Predicting customer emotion in the contact center
At its core, customer service is an emotional transaction. Consumers may not always recall the details of their interaction, but they’ll remember how the agent or the experience made them feel — and that can have a powerful effect on how they perceive your brand.
“Customer experience is the new competitive landscape,” says digital analyst and author Brian Solis. “It’s the sum of all engagements a customer has with a brand, not just any one touchpoint.”
According to Solis, measuring sentiment and emotion are among the primary ways to analyze and optimize your customer experience. By identifying emotional motivators and outcomes, brands are better positioned to up-sell products, anticipate churn and so much more. This strategy works in tandem with collecting reviews and feedback of consumers’ perceptions of a company in order to improve the overall experience.
The blueprint and tools that organizations use to try and impact the way customers view brands can vary. That said, according to analyst firm, IDC, more and more companies are adopting tech-based solutions. In fact, it’s estimated that by 2025, 60% of major consumer brands and retailers will be increasing customer engagement and influencing purchasing decisions through the use of emotion detection and management strategies.
Here’s a look at how they’ll be doing it.
Gauging customer sentiment through data
One of the most effective ways to predict customer emotion — and certainly the most popular — is using customer data. In this regard, leveraging data analytics can lead to establishing a major competitive advantage.
Forrester recently shared its predictions for companies in 2020 with Forbes, writing: “Firms will add data science and analytics skills to their customer experience (CX) teams. And they’ll invest in text and speech analytics technologies to mine data from customers’ digital and call center interactions to understand customer sentiment.”
Speech analytics, wherein brands analyze voice-based conversations for everything from key words to voice inflections to pauses, can help companies optimize their customer service. Similarly, text analytics, which involves collecting and analyzing data from non-voice channels such as chat, email and social media can also provide insights into customer sentiment.
“Like physical interactions, online interactions are based on nonverbal communication. The challenge is to uncover those subtle online signals,” explains Liraz Margalit, a digital psychologist and lecturer based at Israel’s IDC Herzliya.
There are other ‘less obvious’ methods for gathering information about customer sentiment too. Tapping into “unstructured atomic data” that reflects online behavior, like clicks, scrolls and the extent to which internet users hesitate on a web page, can translate into structural behavioral patterns, Margalit says. For example, a customer who scrolls up and down quickly on the page without actually egaging could be what Margalit refers to as “pattern disoriented”. Her research has found it conveys high levels of frustration.
Embracing consumer psychology
Regardless of which type of data you choose to use, you may be presented with the opportunity to use artificial intelligence (AI) to harvest and analyze it. Using a Natural Language Processing (NLP) engine to interpret speech and extract insights about customer sentiment is becoming increasingly common. But, while AI can help companies make sense of huge volumes of information, Margalit maintains that analysis requires taking a step back.
“When developing AI to create a personalized customer experience, we spend far too much time analyzing the raw data and not enough time trying to understand what caused the behavior,” she says. “Data isn’t knowledge until you see patterns and make sense of them.”
Margalit believes that organizations should take the underlying drivers of human behavior into account. Analyzing data “through the lens of psychology” — for example, by considering how customers engage differently with desktop content versus mobile — is vital to understanding how they feel about your customer service.
Predictive technology is just one part of solving the equation; it also requires human oversight in the form of “digital psychology” and empathetic human agents. “Psychological models can help us better understand cognitive biases and human irrationality,” Margalit says. “People may believe their behavior and decisions are a result of a rational, conscious process, but in reality many of our online decisions are done unconsciously with little or no conscious thought.”
Harnessing high-tech, high-touch
In the end, balancing tech with a human touch helps build that emotional connection with consumers. “When customers feel their time and business is appreciated, reciprocity becomes part of the equation,” says Solis. “Their gratitude is returned in brand loyalty and advocacy. Emotions become memories, whether they’re good or bad — and those memories, when shared, shape the brand.”
As Solis points out, brands that fail to “design and align” their customer service to emotion are essentially leaving it to chance. Investing in data analytics and embracing consumer psychology puts brands in a much better position to understand their customers’ needs, and ultimately deliver a more satisfying customer experience.