How To Calculate Statistical Significance To Measure Success Rate

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Data plays a crucial role in every aspect of your marketing campaigns. Statistics is a way to represent and understand the data in the most convenient way possible. Thus, statistical significance is very vital for every business.

To make strategic decisions, you need analytics for everything from sending emails to optimizing landing pages.

It is indisputable that data analytics are consistent, regardless of very volatile marketing trends.

With ever-smarter tools, we learn more about user behaviour. Our ability to react to, or ideally, be proactive towards, this user behaviour will drive our campaigns and adjust existing strategies.

The statistical significance of statistics is one of the most valuable metrics for marketers as they decide to become more data-driven.

It’s time for anyone who isn’t already using this metric to begin.
Since statistical significance helps your campaigns, it is a must for marketers.

Before we move on, have a look at what I will be covering.

Understanding Statistical Significance?

You should include statistical significance to your list of to-tests, whether you’ve been tracking metrics for years or just started testing.

A test outcome must not have occurred by chance but somewhat was influenced by an outside factor to be statistically significant.

Although this sounds like an obscure statistics term, determining whether any particular feature of your campaign is statistically significant. For instance, your blog can yield effective results that could impact future campaigns.

Importance Of Statistical Significance For Digital Marketers

Marketing analysts can use statistical significance to grow their campaigns. With it, you can gauge your efficacy from several perspectives.

Using data metrics, one can determine whether Conversion Rate Optimization (CRO) efforts were practical.

3 Major Importance Of Statistical Significance

It is possible to turn your projections into almost absolutes when using this method, which will allow you to predict outcomes better.

Have a look at these three methods to 3 ways to analyze your CRO efforts.

1. Monitoring And Analyzing Your Marketing Budget

In addition to determining the outcome of your marketing campaign, statistical significance can assist you in deciding where to spend your budget wisely.

You can tweak your budget by determining the statistical significance of outcomes, spend on underperforming areas, and divert funds from areas that don’t require extra funds.

As a result, you will be able to save more, spend more intelligent, and have a more optimistic outlook.

2. Helps In Doing Campaign Research

Research must always be formulated while comparing various metrics.

A thorough analysis of the data will help you confirm or disprove your research using statistical significance.

Statistics provide actionable evidence to be confident in the decision that you make before implementing your campaign.

3. Helps In Effective A/B Testing

A/B testing is a crucial asset for marketers, and you have likely used it across your digital marketing strategies and campaigns.

Based on a statistical analysis of these data sets, you can reasonably determine the success or failure of an A/B test.

A/B tests should be statistically significant. A good percentage (>87%) indicates that the change you’re introducing will have a positive or negative impact.

You should run tests on pages with high conversion rates for two months to reach statistical significance when testing site performance.

Also checkout: A/B testing (Optimization glossary) by if you are unaware of A/B testing.

How To Calculate Statistical Significance In 8 Simple Steps

The clock is ticking on our discussion of statistical significance and relevance to marketing efforts, so let’s move on to calculation.

1. Decide What You Have To Test

Start by deciding what you’d like to test.

You can make a comparison between landing pages with different images, emails with other subject lines, or CTA buttons at the end of a blog post. There are endless possibilities.

No matter what set of items you want to test — landing pages with different banners, emails with varying subject lines — pick the group you wish to test.

Keeping it simple can help you to achieve good results. First, decide upon content and set a particular target. For instance, a better conversion rate or more views are good places to start.

Although you can test more variations or even create multivariate tests, I suggest initially starting with two or three pages.

2. Plan Your Hypothesis.

Statistical significance will indeed help strengthen your hypothesis: and I said about this earlier.

Whenever you experiment, you should always state a hypothesis.

My research methods begin with stating my hypothesis and determining the confidence level I wish to test before collecting data.

3. Accumulate Your Data 

You have to decide which sample size is appropriate for each type of test you run.

You may want to set a specific duration for a page to stay active while testing it. To try your email, send variations of your email to a particular group of your audience.

For something like an email, you might pick a random sample of your list to send variations of your emails randomly.

There is no single appropriate sample size for every test, as it varies from one test to another.

In general, you want each variation to have an expected value ideally (>5).

4. Find Out Chi-Squared Results

Although you can use a wide variety of different statistical tests to measure significance, the Chi-Squared test is more likely to be used.

You can organize the collected data in a chart after gathering it.

It’s easy to view your results with a chart if you test two or more variables with two or more potential outcomes.

5. Prepare Your Expected Values

If you want to know what you can expect from each landing page iteration, split the row total by the column total and multiply that by the visitors.

6. Apply The Statistical Significance Formula

Based on the equation,

  • Σ stands for summation.
  • “O” represents observed values.
  • “E” represents expected values.

You calculate everything after the summation(Σ) for each pair of values and then add them all up when running the equation.

7. Correlate The Outcomes With Expected Values

As I mentioned above, I calculate Chi-Square by comparing the observed frequencies (O) to the expected frequencies (E).

In the calculation, one subtracts the observed value from the expected value, squares the result, and divides it by the expected frequency value.

I’m comparing actual results to expectations and trying to figure out how different they are. Then, by dividing by what is expected, the results are normalized.

In a nutshell, the equation looks as follows: (observed – expected)/expected.

The row totals divided by the column total and the total visitors is what you need to know about the effects of each landing page iteration.

8. Find The Total Value

Calculate the Chi-Square number by adding up the results.

You can compare this with the Chi-Square to determine if your results are statistically significant.

The Chi-Square value must be equal to or exceed 3.84 for the results to be statistically significant.

If the results are not statistically significant, there is no correlation between different designs and the conversion rate.

Marketers can measure individual optimizations better with this method, which provides certainty about the results.


Even though statistical significance does not associate with efficacy, you gain a clearer picture of your campaign hypothesis when examining statistical significance.

You’ll feel more satisfied using this technique as you begin to check your CTA button or your email subject line and other CRO tests.

Implementing this method into your marketing efforts will take a more data-driven attitude regarding explicit areas of your campaigns which you wish to run.

Let me know in the comments how do you track and measure your CRO campaigns? Also, if you are trying out different effective strategies, please write them in the comments.

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