Using AI to find and fix hidden patterns in customer reviews

Executive overview

Customer reviews contain complaint patterns that are invisible to manual reading but surface quickly with AI analysis. Feed your reviews into a high-reasoning model and it will rank complaints by frequency and generate a prioritised action plan.

The output is an interactive HTML dashboard — not just a list of problems. Each complaint links to specific recommended actions, real review snippets, and dates.

The core insight: unstructured text data is a high-leverage input that most businesses already have but underuse — AI turns it into a concrete improvement roadmap.

Getting and cleaning the data

• Use Apify to scrape Google, Yelp, or Amazon reviews via URL
• Export results as CSV
• Delete all columns except review text, star rating, and date
• Fewer columns keeps the AI focused — excess data degrades output quality

Choosing the right model

• Use the highest-reasoning model you have access to (e.g. GPT-5 Thinking extended, Claude Opus with extended thinking, Gemini 2.5 Pro)
• The task requires multi-step reasoning: pattern analysis across hundreds of reviews, then code generation for the dashboard
• Maximum compute is justified here

Writing the prompt

• State what the AI will receive, then list the steps in order
• Step 1: identify the most common complaints in the dataset
• Step 2: rank them by frequency
• Step 3: generate an aesthetically pleasing HTML/CSS/JavaScript dashboard via Canvas
• Step 4: attach a hyper-practical action plan to each complaint
• End with a reminder to review all data methodically before starting analysis
• A basic few-sentence prompt works; run it through an AI prompt optimizer (e.g. OpenAI's platform optimizer) to get a stronger version

What the dashboard outputs

• Bar chart of top complaints, filterable by star rating or negative sentiment
• Click any complaint to see: recommended actions, verbatim review snippets, associated dates
• Complaints ranked by number of mentions — highest-impact issues first

Improving the action plan

• Expert filter: append a follow-up prompt asking what a specific expert (e.g. Alex Hormozi) would do given the data — produces more tailored, opinionated recommendations
• 30/60/90-day plan: if the action list feels overwhelming, ask the AI to break it into a phased roadmap — quick wins in week one through four, process changes mid-term, structural fixes long-term

Other data sources this process works on

• Sales call transcripts — identify recurring objections and redirect future calls
• Social media scrapes — surface common questions and objections from target personas
• Support tickets — find the friction points clients report most often
• Customer surveys — any dataset with a comment field qualifies as usable unstructured input