Hero/CTA Optimization
5 experiments testing hero/cta changes across Direct Energy and NRG brands. Win rate: 40%. 2 winners found.
Key Findings
Plan Page Zip Modal CTA Copy
Hypothesis: Changing the CTA from "Continue" to something more relevant and descritpive
Winner: Plan Page Zip Modal CTA Copy. Changing the CTA from "Continue" to something more relevant and descritpive
Hypothesis: Changing the CTA from "Continue" to something more relevant and descritpive
Order Now CTA
Winner: Order Now CTA.
Get Started CTA
Loser: Get Started CTA.
Frequently Asked Questions
What is the "Hero/CTA Optimization" insight cluster?
This cluster aggregates 3 research findings, test results, and optimization principles related to hero/cta optimization. Each entry includes expected lift ranges, confidence levels, and source attribution so you can evaluate applicability to your own tests.
How reliable are the expected lift ranges in this cluster?
Lift ranges represent aggregated outcomes from multiple experiments and research sources. They are directional estimates, not guarantees. Your actual results will vary based on traffic volume, audience, current baseline, and implementation quality. Always validate with your own A/B test.
How do these findings apply to Hero/CTA optimization?
These findings are specifically relevant to hero/cta optimization. Use the expected lift ranges to prioritize your testing roadmap and the key learnings to inform your hypothesis development.
Where does the data in this cluster come from?
Data is sourced from published UX research, aggregated experiment data across multiple organizations, industry studies, and validated internal findings. Each entry includes its source type so you can assess credibility. Entries marked as validated have supporting statistical evidence.
Turn Insights Into Winning Tests
Stop guessing which tests to run. Use GrowthLayer to track every experiment, surface winning patterns, and build on proven findings.