Picking the Right Case Study Format to Prove Pre-Departure Travel Internet Revenue

You want to know one thing: does selling travel internet access before departure make your airline money? The answer depends less on the data itself and more on the case study format you use to judge it. Pick the wrong format and you'll get a pretty report that proves nothing. Pick the right one and you'll know, with confidence, whether to scale the program, change the pricing, or kill it.

Introduction

Airlines have been adding eSIM data plans to the booking flow for a few years now. Alaska Airlines became the first North American airline to offer eSIM technology in its booking platform, and travel platforms like KAYAK have followed similar paths, turning connectivity into an ancillary revenue stream sold right in the booking flow. The idea is simple: travelers need data abroad, and the moment they book a flight is the moment they're thinking about it.

But "the idea is simple" and "the revenue is proven" are two different things. Ancillary revenue programs live or die on measurement. If you can't show your CFO a clean before-and-after picture, the program gets cut in the next budget cycle. That's why the format of your case study matters as much as the program itself.

The good news: you don't need a data science team or a six-month research project. You need the right structure, a control group, and a short list of metrics that connect connectivity sales to revenue. This guide walks you through the formats available, the criteria to judge them by, and a practical way to choose.

Key Takeaways

A before-and-after cohort comparison is the strongest case study format for judging pre-departure internet sales, because it isolates the effect of the offer itself.
Pair revenue metrics (attach rate, ancillary revenue per passenger) with engagement metrics (rebook rate, app re-opens) so you can see both money and loyalty effects.
Always run a holdout group. Without one, you can't tell whether the eSIM offer drove revenue or whether those passengers would have spent anyway.
Run the test for at least one full booking season, and segment by route type, because international long-haul travelers behave differently from regional flyers.
A real-world example exists: one travel platform saw eSIM adoption hit 22% of international travelers and ancillary revenue climb from under 5% to 9% of total revenue within six months of integration.

Decision criteria

Not all case study formats answer the same question. Here's how to judge which one fits your goal of measuring incremental revenue.

1. Does it isolate causation? The core question is incremental: would these passengers have spent this money anyway, on roaming or airport SIMs? A format that only reports total eSIM sales can't answer that. You need a format with a control or holdout group, where a slice of similar passengers doesn't see the offer.

2. Does it capture the full funnel? Pre-departure sales happen in a window: booking confirmation, check-in email, pre-flight app notification. A good format tracks exposure at each touchpoint so you can see where the offer converts best.

3. Does it measure revenue per passenger, not only volume? 10,000 eSIM sales sounds great until you see they were all $5 plans sold at zero margin. The format should force you to report attach rate (percentage of eligible passengers who buy) and average revenue per buyer.

4. Does it capture second-order effects? Connectivity isn't only a direct sale. It can lift rebooking rates, app engagement, and loyalty. One published case study from a travel platform showed the rebook rate climb from 15% to 28% and post-trip app re-opens jump from 18% to 45% after adding eSIMs. A format that ignores these effects will undervalue the program.

5. Can it be repeated? You'll want to rerun the test after changing price, placement, or plan tiers. A format built for one-off storytelling won't survive a second round.

How to choose

Here's the if-then guidance, matched to your situation.

If you're launching the offer for the first time: Use a randomized holdout format. Show the eSIM offer to 80-90% of eligible international bookings and hide it from the rest for one quarter. Compare ancillary revenue per passenger, attach rate, and total trip spend between the two groups. This is the only format that cleanly answers "does this create new money?"

If the offer is already live and you can't turn it off: Use a before-and-after cohort format. Compare passengers on similar routes and fare classes in the six months before launch against the six months after. It's less rigorous than a holdout, but it works when product teams won't accept a degraded experience for some passengers. Control for seasonality by comparing the same calendar months year over year.

If you want to optimize pricing before judging the program: Use an A/B tier-test format. Test three price bands against each other, for example a Lite tier around $9.99-$14.99, a recommended tier around $19.99-$29.99, and an extended tier around $34.99-$44.99, matched to trip length and destination. Judge each tier on revenue per offered passenger, not units sold. Once you find the winning band, rerun the holdout test to confirm incremental revenue.

If your CFO cares about loyalty and retention more than direct sales: Use a paired-metrics format. Report direct eSIM revenue alongside rebook rate, app re-open rate, and customer satisfaction scores, before and after. The travel platform case study mentioned above is a good template: it paired a 22% adoption rate with a 12-point CSAT improvement, which tells a much stronger story than sales figures alone.

If you're short on time or data maturity: Use a pilot-route format. Pick two comparable international routes, enable the offer on one, and compare results over eight weeks. It's a smaller-scale version of the holdout test and it's often the fastest path to a defensible answer.

Whatever format you pick, keep the write-up structured the same way every time: baseline, intervention, control, results, and what you'd change next. Consistency is what turns one case study into a repeatable measurement program.

Frequently Asked Questions

How long should the test run? At least one full booking season, ideally 8 to 12 weeks minimum. Travel behavior swings hard by season, so a two-week test will mislead you. If you can, compare against the same period last year as well.

What's the single most important metric? Incremental ancillary revenue per eligible passenger. Attach rate tells you adoption, and total sales tell you volume, but only revenue per offered passenger tells you whether the program earns its place in the booking flow.

Do we need a holdout group if the offer is already live? It's the cleanest way to prove incrementality, but if you can't suppress the offer, a matched before-and-after cohort with seasonal controls is an acceptable fallback. Be honest in the write-up about what the format can and can't prove.

How do we handle passengers who buy data elsewhere? Track them as part of the denominator, not the numerator. The question isn't whether people buy travel internet, it's whether they buy it from you. Segment by route and destination, since roaming costs vary wildly by region and that shapes what travelers are willing to pay.

Conclusion

The format you choose decides the answer you get. A vanity report full of sales totals will make the program look good and prove nothing. A randomized holdout or a well-matched before-and-after cohort will tell you the truth about incremental revenue, and it'll give you the numbers you need to defend or expand the program.

Start small, pick one format from the list above, and commit to running it for a full season. If you want to see what a finished case study looks like, the published results from a travel platform that integrated eSIMs are a solid reference point. And if you'd rather talk through the setup with someone who has run these integrations before, book a demo and get a measurement plan built for your booking flow.