Predicting intent and its impact on call centers

Predicting Intent and Its Impact on Call Centers

For decades, billions of dollars have been spent each year by companies to better understand how customers will receive marketing campaigns, how best to position and script customer service agents, and the perfect sequence of events for a sales representative to close more deals.

Until recently, the research and technology supporting this were limited. Today, Deep Learning tools allow us to more accurately predict intent and make smarter estimations of a customer’s next step in the decision-making process. For call centers, in particular, this represents a potential game-changer. When we better understand the intent and emotional state of a customer, it is far easier to ensure they are properly matched with the correct sales agent from the start. Let’s take a closer look at how this works, the benefits of call center prediction technology, and how to start implementing it for your organization.

The Role of Intent Prediction

Prediction intent technology allows companies to better understand the specific outcomes likely to occur on a customer-by-customer basis. As such, they can be significantly more proactive in planning how to address those challenges. A lot of the “guesswork” that long defined customer service, marketing, and sales activity can be replaced with data-driven insights

From detecting customers who might churn to better understanding the emotional triggers that can lead to a bad service interaction in the call center, intent prediction plays an important role in improving service agent engagement, customer retention, and more. 

Deep learning enables stronger prediction of intent through cognitive modeling and machine learning algorithms that evaluate not only what someone has to say, but how they say it. Models are developed based on hundreds of hours of data, annotated by humans, to become significantly more accurate in predicting customer intent. The result is an actionable plan that clearly defines what actions should be taken to avoid negative outcomes while increasing the likelihood of positive ones.

Pairing Intent Prediction with the Call Center

While the most obvious applications of intent prediction are in marketing, sales and accounts, call center predictions can help to improve behavioral profile pairing, and subsequently bolster the performance of your call center team. 

In a recent article, we discussed the role of behavioral profile pairing to leverage emotion AI and better understand both the expectations and current emotional state of a customer before pairing them with a service agent. By evaluating behavior on calls, current emotional state, and language used, the system can help expedite the resolution of customer service complaints, minimize escalation of these complaints, and identify the specific skills your customer service agents have and match them with the appropriate customers in real-time. 

It’s now possible to predict specific business KPIs that improve the performance of a call center, such as:

Revenue – When you predict intent, you can better identify customers with specific needs in advance, especially if they have called in before. This allows you to match your best-skilled agents with the customers who need those particular skills and in turn get better results. When you can predict outcomes and subsequently increase overall customer satisfaction, revenue increases.

Agent Performance – Traditional call centers operate based almost entirely on workload. If someone is free, they take the next call on the switchboard. But AI makes this process smarter. By distributing agents to campaigns based on skillset, you better manage your workforce, likely resulting in faster call resolution and a higher overall level of performance. This scales well to multiple campaigns, allowing you to match agents not just on documented skills, but performance level within a specific campaign. 

Agent Engagement – Because agents are put in the best position to succeed, overall employee engagement improves. It becomes easier to recognize top performers, reduce the risk of burnout, and improve retention of your best employees. 

Cost – When you match the right people on the first try, first call resolution rates improve dramatically. Both sides are more satisfied with a job well done, and overall costs are reduced with less time spent on average fixing individual problems.

Customer Satisfaction – This is the big one. Customers who get high-quality service right away and don’t spend an extra 20 minutes bouncing between agents and listening to hold music will be happier and therefore more likely to remain customers for longer. How much so? According to a recent survey, 90% of Americans use customer service interactions as a primary deciding factor on whether to do business with a company.

Intent Prediction Can Inform Behavior

What makes intent prediction in call centers so effective is not just the ability to better understand the eventual outcome of a call. It’s the ability to implement processes that ultimately impact intent. 

Let’s consider a customer support example. If the algorithm identifies a caller as someone with a high likelihood to get upset and a low understanding of technical instructions, you can match that caller with someone who has a strong aptitude for calmly relaying technical directions in a non-technical way. In turn, the caller is less likely to get upset and the intent is shifted. 

By matching an agent for whom you have a strong behavioral and skill set profile with a customer for whom you have a well-documented behavioral profile, you encourage the development of a strong rapport that leads to a higher rate of positive outcomes.

Improving Call Center Performance Is a Holistic Effort

For decades, call centers have relied on baseline metrics like Agent Performance Score that are informed by a qualitative manual review of call recordings and customer outcomes. The result is imperfect at best and can make it difficult to improve. Behavioral intent prediction in call centers can be a game-changer, providing data-driven insights based on the observations of Emotion AI. It improves matching, informs training, and reduces the risk of churn both of your best customer service agents and valuable employees.