a/b-testingvsoptimaliseringsløp

An "optimaliseringsløp" (optimization cycle) in marketing and digital strategy is a structured, iterative process aimed at continuously improving key performance metrics such as conversion rates, engagement, or revenue. A/B testing serves as a fundamental tactical method within this cycle by providing a controlled experimental framework to compare variations of marketing elements (e.g., landing pages, ad creatives, email subject lines) and identify which version performs better against defined KPIs. Specifically, A/B testing enables data-driven decision-making during each iteration of the optimaliseringsløp by validating hypotheses about what changes lead to improvement. This empirical feedback loop reduces guesswork, accelerates learning, and ensures that optimizations are grounded in statistically significant evidence rather than intuition. Without A/B testing or similar experimentation methods, an optimaliseringsløp risks relying on unverified assumptions, slowing progress or causing regressions. Conversely, A/B testing gains strategic value when embedded in a continuous optimization cycle, as isolated tests without a systematic process may yield fragmented insights that do not scale or align with broader business goals. Therefore, A/B testing operationalizes the experimentation phase of the optimaliseringsløp, making the relationship between them direct and essential for effective, iterative marketing and digital strategy refinement.