Profitable metrics drive the success of any call center. First of all, they showcase performance and provide a foundation for future goals. In today’s IoT (Internet of Things) world, analyzing big data is crucial. Therefore, call centers must embrace the results in every department.
How Predictive Voice Analytics Works
Predictive voice analytics software processes the recording of all voice on a call and uses digital signal processing to distill it into specific voice elements data. These elements include:
- Resonance amplitude
- Vocal pitch
- Speech patterns
- Emotions expressed by the customer
After recording the voice data, a program that utilizes artificial intelligence (AI) processes the data. As a result, the AI produces an objective assessment of the caller’s emotional tone and behaviors. This method results in a more reliable prediction of who will pay in contrast to the intuition of the call center employee. Even more, the software handles hundreds of thousands of calls quickly, each of which produces additional data. Hence, the continuous supply of real-world data improves the accuracy of the predictive model.
4 Ways Call Centers Use Predictive Voice Analytics
Essentially, by collecting and analyzing the data from so many calls so quickly, a voice analytics program ranks the debtors faster than any manual method could. Below we have an overview for each aspect of a call center:
The days of cold calling in the hopes of finding the one debtor who will pay are fading away. Today, collection centers must be able to liquidate a high number of delinquent accounts with minimum effort to remain profitable.
Highly sophisticated voice analytics software makes identifying who is likely to pay a reality for collection centers. This system happens on the first contact with debtors. Mostly, predictive analytics takes the data and ranks all these “first contacts” by their probability to make a payment after a follow-up call.
Customer Service Calls
Customer retention is one of the most highly watched metrics for customer service call centers. It is common knowledge that it costs much more to acquire a new customer than retain an existing one. Is it possible to predict who will become a loyal customer that spreads goodwill for your brand?
Just like above, predictive voice analytics helps to improve this key metric by analyzing customer voices. The system analyzes the data throughout their entire interactions with customer service agents. It’s this analysis that identifies which customers are the most likely to end their relationship with your company. Above all, this insight allows your agents to focus on the issues driving that likelihood and provide the right solutions.
Another benefit to predictive voice analytics is the ability to analyze the voices of the customer service agents. The software can identify both successful agents and those that may need more training. Hence, it goes far beyond the random spot-checking that a quality assurance team provides. The software analyzes every call and pinpoints issues almost instantly.
Follow-Up Sales Calls
In sales, the first contact call is often not the sale. Rather, that first sale may occur one or two phone calls later. It is now possible to predict which customers are more likely to buy with predictive analytics.
Making this determination and prioritizing which potential customers to contact a second time is now more accurate. Most noteworthy, it knows when customers give ambiguous answers. For the human sales center agent, it takes a tremendous amount of experience to accomplish this task. While some agents may be better than others, the learning curve is pretty steep.
Again, predictive voice analytics will process automated analyses of 100 percent of your sales center’s interactions. The software studies voice elements and ranks potential customers on things like likelihood of sale or payment. As a result, sales agents should focus on those at the top of the list knowing that their success with these customers will be higher.
This data accelerates your conversion cycle and improves your metrics. Your experienced agents have a stronger foundation, and your new hires have a more significant opportunity to meet and exceed goals.
Whether it is a collection, customer service, or sales center, assessing agent performance is a vital task. Without proper analytics, this task can encompass weeks of work. Above all, added work hampers your ability to recognize superior performance while trying to pinpoint poor performance.
This most critical limitation that faces a QA team is that humans perform only a certain number of assessments. Typically, this means only one to two performance evaluations on a specific agent per week. If an agent is doing poorly, but not poorly enough to raise red flags, that performance may go unnoticed, eventually dragging down the KPIs.
Predictive voice analytics improves the efficiency of a QA team by processing everything possible. The QA team then has a complete data set, which is objective and accurate. This data improves their ability to address problematic performances and enables them to recognize excellence.
The data also facilitates higher profits by helping the entire call center improve overall quality. Instead of individual assessments, it provides high-quality information upon which to build performance reviews.
Using AI To Increase Call Center Proficiency
It’s clear that a computer can make and analyze data much faster than a human. Combining voice analytics services with your CRM and a predictive dialer should illicit results and fast. If you don’t want your call center to get left in the dust, you should take note and apply what you’ve learned here. It’s never too late to start on the right foot.