You might come across terms such as hypothesis testing when optimizing your web presence to maximize leads and conversions.

Although the term may sound like something out of a science experiment, marketers who want to boost their digital results are turning to scientific methods to extract more juice from their online campaigns.

These are the steps you need to take to get the best leads, revenue, and rate of return. You’re about to reap all the benefits of the scientific method in digital marketing in Las Vegas.

This is how hypothesis testing can be used to improve the results of your B2B marketing campaigns.

What is Hypothesis Testing?

Hypothesis testing, just like science class, is the act of making observations, creating questions based on that information, and then trying to solve the problem using scientific methods.

This is the simplified version. This is the beauty of scientific methods: they are gradual and deliberate, and each phase is documented – your hypothesis is constantly altered – until you reach a solid conclusion.

Although hypothesis testing and using the scientific method to increase marketing results isn’t a new concept, it gives you an advantage over the marketers of the past.

This is because there are many tools that you can use to test your hypotheses and gather relevant data to support them.

Marketing can be seen as a science. It is your job to ask the right questions, then create the tests that will prove your theories.

Your audience will expect a website presence as a result. It draws leads in large numbers and converts them while increasing revenue and ROI.

It sounds amazing, right?

Here are some ways to begin to form hypotheses for your B2B campaigns.

What a Hypothesis is – and What it Isn’t

Hypotheses are nothing more than questions based on an observation you want to prove.

A hypothesis must be proven using real data to make a question a question.

You can show that changing a headline can increase conversions by up to 20%. A hypothesis such as “Will changing the title increase conversions” is not a good idea.

Also, you should make your hypotheses concrete and not vague.

You are encouraged to continue to test, prove, or disprove your hypothesis until you reach valid and proven conclusions.

You must have an insatiable curiosity about every detail of your web campaigns to form a valid hypothesis. It is important to be open to digging into the details.

Here’s an excellent example.

Let’s suppose you have ten blogs. Two of them are extremely successful. They get a lot of views and are shared on many social media channels.

These blogs are ones to follow, according to all accounts.

It is obvious that successful blogs use larger images and have shorter paragraphs than those with less success.

Then, you might ask yourself: “Will other blogs get as much attention with bigger images and better formatting?”

To verify your hypothesis, you can run tests. Did more traffic come to your blog by making other blogs’ images bigger and paragraphs shorter? Did there be more sharing?

This is hypothesis testing in its simplest form.

Hypothesis Testing: Why Multivariate and A/B Testing are Qualifies

As you can see, hypothesis testing is possible with popular methods such as A/B testing or multivariate testing.

You can use a variety of tools to run numerous tests on web elements to test your ideas and prove your theories.

If you are running a retargeting campaign, you might want to know if your copy, image, and demographics match the target audience. To see if sales and traffic are increasing, you can perform A/B tests.

Another Hypothesis Testing Examples

• Two landing pages convert well, but one does not.

Hypothesis: The headline of the dud is written like a question. Is it possible to change the headline to make a stronger announcement?

A/B Test headlines can be used to determine if the slower-performing landing page is performing better.

• The open rate of your emails is lower than in the past.

Hypothesis: You notice that you were using a new template for your email at the time of the drop. Is it possible to convert by changing the template or going back to plain text?

To see if your traffic is returning to normal, you can A/B-test different email templates or plain text emails.

• Your website has a higher-than-average bounce rate when compared to other times in the past.

Hypothesis: Would changing your website’s color to a more relaxing shade of blue help people stay on-site for longer?

Your images might be too small. Also, your headlines may need some tightening.

Multivariate testing allows you to test all of these elements simultaneously until you can determine whether the changes have a positive or negative effect, or if you get the same results.

All of these hypotheses are possibly to be tested with data. This is the most important aspect of hypothesis testing. Without data, you can’t answer your questions and will never know the validity of your conclusions.

The Alternate Hypothesis and the Null Hypothesis

The inferential statistics term null hypothesis can be used to describe a term that is not related to the hypothesis you are testing. A null hypothesis is a statement that does not relate to the hypothesis you are trying to prove.

