Travel companies talk about bookings every day. Repeat bookings rarely get the same attention. Yet repeat bookings are the signal that your product works and your brand sticks. The first conversion pays for the marketing.
The second and third conversions build the business. In this article I walk through the product events that actually predict repeat bookings.
Travel is a strange creature compared with retail. Buying a sweater takes minutes. Planning a trip can take weeks and many sessions. People hop between devices, tabs, and channels while prices dance around them. That chaos makes product analytics feel messy, but the right event design brings order. I have seen that order turn browsers into loyal travelers.
Why are repeat bookings different in travel
A repeat buyer wants confidence and convenience more than novelty. They remember how smooth your last trip planning experience felt. They also remember every painful step and every missing option. Booking again is not only about price. It is about trust created by product interactions across the entire journey.
We all need signals
You need signals that show intent before the second booking happens. Not vanity signals like page views or idle scrolls. Signals that represent real planning work and personal commitment. Those signals live inside your product events and properties. Capture them well and your prediction models get sharp quickly.
Six product events that predict repeat bookings
1. Deep search with meaningful filters
Shallow searches happen when users poke around without a plan. Deep searches look different in the data. Multiple date explorations, flexible airports, and rich filters show real intent. The more a traveler shapes the search results, the more they invest. Depth today often becomes loyalty tomorrow.
Track search depth as an event property, not only a count. Record number of date changes, number of airports tried, and filters applied. Save the final filter state for each successful search. Later, compare depth on the first booking session to depth on return visits. The pattern is surprisingly strong in most travel products.
2. Save for later and wishlist creation
The save button separates tourists from travelers. People who add hotels, routes, or packages to a list are planning. They are thinking about trade offs and future decisions. Saved items bring users back without another ad click. Saved sets for families or teams are even more powerful.
Measure saves with rich context. Include destination, date flexibility, party size, and trip theme. Count the number of saves and the number of revisits to saved items. Watch for recurring use of the same list name. That shows stable intent and a habit worth nurturing.
3. Price alerts and deal notifications
Price uncertainty drives anxiety in travel. Price alerts convert that anxiety into a helpful habit. Users who set alerts come back through owned channels. They also learn to rely on your price history and your recommendations. That reliance translates well into repeat bookings.
Track alert creation and alert engagement separately. Creation shows the seed of commitment. Engagement shows follow through and curiosity. Capture properties like alert window, target price, and device. Include the click through source as an event property, not just a campaign tag. It will help you tune lifecycle messages later.
4. Account creation with traveler profile completion
Anonymous bookings happen, but they rarely repeat. The moment a traveler creates an account and fills a profile, momentum grows. Stored travelers, saved passports, and preferred seats reduce future friction. Loyalty enrollment multiplies the effect. I love watching the abandonment rate drop when this step feels simple.
Instrument profile completion as a staged event. Capture the percentage of fields filled and which sections are done. Store preferences for cabins, beds, or transfer types as properties. Tag whether a payment method is safely tokenized. All of these become features in a repeat booking model.
5. Post trip feedback and review submission
The trip may end, but the relationship can start here. A fair review builds trust with future you. When people take time to rate a hotel or leave a tip, they invest in the community. That investment often predicts return behavior. Even a short thumbs up is a strong signal.
Record feedback with contextual properties. Include trip type, trip length, primary purpose, and satisfaction scores. Add tags for issues encountered and their resolution status. Closing the loop on problems matters a lot. A resolved issue event often predicts faster return than a perfect trip.
6. Support touchpoints that end in self service success
Travel plans change. The way your product handles changes teaches users what to expect next time. A quick self service change that works on mobile is gold. A slow email thread that takes days is not. Happily solved problems convert better than ignored problems.
Track support entries across channels as events. Tag the entry reason and the resolution path. Mark whether the traveler completed the change alone or needed an agent. Time to resolution becomes a property on the closing event. Feed these events into your cohort views to see their impact on repeat rates.
