How AI-Powered Optimization is Different from A/B Testing
Conversion rate optimization (CRO) is an important and widely used strategy in enterprise marketing and product management. CRO teams at large companies run hundreds or sometimes thousands of experiments per year in an attempt to continuously optimize customer experience.
A/B testing just isn't that efficient.
Experimentation has traditionally been limited to A/B testing as the principal tool for validating/rejecting conversion and personalization hypotheses. However, most A/B tests do not produce positive results, and most companies do not have the necessary resources or traffic levels to run the number of A/B tests required to see a consistent ROI for money spent on website optimization. While A/B testing is still as an important tool for risk mitigation and data-driven decision making, gains from optimization remain out of reach for the majority of businesses.
Test more ideas, faster.
Evolv uses artificial intelligence (AI) to improve the ROI of experimentation by increasing both test velocity and win-rate without increasing manual resources dedicated to optimization. Evolv accomplishes this by efficiently evaluating a broad set of hypotheses within a single experiment. During an experiment, the system identifies which hypotheses are positively impacting performance and those which are not. Evolv uses this data to automatically generate new experiments consisting of a combination of the high-performing hypotheses—continually searching for higher-and-higher performance within an experiment. This enables businesses to quickly evaluate many designs without the manual effort and low win-rate of running a series of sequential A/B tests.
A/B Testing Limitations
The Power of Evolv
Many companies cannot afford to dedicate multiple human resources that are required for success in an A/B testing framework.
Evolv’s ability to automatically evaluate many hypotheses at once allows a single resource to set up an experiment that is equivalent to running hundreds of single-variable A/B tests. This allows a single resource to accomplish much more than ever possible with A/B testing.
Having a large enough sample size to declare a statistically significant result requires a large amount of traffic. Dedicating time and traffic to a single-variable test means learning and optimization happens extremely slowly.
By efficiently evaluating many hypotheses at once, a single Evolv experiment provides learnings that would have required many months of sequential A/B tests to obtain.
Most A/B tests fail
Industry standards for A/B testing typically result in about 10 – 20% of tests finding improved performance. This puts great importance on prioritizing which hypotheses to test and requires increased testing velocity to find improved performance.
More chances for improvement
Testing more hypotheses gives your team more chances to find improvements in performance and decreases the need to prioritize which ideas you’d like to test.
Isolating a single variable on a single page means it requires many tests to improve overall performance of multiple-page conversion funnels that are common for many businesses.
Evolv is built to evaluate and optimize many changes across a multiple-page funnel as if it were a single page. This helps you understand how changes across pages affect bottom-of-funnel conversion rates and overall performance. Full-funnel capability greatly accelerates optimization efforts and creates a more holistic digital experience for users.