Debt collection agencies have an uphill battle ahead of them. According to statistics cited by the Motley Fool, Student loan debt has climbed from $1.21 trillion to $1.3 trillion; auto loans outstanding have grown from $866.44 billion to $943.76 billion; and mortgage debt outstanding increased $35 billion between the second and third quarter to $8.13 trillion. Getting consumers to pay on this massive pool of debt is no easy task.
However, as challenging as the debt collection industry can be, there are great opportunities for collection centers that can efficiently and accurately target their calls to the debtors who are most likely to pay.
One way that debt collection call centers can significantly improve their liquidation rates and increase overall collections is to use predictive voice analytics to automatically assess interactions between their phone agents and debtors.
Integrating Predictive Analytics with Debt Collection Call Centers
Predictive voice analytics can be a very powerful tool for driving results in a debt collection call center, provided that the analytics tool is properly integrated with call center practices.
Tips for integrating predictive analytics with your debt collection centers practices include:
Analyzing 100% of Call Results
RankMiners predictive voice analytics solution uses a series of machine-learning algorithms to assess calls and emotional behaviors. As these algorithms collect more data, they are better able to mathematically model your business, current operations and future outcomes. This increases the accuracy of the predictive model, making it more reliable and consistent.
Establish a Clear Doctrine for Using Predictive Call Suggestions
Once the analytics program has had time to process your calls and analyze the results, it identifies which debtors are the most likely to pay. This output can be in a report form or can be automatically appended to your dialer file. Either way the information is prioritized from most to least likely to pay so incorporating the predictive outcomes is easy. Having a strategy in place for using these predictive suggestions can help improve results, or even let you A/B test between the predictive suggestions and your own normal call practices.
Combining Multiple Analytics Solutions
You may already use a speech analytics solution to monitor what your phone agents say on the phone for compliance purposes. While this can tell you what a phone agent says, youll miss out on the context of the conversation because you still dont how the phone agent spoke. Adding voice analytics can help you spot inconsistencies between the words spoken and the underlying meaning, which may provide valuable insight into why a given call was successful or unsuccessful. This information can also be helpful for guiding agent training later on.
By giving your predictive voice analytics solution more data, comparing data from other analytics tools with your voice analytics data, and having a solid strategy for using this information, you can more completely integrate predictive analytics and maximize results for your call center.