Customer Service
Customer feedback synthesis
Aggregate and summarise customer reviews, survey responses, and support tickets into themes. Weekly digest for product and commercial teams.
70–85%
reduction in feedback analysis time
Opportunity assessment
Minor improvement. Small efficiency gain with limited effect on overall turnover or bottom line.
Very easy to implement. Ready-to-use tools exist. Can be up and running in days or weeks with minimal technical resource.
Light data requirements. Uses straightforward inputs — documents, product descriptions, customer records — that are usually accessible with minimal prep.
Very low risk. Fully internal use. A person checks everything before it goes further. Worst case is a minor internal inconvenience.
Very easy to absorb. Runs quietly in the background or gives people a helpful new input. Makes everyday work easier with almost no friction.
Tooling required
Things to consider
T-Mobile's AI-powered analysis of customer feedback resulted in a 73% decrease in client complaints (SentiSum).
Consistent data export format across review platforms and survey tools makes the synthesis significantly more reliable.
Theme taxonomy should be agreed in advance — tell the AI what categories to classify into, rather than asking it to invent its own.
The digest is only valuable if the teams receiving it act on it. Agree on a standing agenda item before investing in the analysis infrastructure.
Sentiment analysis without action routing is noise. Each theme in the digest should have a named owner in the business who is accountable for responding to it — agree this before building the tool.
Experiment starter: Export one month of reviews and support tickets as a one-off exercise. Prompt an LLM to categorise into 8–10 agreed themes and summarise the top three issues per theme. Share the output in the next trading meeting and test whether it changes the conversation or just confirms what the team already knew.
Go deeper in the playbook
Section — Opportunity Identification, including the AI Opportunity Assessment ScorecardSection — RAG and Knowledge Systems: Unlocking Proprietary DataThis is an decision support opportunity — see the relevant playbook section for how to approach it.More in Customer Service
Complaint triage and first-draft response
Classify incoming complaints by type and urgency, then generate a first-draft response. Agent reviews, personalises, and sends. Faster turnaround, consistent tone.
Knowledge base chatbot (FAQ deflection)
Answer common customer questions using a RAG system over the knowledge base. Deflects repeat queries from contact centre. Escalates complex issues to humans.
Return reason categorisation
Automatically classify free-text return reasons into structured categories. Feeds merchandising and quality teams with actionable data.
AI Transformation Playbook
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