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The questions people always ask about AI feedback analysis
Most answers come back to one test: can you open any number and see the words behind it? The questions that come up on every call, answered.
AI adoption in CX teams: the four fears and what to do about them
CX teams’ concerns about AI are about the black box, not the AI. Each of the four fears has a good answer when the platform shows its work.
How to summarise customer feedback without losing the point
Structure the feedback first and summarise second. A summary built on stored, scored topics points at what matters, and you can open any claim.
Using Copilot to analyse customer feedback: structure the data first, then let it count
Copilot should not be what structures your feedback. Structure it first, send the data to Power BI or Excel, and let Copilot count.
Using ChatGPT to analyse customer feedback: where it works and where it breaks
ChatGPT is good at telling you what feedback says. What you do not get is a dataset: nothing stored, counted consistently or traceable.
How to validate AI insights and turn them into action
Open the finding, read the comments behind it and confirm it says what it appears to say. Then investigate the cause, get the fix to its owner and measure it.
How accurate is AI feedback analysis? The honest answer
There is no right answer to mark AI against. The question you can answer is whether the analysis is consistent, traceable and open to checking.
AI text analysis for customer feedback: what good looks like
Good AI text analysis turns every comment into structured data you can count and trace back to the words. Bad analysis just tells you what it thinks.
NPS isn't broken. Disconnected NPS is.
Every serious criticism of NPS is a criticism of a score cut off from its context. Connect it to the why, the who, the where, the owner and the outcome.