Customer service data originates from a variety of sources like support tickets, chat logs, surveys, social media interactions, customer feedback, and user adoption metrics, among others.
While it reveals insights on common issues, response times, and customer satisfaction levels, it often hides deeper, nuanced insights that are critical for long-term success.
Consider these numbers:
A Harvard Business Review study suggests that 91% of unhappy customers will leave without complaining, making it hard for companies to identify dissatisfaction through customer service data alone.
Forrester Research reports that 20-30% of customers are at risk of churn despite seemingly favorable metrics, but customer service data won’t always pick up on these signs.
While businesses collect and analyze customer service data extensively, they often fail to capture the full picture of customer experiences, leading to potential blind spots in organizations.
So, there is a huge need for organizations to relook at their customer service data and see how they can ensure exceptional customer experiences. In this panel, we want to discuss specifically:
How do you avoid incomplete data narratives? What are the ways to address silent signals like non-complaints, churn precursors, and emotional context?
Is there a need for qualitative insights? Should organizations move towards delivering personalized quality experiences that discount the quantitative metrics?
What role does AI have in collecting and analyzing customer service data – both quantitative and qualitative?
How do you narrow the gap between customer service data and churn prediction?
Real-life examples of early warning signs that organizations should be wary of when it comes to customer service data.