Marketing
AI for loyalty offers and promotions
Group customers by how recently they bought, how often and how much, then let AI suggest one offer per group and draft the messages. Give thank-you rewards to loyal customers, gentle reminders to those drifting away, and no discount to people who would buy anyway. Always keep a small control group without the offer, so you can see what the promotion really added.
Effort:
Medium
Time saved:
Often 30–50% less time planning each promotion (estimate; test it)
The problem today
Many SMEs send the same discount to everyone, which teaches customers to wait for sales and eats into margin. Large supermarket chains and retailers already use AI to tailor offers to each customer's buying habits. A small business can do a simpler version with its own sales export, a few customer groups and the email or SMS tool it already uses.
How it works, in plain words
Picture a shop owner who remembers every regular and knows who needs a nudge and who does not.
What goes in: a year of purchase history per customer ID (dates, amounts, product categories), with names and contact details removed.
What the AI does: it sorts customers into groups by how recently, how often and how much they buy, then suggests one offer per group and drafts the messages.
What a person checks: the owner approves the groups, the discount limits and every message, and keeps a small control group that gets no offer.
What comes out: targeted emails, SMS or in-store offers per group, plus a simple comparison with the control group that shows what the promotion really added.
It cannot tell you why a customer left, and it should not decide on its own who gets a bigger discount.
How it could work for businesses like yours
Illustrative examples, not client case studies.
A bakery chain with three shops in Heraklion
It runs a paper stamp card and has no view of who stopped coming. The owners would move to a simple loyalty app linked to the till, group customers after six months of data and send a "we miss you" offer only to the drifting group. They would compare return visits in that group with a control group that received nothing.
A 10-person pet supplies e-shop in Ioannina
Every promotion today is a site-wide discount, which cuts margin on customers who would buy anyway. The team would use RFM groups in its email tool, give loyal buyers early access instead of a discount and time reorder reminders to the usual pet food cycle. A realistic first target could be more repeat purchases without a lower average margin.
A 25-room hotel on Paros with many returning guests
Repeat guests book through travel platforms because nobody invites them back directly. The hotel would group past guests who agreed to receive emails by stay dates and frequency, and draft a direct-booking offer for each group, approved by the owner. It would measure the share of returning guests who book direct the following season.
How to do it, step by step
Export a year of sales by customer. You need a customer ID, the date and value of each purchase and the product category. Remove names and contact details before any analysis in a general AI tool.
Build simple customer groups. Ask the AI to sort customers by how recently, how often and how much they buy (often called RFM). Typical groups are loyal, new, drifting away and one-time buyers.
Match one offer to each group. Loyal customers get early access or a thank-you gift, drifting customers a time-limited reminder and new customers a reason for a second purchase. Set a maximum discount per group to protect margin.
Let AI draft the messages. Ask for two versions of each email or SMS in your brand voice, with clear terms and end dates. Check every offer for accuracy before sending.
Keep a control group. Hold back about 10% of each group from the offer. Compare their purchases with those who got it, so you measure extra sales, not sales you would have made anyway.
Review after each campaign. Look at extra revenue, discount cost and unsubscribes per group. Drop offers that only pay for purchases that would have happened without them.
Tools to consider
Klaviyo: email and SMS tool for e-shops with built-in customer groups and predictions such as the expected next order date. Caveat: predictions need enough order history, so very small shops may not get reliable numbers.
Mailchimp: familiar email tool with segments, AI help for content and some predictive audience features. Caveat: its e-commerce data is less detailed than tools built only for online shops.
ChatGPT (data analysis): can group an exported, anonymized sales file and suggest offers per group. Caveat: it works on a one-off file, so you must repeat the export each time and check its calculations.
Data protection and GDPR
Grouping customers by purchase history to target offers is profiling under GDPR. You need a lawful basis, a privacy notice that explains it, and valid consent for marketing emails and SMS, with an easy way to opt out. Use pseudonymized exports (customer IDs, no names) when you analyze data in a general AI tool. Avoid groups based on sensitive data, such as health-related purchases. Offer terms must be clear and truthful under consumer law. If you profile a large customer base, consider a DPIA and check with your DPO or legal advisor.
Mistakes to avoid
Sending the deepest discounts to your best customers, who would have bought anyway.
Skipping the control group, so every sale after a campaign looks like a success.
Uploading full customer lists with names and emails into a personal AI account.