VoC Trends in Customer Experience
- Direct insights. AI in customer experience revolutionizes direct feedback collection. Utilizing NLP and sentiment analysis, it comprehends customer sentiments on a large scale.
- Indirect interpretation. AI technologies enable brands to mine VoC data from unstructured sources such as social media. Automated sentiment analysis helps understand product and service impressions.
- Inferred wisdom. AI unveils unspoken customer behaviors and nuances. It offers a comprehensive view of the customer journey, aiding businesses in anticipating and navigating every intricate customer interaction.
A group of customer experience (CX) leaders are seeing several trends in how companies are deploying artificial intelligence (AI) in their voice of the customer (VoC) practices: for direct, indirect and inferred feedback — as well as feedback-based analytics and action. They say companies are using AI to better understand and address customer feedback at scale. The CX leaders shared details on each of those AI in customer experience trends with CMSWire:
1. AI in Direct Customer Feedback
Daniel Ziv, VP of experience management and analytics and go-to-market strategy for the Melville, New York-based customer engagement platform Verint, said the best way for companies to collect feedback from customers is to ask “more open-ended questions” and leverage AI to analyze unstructured voice or text comments, pulling out recurring themes or trends on areas for improvement or attention.
For customer surveys, companies apply natural language processing (NLP) to categorize and extract insights from questions and identify trends, said Gabe Larsen, CMO for the Short Hills, New Jersey-based CRM platform Kustomer.
Organizations are employing AI in customer experience to transcribe calls through voice recognition and train agents, summarize customer emails through machine learning (ML) to generate the next best actions, and provide real-time responses to customer queries through AI chatbots, said Olusegun Obafemi, customer experience leader at the consulting firm EY Americas.
Fabrice Martin, chief product officer of XM for customer frontlines for the experience management company Qualtrics, said generative AI “allows us to go deeper than ever before and fundamentally transform how companies learn about their customers.”
“Conversational feedback provides a dynamic, human-like way to probe customers for answers,” Martin said.
He said companies use AI in customer experience to initiate a conversation and understand the context of customer responses to determine if they had a positive or poor experience.
CX pros then use AI sentiment analysis to “understand the emotional tone of customer comments,” said Monica Ho, CMO for San Diego-based SOCi, a marketing platform for multilocation brands.
Related Article: AI in Decision Making: The Revitalization of Voice of the Customer
2. AI in Indirect Customer Feedback
Martin with Qualtrics said advances in AI and conversational analytics technology enable companies to uncover insights from “all kinds of unstructured feedback” that customers share, such as social media posts and online reviews.
He said AI reviews high volumes of posts to identify key aspects of the experience, such as customer intent, sentiment, emotion and personal preference.
“AI will allow brands to tap into the untouched goldmine of VoC data floating around in unstructured sources,” Martin said.
Obafemi with EY said companies are turning to large language models (LLMs) to automate customer sentiment analysis to understand product and service perceptions as well as classify data into categories and themes and pinpoint root causes.
Teresa Anania, SVP of customer experience for the San Francisco-based maker of CX software Zendesk, agreed that through AI sentiment analysis, CX teams analyze large volumes of data and different types of data to identify sentiment and “messaging pull through” — as well as patterns, trends, and common problems to “better understand needs and expectations.”
Ziv with Verint stressed that with siloed unstructured data, it’s important for companies to use APIs to efficiently move data and “join it to other data sources within the CX environment and outside it,” with AI being used to unify the data.
Related Article: AI in Customer Experience: 5 Companies’ Tangible Results
3. AI in Inferred Customer Feedback
Michelle Huff, CMO for the San Francisco-based provider of UX insights UserTesting, said companies are deploying AI to decode “the unspoken” by interpreting behaviors, nuances, facial expressions, voice tones and biometric signals to “better understand customer emotions and feelings.”
“We’re not just listening,” Huff said. “We’re predicting and navigating every nuance of the customer journey.”
