How do you choose the right sample size and duration for your marketing channel experiments?

1 Why sample size and duration matter

Sample size and duration are two key factors that affect the validity and reliability of your marketing channel experiments. Sample size is the number of people or units that are exposed to your experiment, and duration is the time period that you collect data from them. The larger your sample size and the longer your duration, the more likely you are to detect a meaningful difference between your control and treatment groups, and the less likely you are to be influenced by random noise or external factors. However, increasing your sample size and duration also comes with costs, such as resources, time, and potential harm to your participants or your brand.

2 How to estimate your sample size

To estimate your sample size, you need to define your hypothesis, your outcome metric, your baseline value, your minimum detectable effect, and your desired level of significance and power. Your hypothesis is the statement that you want to test, such as "Adding a testimonial to the landing page will increase conversions". Your outcome metric is the measure that you use to evaluate your hypothesis, such as conversion rate. Your baseline value is the current or expected value of your outcome metric in the control group, such as 10%. Your minimum detectable effect is the smallest difference between the control and treatment groups that you consider meaningful, such as 2 percentage points. Your level of significance is the probability of rejecting the null hypothesis when it is true, or making a false positive error, such as 5%. Your power is the probability of rejecting the null hypothesis when it is false, or making a correct positive decision, such as 80%. Based on these parameters, you can use a statistical formula or an online calculator to estimate your sample size for each group.

3 How to estimate your duration

To estimate your duration, you need to consider your sample size, your expected traffic or exposure, and your data collection frequency. Your sample size determines how many people or units you need to reach to complete your experiment. Your expected traffic or exposure is the number of people or units that you can realistically reach through your marketing channel, such as visitors, clicks, or impressions. Your data collection frequency is how often you monitor and analyze your data, such as daily, weekly, or monthly. Based on these factors, you can calculate your duration by dividing your sample size by your expected traffic or exposure, and multiplying by your data collection frequency. For example, if you need 1,000 people per group, and you expect 500 visitors per day, and you collect data daily, your duration is 1,000 / 500 * 1 = 2 days.

4 How to balance trade-offs and constraints

While sample size and duration are important for statistical reasons, they are not the only factors that you should consider when designing your marketing channel experiments. You should also balance trade-offs and constraints that may affect your practical and ethical decisions. For example, you may have a limited budget, time frame, or audience size that prevents you from reaching your optimal sample size or duration. You may also have to weigh the benefits of running a longer or larger experiment against the risks of losing customers, damaging your reputation, or violating regulations. You should always align your experiment goals with your business objectives, and evaluate the costs and benefits of your experiment design.

5 How to monitor and adjust your experiment

Once you launch your experiment, you should monitor and adjust it as needed to ensure its quality and validity. You should track your key metrics, such as exposure, response rate, and outcome value, and compare them across your control and treatment groups. You should also check for any errors, anomalies, or biases that may affect your data, such as technical issues, outliers, or seasonality. You should avoid peeking at your results or stopping your experiment too early or too late, as this may lead to false conclusions or missed opportunities. You should use a pre-defined stopping rule or a sequential testing method to determine when to end your experiment based on your data and criteria.

6 How to interpret and communicate your results

After you end your experiment, you should interpret and communicate your results in a clear and meaningful way. You should summarize your findings, such as the effect size, the confidence interval, and the p-value, and explain what they mean for your hypothesis and your outcome metric. You should also acknowledge the limitations and assumptions of your experiment, such as the sample size, the duration, the external validity, and the causal inference. You should provide actionable recommendations based on your results, such as scaling up, iterating, or abandoning your experiment. You should also share your learnings and insights with your stakeholders, such as your team, your manager, or your clients, and use visualizations, dashboards, or reports to convey your message.

7 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?