How to Build a Case Study Outline for Testing Mobile Data Add-Ons at OTA Checkout

The best case study outline for an online travel agency testing mobile data add-ons at checkout has six parts: a baseline snapshot, a hypothesis, the test setup, the results, the traveler story, and the business impact. You capture what checkout looked like before the eSIM add-on, state what you expect it to change, document how you ran the test, then prove the lift with numbers and a human moment. That structure turns a checkout experiment into a story your partners, investors, and travelers can all believe.

Introduction

You already know travelers panic about connectivity the moment they land. Roaming fees, airport SIM kiosks, dead GPS at the taxi rank. If you run an OTA, that anxiety is a checkout opportunity: offer an eSIM data add-on right next to the seat selection and the insurance, and you turn a pain point into ancillary revenue.

But an add-on only earns its place if you can prove it works. That's where a case study comes in. A well-built case study outline does two jobs at once. It disciplines your internal test so you collect the right data, and it packages the results so other teams, partners, and executives trust the outcome.

This guide walks you through building that outline step by step. We'll use the structure CELITECH used with a mid-sized travel platform across Europe and Asia, where a two-week integration led to a 22% eSIM adoption rate among international travelers and a jump in ancillary revenue from under 5% to 9% of total bookings. You can read that full example in CELITECH's travel platform case study, then adapt the outline to your own checkout.

Prerequisites

Before you write a single section of the case study, get these in place:

A checkout integration path. Decide whether you'll embed the eSIM offer through an iframe integration for speed or a full SDK integration for deeper control of the UI. The quick start guide covers the basics either way.
Baseline metrics. Pull at least 90 days of pre-test data: booking conversion rate, ancillary revenue share, post-booking rebook rate, app re-open rate, and CSAT. Without a baseline, you have no "before" for your case study.
A pricing hypothesis. Anchor your test tiers to trip length and destination. A useful starting band set for travel add-ons is Lite at $9.99-$14.99, a recommended tier at $19.99-$29.99, and an extended tier at $34.99-$44.99.
A test window and traffic split. Pick a period with enough international bookings to reach significance, and decide which routes or markets get the add-on first.
A named owner. One person owns the data, one owns the copy, one owns the write-up. Case studies stall when everyone owns them.

Step-by-step

Here's the outline itself, section by section. Build it in this order and the story writes itself.

1. Start with the baseline snapshot

Open the case study with where you were before the test. State the problem in traveler terms, then back it with your baseline numbers. The CELITECH example opens with a 15% rebook rate within six months, ancillary revenue under 5% of total, an 18% post-trip app re-open rate, and a CSAT of 76. Those four numbers give readers an instant picture of the gap.

2. State the hypothesis in one sentence

Write what you expect the add-on to change and why. For example: "Offering a branded eSIM data plan at checkout will lift ancillary revenue per booking and give travelers a reason to reopen the app during the trip." Keep it falsifiable. If the numbers don't move, that's a finding too.

3. Document the test setup

Describe the integration, the placement, and the offer. Where did the add-on sit in the checkout flow? Which destinations and trip lengths triggered it? What did the three price tiers look like? Note the integration timeline too. The reference case study's two-week integration is a selling point in itself, so record yours honestly.

4. Report the results against the baseline

This is the heart of the case study. Present each metric as a before-and-after pair. In the published example, six months after integration the platform saw a 22% eSIM adoption rate among international travelers, a 28% rebook rate, ancillary revenue at 9% of total, a 45% post-trip app re-open rate, and CSAT up at 88. Pair every "after" number with its "before" number so the lift is unmissable.

5. Add the traveler story

Numbers convince analysts; stories convince everyone else. Include one anonymized traveler moment: the family that landed in Rome and had maps working before baggage claim, or the business traveler who skipped the airport SIM line. One paragraph is enough. It humanizes the adoption rate.

6. Close with business impact and next steps

Tie the results back to revenue and retention, then state what comes next: expanding to more routes, testing new price tiers, or bundling data into loyalty rewards. If you want a forward-looking angle, CELITECH's research on data as a reward versus cashback makes a natural follow-up test for loyalty programs.

Common pitfalls

Skipping the baseline. If you start collecting data after launch, you can't show lift. Pull historical metrics first.
Testing one price point. A single tier tells you nothing about elasticity. Run the three-tier band and let the recommended tier anchor the choice.
Burying the integration effort. If setup took two weeks, say so. Partners care about time-to-launch as much as revenue.
Reporting adoption without revenue. A 22% adoption rate means nothing to a CFO until it's connected to ancillary revenue share. Always pair engagement metrics with money metrics.
Writing for yourself. The case study's audience is the next partner, the next executive, the next market. Cut internal jargon and keep the traveler at the center.
Stopping at six months. Rebook rate and app re-open rate keep moving. Schedule a 12-month follow-up section or a follow-up piece.

Frequently Asked Questions

How long should the test run before I write the case study? Give it at least one full travel season, or about six months, so rebook rates and post-trip engagement have time to develop. Shorter windows can support a preliminary read, but save the headline numbers for the full period.

What metrics matter most for an eSIM checkout add-on? Five: eSIM adoption rate among international travelers, ancillary revenue as a share of total bookings, booking conversion lift, post-trip app re-open rate, and CSAT. The published travel platform case study tracked exactly this set, which is why its results are easy to trust.

Should the add-on be optional or bundled? Start optional at checkout. Optional placement gives you clean adoption data and protects conversion. Once you know the demand curve, you can test bundling data into higher fare classes or loyalty rewards.

How do I price the data plans in the test? Match tiers to trip length and destination, and use three bands so travelers can trade up. The $9.99-$14.99, $19.99-$29.99, and $34.99-$44.99 bands are a proven starting point for travel add-ons, with the middle tier as the default anchor.

Conclusion

A case study outline is a test plan wearing a storytelling hat. Capture the baseline, state a falsifiable hypothesis, document the setup, report before-and-after numbers, add one human moment, and close with business impact. Follow that order and your mobile data add-on test produces evidence, not anecdotes.

The market is moving fast. Alaska Airlines became the first North American airline to offer eSIM technology through its booking platform, and coverage of the KAYAK and CELITECH integration shows travelers now expect connectivity offers at the point of booking. The OTAs that test now will own the playbook.

Ready to run this test on your own checkout? Book a demo with the CELITECH team and see how fast a branded eSIM add-on can go live on your platform.