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Utilities jump to the head of the payment line with predictive analytics
Forsyth Alexander 060001T8Q2 FMALEXAN@US.IBM.COM | | Tags:  predictive utilities ibm industry analytics solutions
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Today's guest blogger is Tom Stribling from the Business Analytics software team.
No one wants to be last in line, but energy and utility companies are often the last to be paid by their customers. A recent Federal Reserve statistical release
The big questions facing utilities are:
The answer is in the data
If the devil is in the details, the shining angel is in the data utilities are already collecting. Utilities can take this data and use predictive analytics to make better debt collection decisions. Predictive analytics encompasses a variety of statistical techniques that can identify patterns in historical and transactional data to enable better decision-making. By using predictive analytics, utilities can dramatically improve the efficiency of their debt collection while guarding against future losses from potential non-paying customers.
Predictive analytics improves debt collection
Unlike spreadsheet software programs, predictive analytics can quickly pull together massive amounts of all sorts of data for analysis – including free-form text data (for instance, customer service center notes) – that capture detailed conversations with customers and payment information. This data can help utilities better understand what collection efforts have been successful as well as identify the patterns of how customers might get into trouble.
Predict who will likely not pay
Not only do you want to improve your debt collection, but wouldn’t it be great to predict which customers are likely not to pay? Using predictive analytics, utilities can segment customers and apply the right interventions to each unique group of customers such as offering payment plans, interest penalty forgiveness or going straight to collections.
For customers already in debt, ideally you´d like to recover as much as possible. But a heavy-handed approach can be counterproductive. Predictive analytics can provide utilities valuable insights about customers and collection case loads.
Predictive analytics can deliver a clear picture of what collection strategies have worked and how well they’ve worked. And it can help companies better allocate debt collection resources, so that collections personnel can zero in on customers most likely to repay.
It’s tough for utilities to keep a healthy bottom line in today’s economy with challenges such as costly upgrades to aging infrastructure and government regulations to both keep prices down and constrain timely consumer debt repayment. Predictive analytics is an innovative strategy to limit losses from customers.
IBM recently produced a short presentation that demonstrates how predictive analytics can help utilities:
All while meeting governmental regulations.
Watch this presentation and learn how IBM SPSS predictive analytics solutions for utilities can support utilities’ debt recovery strategies.