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Measuring customer effort score

Moving beyond traditional metrics to gain deeper insights into support quality and customer experience.

How do you capture the quality of a support interaction?

It’s a nuanced question. A short chat can still feel draining, while a longer, step-by-step exchange might actually reduce customers’ effort if it effectively guides them or teaches something new.

At Markprompt, we’re tackling this problem in multiple ways. One of them is using a Customer Effort Score (CES), powered by LLMs.

Pre-LLM, traditional measurements either demanded manual review or relied on crude metric combinations. This has some obvious pitfalls:

  • Fragmented analysis: Counting messages or flagging sentiment often misses the full story.
  • Rigid models: Fixed scoring rubrics don’t adapt well to evolving behaviors and don’t account for cross-correlations.
  • Lack of “Why?”: Traditional scores rarely tell why an interaction feels easy or difficult, especially when qualitative factors are linked.

In contrast, using LLMs offers a much richer and detailed perspective:

  • Contextual understanding: Our LLMs read entire transcripts, evaluating multiple aspects—tone, complexity of the problem, time to the “aha” moment, and so on.
  • Adaptive: The scoring logic can be tailored to the specific factors that matter to you, while omitting others.
  • Explanatory feedback: Instead of a single score, our LLMs also explain why they gave a particular assessment, highlighting, for instance, the main friction points that led to a high effort score. When tracked at scale, these insights become a clear roadmap for product improvements.

In short, that’s how we as humans would look at it—if we only had the resources!