Appointment count is easy to see. Loyalty is harder to read. One client may book often for a single recurring service; another may visit less often but use more than one service, keep a steady routine or return after a pause. Those patterns can call for different rewards, reminders or personal check-ins.
Why visit counts can mislead
Visit frequency is a useful starting point, but it is only one signal. A high count does not explain what a client values or whether their relationship with your business is changing. Before treating frequent visits as proof of loyalty, look at three things alongside the count:
- Service mix: Does the client book one service repeatedly or choose from several?
- Consistency: Are the gaps between visits fairly steady for that client’s routine?
- Lapses: Is a long gap unusual for this client, or does it fit the service they usually book?
These are not universal loyalty scores. They are ways to ask better questions about individual behavior. A client whose regular pattern has changed may be worth a different kind of outreach than someone who simply books less often.
What recommendation research can suggest
An arXiv paper on recommendation systems, software that suggests what a person may want next, points to a similar challenge in digital services. The authors note that industrial systems often truncate behavior histories to a few hundred actions because of latency and memory limits, potentially leaving long-term interests underused. A short history can make it harder to see preferences that show up over time.
The paper also describes users as pursuing multiple, different interests across formats such as news, Q&A and short video. Its MuSeR framework combines a way to retain recent activity while compressing older actions, a way to model multiple interests and text summaries of items. That is a sophisticated system built for large digital services, not a ready-made retention method for a salon or clinic.
The reported results should be kept in context. In online tests on Baidu APP’s homepage feed, discovery feed and short-video scenarios, MuSeR yielded +0.26% daily active users and +0.89% total session duration. These figures describe those digital scenarios. They do not show what a similar approach would do for an appointment-based business.
A separate MSN headline reports that Churn Solution secured investment to scale its AI-powered customer-retention platform globally. The supplied item has no readable full text, so the headline alone does not establish the investment terms or the platform’s results. It is a signal of interest in retention tools, not evidence that a particular tool will improve your business.
Apply the idea with the records you have
You do not need a complex AI system to look beyond appointment totals. If your booking records include the details, start with a simple view of each client’s last visit, typical gap between visits, service mix and any change from their own routine. Keep these as descriptive clues rather than combining them into a score that treats every client the same.
Use the patterns to guide a more relevant question. Has someone who usually books two types of service stopped choosing one? Has a client’s usual return interval stretched? Is the person still coming in regularly for one service but not engaging with the rest of your offer? The answers can help you decide whether a reminder, a service suggestion or a personal check-in makes sense.
When you test an outreach message, look beyond whether someone clicked or used an offer. Track whether the client booked again and whether their service pattern changed. Compare similar groups over a period that fits the normal cadence of the services involved. Avoid assuming that a gap of the same length means the same thing for every service or every client.
What to do next
- Choose one client group, such as people who have not returned for a service they previously booked.
- Review visit count, time between visits, service mix and changes in routine.
- Pick one relevant message and track return bookings as well as offer use.
- Adjust the definition of an overdue visit to fit the service rather than applying one cutoff to everyone.
The goal is not to label every frequent visitor as loyal or every quiet client as lost. It is to use activity as a clue and make retention decisions with more context. That is the distinction behind Vetraeus’s Loyalty and Engagement: usage versus loyalty service.
- client retention
- customer loyalty
- appointment businesses
- marketing analytics
- ai
Alan Jordan, founder and principal solutions architect. Data engineering, data science and business analytics for appointment-based businesses.
