The travel industry often gravitates toward emotional messaging, but this case proves that addressing multiple core customer needs in a clear way can significantly outperform single-benefit value propositions. Here's how we transformed a cruise booking site's performance by expanding their value proposition from just "savings" to include both "choice and savings."
Through strategic A/B testing, we achieved:
Our client operates in the competitive online cruise booking space, targeting both budget and luxury travelers across North America. Despite having great pricing and excellent customer service, their homepage wasn't effectively communicating these benefits to first-time visitors.
Previous optimization attempts focused on design tweaks rather than messaging. We knew there was a bigger opportunity: refining their core value proposition.
When data reveals a conversion problem, the key is developing a focused testing strategy that isolates the most impactful variable. In this case, we knew that value propositions directly influence how visitors perceive and engage with a brand, so we designed a test to identify which message would resonate most strongly with cruise shoppers.
We implemented a focused A/B test comparing three distinct value propositions:
To ensure accurate tracking, we:
Results Table:
| Metric | Control | Variation 1 | Variation 2 |
| Users | 9,237 | 8,714 | 8,858 |
| Transactions | 42 | 27 | 54 |
| Conversion Rate | 0.45% | 0.31% | 0.61% |
| Conversion Lift | — | -31.1% | +34.1% |
| Revenue Lift | — | +12.4% | +21.0% |
Variation 2 ("More Choices, More Discounts") was the clear winner, outperforming both the control and Variation 1 with a confidence level of 95%. Not only did it produce the highest conversion rate, but it also delivered the strongest revenue results.
We made sure to advise the client that test results can sometimes show different outcomes in the long term due to sample variability and the shorter test period. What's most important is that the test version performed statistically better than the control. If you wanted a more precise estimate of long-term revenue lift, you'd need to run the test longer—but this comes with trade-offs. Longer tests can delay rolling out the winning version, and for most tests, the opportunity cost of waiting isn't worth it.
Our data revealed two key insights about cruise shoppers:
✅ Adding choice to a savings message creates a more complete value proposition
✅ Both versions were clear and direct - but one told a fuller story
The control's "Pay Less" message wasn't wrong - it was clear and focused on a key benefit. But by simply adding "More Choices" to the savings message, we created a more complete picture of what matters to these customers. While both versions were straightforward, the winning variation succeeded by addressing two core customer needs instead of one. Think of it like the difference between "We have cheap shoes" versus "We have lots of shoes at great prices" - the second just feels more compelling because it addresses both selection and savings.
Ready to discover what data-driven value proposition testing could do for your conversion rates? Let's talk about finding your winning message.
Appendix: Detailed Test Parameters and Statistical Analysis
Test Configuration
| Parameter | Details |
|---|---|
| Device Targeting | All devices |
| Page Scope | Homepage only |
| User Segments | All traffic |
| Test Duration | Standard test period |
| Implementation | Nantu AB Test Tool |
| Traffic Distribution | Even split between variations |
Statistical Results
| Metric | Value |
|---|---|
| Total Sample Size | 26,809 users |
| Confidence Level | 94.25% |
| Conservative Lift Estimate | 34.1% |
| Statistical Significance | Achieved |
Field Notes
