Collection agencies face a constant uphill battle to collect on delinquent receivables for their clients. Whether you’re collecting credit card debt, telecom debt or student loans, meeting collections goals can be tough.
The restrictions on collections agents under legislation such as the Fair Debt Collection Practices Act (FDCPA) are constantly evolving, continuously restricting what phone agents can and can’t say in their conversations with debtors. Because of this, it’s important to be able to concentrate on what you can learn from the voice patterns – not the words. With this information, you can focus on the calls that can be converted to “YESes” without wasting too much time on the “NOs.”
This is where predictive analytics platforms such as RankMiner’s Customer Insight software can help collection agency call centers. The voice analytics aspect of RankMiner’s Customer Insight product go beyond what was said in a conversation and focuses on how it was said. This allows RankMiner to perform a predictive analysis of future behaviors based on the emotional behavior and tone of each customer.
Automating the Analysis of Every Collections Call
Analyzing collection calls is a huge part of not only monitoring agent success and adherence to your collection agency’s guidelines, but of optimizing future calls.
A lot of analytics programs merely transcribe the dialogue from a collections call, putting it into a text format so that your team can review it. The process of manually reviewing call text is slow and inefficient. The time it takes to do a manual review of a call and produce an analysis of that customer’s past conversations means that your team is constantly looking back. And you will always rely on the analytical skills of the employee responsible for the review. Wouldn’t it be better to apply self-learning, predictive algorithms to the task?
RankMiner’s Customer Insight product not only analyzes every phone interaction between customers and your collection agents, it also predicts the likely outcome of future interactions automatically.
Identifying the Potential “YES” and the Hard “NO” Customers
Another weakness of manually reviewing call transcripts is that text on a page can’t do much to convey the emotions behind what customers say. Text won’t tell you which words a debtor stressed in their responses or the pitch of their voice, both of which can be used to indicate the emotional context of what was said.
The voice analytics aspect of RankMiner’s Customer Insight product go beyond what was said in a conversation and focuses on how it was said. This allows RankMiner to perform a predictive analysis of future behaviors based on the emotional behavior and tone of each customer.
These analyses can then be compiled into a report that sorts customers into “YES,” “NO,” and “MAYBE” categories so you can focus on the customers that will drive real results for your business. This means agents will spend less time on calls that go nowhere, and more time collecting money.
Making more successful collections calls in less time equals more gross collections for your agency’s call center.