managing field disconnects, utility collections are expensive.

Additionally, as more customers struggle to pay their bills, utilities are competing with other creditors for a share of the consumers' shrinking wallet, while sustaining a balance between customer service, customer satisfaction and consistent recoveries. Increased credit losses, rising Days Sales Outstanding (DSO) or Average Days to Pay (ADP), and human resource constraints and increasing collection costs have driven utilities to search for new technology to improve collections processes.

For this reason, companies are turning to statistical payment behavior modeling to help predict the likelihood of delinquency, probability of a shut-off or even the likelihood of a direct debit payment not going through.

The models will identify which residential, small business and commercial accounts are likely to pay on a timely basis or self-cure, even if past due, and which accounts are likely to become seriously delinquent. Knowing and using the probability and odds of the occurrence of serious delinquency or write-off enables companies to develop a risk-based collections strategy to work the right accounts. By using this methodology, the use of final notices, field visits, collection calls and letters can be more productive. The models evaluate the risk associated with each customer based on statistical relationships associated with previous payment behavior in order to proactively identify future delinquencies. Once the models detect an issue, the company can then initiate the most optimal collections strategy to mitigate and control this payment risk.

By identifying late payment behavior proactively, companies can then implement a stringent collections plan associated with riskier accounts. The scoring models help to accurately prioritize dunning strategies, outbound Interactive Voice Response (IVR) dialing strategies, field visits, disconnect strategies and deposit requirements based on risk rather than how much money is owed and the age of the account. Also, this scoring technology helps improve customer service and the customer experience by minimizing collection treatments on low risk accounts that are very likely to self-cure

Utilities who have implemented risk based collections based on statistical payment behavior models have seen a tremendous reduction in the cost of collections while increasing cash flows by aligning the right customer with the correct treatment path based on risk. While the long term goal of improving infrastructures or even increasing cash flow to manage the business itself may be daunting to many utility companies, those who are taking steps to improve their processes will be in better shape for the future.
Authored By:
Douglas Picadio is the Director of Business Development for SunGard Predictive Metrics. He has over 12 years of experience in the information and software industry. His roles have included product management and development, risk consultation and sales. Doug has worked with a varied market base including clients within academic, commercial lines insurance, utilities, leasing and financial services industries. Doug’s main focus has been consultation on the use of predictive modeling

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March, 21 2013

Rasika Athawale says

That could be a great strategy, given billing & especially collection is 'the' pain point for almost all utilities. I sometimes wonder why is it that only for electricity we pay after we consumer; whereas for almost all other things we have to pay upfront! Will the regulators allow for a change in this basic principle of utility business model? Rasika Athawale

March, 21 2013

Len Gould says

Rasika. Just get month behind in your payments, they'll rapidly demand payment up front lol.

March, 23 2013

Jerry Watson says

You all must live in those more enlightened areas that do not charge a $200 deposit before even turning on the power or charge a fee to turn it back on if you were delinquent.

March, 26 2013

Eric Christenson says

Incremental gains from improvements to collections and lost revenue from uncollectible accounts will do little to fund capital investments.

And a large portion of high-bill write-offs and collection activity are from winter months, when many utilities have mandated service / no disconnect rules in place.

Add to this the problem that % write-off is generally applied as additional O&M in a rate case, and any temporary increase in the collection rate actually poses a long-term risk if the revenue requirement is reduced.

That being said, I agree that predictive metrics does reduce the cost to collect after a charge-off by identifying accounts with payers versus those who won't (or cannot) pay. I did this for 3 years as the Strategy and Analysis Manager at a debt recovery company and it was easier than printing money.

March, 27 2013

Ken Zimmerman says

Very interesting. But two problems, at least. First, this statement defeats the entire scheme -- "The models evaluate the risk associated with each customer based on statistical relationships associated with previous payment behavior in order to proactively identify future delinquencies." Statistical relationships cannot be employed to assess future behavior of individuals. That's just basic statistics. Statistical relationships only work with group tendencies. Second, if the circumstances of current behavior are different from those of past behavior, then statistical relationships developed using that past behavior cannot be applied to current (group) behavior Finally, as any modeler knows all models are always wrong. We're just never certain how much and in what ways they are wrong. Second problem. This statement put into practice is a lawsuit waiting to happen - "By identifying late payment behavior proactively, companies can then implement a stringent collections plan associated with riskier accounts." I hope utilities are able to defend the assignment of customers to "riskier accounts" in court. Because that's were it's going.

April, 10 2013

Ted Phillips says

Oh, I don't know, Ken. In banking, we approve client financing on their willingness and ability to pay. The former measured by their credit score and the latter their income. As utility markets become increasingly de-regulated, Mr. Picadio's suggestions seem more realistic and defendable due to competitive forces. As far as statistical relationships being used for individuals, an underwriter's feedback on this would be interesting. Their use of correlation analyses is crucial for preventing discriminatory practices.

.. just a thought. I loved the article and would like to see more like it.

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