How to calculate customer lifetime value for a hospitality business

Customer lifetime value (CLV) is the total a guest is worth to your business over the whole relationship. The simplest way to calculate customer lifetime value for a hospitality business is average spend per visit × visits per year × expected years as a customer. Historic CLV adds up what a guest has already spent; predictive CLV estimates what they are likely to spend from now on. If you want to know how much you can afford to spend keeping a guest, calculate it on gross margin as well as revenue.

What is the difference between historic and predictive CLV?

Historic CLV is a sum: every bill, stay, treatment and membership payment linked to a guest, from their first visit to today. It is exact as long as the guest's spending sits on one profile, which makes it a data quality question more than a maths one. A guest with two profiles (one from an online booking, one from a walk-in) looks like two smaller customers.

Predictive CLV looks forward. It uses patterns in past behaviour, such as how often someone comes, how much they spend and how recently they last visited, to estimate future value. Historic value tells you who your best guests have been. Predictive value is more useful for deciding where to spend marketing money next.

What is the formula for customer lifetime value?

The standard simple formula, as Stripe sets it out, multiplies three averages:

CLV = average transaction value × purchase frequency × customer lifespan

For a restaurant or hotel the three inputs are:

• Average transaction value: revenue from identified guests in a period divided by the number of bills or stays in that period
• Purchase frequency: the number of bills or stays divided by the number of unique guests in the same period, counting each guest once
• Customer lifespan: the number of years a typical guest keeps coming back

Lifespan is the hardest input, because hospitality guests rarely tell you they have left. A common shortcut uses churn, the share of guests you lose each year. If that share stays roughly the same from year to year, the average guest stays for one divided by the churn rate: lose 25% of guests a year and the average guest stays for about four years. Stripe's Billing analytics uses the same approximation when it estimates lifetime value as average revenue per subscriber divided by the churn rate. You will need your own definition of a lost guest, such as no visit in the past 12 months.

A worked customer lifetime value example for a restaurant

Take a hypothetical neighbourhood restaurant. Last year it took £360,000 from 8,000 bills that could be linked to 2,000 identified guests. Its gross margin on food and drink is 70%, and about a quarter of the identified guests from the year before did not come back.

Gross margin CLV here is gross profit after food and drink costs, before labour, rent and other overheads. If the restaurant spends £30 on average to win a new regular, through a first-visit offer and advertising, a guest worth £504 in gross margin pays that back many times over. If the lifespan estimate were one year instead of four, the same guest would be worth £126 in gross margin, and the restaurant would look at acquisition spend very differently.

Decide whether you measure per bill or per guest at the table. Most restaurants can only link a bill to the person who booked or paid, so a regular who always hosts will look like a bigger spender than the friends they bring. That is usually acceptable, as long as you measure the same way every time.

How do you calculate CLV on margin?

The margin-based version swaps revenue for gross profit and uses the same churn shortcut: annual gross profit per guest ÷ annual churn rate. For the restaurant above, that is £45 × 4 × 70% = £126 a year, divided by 0.25, which gives the same £504 as the table. Stripe's guide notes that most businesses add measures such as customer acquisition cost and gross margin to the simple equation to get a fuller picture of profitability, and the margin version builds in one of them. Finance teams sometimes go further and discount future years back to today's money, which lowers the figure. For comparing segments inside one business, the undiscounted version is usually enough.

How does CLV work for hotels and members clubs?

Hotels see fewer visits at a higher spend, so frequency might be 1.2 stays a year and the transaction value is the whole folio: room, dining, spa and bar. Keep ancillary spend on the same guest profile, or a guest who spends heavily in the spa but books through a travel agent will look like a room-only customer.

Members clubs are closer to a subscription business, and churn is easier to measure because members cancel or fail to renew. Say a club charges £1,200 a year, members spend another £1,800 a year on food and drink, and 15% of members leave each year. Annual revenue per member is £3,000, expected membership is 1 ÷ 0.15, or about 6.7 years, and revenue CLV is about £20,000.

What are the limits of customer lifetime value?

• Averages hide spread. A handful of regulars can carry the figure, so calculate CLV by segment as well as for the whole guest base.
• Unlinked spend is invisible. Walk-ins who pay without giving details, and duplicate profiles, both pull historic CLV down.
• Churn needs a definition. Pick a lapse window that matches your normal visit pattern and keep it fixed so figures stay comparable.
• Predictions depend on history. A new venue, a refurbishment or a menu change can make last year's pattern a poor guide to next year's.
• Even margin-based CLV leaves out labour, rent and the cost of the rewards you give to keep guests, so treat it as a ceiling on what a guest contributes.

How Tiquo shows historic and predictive customer value

Tiquo shows predictive customer lifetime value and expected next-order ranges alongside historic value, on customer profiles and in analytics dashboards. The estimates come from prior order and activity signals and are shown as directional ranges that reflect uncertainty, so a team can see how predicted value compares with what a guest has already spent. The release notes on predictive customer value describe them as decision-support signals to read with current customer history and your team's judgement.

Because orders, bookings, payments and memberships share one customer record, the history behind each prediction sits on the same profile. Staff can review suggested duplicate profiles, matched on details such as name and email, and merge them so that the bookings, orders and memberships from both sit on one record. Teams can then use predicted value to decide who belongs in a reviewed segment.

Tiquo's customer analytics also report retention rate and returning customers, and the reporting library measures repeat visits and customer lifetime value across locations. Those figures are a starting point for the lapse window and churn rate the formula needs.

If your guests are spread across several venues, our guide to reliable reporting across several sites covers how to keep customer records consistent. For clubs, our guide to pricing membership tiers looks at what each tier's benefits cost to deliver.