Marketing
AI for customer review analysis
Export your reviews from Google, your e-shop and marketplaces into one spreadsheet, remove names, and ask an AI tool to group them into themes with counts and example quotes. Read a sample yourself to confirm the themes. Repeat monthly to spot trends, and turn the top issues into fixes in products, delivery or service.
Effort:
Low
Time saved:
Often several hours of manual reading saved per month (estimate)
The problem today
Reviews pile up across Google, Skroutz, the e-shop and social media. Owners read a few, react to the angry ones, and miss the patterns that cost repeat sales.
How it works, in plain words
It is like an intern who reads every review in the pile, sorts them into themes and counts how often each one comes up.
What goes in: reviews from Google, your e-shop and marketplaces in one sheet, with date, rating, channel and text, and names removed.
What the AI does: it groups the comments into positive and negative themes, counts the mentions and pulls short anonymized examples.
What a person checks: someone reads 20 to 30 reviews to confirm the themes are real and the counts make sense.
What comes out: a monthly one-page summary of the top issues, each with an owner and an action for products, delivery or service.
It summarizes what customers wrote. It does not explain why, and it can miss sarcasm or rare but serious complaints.
How it could work for businesses like yours
Illustrative examples, not client case studies.
A 7-person pet supplies e-shop in Chalkida
Reviews arrive on Skroutz, Google and their own site, and nobody reads them together. Each month they would export all channels, remove names and ask for themes with counts. They would track whether complaints about delivery or packaging fall after each fix.
A regional supermarket with six branches around Kozani
The marketing manager hears complaints one at a time from branch managers. She would group a quarter's Google reviews by branch and compare themes such as queues, fresh produce and staff. A realistic aim could be one clear priority per branch each quarter, checked against the next quarter's reviews.
A physiotherapy clinic in Alexandroupoli
The clinic has many Google reviews but no view of the patterns in them. The office manager would remove names and any health details before the analysis, then look for themes about booking, waiting time and follow-up. They would measure the average rating and the recurring themes over six months.
How to do it, step by step
Collect in one place. Export or copy reviews from each channel into one sheet with date, rating, channel and text.
Remove personal details. Delete reviewer names, order numbers and anything else that identifies a person.
Ask for themes with evidence. Prompt the AI to list the main themes, count mentions and give two anonymized examples for each, split into positive and negative.
Spot-check the result. Read 20–30 reviews yourself and confirm the themes match what you see.
Turn themes into actions. Give each top issue, such as packaging, sizing or delivery times, one owner and a deadline.
Repeat monthly in a three-month pilot to see whether the issues shrink and ratings move.
Tools to consider
ChatGPT or Claude: can read a spreadsheet of reviews and summarize themes quickly. Caveat: counts can be approximate, so verify important numbers with a filter or pivot table.
Microsoft 365 Copilot in Excel: analyzes and summarizes review sheets inside Excel. Caveat: it needs a suitable licence and well-structured tables.
Gemini in Google Sheets: summarizes and categorizes review text where your team already works in Google Workspace. Caveat: availability and features depend on your Workspace plan.
Data protection and GDPR
Reviews often contain personal data: names, photos, order details and sometimes health mentions. Remove identifiers before pasting reviews into an AI tool, and prefer business plans whose terms exclude using your data for model training. Use the results to improve the business, not to profile individual reviewers. When you reply publicly, never reveal order details or private information. If you analyze large volumes of feedback together with purchase data, a DPIA may be needed. Check with your DPO or legal advisor.
Mistakes to avoid
Trusting the AI's theme counts without reading a sample, and fixing the wrong problem.
Analyzing once and stopping, so you never see whether your changes worked.
Posting AI-written replies that sound robotic or repeat the same text to every customer.