Tourism & Hospitality · Scenario 22

Tour Operator

Plenty of people visit the booking page; far too few finish. We A/B test the funnel scientifically and turn the same traffic into measurably more bookings.

Method · Controlled experimentation

The situation

A tour operator pays to bring visitors to its site, then loses most of them somewhere between ‘browse’ and ‘paid’. The booking flow was designed once, by opinion, and never tested — a confusing date picker, a long form, hidden fees at checkout. Every lost visitor is marketing money already spent and wasted.

Nobody runs controlled experiments, so ‘improvements’ are guesses and the real friction points stay invisible. Conversion sits where it’s always sat.

3.1%
visit-to-booking rate
Never tested
the booking flow
Checkout
where most drop
Opinion
drives changes

Where we dig for the truth

We map the booking funnel, find where visitors abandon, and run controlled A/B tests so every change is proven, not guessed.

Funnel / page analyticsDrop-off by stepDevice & sourceCheckout abandonmentA/B experiment resultsBooking value & type
The booking funnelWhere visitors abandon todaySTEP RATEVisited booking page9,000Selected a tour & date3,60040%Started checkout1,50042%Completed booking27919%

The biggest leak is checkout — barely a fifth of those who start actually finish. That single step is where testing pays back fastest.

Our approach — A/B Testing & Conversion Optimization

We instrument the funnel and run structured A/B tests on the worst-converting steps: a simpler date picker, fees shown upfront, fewer form fields, trust signals at checkout. Each test is measured for statistical significance, so only changes that genuinely lift bookings ship.

Winning variants compound across the funnel, and the same ad spend suddenly produces far more paid bookings — without buying a single extra visitor.

From guesswork to tested conversion1Map the funnelMeasure drop-off atevery step of thebooking flow.2HypothesiseTarget the worst stepswith specific, testablechanges.3A/B testRun controlledexperiments tostatisticalsignificance.4Ship winnersKeep only changes thatprovably lift bookings.
Checkout completion by test variantShare who finish, with the winning design0%10%20%30%40%19%Control24%Upfront fees27%Short form23%Trust badges34%Winner

Each tested change adds a few points; combined, the winning checkout nearly doubles completion — all from the same incoming traffic.

What changes

Same traffic, a funnel tuned by experiment. Representative for an experiences/tours operator.

Representative 90-day movementVisit-to-booking3.1%5.4%▲ +2.3 ptsCheckout completion19%34%▲ +15 ptsCost per booking$31$18▼ -42%Monthly bookings279486▲ +74%
Monthly bookingsSame ad spend, tested funnel0140279419559M1M2M3M4M5M6
Why this is not "social media management"
We didn't just pour more money into ads to drown the leak. We tested the booking flow scientifically and fixed the steps quietly losing visitors the operator had already paid for. Conversion optimisation is experimentation, not opinion.

Frequently asked questions

How do you increase online bookings without more traffic?
We map the booking funnel, find where visitors abandon (usually checkout), and run controlled A/B tests on the worst steps — a simpler date picker, upfront fees, fewer form fields. Only changes proven to lift bookings ship.
What is A/B testing?
A/B testing shows two versions of a page to comparable visitors and measures which converts better, to a level of statistical significance. It replaces opinion-led design changes with evidence.
Isn't buying more ads simpler?
More ads just pour visitors into a leaky funnel. Fixing the funnel lifts bookings from traffic you already pay for. Book a marketing audit.

Want this run on your numbers?

Share your funnel analytics and we’ll find the tests that lift your bookings.

Book a marketing audit
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