Obafemi with EY said CX pros are relying on AI in customer experience to track and analyze historical customer data, interactions, and transaction patterns across multiple customer touch points.
For instance, limited or targeted website traffic and abandoned carts can mean a “broad swath of issues,” said Nelson Haung, senior director of product marketing for the San Mateo, California-based business software provider Freshworks.
AI, however, gives companies insight by “taking a holistic look at a company’s customer database and all of the interactions within it,” he said.
“AI takes the guesswork out of analysis by efficiently connecting the dots for agents and identifying commonalities between customer behavior,” Haung said.
Larsen with Kustomer said AI algorithms identify patterns in user engagement data to “infer” the level of interest and satisfaction with a product, feature, or piece of content.
Streaming companies, for example, depend on AI to learn subscriber behavior and preferences from the “fully consented and actionable” data customers provide as they view content, according to Ted Sfikas, senior director of digital strategy and value engineering for the San Diego-based customer data platform Tealium.
4. AI in VoC Analytics
Anania with Zendesk said CX teams are using AI to streamline and expand VoC data analysis processes.
“Automation allows us to analyze data in real-time as opposed to having to wait weeks or months to gather insights,” Anania said. “Data mining does not need an army of data scientists to be accurate or efficient.”
Huff with UserTesting said companies are implementing AI for predictive analytics to help “illuminate a path ahead, revealing opportunities and cautioning against pitfalls.”
“We’re no longer just listeners but forecasters,” Huff said.
CX teams are combining linguistic-based NLP, multi-channel data analytics and predictive analytics to categorize feedback, extract insights, and enhance customer feedback dashboards, identifying areas of improvement and customer preferences, according to Obafemi with EY.
AI-driven dashboards and monitoring systems give companies real-time insights and trigger notifications based on predefined criteria, allowing them to respond “quickly and efficiently” to customer feedback and issues, said Larsen with Kustomer.
Martin with Qualtrics said AI in customer experience is “transforming” customer segmentation based on patterns of real-world behaviors as well as automatically discovering and building customer segments based on common behaviors across a cohort of customers — and “not only simple demographics.”
“Given the right data streams, AI can update these segments with every customer interaction in real-time, allowing organizations to stay ahead of trends and capitalize on opportunities faster than they ever could with manual analysis,” Martin said.
5. AI in VoC Action
Obafemi with EY said companies are applying AI-based VoC insights to inform product design and service improvements, tailor feature development and market messages, optimize R&D, and reduce waste.
CX teams employ AI to anticipate customer needs, preferences, and potential issues, allowing for proactive engagement, said Ho with SOCi.
For instance, Ho said, organizations lean on AI-powered recommendation engines to suggest personalized solutions or offers to customers based on their feedback and preferences.
AI also enables workflows that help automate follow-up actions, ensuring that customer issues are addressed “promptly and consistently,” she said.
Larsen with Kustomer noted that AI performs root-cause analysis on negative feedback and low Net Promoter Scores, helping identify recurring issues or patterns in customer comments and complaints.
Based on real-time feedback, companies are taking real-time actions through AI, identifying positive and negative sentiment and providing next action recommendations to human-assisted interactions, “leading to better outcomes,” said Ziv with Verint.
He said CX teams are now looking to AI to help automate case management and “close the loop with customers faster.”
Gaurav Jain, assistant professor of marketing at the Rensselaer Polytechnic Institute (RPI) Lally School of Management, added that AI in customer experience is “ensuring that every customer who takes the time to share their thoughts receives an immediate and appropriate response.”
“It’s not just about the efficiency that AI brings to VoC programs,” Jain said. “It’s about the opportunity to deepen our connection with customers.
“By truly understanding their words, their sentiments, and even their behaviors, we can craft experiences that resonate on a human level. And in a world that’s increasingly digital, that human touch is what sets a brand apart.”