How can you run and track marketing experiments?

1 Plan your experiment

Before you run any experiment, you need to have a clear goal, a hypothesis, and a metric. Your goal is what you want to achieve with your experiment, such as increasing conversions, engagement, or retention. Your hypothesis is your prediction of how a certain change will affect your metric, based on your existing knowledge or assumptions. Your metric is the measurable indicator of your goal, such as click-through rate, bounce rate, or revenue. You should also define your target audience, your sample size, and your duration for your experiment.

2 Execute your experiment

To execute your experiment, you need to create different versions of your campaign element, such as A, B, C, etc. You also need to randomly assign your target audience to each version, so that you can compare them fairly. You can use various tools and platforms to help you run your experiment, such as Google Optimize, Optimizely, or Unbounce. You should also make sure that you run your experiment long enough to collect enough data and avoid external factors that may skew your results, such as seasonality, holidays, or events.

3 Analyze your experiment

To analyze your experiment, you need to compare the performance of each version of your campaign element based on your metric. You can use statistical methods, such as t-tests, ANOVA, or chi-square tests, to determine if there is a significant difference between the versions. You can also use tools and platforms that provide these analyses for you, such as Google Analytics, HubSpot, or Kissmetrics. You should also look for any insights or patterns that may explain the results, such as user behavior, preferences, or feedback.

4 Act on your experiment

To act on your experiment, you need to decide what to do with the results. If you find a clear winner, you can implement it as your new campaign element and monitor its performance over time. If you find no significant difference, you can either keep the original version or try a different change. If you find conflicting or inconclusive results, you can either run another experiment with a larger sample size or a different metric, or explore other factors that may affect your goal. You should also document your experiment and share your learnings with your team or stakeholders.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?