9 Steps to Improve Contact Data Quality in Debt Collection

Contact data quality in debt collection can make or break your success — here are best practices for improving it

Debt collection is about contacting customers, and that only works with correct contact data. Contact data quality in debt collection is a common challenge, and we often get asked how to best address this.

Contact data quality steps

1. Get Contact Data Before You Need It

It is much easier to get valid contact data from your customers than from a third party. And updating contact data when you need to contact a customer as part of a collections process is typically much harder than in originations or account management. Hence managing contact data should be an enterprise task.

Data capture at customer acquisition should not be limited to data that is required to complete the originations process. Specifically, mobile phone number and email addresses should be captured in addition to the physical address whenever possible, as they facilitate digital engagement and hand-off to self-service processes.

2. Test Your Data Quality

Whenever contact data is acquired, it should be checked for syntactical plausibility – two-digit phone numbers and email addresses without an @ or without a valid top level domain will not be of much use later. And ideally, contact data should be tested. At originations, such tests can be branded as a customer satisfaction survey, which is a sensible thing to do anyway. Switch channels if you can — for example, if emails are used to update your customer on progress during the originations process, use phone or text for a follow-up survey.

3. Evaluate Your Communication Results

It is a common bad practice to not process physical communication results from automated bulk communications. Typically, such shortfalls are driven by implementation budget restrictions, but tend to be expensive in the long run. If a customer cannot be contacted on a phone number because the number is invalid, this is not going to change in later attempts.

For every outbound customer contact, the physical outcome should be evaluated; failures should be logged and should trigger a respective rectification process. Negative outcomes include undeliverable emails and text messages, invalid phone numbers and returned physical mail. Addressing the problem when it first occurs increases the likelihood that customers can still be reached on another channel.

4. Periodically Confirm Contact Data

Over time, customers will change their contact details, and specifically for low-interaction, long-term products like mortgages or credit cards, it is important to periodically validate contact data with the customer. This can be achieved through pop-ups on the customer portal or mobile app or as part of the call script during customer service contacts. For a reasonable customer experience, the date of the last validation should be stored, and the next confirmation should be triggered based on that date.

Contact data quality steps

5. Process Contact Changes

It sounds too obvious to state, but when customers provide updated contact data, these data need to be processed. In too many organizations, this remains a challenge. There are usually two main drivers for this: The first one is insufficient integration of satellite systems or product-specific platforms that lead to address changes not being replicated across the entire organization. The second one relates to policies which make it unnecessarily difficult for customers to share their address.

While it might be appropriate to insist on a written confirmation for changes to the primary legal address, contact changes provided by the customer should never be disregarded just because they don’t meet certain formal requirements. Instead, such contact data should be captured and flagged as unconfirmed. Where required, confirmation can be gained via pop-ups in customer portals or via pre-filled forms that are sent to the customer.

6. Define a No-Contact Strategy in Debt Collection

Even with valid contact data, you might not be able to contact some of your customers. For example, you might repeatably attempt to contact customers at a time where they are unable to answer your call.

In highly automated collection environments, it is even more important to keep track of customers you have not been able to contact. Such customers need to be removed from standard processing and assigned to a dedicated no-contact strategy, where the reason for the failed attempts gets evaluated and addressed.

Contact data quality steps

7. Professionalize Data Research

Even if you follow the above recommendations, you might end up without valid contact data. Then it is time for data research.

The value of valid contact data might vary from customer to customer, depending on customer value, outstanding balance, and phase of the customer life cycle. In consequence, a segmented approach should be taken when deciding on method and effort for contact data retrieval.

Leaving contact data research to the individual collector is probably the most expensive approach. Research should be a specialist task, executed by dedicated resources. This is the only way to get a grip on which methods work best, and to gain an understanding of costs and benefits of alternative approaches. In smaller organizations that don’t have a dedicated team, contact data research should still be handled by specialized resources, even if this is not their sole responsibility.

8. Automate and Escalate

Even if accounts missing contact data should be the exception, they typically lead to high-volume processes. For this reason, automation of data retrieval is key.

Wherever possible, initial data retrieval attempts should be undertaken in bulk, e.g., by contacting the customer on an alternative channel and asking for updated contact details. In many geographies, address research services provide updated contact information and charge on a success basis only. Where multiple providers exist, you might pit providers against each other in champion/challenger tests, or rotate failed attempts from one provider to the next. Implementing processes in your decision engine can provide the structure and agility to dramatically accelerate the test and learn.

For customers without any valid contact data, larger institutions have internal mini trace teams and outsource the qualified Gone Away for Trace and Collect to specialist agencies. Smaller organisations might go straight to Trace and Collect. What is important is that Gone Away accounts are not immediately assumed to be a much higher risk if they are easily traced and contacted. A Gone Away tag or label should not dismiss the need to validate the financial vulnerability of the customer.

Manual research activities are typically much more costly and might include a review of previous customer correspondence and origination files. Depending on customer value and lifecycle phase, field visits and third-party manual research might be your last resort.

9. Measure What You Do to Improve Your Data Quality

Whatever your approach to contact data retrieval is, activities should follow a structured process, should be logged, and should be monitored for efficiency and effectiveness. This is the only way to improve your processes and data quality over time, to understand what works for which type of customer, and to get the most out of your research efforts.

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