Artificial Intelligence (AI) to Measure Customer Experience
By now, you already have understood that customer experience is a great driver of growth, but it can be the greatest source of risk if it fails.
You’ve already figured out that while customer service is a great engine of success, it can also be the greatest source of risk if it goes wrong.
Customers today are living in an omnichannel world. Customer insights are quickly becoming one of the most important metrics for measuring and improving CX. Customer behavior, like data, is becoming increasingly dynamic. However, it is precisely because of this uncertainty that AI can uncover so much value through the consumer interface.
Customer Experience Managers, Marketing Managers, Brand Managers, and customer-facing employees cannot be expected to know the customer experience across their entire journey.
This is where AI comes into the picture.
Mining customer insights from thousands of customer journeys with the help of traditional analytic tools is time-consuming and laborious. AI-enabled analytics sifts through larger and more complex data and thereby uncovers consumer behavior.
Customer insights platforms are gaining traction with a host of APIs and development kits. These platforms can offer real-time touchpoint integration with minimal investment.
AI to Understand Customer Experience Data
Former Netscape CEO Jim Barksdale said, “If you have facts, present them and we’ll use them. But if you have opinions, we’re gonna use mine.”
Making informed judgments on how to maximize your consumers’ (and future customers’) experience necessitates a thorough examination of both qualitative and quantitative data from the company and the market. This can be difficult for our egos to accept.
Former Google Ventures Partner Ken Norton explains:
“A delightful thing happens when you stop relying on the opinion of the highest paid person in the room and start demanding data: you move faster. Rather than arguing for weeks, you test your assumptions and see what works and what doesn’t.”
In the form of product feedback, star ratings, user forums, social media, call records, website reports, and chat transcripts, today’s new tech-savvy consumers are leaving a huge trail of customer service data. This provides a wealth of customer service info. CX administrators may examine this data in order to assess customer satisfaction.
1. Text Analytics Based on AI
Text analytics is the process of extracting meaning from text. The aim of text analysis is to find consumer input so that the customer experience can be improved. To discover customer sentiments, businesses use text analytics tools, artificial learning, and natural language processing algorithms.
For example, Clootrack derives consumer insights using text analysis. It identifies topics and sub-topics that are spoken by customers. It identifies the tone in which it is spoken.
See how the tone is analyzed – “The resort is good” is scored lower than “The resort is fantastic.”
The conversations are segregated into positive and negative sentiments based on text analysis. Companies can deep dive into positive and negative comments to get insights into the customer experience.
In addition, it also identifies the relationships between the topics spoken. A customer conversation can contain several topics, with customers expressing separate emotions for each topic.
For example, “Service is good, but the location is bad. Overall OK.” Here each aspect, such as the service, the location, and overall is analyzed.
This methodology helps to get a list of all topics that matter to the customer with the emotions attached to each. Not just this, the topics are grouped based on their relationships at multiple levels for analysis.
Thus, text analytics is helping to add context and color to consumer information.
2. Emotion Analysis
Many motivational expressions may be used to communicate a customer’s experience. Anger, hatred, sadness, and happiness are all emotions that can be conveyed by facial expressions. The tone and pitch of the customer’s voice will reveal whether or not they are pleased. Body language can convey valuable information about a customer’s experience. For years, such consumer actions could only be known when the customer talked of it.
However, with AI, it is possible for a machine or a device to detect the emotion of a person – called artificial emotional intelligence.
Sentiment analysis and emotion identification and recognition from the text are inextricably linked. Emotion analysis attempts to recognize emotions in language, while sentiment analysis extracts positive, neutral, or negative thoughts from the text. Emotion analysis can identify frustration, sorrow, disgust, satisfaction, anxiety, and surprise.
3. Speech Analytics
Speech analytics is quickly becoming a valuable platform for gauging consumer experience. Since the call center is often a customer’s first point of contact with a brand, speech analytics is often used here first.
Speech data in call centers are limited to transcripts. The auditory cues on how it was conveyed are often overlooked in these transcripts. The transcripts do not catch whether the customer raises his voice or becomes enraged. Speech is effective because it provides important customer information. However, when a call is not analyzed and only a transcript is available, both are missed.
Deep learning algorithms are presenting a revolutionary change in speech to a transcript. AI makes it accurate for understanding the emotions, intent, relevancy, and topics discussed by the customer. With AI, it is becoming possible to process larger amounts of speech data quickly.