How do you manage external factors in your campaign experiments?

1 Identify potential external factors

Before you run your experiment, you should do some research and analysis to identify the possible external factors that could impact your campaign. For example, you could look at historical data, industry trends, competitor activity, or customer feedback to see what factors have affected your campaign performance in the past or are likely to do so in the future. You should also consider the timing, duration, and scope of your experiment, and how they might coincide with or expose your campaign to external factors.

2 Design your experiment to minimize external factors

Once you have identified the potential external factors, you should design your experiment to minimize their impact or account for them in your analysis. For instance, you could choose a control group that is similar to your test group in terms of exposure to external factors and compare their performance to isolate the effect of your campaign variable. Alternatively, a pre-post design might be used, where you measure the performance of your test group before and after the experiment and compare it to the performance of a similar group that did not receive the campaign treatment. Additionally, a factorial design could be utilized to test multiple campaign variables at once and analyze the interactions and main effects of each variable, as well as the external factors. Lastly, a time series design can be employed where you measure the performance of your test group over a long period of time and use statistical methods to detect and adjust for trends, cycles, or outliers caused by external factors.

3 Monitor and measure external factors

During and after your experiment, it’s important to monitor and measure the external factors that you identified, and observe how they influence your campaign performance. Tools like Google Trends, social media analytics, or media monitoring can track changes in customer demand, sentiment, or awareness related to your campaign or industry. Surveys, interviews, or feedback forms can provide qualitative data from customers or prospects regarding their motivations, preferences, or pain points that may affect their response to your campaign. Analytics platforms, dashboards, or reports can measure the metrics and KPIs that reflect your campaign goals. Additionally, statistical tests such as ANOVA, t-test, or chi-square test can compare the performance of your test and control groups to determine if the differences are significant or due to external factors.

4 Learn and improve from your experiment

Finally, you should use the data and insights that you gathered from your experiment to learn and improve your marketing strategies. Evaluate the results to see if they confirm or reject your hypothesis, and interpret them to understand what they mean for your campaign objectives. Communicate the results with your team, stakeholders, or clients, and apply them to optimize your campaign elements. Repeat the experiment and test different variables, or run it again in a different context, to validate or generalize your results. By doing so, you can manage external factors in your campaign experiments and get accurate insights that help you improve your marketing performance.

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?