Many marketers equate CRO with just A/B testing. However, a successful program is built on two pillars: research (gathering quantitative and qualitative data) and testing (experimentation). Overlooking the research phase leads to uninformed tests and poor results, as it provides the necessary insights for what to test.
A CRO program's primary metric must directly impact the business bottom line (revenue, MQLs, SQLs), not vanity metrics like bounce rate. The argument that bottom-line impact is "too hard to measure" is an unacceptable excuse that undermines the program's strategic value and executive buy-in.
Direct-to-consumer (D2C) brands often excel at straightforward messaging and simple user journeys. B2B marketers should emulate this clarity. Complex B2B products often lead to jargon-filled copy and convoluted website flows, creating friction that a D2C mindset can help solve.
Don't waste resources on advanced CRO tactics like personalization if your website's foundation is weak. If your messaging is unclear, your value proposition is confusing, or you lack social proof, these core issues must be addressed first. Advanced tactics on a cracked foundation will inevitably fail.
Effective CRO research goes beyond analytics. It requires gathering data across two spectrums: quantitative (what's happening) vs. qualitative (why it's happening), and behavioral (user actions) vs. perceptive (user thoughts/feelings). This dual-spectrum approach provides a complete picture for informed decision-making.
To get company-wide buy-in for CRO, focus reporting on program-level metrics, not just individual test results. Share high-level insights like win/loss rates and cross-departmental impact in quarterly reviews. This frames CRO as a strategic business function, not just a series of tactical marketing experiments.
Don't attempt traditional A/B testing on a low-traffic website; the results will be statistically invalid. Instead, use qualitative user testing methods like preference tests. This approach provides directional data to guide decisions, which is far more reliable than guesswork or a flawed A/B test.