Conversion rate optimization is a website and app improvement model that aims to turn more users into customers. It cycles through four stages: research, hypothesize, test, and evaluate.
Research. The first stage in the conversion optimization process is discovering where a website or app’s problems are. This involves teams looking at which areas have high bounce rates, low conversion rates, and other key metrics.
Hypothesize. Once a team has identified where problems are, they can begin to think of fixes and improvements. These changes are then formalized into testable hypotheses, tying the performance of the change to a specific, trackable metric. For example, the basic structure of a hypothesis might look like this: ‘based on this data, I believe making this change will lead to an increase in this metric on this page.’
Test. The team tests its hypothesis by making the suggested change and then splitting website or app traffic between the original and the page with the change. The performance of each page is then tracked, and whichever one leads to more instances of desired user behavior is declared the winner.
Evaluate. If the change wins, great – roll it out. If not, it’s back to the drawing board – now armed with more data from the failed test. The whole process is continuously repeated, the idea being that websites and apps are continuously optimized – and it’s all backed by the scientific method of testing.
Conversion rate optimization is the go-to optimization framework for digital teams who use only quantitative user data when it comes to improving their websites and apps.
As conversion rate optimization revolves around tracking specific metrics and carefully testing hypotheses, it’s an effective method for ensuring that – even with little insight or support from across an organization – any improvements made are backed by data and based on solid scientific method.