For years, businesses have been engaging in the art of studying vocal emotion, whether they realize it or not. Almost every business relies on its customer-facing personnel to be able to empathize with and understand the problems of their customers, a task that requires a deep understanding of the customers emotional state.
In recent years, the study of vocal emotion has become more formal, with major universities running behavioral studies of real people in an attempt to garner more data about how emotions influence a speakers tone.
In fact, back in 2010, Stanford University ran a study of how children interacted with the Aibo dog robot toy to collect data on this subject. The goal of the study was to create a classification of the subjects actual emotional state (some sort of lie detector for emotions).
Applications for this lie detector for emotions were initially academic, with some organizations using the rudimentary, early-stage technology for assessing behavior in test subjects. Over time, companies began applying the technology for recognizing vocal emotion to their businesses.
For example, a Japanese company called Smartmedical is launching a vocal emotion product that theyve integrated into a demo with an online videogame that analyzes the tone of players to create a report of their emotional state at the end of the game. This kind of application could allow game developers and psychologists to better analyze how games influence the mood of players based on their emotional behavior and tone.
Recently, RankMiner released a series of automated predictive voice analytics products that can assess current behaviors and predict future ones based on the vocal emotions displayed by speakers. This turns vocal emotion into an incredibly useful source of business intelligence, or, better yet, predictive and prescriptive business intelligence.
How Does Understanding Emotional Behavior and Tone Affect Business Outcomes?
As mentioned before, businesses have been engaged in studying vocal emotion for years, whether or not they actively realize it. The best salespeople and phone agents are highly skilled at listening to customers and gauging their emotional state by voice alone.
Knowing when a customer is outright angry or happy helps a phone agent guide the conversation towards a more positive outcome. For example, if mentioning a specific situation or object arouses a customers anger, the agent knows to back off of mentioning that unless necessary, or to work towards resolving that particular issue first.
Being able to interpret other, more complex emotions such as passive aggressive or apathetic behaviors can also help phone agents. An apathetic customer is one thats not engaging with the brand, and identifying when customers get bored and why can help phone agents identify what causes that reaction so they can avoid it and keep listeners engaged over the phone.
Overall, knowing the characteristics of a voice well enough to spot specific emotional states is crucial to improving the success of phone agents. Programs that automate the study of emotions based on vocal characteristics can help businesses take this individual agent skill and apply it throughout the organization.
How Can Businesses Apply Vocal Emotion Data?
Having access to an objective, automatic assessment of vocal emotions can have a huge impact for success, particularly for call centers that routinely interact with customers over the phone.
For example, with a predictive voice analytics program assessing your customer interactions, you can gain a better understanding of how consumers interact with your brand. By reviewing broad data sets of how customers interact with your phone agents, you can establish trends for customer interactions with your brand based on detailed analyses of each interaction.
If you were to sort customer interactions based on a specific type of call or issue, you could evaluate the emotional state of all customers dealing with that issue. This type of data could be used to assess what products or features of your service cause the most stress to customers, helping you clearly identify your largest opportunities for improvement to reduce customer complaints.
Or, a collections-oriented call center can use predictive voice analytics to improve the decision-making process for targeting repeat calls. This is useful for sorting through calls that have an ambiguous end result (not NO, but not YES either), and targeting the customers who are most likely to make a payment agreement on a future call.
As the database you have on your customers emotional behaviors grows, your call center will be able to leverage these findings to repeat successful customer interactions and avoid negative ones.
Case in Point: DCI
DCI used RankMiners predictive voice analytics solution to review their customer interactions with customers who did not commit to paying outstanding balances on the first call. Previously, DCIs conversion rate on a second call was 8.99%. With predictive voice analytics identifying which customers were most likely to pay, DCIs conversion rate jumped to 36.7%, nearly four times the rate that it was without vocal emotion analytics.
This one change made a huge difference for DCIs revenue generation. Previously, DCI generated $3,294 for every 1,000 customer accounts. After adding predictive voice analytics, DCI began generating $13,449 per 1,000 accounts.
The vocal emotion of customers is a critical piece of data to have for call centers moving forward from now on. With this information, its easier than ever to improve business outcomes for call centers. Dont overlook this critical source of business intelligence.