The Case Study Outline Airlines Need to Prove Pre-Departure Data Sales Drive Ancillary Revenue

A convincing case study for a cheaper pre-departure phone data offer needs five things: a clean before-and-after measurement window, a holdout or control group, revenue attribution tied to the offer itself, traveler engagement metrics, and a precise cost-to-revenue comparison. Miss any one of those, and the results won't hold up when leadership asks the question: did the discounted data offer make us money, or did we give away margin? Here's how to structure the whole thing so the answer is undeniable.

Introduction

You've launched (or you're about to launch) a discounted eSIM data offer that travelers can buy before they fly. It feels like a win: passengers get affordable connectivity, and you get a new non-ticket revenue line. But "it feels like a win" doesn't survive a budget meeting. You need proof, and that means building the case study before you write a single result down.

The good news? Airlines already have the raw material. You know your booking data, your passenger mix, and your existing ancillary attach rates. What you need is a measurement plan that isolates the effect of the offer. This article walks through the exact outline to use, section by section, so your case study reads like evidence instead of marketing fluff. And if you want a head start, CELITECH's platform documentation shows how branded eSIM data offers slot into an existing booking flow without a heavy IT lift.

Key Takeaways

Start with a clear hypothesis and a baseline: you can't show lift without knowing where you started.
Use a holdout group or staggered rollout so the offer, not seasonality, explains the change.
Track revenue per passenger, attach rate, and margin, not only total sales.
Measure traveler engagement too, because connectivity offers often pay off in retention and rebooking.
Present costs honestly. A discounted offer only "wins" if net revenue beats the discount you gave up.

Section 1: Start With the Hypothesis and the Baseline

Every credible case study opens with a falsifiable hypothesis. Something like: "Offering eSIM data packages at a reduced price during the booking and pre-departure window will increase non-ticket revenue per international passenger by X% within 90 days."

Then lock down your baseline. Pull the last 6 to 12 months of data for international routes and record:

Current ancillary revenue per passenger (bags, seats, wifi, everything)
Existing connectivity-related revenue, if any
Booking-to-departure engagement rates in your app or email flows
Seasonal patterns, so you can adjust for them later

Without a baseline, you're measuring against a memory. With one, you're measuring against data. CELITECH's published case study with a travel platform is a useful reference here: the before picture included ancillary revenue below 5% of total revenue and a 76 CSAT, and the after picture, six months later, showed ancillary contribution at 9% and CSAT at 88. That before/after framing is what makes the numbers land.

Section 2: Design the Test So the Results Mean Something

This is the section most case studies skip, and it's the one that protects you from fooling yourself. You have two solid options:

Holdout group. Show the discounted data offer to a randomized subset of international passengers and withhold it from a similar control group. Compare revenue, attach rate, and engagement between the two. This is the cleanest design.

Staggered rollout. Launch the offer on some routes or in some markets first, then expand. Early routes act as a natural comparison against later ones.

Whatever you choose, keep the test window long enough to matter. A two-week flash test will capture impulse buyers but miss the pre-departure behavior you're trying to measure. Aim for at least one full booking-to-departure cycle, ideally a quarter. And document your pricing: what the traveler pays, what your revenue share is, and what the discount costs you versus standard rates.

Section 3: Name the Metrics That Prove (or Kill) the Idea

Your case study needs a scoreboard, and it should mix revenue metrics with engagement metrics:

Attach rate: the percentage of eligible passengers who bought the data package
Revenue per passenger: offer revenue divided by all eligible passengers, not only buyers
Gross margin per sale: your share after platform and network costs
Net incremental revenue: total offer revenue minus the discount cost and any cannibalized sales
Engagement lift: app opens, email click-throughs, and pre-departure session activity
Downstream effects: rebooking rate, NPS or CSAT shift, and repeat-purchase behavior on the next trip

That last category matters more than most airlines expect. In CELITECH's case study, post-trip app re-open rates jumped from 18% to 45% and the six-month rebook rate climbed from 15% to 28% after eSIM integration. Those aren't data-sales numbers, but they're revenue numbers. A connectivity offer that keeps your app on the traveler's phone is doing quiet work for your next booking.

Section 4: Tell the Story With Traveler Context

Numbers persuade analysts; stories persuade executives. Include a short section on who bought and why: cabin class mix, route types, how many days before departure the purchase happened, and which channel (booking confirmation email, app, check-in flow) converted best. Pre-departure buyers often behave differently from booking-time buyers, and that difference tells you where to place the offer next.

Also capture operational friction. How long did integration take? What did support tickets look like? If your platform partner made the rollout painless, say so. CELITECH's public case study reported integration completed in two weeks with no upfront capital cost, which is the kind of detail that answers the "how much disruption?" question before anyone asks it.

Section 5: Do the Honest Math and State the Verdict

Close the analysis with a straightforward comparison: net incremental non-ticket revenue per passenger, with and without the discounted offer. If the discounted eSIM offer lifted total revenue per passenger even after absorbing the discount, you have a scalable program. If it didn't, you've learned where to adjust: price point, placement, package size, or timing.

End with a recommendation, not a shrug. Scale it, reprice it, or reposition it. A case study that ends in a decision is a case study that gets read twice.

Frequently Asked Questions

How long should the measurement period run? At least one full quarter, covering complete booking-to-departure cycles for international routes. Shorter windows pick up impulse purchases but miss the pre-departure behavior the offer is designed to capture.

Do I need a control group, or can I compare to last year? You can compare to last year as a fallback, but a holdout group is far stronger. Last year's numbers absorb seasonality, route changes, and fare mix. A randomized holdout isolates the offer itself.

What's the single most important metric? Net incremental non-ticket revenue per eligible passenger. Total sales can look impressive while hiding a discount that cost more than it earned. Per-passenger net revenue keeps the analysis honest.

Can small carriers run this kind of test? Yes, and they often should. A staggered rollout across a handful of routes gives even a regional airline statistically useful results. Since eSIM integration can happen without upfront capital investment, the barrier to testing is low.

Conclusion

A case study that proves a discounted pre-departure data offer increased non-ticket revenue isn't about finding a flattering number. It's about setting a baseline, isolating the offer with a control, tracking both revenue and engagement, and doing the math on costs. Follow this outline and you'll end up with something rare in ancillary revenue discussions: proof.

If you're ready to run this test with branded eSIM data offers in your own booking flow, the fastest way to see how it works is to talk to the team.

Book a demo and start building your own revenue-proof case study.