The Future of CX Personalization: AI and Data Analytics


The Gist

  • Data harnessing. AI analytics empowers businesses to use data effectively in CX.
  • Enhanced personalization. AI tools deliver personalized experiences, boosting customer retention.
  • Operational efficiency. Automation in AI analytics streamlines processes and saves valuable time.

Customer experience (CX) professionals say they and their clients are using artificial intelligence (AI) analytics to harness data and drive customer personalization in several key areas: from customer friction to churn, sentiment and engagement. CX executives in various industries — such as software, marketing and finance — are seeing multiple trends in how customer data and AI analytics are being combined to improve overall CX. A group of CX, data and AI execs shared details on those trends with CMSWire:

1. Cutting Customer Churn and Increasing Retention 

Cristina Fonseca, head of AI at San Francisco-based Zendesk, a maker of CX software, said two-thirds of consumers are “willing to share additional data to enable AI-driven personalized experiences,” according to the Zendesk “CX Trends Report.” 

Fonseca said the finding presents companies with an “unprecedented chance to cultivate stronger relationships and enhance customer retention.”

For instance, Zendesk is training its AI capabilities on its large CX data sets to “better understand consumers” and help customer service agents “increase their knowledge and provide personalized experiences,” Fonseca said.

AI analytics models built on these types of customer profile information, transactional data and external data are predicting customer churn as well as upsell opportunities, said Zohar Bronfman, co-founder and CEO of Pecan AI , an automated predictive analytics platform based in Ramat Gan, Israel.

“In both of these cases, AI can anticipate customer behaviors in advance,” Bronfman said.

Predictive AI analytics give companies “a heads-up about what customers are likely to do, so they can take personalized action in advance,” Bronfman said.

Monica Ho, chief marketing officer at San Diego-based SOCi, a marketing platform for multilocation brands, said 92% of customer questions posted on retailer Google profiles go unanswered, and marketers “ghosting” customers online costs retailers $2.4 billion annually, according to the SOCi “Local Visibility Index.”

Ho said that with data on customer inquiries, AI chatbots efficiently handle customer questions and provide real-time responses, “ensuring that no customer is left waiting or feeling ignored.”

Marketers apply AI analytics in the customer service case to enhance “brand reputation, deliver exceptional customer experiences, and foster long-term loyalty,” Ho said.

Related Article: The 5 Stages of Predictive Analytics for CX Success

2. Improving Customer Insights With Sentiment Analysis

Fonseca with Zendesk said companies are using AI analytics to drive “business value” through “intent detection and sentiment analysis for customer personalization.”

When AI analysis, for instance, detects a customer’s “specific intent” is to make a return, their inquiry can get “instantly routed to the correct customer service representative instead of routing them through a generic queue,” Fonseca said.

Jonathan Moran, head of martech marketing at Cary, North Carolina-based SAS, a maker of analytics software, said text analytics and sentiment analysis are “a must-have for conversational AI.” 

“By analyzing chat text strings and the sentiment in those text strings, brands can understand customer attitudes and intent,” Moran said.

Companies are training their AI chatbots to “analyze the text from customer interactions, understand the sentiments, identify common product inquiries, and even predict possible issues before they arise,” said Tim Shi, co-founder and chief technology officer of Palo Alto, California-based Cresta, a maker of generative AI for contact centers.

Shi said CX professionals are using AI analytics to “track and interpret customer behavior and sentiment trends over long periods.”

“This capability provides an understanding of the customer’s journey and allows businesses to identify and respond to any anomalies or sudden changes in these trends,” Shi said.

AI analytics enable CX teams to analyze customer feedback, reviews and survey responses to extract valuable insights on sentiment and preferences, said Rhodette Zuñiga, VP of customer success at Seattle-based Textio, a maker of HR software.

Zuñiga said that with AI sentiment analysis, CX teams “aggregate and assess customer feedback across various channels, effectively identifying common pain points and areas for improvement.”

Related Article: Sentiment Analysis Improves the Customer Experience

3. Reducing Customer Friction

Frank Schneider, VP and AI evangelist at Melville, New York-based Verint, a customer engagement platform, said brands integrating customer data and conversational AI are “less interested in preventing a human contact and more interested in reducing friction at the customer outreach moment of truth.”

CX teams employing conversational AI analytics are “at the forefront of the conversational economy’s ability to think well beyond the archaic metrics of contain and deflect,” Schneider said.

Schneider said conversational AI is opening “front doors across all user experiences and touch points,” such as asynchronous digital messaging and self-service task completion.

“This creates an end-to-end life cycle of conversational access to analytics for CX leaders, customized workflows for contact center agents, and personalized customer journeys,” Schneider said.

For example, conversational AI data is being used, Schneider said, for channel switching, such as a follow-up SMS message, to deliver an “elegant experience” that creates both revenue and contact center efficiency. 

Ranjitha Kumar, chief scientist at San Francisco-based UserTesting, a provider of user experience insights, said companies are employing AI to analyze clickstream data to “identify the parts of an interaction where a user struggles or experiences friction — where someone gets stuck or has to try several times to make progress.”

Kumar said, for instance, if a company launched a new product that didn’t reach sales targets, “these sorts of models can help you quickly understand whether people were confused by your checkout flow or if they just thought the product was too expensive.” 


Source link