Baseline Conversion Rate

The baseline conversion rate is simply the current performance of whatever you're testing against — the percentage of visitors who complete the desired action under the existing, unmodified experience. It serves as the anchor point for nearly every planning decision in an experiment: sample size calculators, minimum detectable effect estimates, and test duration projections all require an accurate baseline as an input.

Getting this number right matters more than most people assume. If the baseline is estimated from too short a window, a seasonal spike, or a non-representative traffic segment, every downstream calculation — how long the test needs to run, how big a sample is required, whether an observed lift is meaningful — will be off. Teams that skip this step often end up either underpowering a test (running it too briefly to detect a real effect) or overestimating how quickly they'll reach significance.

A concrete example: if a checkout page currently converts at 3.2% based on the last eight weeks of stable traffic, that 3.2% becomes the baseline used to calculate how many visitors are needed to detect, say, a 10% relative lift with 80% power. If the team instead used a single unusually strong week where conversion spiked to 4.5%, they'd underestimate the sample size needed and risk ending the test too early.

Because baseline conversion rate directly feeds into test planning, it's good practice to pull it from a period long enough to smooth out day-of-week and seasonal variation, and to segment it appropriately if traffic sources or devices convert very differently.