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Conversion Uplift

Hypothesis

Definition

In website optimisation a hypothesis is a change and effect statement that is based upon qualitative (e.g. customer complaints) and/or quantitative data (e.g. web analytics). This should be measurable and ideally provide a rationale for why a change to the customer experience will influence the success metrics. Creating an hypothesis is a critical step in the test and learn process outlined below.

Without a strong hypothesis for each A/B test the optimisation process is likely to become random and not reach its full potential to improve the bottom line. Without a hypothesis there is also little, if any, learning from tests as there needs to be a logical reason to believe a new experience is better than the default. Failure is how we learn, but it is also important to try and understand why something didn’t work.

Resources:

Conversion marketing – Glossary of Conversion Marketing.

Over 300 tools reviewed – Digital Marketing Toolbox.

A/B testing software – Which A/B testing tools should you choose?

Optimisation process – 8 steps guaranteed to boost your conversion rate.

Prioritise tests – How should you prioritise your A/B test ideas?

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Neal Cole

Neal Cole

Neal is the founder of Conversion Uplift and the author of the official GA4/GTM audit course for CXL. It's the course much of the industry trains on. He's spent over 20 years in digital analytics, including senior roles in online gaming and financial services, working out where businesses' numbers go wrong and what it costs them. His work now focuses on server-side tracking, GA4 and BigQuery: getting the data right, and being the person who's accountable for it when a decision depends on it.
Neal Cole

Neal Cole

Neal is the founder of Conversion Uplift and the author of the official GA4/GTM audit course for CXL. It's the course much of the industry trains on. He's spent over 20 years in digital analytics, including senior roles in online gaming and financial services, working out where businesses' numbers go wrong and what it costs them. His work now focuses on server-side tracking, GA4 and BigQuery: getting the data right, and being the person who's accountable for it when a decision depends on it.

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