How do you test content hypotheses with metrics?

1 Formulate content hypotheses

Before you can test your content hypotheses, you need to formulate them clearly and specifically. A good content hypothesis should have three components: a variable, an outcome, and a rationale. For example, "Adding more images to blog posts will increase engagement, because visuals attract attention and illustrate concepts". The variable is the content element you want to change or test, the outcome is the metric you want to improve or affect, and the rationale is the reason or logic behind your hypothesis.

2 Choose relevant metrics

Once you have your content hypotheses, you need to choose the metrics that will help you measure them. Metrics are quantitative indicators of how your content is performing, such as page views, bounce rate, conversions, etc. You should choose metrics that are relevant to your content goals, aligned with your hypothesis outcome, and easy to track and collect. For example, if your hypothesis is about increasing engagement, you might choose metrics like time on page, scroll depth, social shares, or comments.

3 Set up a testing method

After you have your metrics, you need to set up a testing method that will allow you to compare your content variations and see which one supports or rejects your hypothesis. There are different ways to test your content hypotheses, such as A/B testing, multivariate testing, or before-and-after testing. The best method depends on your hypothesis, your resources, and your tools. For example, if you want to test different headlines for a landing page, you might use A/B testing to split your traffic and see which one generates more conversions.

4 Collect and analyze data

The next step is to collect and analyze the data from your testing method. You should run your test for a sufficient period of time and sample size to ensure the validity and reliability of your results. You should also use statistical tools or methods to calculate the significance and confidence of your results. For example, if you use A/B testing, you might use a calculator to determine the p-value and the confidence interval of your test. The data analysis will help you accept or reject your hypothesis based on the evidence.

5 Apply learnings and iterate

The final step is to apply the learnings from your data analysis and iterate on your content strategy. You should document your findings and share them with your team or stakeholders. You should also implement the changes that support your hypothesis and improve your content performance. For example, if you find that adding more images to blog posts increases engagement, you might update your content guidelines and editorial calendar to include more visuals. You should also continue to test new hypotheses and refine your content strategy.