Marketers know the value of data for every aspect of your campaign.
You need to have a complete suite of analytics in order to make strategic decisions, from sending emails to optimizing landing page pages to sending them.
Marketing trends can change, but it is indisputable that data analytics will be around for the long-term.
Our tools are getting smarter and we can find out more about user behavior. This data can be used to optimize campaigns and adapt existing strategies to react to or, ideally, proactive about user behavior.
Statistic significance is one of the most valuable metrics you can use to help with almost every aspect of your marketing campaign as we all move towards being data-driven marketers.
Have you ever heard of it before? This metric was used historically to predict the likelihood of a hypothesis being true. This term is not necessarily in line with your marketing strategy, but it can be extremely useful.
This metric is not yet used by many people.
This blog post explains what statistical significance means, how it can be used to help you with your campaigns, as well as how to calculate it.
What is statistical significance?
You should include statistical significance in your to-test list, regardless of whether you are new to testing marketing campaigns or have been tracking metrics for years.
A test's statistical significance is the probability that an outcome was not random but was influenced by outside sources.
Although this might seem like a simple statistical term, it can help you to change how you run, optimize and execute your future campaigns.
Why is Statistical Significance Important for Marketing?
Marketers don't need to know everything about your campaign in order to understand statistical significance.
You can view your efficacy through many lenses.
This method allows you to turn your projections into near absolutes. It will allow you to better understand and project the outcome.
This metric can also be used to measure landing page success, effectiveness of call-to-action phrasing, success with subject line optimization, and many other variables.
Marketers know the importance of A/B testing. You have probably used it throughout your digital strategy. These data sets can be used to determine whether or not an A/B experiment was successful. Ideal results for A/B tests are 90 percent statistically significant. This means that the predicted change in the environment will have a positive or negative impact on it.
To test site performance, it is best to test pages that have high traffic or conversion rates. Test times should be within 2-8 weeks.
Application to Marketing Budget
Statistics can be used to help you decide the outcomes of campaigns. However, they can also be used to help you plan where your marketing budget should go. You can optimize your budget by identifying the statistical significance of results. This will allow you to spend on areas that are underperforming and redirect funds from other areas that don't require extra money.
This lens will help you save more, spend smarter, and have confidence in your future results.
You will always have a hypothesis when you test any two or more variables against each other. You can use statistical significance to test the variables. This will allow you to prove or disprove your hypothesis using in-depth data analysis. You can prove or disprove your original hypothesis using statistical significance.
Calculating Statistical Significance
We've now discussed what statistical significance could mean for your marketing efforts. Let's talk about calculation.
This tool is useful, but you need to understand how it works.
1. Establish Test Subject
You can compare the conversion rates of landing pages with different banners and clickthrough rates (CTRs), on emails with different subject lines or the success of different call to action buttons. Simply choose the items that you wish to test.
It was mentioned earlier that statistical significance can be used to strengthen hypotheses. To assess your level of confidence, you should always have a hypothesis ready for each test.
3. Collect your data
No matter what your purpose is, you must determine the size of your sample. This may be the time your landing page is available. You can select a random sample from your audience to test your email.
There are many statistical tests that can be used to measure significance. However, the Chi-Squared method is most commonly used.
For easy organization, you can enter the data into a chart after collecting it. You can make a 2×2 chart if you are testing two variables that have two possible outcomes. This allows you to quickly view your results.
5. 5. Determine your Expected Values
You can assess the expected outcome of each landing page iteration by multiplying the column total by the row total, and then divide it by how many actions you have (visitors, etc.).
6. Reexamine Your Hypothesis
Once you have all the data you need, it is time to review your hypothesis to see if your expectations were correct. To guarantee you have the best visibility into the difference between projection and outcome, square the difference by using the equation: (expected – observed)^2)/expected.
7. Find your Sum
Add the numbers to find your Chi-Square number. The sum of the above table is.95. Compare the table above with a Chi-Square to determine if there are statistically significant differences.
For statistical significance, the Chi-Square value in the example above must equal or exceed 3.84
The statistical significance of the above table is not statistically significant because.95 is lower than 3.84. This means that there is no correlation between different versions or the actions taken by users.
This gives marketers greater certainty over the outcomes and allows for better visibility into individual optimizations.
Avoiding Inconclusive Results
There are steps you can take to get better insight from statistical significance tests that keep returning inconclusive results.
Segment Your Data
If the test is not conclusive, you can assess performance across segments such as traffic sources and devices.
Segmenting data can give you a better understanding of the workings within each segment, as opposed to aggregating all data.
Your Hypothesis to the Extreme
It can be costly to test small changes over and over again. Bold changes can dramatically change user behavior if you really want to see dramatic results.
You can take your hypothesis to the extreme and change the outcome of your campaign by changing your engagement goals.
Assess Your Hypothesis
Your results may not be conclusive. It might be time to rethink your hypothesis.
To determine if your hypothesis requires fine-tuning, ask yourself these questions.
- Are you able to back up your hypothesis with behavioral insight?
- Are users concerned about the changes that you make?
- Are your changes too subtle?
Frequently Asked Question about Statistical Significance In Marketing
What is statistical significance?
Although statistical significance might seem complicated, it is actually quite simple. The probability that a test result was not random but was influenced or influenced by outside sources is called statistical significance.
What is the point of statistical significance for marketers?
Although this test might seem unnecessary in comparison to other marketing metrics such as A/B testing and budget allocation, it allows marketers to prove their hypotheses, perform smarter A/B tests, and better allocate resources.
Do I have to conduct my own statistical significance testing?
You sure can. You can do your own statistical significance testing using the seven-step process described in this blog. However, you can also use our tool to calculate significance.
What is the best outcome for a statistical significance testing?
You can be certain that statistical significance is achieved if your test returns a score of 90 percent or higher
It is important to remember, however, that not all significance equals validity. However, statistical significance can help you see your digital marketing hypotheses more clearly.
You will feel more confident and comfortable using this method to determine whether your call to action button (CTA) needs to be refreshed or if your email subject line requires a refresh.
This method can be integrated into your digital marketing strategy to help you take a more data-backed position about certain aspects of your campaigns.
Which statistical significance did you use in your marketing most effectively?
By: Neil Patel
Title: What is Statistical Significance and Why Does it Matter?
Sourced From: neilpatel.com/blog/statistical-significance/
Published Date: Tue, 05 Oct 2021 13:00:00 +0000
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