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What are common campaign testing pitfalls and how can you avoid them?

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Campaign testing is a vital part of marketing operations, as it allows you to measure and improve the performance of your campaigns across different channels, audiences, and messages. However, campaign testing is not as simple as setting up a few variations and comparing the results. There are many pitfalls that can compromise the validity, reliability, and usefulness of your tests. In this article, we will explore some of the most common campaign testing pitfalls and how you can avoid them.

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1 Lack of clear goals

One of the first pitfalls to avoid is testing without a clear and specific goal. A goal is not just a vague idea of what you want to achieve, but a measurable and relevant outcome that aligns with your overall marketing strategy and objectives. For example, instead of saying "I want to increase conversions", you should say "I want to increase conversions by 10% in the next quarter for this segment". Having a clear goal will help you design your test, choose your metrics, and analyze your results.

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Adam Freeman

Making Data, Tech & Processes Work For Marketers || 10yrs - Marketing, Email & Digital Operations || 2x Marketo Certified Expert
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You don’t need a clear goal… but you do need a hypothesis. There’s a distinction between strategic objectives, tactical goals and testing hypotheses. A strategic goal could be “to improve marketing revenue generated from the email program by 2%” Relationally, a tactical goal could be to enhance segmentation or to improve email design. Beneath this, there are known optimisations and testing hypotheses. Hypotheses aren’t goals. Simply because they’re unproven/unknown. You’re trying something new with the belief that it’ll turn the needle. I’ll caveat this by saying that it doesn’t mean that a test is a complete guess. There should be a foundational knowledge of the cause and effect relationship between the variable and the key metric.

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2 Insufficient sample size

Another common pitfall is testing with an insufficient sample size. A sample size is the number of observations or participants in your test, and it determines the statistical significance and confidence level of your results. If your sample size is too small, you may not be able to detect meaningful differences between your variations, or you may get false positives or negatives. To avoid this, you should use a sample size calculator to determine the minimum number of observations you need for your test, based on your expected effect size, significance level, and power.

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3 Confounding variables

A confounding variable is a factor that affects both your independent variable (the one you are testing) and your dependent variable (the one you are measuring), and thus confuses your results. For example, if you are testing different email subject lines, but you also change the sender name, the time of day, or the content of the email, you are introducing confounding variables that may influence the open rate or the click-through rate. To avoid this, you should control for any potential confounding variables, and only test one variable at a time.

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Pierre Elisseeff

Founder | G2M Insights
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This is the most common mistake we see when working with marketing organizations performing 'a/b testing'. There is no point in testing if you aren't going to take an apples-to-apples approach. When measuring campaign effectiveness, if you don't account for confounding variables, you often report biased results that cannot be reproduced by the business. There are several ways you can solve for this - such as testing variables one at a time or using a/b matching algorithms that create a better control group for you to compare to the treatment group.

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4 Selection bias

Selection bias is a type of bias that occurs when your sample is not representative of your population, or when your participants are not randomly assigned to your variations. For example, if you are testing different landing pages, but you only send traffic from one source or one device, you are creating a selection bias that may skew your results. To avoid this, you should ensure that your sample is as diverse and representative as possible, and that your participants are randomly and evenly distributed among your variations.

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5 Testing too many variations

Testing too many variations at once is another pitfall that can dilute your results and make them harder to interpret. While it may be tempting to test multiple elements or combinations of your campaign, such as headlines, images, colors, buttons, etc., this can increase the complexity and duration of your test, and reduce the power and significance of your results. To avoid this, you should prioritize and test the most impactful variables first, and limit the number of variations to two or three per test.

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Jordan Resnick
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While I agree that by the time you're ready to QA, it's best to narrow down variations in terms of content (i.e. that should be worked out ahead of time), one of the biggest pitfalls in QA'ing campaigns is not taking into account all variables/variations of campaign actions and possible outcomes. If you don't test ALL possible variations, then you might as well not test at all. Make a QA spreadsheet and meticulously outline a new flow/possibility on each row, and then test to ensure the expected outcome matches the actual result. This way, you can go live with confidence that everything has been taken into account and no errors/unexpected results will occur.

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6 Stopping the test too soon or too late

The last pitfall to avoid is stopping the test too soon or too late. Stopping the test too soon can lead to inaccurate or incomplete results, as you may not have enough data to reach a valid conclusion. Stopping the test too late can waste your time and resources, as you may not gain any additional insights or benefits from continuing the test. To avoid this, you should define the duration of your test before you start, based on your sample size, expected effect size, and confidence level, and stick to it unless there is a compelling reason to stop or extend the test.

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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?

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What are common campaign testing pitfalls and how can you avoid them?

1

2

3

4

5

6

7

1 Lack of clear goals

2 Insufficient sample size

3 Confounding variables

4 Selection bias

5 Testing too many variations

6 Stopping the test too soon or too late

7 Here’s what else to consider

Marketing Operations

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What are common campaign testing pitfalls and how can you avoid them?

1

2

3

4

5

6

7

1 Lack of clear goals

2 Insufficient sample size

3 Confounding variables

4 Selection bias

5 Testing too many variations

6 Stopping the test too soon or too late

7 Here’s what else to consider

Marketing Operations

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