If you decide to increase the size of images on three blogs to match your high traffic rates, your null hypothesis could be that there will be no difference between the two.

Although this is not the outcome you want, it will give you some context for what you are trying to prove.

You hypothesize that increased traffic will result from larger images. However, your null hypothesis states that it doesn’t matter how large the image is.

An alternate hypothesis, also known as a research hypothesis or a hypothesis de recherche, is the alternative to the null hypothesis.

It is designed to prove your null hypothesis. In the case we used, the alternative hypothesis was that simply enlarging images on three blogs would help them attract the same attention as the other two.

How to use Hypothesis Testing for your Benefit

Step 1: Decide what to test

This is where you will select the web elements that you want to improve. Are you looking to improve the performance of your blogs? Increase the open and read rates of your email newsletters. Perhaps you just want to increase conversions on your website.

Your experiments can begin once you have your subject.

Step 2: Define your Hypothesis

This is the core of hypothesis testing. This is the question that you will ask and then try to answer using relevant data.

Your website may not be converting. You might want to check if your copy is convincing enough. You might wonder if your email template is the problem.

These questions will be the foundation for all the experiments that you engage in.

Step 3: Define your Variables

You can use A/B or multivariate testing to test your hypothesis.

You might decide to modify the title. Then, you can record the data and make your observations. You might also want to modify the title, image, and button color in multivariate tests.

Step 4: Check Your Hypotheses 

Start the testing process with your original hypothesis, null hypothesis, and alternative hypothesis in your mind.

You have many options, including Kobe Digital and many others. Each tool allows you to test your site and record visitor data in real time.

Step 5: Analyze your results, Calculate, and Act

Start testing your experiment once you have it ready. Each test should be given time to collect enough data to avoid making any alterations based on emotion or whims.

Instead, look at the facts. Your hypotheses will be proven if you have concrete evidence that your alterations are effective. 

If you find that your alternate or null hypotheses have been proven, you can go back to the drawing board and try other variables. You’ll need to do more tests to find the right combination for your audience.

Q&A about Hypothesis Testing

Q. How long should you wait before calibrating your hypothesis testing results?

To be able to allow A/B and multivariate tests to run for seven consecutive days, it is a good idea. You might test from Monday to Sunday and then calibrate the results. Although you can go on for longer than seven days, a week of data analysis will provide a solid foundation.

Q. Should you take multiple tests to prove your hypotheses?

This is an excellent question. You can always run the results again with different variables if you think your results were a fluke.

Hypothesis testing should not be done once. Hypothesis testing is an ongoing process that can easily be replicated through different channels and over time.

It is encouraged that you test your hypotheses as often as possible to give them at least 95% certainty.

Q. Which variables should I test for?

It should be tested if it is possible to test it. Your headlines, persuasive copy, and image sizes can all influence how visitors stay on your site.

It is your job to be as curious as possible and to ask lots of questions to make your tests more real.

You’ll be able to draw evidence-based conclusions and improve your results, even if you don’t know how to do it.

Q. What tools should I use to perform hypothesis testing?

Kobe Digital, one of the aforementioned tools, can be used to run live testing at any scale. You can run tests as long as necessary and reproduce your results across all variables.

Get Kobe Digital now!

These are the early pioneers of web-based hypothesis testing tools.

You can use the trial to test your hypotheses by A/B testing and multivariate testing. To see which visitors are clicking and pausing, you can explore scroll maps and heatmaps for your test variants.

You can even log visitor sessions to see how users use your site and identify any roadblocks or problems.

Conclusion

Hypothesis testing can help you improve your web presence.

Find out what you are trying to improve and then get creative with your observations. Ask lots of questions to form hypotheses that you can test.

Use the right tools like Kobe Digital to gather your data and then evaluate your test results. Did your hypothesis prove true or was it a false one?

This allows web testing to be more systematic and scientific for Miami’s digital marketing improvements.

Hypothesis testing is a must-have tool in any marketing campaign, no matter what your goals are.