Marketing
AI for social listening and customer insight
Collect what people say about your brand, products and competitors in comments, mentions, messages and forums, then let AI sort it into themes and tone every week or month. Read the summary, check a sample of the original comments, and turn the top two or three themes into actions for marketing, product or service. Focus on patterns, not on individual people.
Effort:
Low
Time saved:
Often 40–60% less time reading and sorting comments (estimate)
The problem today
Comments, mentions and messages are scattered across Instagram, Facebook, TikTok, Google and forums, and nobody has time to read them all. Large consumer brands and hotel groups already use AI to track what people say and spot issues early. For an SME, the simpler version is one monitoring tool or a monthly export, summarized by AI into a one-page report.
How it works, in plain words
It is like having someone read every comment and review each week and hand you a one-page summary of what people keep saying.
What goes in: comments, messages and reviews from your channels, public mentions from a monitoring tool, and your brand, product and competitor names in Greek, Greeklish and English.
What the AI does: it groups the texts into themes such as price, quality, delivery or staff, marks a rough positive, neutral or negative tone and picks example quotes.
What a person checks: someone reads a sample of the original comments, especially where sarcasm or slang could fool the AI.
What comes out: a weekly or monthly summary with the top two or three themes and a clear action for marketing, product or service.
It shows patterns, not the full picture: quiet customers rarely post. Never use it to profile individual people.
How it could work for businesses like yours
Illustrative examples, not client case studies.
A regional supermarket chain with eight stores in Achaia
Comments about queues, fresh produce and the loyalty card are spread across Facebook, Google reviews and the contact form. The chain would export these sources every month, let AI group them by store and theme, and have the marketing manager check a sample. It would aim to fix one issue per month and see whether negative mentions on that theme fall.
A 7-person PR agency in Kolonaki
Its monthly client reports list mentions but say little about what they mean. The agency would add an AI theme summary with example quotes, always checked by the account lead, and compare each client with two competitors. It would measure time spent on reports and whether clients act on the themes it highlights.
Two family-run tavernas on Corfu
Hundreds of reviews in English, Italian and Greek pile up every summer, and nobody has time to read them. The owners would paste each month's reviews, with reviewer names removed, into an AI assistant, ask for themes per language and spot recurring issues such as waiting times or portion sizes. A realistic first target could be one clear operational change per season, followed by a check on review tone.
How to do it, step by step
Decide what to listen for. List your brand name and common misspellings, product names, two or three competitors and topics you care about. Write them in Greek, Greeklish and English.
Choose your sources. Start with comments and messages on your own channels, then add public mentions through a monitoring tool. Respect each platform's terms instead of scraping.
Let AI group by theme and tone. Ask for themes such as price, quality, delivery, staff and new ideas, with a rough positive, neutral or negative tone and example quotes. Ask it to flag comments where it is unsure about sarcasm.
Check a sample by hand. Read 20–30 original comments each month to confirm the themes are right. AI often misreads Greek irony and slang.
Set alerts for spikes. Ask to be notified when negative mentions jump or a new topic appears, so you can respond within hours, not weeks.
Turn insight into one action a month. Share a one-page summary with the team and pick one change, such as a clearer delivery message or a new FAQ.
Tools to consider
Brand24: tracks public mentions of your brand online and adds AI summaries and sentiment. Caveat: coverage of Greek sources and accuracy of Greek sentiment vary, so check a sample.
Meta Business Suite: one inbox for Facebook and Instagram comments and messages, with exports for analysis. Caveat: it covers only Meta platforms and offers little analysis of its own.
ChatGPT or Claude: sort a monthly export of comments into themes, quotes and suggested actions. Caveat: remove names and handles first, and spot-check its counts.
Data protection and GDPR
Public posts are still personal data when they can be linked to a person. Use listening to understand themes, not to build files on individual users. Remove names, handles and photos before you paste comments into a general AI tool, and keep only grouped results. Follow platform terms and avoid scraping tools that break them. Don't record sensitive details, such as health or political views, that people mention in passing. If you monitor at scale or combine this data with your customer records, consider a DPIA and check with your DPO or legal advisor.
Mistakes to avoid
Trusting the sentiment score without reading real comments, so irony is counted as praise.
Collecting data every week but never deciding on an action.
Tracking only your exact brand name, so you miss misspellings, Greeklish and what people say about competitors.