When I wrote Listen or Die in 2017, I made the case that NPS is invaluable for understanding the overall health of your customer relationships, but it’s not enough—especially in a transactional VoC survey.
To truly improve the customer experience, you need to combine NPS with metrics like Customer Satisfaction (CSAT), Customer Effort Score (CES), or overall experience ratings to evaluate specific interactions.
But knowing the score is just the starting point.
The real work begins when you take action to improve those metrics.
And while CX professionals and market researchers have always worked hard to connect metrics to actions, AI now makes that process faster, smarter, and more focused.
With AI, we’re no longer guessing or spending weeks analyzing data manually. Instead, we can pinpoint exactly what needs to change to boost metrics like CSAT—and by extension, the entire customer experience.
How AI Pinpoints the Actions That Matter
AI doesn’t just give you data—it connects the dots, helping you understand what’s behind your scores and what to do about them.
Here’s how AI makes this possible:
1. Identifying the Root Cause Behind Metrics
One of the biggest challenges in CX is understanding why a score is low. AI-powered text analytics processes open-ended survey responses, social media comments, and support tickets to identify recurring themes and sentiments.
Example:
A retail chain sees declining CSAT scores for its online checkout process. AI analyzes customer comments and finds that complaints center on confusing discount codes. This insight allows the company to simplify the checkout interface, directly addressing the root cause of dissatisfaction.
2. Recommending Specific, Actionable Fixes
AI doesn’t just highlight problems—it suggests solutions. By analyzing historical data and outcomes, AI can recommend specific actions that have previously improved metrics in similar situations.
Example:
A telecom provider notices low CES scores in its contact center. AI identifies long hold times as the primary driver of effort and recommends reallocating staff during peak hours to reduce wait times. This targeted action leads to measurable improvements in CES.
3. Highlighting Hidden Opportunities
Sometimes, the biggest opportunities for improvement aren’t obvious. AI excels at finding patterns and trends that humans might miss, uncovering hidden opportunities to enhance CX.
Example:
An e-commerce company analyzes CSAT feedback using AI. While most complaints are about late deliveries, the AI also flags a smaller but consistent issue: unclear product descriptions. By addressing this secondary issue, the company not only improves CSAT but also reduces returns.
4. Prioritizing What to Fix First
Not all problems are equal. AI helps CX professionals and market researchers prioritize by predicting which fixes will have the most significant impact on metrics like CSAT or NPS.
Example:
A hotel chain uses AI to evaluate detractor feedback from post-stay surveys. The system identifies three recurring issues: slow check-ins, noisy rooms, and inconsistent housekeeping. AI ranks these issues by their potential impact on NPS and suggests focusing on housekeeping first, as it’s driving the most dissatisfaction.
5. Predicting the Ripple Effects of Actions
AI doesn’t just help with today’s problems—it predicts how changes will impact metrics in the future. This allows CX teams to act proactively and prevent future declines.
Example:
A SaaS company tracks CES scores during onboarding. AI predicts that customers with high effort scores are more likely to churn within six months. The company uses this insight to redesign its onboarding process, reducing friction and increasing retention.
AI and Humans: A Powerful Partnership
As always, while AI excels at identifying patterns, predicting outcomes, and recommending actions, it’s still up to humans to bring these insights to life. The best results come when CX professionals and market researchers combine AI-driven insights with human judgment, empathy, and creativity.
Here’s why humans are essential:
- Interpreting Context: AI highlights trends, but humans understand the nuances of the business and customer expectations.
- Building Relationships: AI can flag at-risk customers, but personalized outreach is what rebuilds trust and loyalty.
- Executing Change: AI might suggest actions, but it’s people who collaborate across teams, implement solutions, and monitor the results.
The Bottom Line
In transactional VoC surveys, metrics like NPS, CSAT, and CES are just the starting point. The real value lies in the actions you take to improve those scores and the experiences they represent.
AI has made it easier than ever to identify what’s behind your metrics and what to do about it, but it’s still up to CX professionals and market researchers to execute the changes that matter.
Here’s the takeaway: Use AI to uncover root causes, prioritize actions, and predict future trends.
Then leverage the human element to connect with customers, implement improvements, and deliver experiences that go beyond the numbers.
Because at the end of the day, metrics don’t matter nearly as much as the actions you take to improve them.