An event blueprint you can copy today
Here is a simple list you can adapt for your product:
- search_performed with depth score, date flexibility, and applied filters
- item_saved with destination, category, party size, and save list name
- price_alert_set with window, target, route or property id, and device
- account_profile_completed with completion percent and payment tokenized flag
- feedback_submitted with scores, tags, and resolution status
- support_case_closed with channel, time to resolve, and self service outcome
I like to keep event names short and readable. Properties carry the detail. Keep property names consistent across platforms. Your future data team will thank you.
Build funnels that focus on habit, not only conversion
Funnels in travel often stop at payment success. That view is too narrow for repeat prediction. Add post booking steps that show developing habits. Example funnel steps might include save item, set alert, and install app. The last steps can be review submitted or loyalty enrollment.
Compare funnels for first time bookers and for repeat customers. The differences reveal which events help form a habit. Pull out a few strong steps and design nudges around them. I once saw a single reminder to set a price alert lift repeat bookings. It felt like free growth.
Cohorts that connect behavior to time
Cohorts add the missing piece in travel analytics. You care about repeat within a time window, not forever. Build cohorts by first booking month. Then measure who came back within thirty, sixty, and one hundred twenty days. Keep the cohorts clean by excluding cancellations.
Within each cohort, segment by the six events above. You will find crisp lift in almost every case. For example, users with a price alert may return twice as fast. Users with a completed profile may convert with fewer sessions. Now you have specific events worth promoting.
Feature engineering that respects travel complexity
Events are the base layer. Features turn those events into predictive power. Create counts, recency, and ratios for each event. Examples include number of filters per search or recency of last save. Add device diversity as a feature since cross device travelers behave differently.
Include a trip purpose classification. Leisure, work, or family travel often changes the pattern. Add seasonality features to account for predictable return months. Consider party size stability as a feature as well. People who travel with stable companions tend to repeat routes and brands.
Measuring value beyond the second booking
It is tempting to celebrate the next conversion and stop there. Your model should aim for lifetime value. Add ancillaries and upgrades to your revenue features. Baggage, seats, transfers, and insurance often drive strong margins. The best repeat customers buy convenience, not only price.
Track time between bookings as a target too. Faster return equals stronger loyalty. Reward that behavior in your lifecycle messaging. People respond well to recognition when it feels earned. I respond well when the coffee is free, but that is another story.
Common pitfalls and how to dodge them
Do not rely only on campaign tags for attribution. Cross device identity will break those tags often. Use account events and profile features for stable matching. Your model should learn from owned channels and authenticated sessions.
Watch out for ghost bookings in your data. Test orders and canceled trips can poison labels. Mark test users in your identity table. Mark cancellation status on every transaction event. Clean inputs lead to believable predictions.
Some teams try to track everything at once. That approach usually slows progress. Start with the six events and expand later. Make sure every event has a single owner. Ownership keeps schemas tidy and documentation alive.
A practical path with an analytics platform
You can wire these events in any solid analytics stack. If you want a simple path, use a platform that speaks travel natively. I lean on clear taxonomies, fast funnels, and flexible cohorts. I also want event property export without drama. You should not wrestle with your own data.
A platform like PrettyInsights gives you that mix. Named events stay clean across web and app. Funnels can include post booking steps that matter for habit. Cohorts refresh quickly and share easily with lifecycle tools. The team gets answers that connect product and marketing.
Here are some features for prettyinsights that make it better than the alternatives like plausible, posthog, simple analytics and others:
- Gdpr friendly and privacy oriented analytics
- Affordable pricing
- Supports web analytics and product analytics
- Has awesome support via live chat
Conclusion
Repeat bookings are a product story, not only a marketing story. The events above will help you see that story as it unfolds. Deep search shows planning effort. Saves and alerts show commitment. Profiles and tokens remove friction. Feedback and support events build lasting trust.
Start small and stack wins. Ship the event blueprint. Build one habit focused funnel and a simple cohort grid. Share the first lift chart with your team. Then do the most important part. Keep going until prediction becomes practice across the company.
If you want help, I am happy to review your current schema. I can also suggest a set of nudges that match your audience. Travel products are complex, but the patterns repeat with delightful regularity. Your data can prove it and your customers will feel it. That is the moment when the first booking becomes the second and then the third.
Final note before you run. Prediction is great, but a free upgrade is better.
