The purpose of a Hypothesis Statement is to articulate a prediction that your research will test. A hypothesis is the basis for designing an experiment. In marketing, we often use experiments to optimize elements of campaigns and validate different concepts. The volume and sophistication of experimentation and testing continues to grow as data, testing tools, and agile principles are more widely adopted by marketing teams. Without an effective hypothesis, it impossible to design a test that produces valid results or learning (i.e., garbage in, garbage out). Establishing a template that is consistently used by your team can help you capture your hypothesis in a way that is clear, specific, measurable, and relevant to your organization.
What is the underlying assumption that we are testing to see if it is true?
- Start by writing down your research question. This question represents the underlying reason why you are choosing to design a test in the first place. Remember to be as focused and specific as possible.
- In the ‘IF’ field, capture the context of the potential test that relates to your research question. This represents the area that you are interested in testing. For example, updating the cover of your new book.
- In the ‘BY’ field, capture the independent variable that you intend to change that relates to the context of your test in the ‘IF’ field. For example, including positive testimonials on the cover.
- In the ‘WILL’ field, capture the dependent variable and result that you believe will be impacted by the independent variable captured in the ‘BY’ field. For example, increase book sales by 20%.
- In the ‘BECAUSE’ field, capture your rationale that describes why you expect the result captured in the ‘WILL’ field. For example, people are more motivated to purchase books with positive reviews.
- Make sure that your hypothesis is actually testable given the resources and capabilities of your team.
- Make sure that your team has the same shared understanding of the terms you use in your statement.
- Keep in mind that you may need several hypotheses and tests to fully explore a research question.
Luca, M., Bazerman, M., “The Power of Experiments: Decision-Making in a Data-Driven World”, The MIT Press, 2020