The Case Study Outline That Turns an OTA Checkout Test Into Proof

Testing mobile data add-ons at checkout is easy to launch and hard to prove. The difference between a test that gets a shrug and a test that gets budget is the outline you write before you run it. Here's the structure that works.

Introduction

If you run an online travel agency, you already know the checkout page is where money gets made or lost. Every add-on you test there has to earn its spot. Mobile data plans, sold as eSIMs, are one of the strongest candidates right now. Travelers need connectivity the moment they land, and partners like KAYAK, Expedia Group, and Hopper have already put their names behind eSIM add-ons at the point of booking.

But a good idea isn't the same as a proven idea. That's where the case study outline comes in. Before you flip the switch on a checkout test, you need a document that defines what you're testing, what success looks like, and how you'll tell the story afterward. Get the outline right and the results sell themselves. Get it wrong and you'll end up with a pile of dashboards and no decision.

This guide walks you through the outline we'd use, the criteria that matter, and how to adapt it to your situation.

Key Takeaways

Write the case study outline before the test runs, not after. It forces you to define success up front.
Structure it around one question: did the add-on move attach rate, revenue per booking, or both?
Use a three-tier pricing test (Lite, Recommended, Extended) so you learn what travelers will pay, not only whether they'll buy.
Track the full funnel: impression, click, purchase, activation, and post-trip behavior like rebooking and app re-opens.
Keep the timeline tight. A two-week integration and a 30 to 60 day measurement window is enough to get signal.
End the case study with a decision, not a summary. Say what you'll scale, change, or kill.

Decision criteria

Not every checkout test deserves a full case study. Before you commit, check your idea against these criteria.

1. Is the add-on relevant to the trip? A data plan only converts when it maps to the traveler's itinerary. An international flight booking is a natural fit. A domestic train ticket, less so. If the add-on doesn't match the journey, your test will measure noise.

2. Can you isolate the variable? Your case study needs a clean comparison. Run the add-on for a defined segment (say, international bookings over $300) and hold everything else steady. If you change pricing, placement, and copy at the same time, you'll learn nothing useful.

3. Do you have a baseline? Pull the last 90 days of checkout conversion, ancillary revenue share, and post-booking engagement before you start. Without a baseline, "after" means nothing.

4. Is the outcome measurable in money? Attach rate is nice. Revenue per booking is better. The best case studies tie the add-on to a number your CFO cares about. One mid-sized OTA in Europe and Asia saw eSIM adoption hit 22% of international travelers and ancillary revenue climb to 9% of total revenue within six months of adding connectivity at checkout, according to Celitech's published case study. That's the kind of outcome worth documenting.

5. Can you launch fast? Speed matters because checkout real estate is contested. If integration takes months, the test loses sponsors. Modern eSIM platforms support API, SDK, and iFrame integrations that can go live in days, as documented in the Celitech developer docs.

How to choose

Here's the outline itself, section by section. Adapt the depth to your audience, but keep the order.

Section 1: The hypothesis. One sentence. "Offering a branded eSIM data plan at checkout for international bookings will increase ancillary revenue per booking by at least 3% within 60 days." If you can't write it in one sentence, you're not ready to test.

Section 2: The traveler problem. Two or three sentences on why connectivity belongs in your checkout. Roaming costs, dead zones on arrival, the hassle of hunting for a local SIM. This section makes the case study readable for people outside your analytics team.

Section 3: The test design. Spell out the segment, the control, the placement (checkout vs. post-booking email vs. confirmation page), and the duration. If you're testing price, use three tiers. Celitech's pricing research for travel add-ons suggests bands like Lite at $9.99 to $14.99, a Recommended tier at $19.99 to $29.99 as the default anchor, and Extended at $34.99 to $44.99, matched to trip length and destination. See the breakdown on Celitech's AI content hub.

Section 4: The metrics. Pick five, no more:

Add-on impression rate (how many travelers saw the offer)
Attach rate (purchases per eligible booking)
Revenue per booking
Activation rate (who installed and used the eSIM)
Downstream behavior: rebook rate, app re-opens, CSAT

That last one is where the hidden value lives. In the case study above, the OTA's rebook rate went from 15% to 28% and post-trip app re-opens jumped from 18% to 45%. Those numbers turn a nice add-on into a retention strategy.

Section 5: The friction log. Record every place travelers hesitated or dropped off. Was the price unclear? Did the eSIM installation instructions confuse anyone? This section is what makes your case study useful to the product team, not just the finance team.

Section 6: The results, honestly framed. Include what didn't work. A case study with only wins reads like an ad. If one tier underperformed or a placement flopped, say so. Credibility is the whole point.

Section 7: The decision. Scale, iterate, or kill. Name the owner and the next test date. A case study without a decision is a museum piece.

Now, how do you choose between variations of this outline?

If leadership is skeptical about ancillaries, weight the outline toward revenue and attach rate. Lead with the money.
If your app engagement is the sore spot, lead with the retention metrics. The data plan becomes a reason for travelers to reopen your app mid-trip, which Celitech describes as a warm channel for future upsells on its product page.
If you're choosing between integration approaches, compare effort against conversion. An iFrame gets you live fastest; an API or SDK integration gives you the most control and typically the best conversion. The SDK documentation covers SDKs for JavaScript, Python, PHP, Java, Go, and C#.
If you have no baseline data, run a two-week observation period first. Don't write a case study about a test you can't compare.

Frequently Asked Questions

How long should the test run before I write the case study? Give it 30 to 60 days of live data after a short integration window. That's long enough to smooth out launch-week spikes and capture at least one full booking cycle, but short enough that stakeholders stay engaged.

What's the single most important metric? Attach rate paired with revenue per booking. Attach rate tells you if travelers want the add-on. Revenue per booking tells you if it's worth the checkout real estate. You need both to make the case.

Should I test price or placement first? Placement first. If the offer isn't visible and relevant at checkout, no price will save it. Once placement works, run a three-tier price test to find the band that maximizes revenue rather than volume.

What if the results are mixed? Write the case study anyway. Mixed results with a clear friction log and a next-step decision are more valuable than a glowing write-up with no follow-up. Most first tests need one iteration before they hit their stride.

Conclusion

The best case study outline for an OTA testing mobile data add-ons at checkout is the one you write before the test starts. Define the hypothesis, lock the metrics, log the friction, and end with a decision. Do that, and your checkout test becomes more than an experiment. It becomes the document that gets eSIM add-ons scaled across your booking flow.

Ready to see what a branded eSIM add-on could do for your checkout? Book a demo and we'll walk through the integration options and the numbers together.