Sales
AI for demand-based pricing
Gather your past sales or bookings, the prices you charged and a calendar of busy periods. Use AI to suggest price changes within limits you set, such as a minimum and a maximum price. A person approves every change at first. Show prices clearly, never use a fake "was" price, and check your approach with your legal advisor before going live.
Effort:
High
Time saved:
Roughly 1–3 hours a week on price reviews; measure it in a pilot
The problem today
Many SMEs set prices once a season and change them by gut feeling, so they leave money on the table in busy weeks and stay empty in quiet ones. Large hotel groups, airlines and retailers already adjust prices automatically from demand data. For a Greek SME the simpler version is a weekly price suggestion based on your own data, inside rules you set, with a person making the final call.
How it works, in plain words
It works like a revenue manager who studies last year's calendar every Monday and hands you price ideas, but never touches the till.
What goes in: past bookings or sales, the prices you charged, stock or occupancy, a calendar of holidays and events, and your pricing rules.
What the AI does: it compares the coming weeks with similar past weeks, spots where demand looks higher or lower, and suggests a price within your minimum and maximum, with a short reason.
What a person checks: one person reviews every suggestion, rejects anything that feels wrong for customers and approves the changes, at least for the first months.
What comes out: a weekly price table for the next 2–4 weeks that you update by hand in your booking system or e-shop.
It cannot see competitors' live prices or sudden events, and it does not set prices on its own.
How it could work for businesses like yours
Illustrative examples, not client case studies.
A 12-apartment family complex on Paros
The owners set one price for July and one for August, so some weeks sell out months early and others stay half empty. They would export two seasons of bookings, add the island's festival dates and ferry schedule changes, and ask for weekly suggestions within fixed minimum and maximum rates. They would compare occupancy and average nightly rate with the previous season, treating the result as an estimate to confirm over a full summer.
A 15-person sports nutrition e-shop in Patras
Promotions are planned by feel, and slow-moving flavours sit in stock until they near expiry. The shop would feed sales, stock and expiry dates into a weekly analysis and get suggested discounts per product, never below a set margin and never with a fake 'was' price. It would measure stock left close to expiry and margin per order over a 3-month pilot.
A 4-person performance marketing agency in Athens
The agency plans seasonal sales for client e-shops and spends hours deciding discount levels. It would build one shared spreadsheet per client with past campaign results and ask AI to propose discount ranges for the next campaign, with the client approving every price. It would track the time spent on planning and whether margins hold up compared with earlier campaigns.
How to do it, step by step
Collect 1–2 years of data: daily bookings or sales, prices, occupancy or stock, plus holidays, local events and other busy periods. A spreadsheet export is enough to start.
Write your pricing rules before any AI is involved: a minimum and maximum price per product or room type, the largest change allowed in one step, and which customers or channels keep fixed prices.
Ask AI to analyse the data and suggest prices for the next 2–4 weeks, with a short reason for each. If you are new to this, start with the approach in AI for spreadsheet and report analysis.
Have one person review and approve every suggestion during a pilot of 8–12 weeks. Compare revenue and occupancy or sell-through with the same period last year.
Check your price display with your legal advisor before going live. Greek and EU consumer rules require clear final prices, and a "was" price in a discount must generally be the lowest price of the previous 30 days.
Automate only small, routine changes once the suggestions have proved reliable, and keep the floor, the ceiling and a weekly human review in place.
Tools to consider
ChatGPT or Claude: analyse an exported sales or booking file and suggest prices with reasons in plain language. Caveat: they do not see live demand and can make calculation errors, so check the numbers.
Microsoft Excel with Copilot: finds patterns by day, season and price inside the spreadsheet you already use. Caveat: it needs clean, well-structured tables to give useful answers.
Gemini in Google Sheets: summarises trends and builds simple price tables for teams that work in Google Workspace. Caveat: it is an analysis aid, not a pricing system, so changes still happen by hand.
Data protection and GDPR
Pricing on general demand, such as dates, season or stock, uses little personal data. That changes if you set prices per customer from their profile or behaviour. EU consumer rules then require you to tell customers that the price was personalised by automated means, and GDPR applies to the profiling behind it. Show the final price clearly, including taxes and fees. Avoid misleading discounts and do not change a price after a customer has started checkout. Check with your legal advisor, and with your DPO if you use customer data, before going live.
Mistakes to avoid
Letting a tool change prices with no floor, no ceiling and no person checking.
Showing a "was" price or a discount that does not match the real earlier price.
Changing prices so often that regular customers and partners lose trust.