Multiple: Multivariate Testing Program for Online Gift Retailer
Hypothesis
A gift-giving ecommerce platform with chronically low conversion rates has multiple simultaneous friction points that require multivariate testing rather than sequential A/B testing to efficiently identify winning combinations.
Test Results
Key Learning
Context: Multi-step processes on the multiple can overwhelm users if they can't see how far along they are or how much is left.
What was tested: Proprietary ecommerce platforms require custom CRO tool integration — off-the-shelf AB testing tools often require additional engineering for non-standard platforms. Gift-giving purchases have unique friction points: gift messaging, delivery timing, recipient vs. buyer information in checkout. Multivariate testing is appropriate when multiple page elements need optimization and traffic volume supports it. Team education in CRO methodology alongside implementation delivers lasting capabilities beyond the engagement
Result: No statistically significant difference was detected. This null result is still valuable — it narrows the search space and helps calibrate your minimum detectable effect for future tests.
How to Apply This to Your Site
This experiment tested multiple: multivariate testing program for online gift retailer but produced no statistically significant change. The test was run on a landing page page in the e-commerce industry. Inconclusive results suggest this particular change may not be a priority — focus testing effort on higher-impact areas.
Before you test: Consider that layout tests typically require adequate traffic to reach statistical significance. Run your test for at least 2 full business cycles to account for weekly traffic patterns.
What Was Tested
Gift Tree, a Vancouver-based gifting brand since 1997, had consistently low conversion rates despite decent traffic. Complicating factors included a proprietary ecommerce platform (not a standard SaaS platform) requiring custom test implementation. Invesp designed a customized testing model for the platform, trained the team on multivariate testing, and delivered a 10% conversion rate increase.
Methodology
Build On These Learnings
Save your own experiments, spot winning patterns across your test history, and stop repeating what's already been tried.
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