Bad debt: Predicting and preventing risk
Service providers can address risk management from the start.
Delinquent and charged-off subscriber balances are a significant source of revenue loss. Communications service providers (CSPs) have the technological means to identify subscribers who might represent bad debt risks, categorize the nature of the risk and implement policies to respond accordingly – all in advance of a problem developing.
The solution requires a combination of analytical tools running in conjunction with integrated billing and customer care systems.
The amount of bad debt that has to be written off annually across a variety of industries totals billions of dollars. Research shows CSPs lose anywhere from 1 percent to 6 percent of revenues to customers who choose not to pay for service.
In a challenging economy, the problem of collections only increases, and the time and money it takes to retrieve delinquent payments and stem incidences of fraud grows. Reducing bad debt, collections risk and the cost of collections must be addressed throughout the customer lifecycle, but especially at the beginning before they actually become delinquent.
This is a vastly different approach for any CSP. Instead of looking in the rearview mirror to see how a problem could be addressed the next time around, CSPs can flag the potential risks beforehand, saving time and money.
Predicting the future and managing financial risk is no small task, however. To handle this enormous challenge, operators must look for ways to automate risk management functions at critical customer touch points.
Contrary to some industry opinions, these problems cannot be addressed with a single silver bullet. But new methodologies and tools are emerging to help organizations evaluate each stage of the customer lifecycle to reduce bad debt, collections risk and costs.
From the day a new customer signs up for service and throughout their relationship, operators have the opportunity to continually monitor collections risk to protect their bottom lines while also continuing to deliver a high-quality customer experience. A successful outcome, however, is highly dependent on segmenting customers by their collections risk from the very beginning.
By using credit scores and identity verification to assess risk upfront, operators can begin to generally categorize customers and then assign treatment paths based on where that customer falls within those segments.
If, for example, a customer is in a low-risk category but is five days late in making a payment, the operator would likely choose a treatment path that is distinctly different than a treatment path for a high-risk customer whose payment is also five days late.
Customer segmentation is not a new concept, but segmentation can deliver immense possibilities to the collections process when combined with innovative methodologies such as predictive analytics. Such tools can further refine an operator’s collections strategy while also delivering a differentiated customer experience.
Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning and data mining to analyze current and historical facts to make predictions about future events.
Predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. These models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision-making for those transactions.
In a cable provider’s business, a predictive analytics model could leverage customer behavior data to pinpoint which customers are most likely to be delinquent in the first place.
This kind of insight allows cable providers to take specific actions with these “at-risk” customers. The beauty of predictive analytics is that operators can very precisely segment their customer base for delinquency or collections risk and manage that risk without negatively impacting the experience of other customers who are in good standing.
With this in mind, service providers can better align collections strategies to specific customer profiles and avoid generic collections strategies of “one size fits all” where applicable. They can also use analytics to optimize collections strategies to better fit individual customer behavior and align with their paying habits.
For example, if a customer always pays two days after the notice has expired, the provider can begin the collections process two days earlier. Or if a customer pays only after an operator initiates a phone call, the provider can forgo the costs and effort of sending email or printed notices.
Making collections data work
As with one-to-one marketing, a one-to-one approach in managing, monitoring, assessing and preventing collections risk not only reduces bad debt overall, but it also ensures a more individualized and appropriate customer experience based on past behavior.
To achieve this goal, however, organizations have to focus on operationalizing the information that comes from analytics. For example, if the accounts receivable system enables an organization to alter the treatment path of a customer based on the analytics model’s predictions about their payment behavior, it can add real and immediate value. If a business can accurately predict that a customer is going to be sent to collections and their bad debt ultimately written off, a provider can take action much earlier and lower operating costs by writing off that debt sooner.
The value of leveraging analytics in financial management becomes even more demonstrable when it is integrated between other back office, call center and customer communications tools. By having a full picture of customer behavior, companies can empower the entire business with information that can lower costs and deliver a more meaningful customer experience.
When putting a risk assessment strategy into practice for the entire customer lifecycle, there are best practices to consider:
- Automate key financial services processes – Automated processes can significantly reduce the amount of time and employees it takes within your organization to manage collections-related issues. Automated processes also dramatically reduce errors and enable operators to immediately initiate action. An example of this is to use outbound communications strategies, like Interactive Voice Response (IVR) or email tools, to automatically reach out to customers who fall in your segmentation categories.
- Link financial systems to billing and customer care – In order to manage financial risk, operators must integrate risk management tools into their core billing accounts receivable and customer care systems. This gives the operator a real-time view of collections risk at both an aggregated and individual customer level, and it ensures that customer care agents can effectively address bill-related inquiries.
- Automatically align risk scores with deposit amounts – Use business rules based on the risk score with your offers so that the higher the delinquency risk, the higher the deposit. By automating this process, customer service agents know exactly what deposit to collect based on the risk score provided, lowering longer-term capital exposure.
- Initiate check verification/check recovery programs – Check verification and check recovery processes enable operators to automatically manage Automated Clearing House (ACH) returns due to insufficient funds, invalid account numbers or fraud. Deploying programs to automatically manage ACH returns before they are sent to collections can save providers time and money.
- Assess collections agency performance – While the goal is to prevent an account from going into collections, operators must ensure their collections process is streamlined. Many providers use labor-intensive manual processes to collect on unpaid bills, sometimes taking up to four weeks to reconcile an account. Implementing a collections process that proactively identifies the most effective collections agency can dramatically reduce collections-related costs.
- Use the printed statement to help monitor payments – Technologies enable you to trace payments by individual statements using an individual bar code. This information can be invaluable in giving customers a cushion for payments that are already on their way, thus preventing a negative customer experience.
- Adopt an integrated communications strategy – Use automatic emails, SMS or IVR messages to alert at-risk customers that payments are due before they become delinquent. This has been demonstrated to be highly effective in securing payments from at-risk accounts.
By addressing risk management from the start and tapping into new methodologies, providers can leverage existing customer information and behavior data to accurately identify those most likely to be a collections risk. And they can do it before it even occurs. The ability to forecast collections risk and take appropriate action will enable CSPs to further refine their collections strategy while also delivering a differentiated customer experience.