Creating a solid marketing strategy requires constant experiments and learning through doing to succeed. They help you discover what works or doesn’t work for your business without spending large sums of money and investing a lot of time. The most challenging part is knowing which tests to run and how to interpret the data you get.
If you conduct the wrong experiment, you will gather skewed data and have an inaccurate picture of the possible outcome. And even the most seasoned marketers and entrepreneurs sometimes make mistakes. The good news is, you can avoid making these errors resulting in an inefficient marketing strategy, wasting your efforts and finances. Here we have five common mistakes in marketing experiments.
Setting a clear hypothesis is the first step when conducting a marketing experiment. It’s merely a statement that outlines the predictive outcome of your test. It needs to illustrate what you are trying to prove (or disprove), and it needs to be measurable as well. It is where most marketers make a mistake. For instance, some people hypothesize that marketing experiment design A will improve the user experience or feature X will drive more engagement. It’s difficult to measure an increase or decrease in user experience or involvement in such a case.
On the other hand, a reasonable hypothesis would be, ‘our website will see a 60% increase in conversions when we include a call to action on the home page. ‘ Such a statement is measurable, and you can even change the call to action to get different results. Again, the idea is disprovable if you fail to get a 60% conversion rate. Now, this disapproval aspect of your experiment will become a null hypothesis.
A well-structured statement should clearly explain whether the results are inconclusive, proved, or disapproved. Again, avoid writing your hypothesis in the form of a question. It should appear as an objectable statement. Also, make sure that your idea has an exact reason. For instance, ‘my page needs a new call to action to increase conversions.’
To develop a strong hypothesis, collect data through observation, and create a detailed description of your results. Next, form a thesis describing the observations’ different outcomes, test the hypothesis, analyze data and draw conclusions. It will help you eliminate unnecessary tests to produce actionable insights even when the idea gets proven wrong.
Biases occur when your expectations influence the marketing experiment design. Sometimes it can be choosing a more comfortable option, other times focusing on results that support your hypothesis. You might not even realize that your thinking is biased, but it still will affect the outcome’s credibility.
Asking the wrong questions will attract the wrong answers. For instance, if you are doing a Net promoter score (NPS) survey and only give respondents multiple option questions, you might limit their options. In surveys, it’s important to include open-ended questions to gain a better perspective and avoid overlooking some possibilities that might be important to your respondents.
Or when you gather the feedback only from customers who are satisfied with your service, you miss out on the opportunity to learn from those who had issues with it and improve further. Or when you choose a specific experiment method only because “we’ve always done it this way.”
The best way to avoid biases is to be aware of why you are doing things the way you do and not to do them on autopilot.
Another grave mistake in marketing you can make is relying on a single experiment result. This way, you are more likely to make conclusions without enough evidence. To eliminate variables that could skew results, you need to run your experiments several times and over a decent period. It also helps achieve enough statistical significance.
Yes, it takes time. Yes, it can be tedious. But doing repeated experiments several times will help eliminate errors. When something happens once, it’s an accident, twice – it’s a coincidence, three times – it’s a trend. Repetitive tests show you clear patterns and trends in your experiment, which helps you develop a more affirmative conclusion.
The statistical significance test helps you determine whether your experiment results are due to chance or some factors of great interest. For example, you can check whether the difference in conversion rates between a variation and baseline is due to chance or not.
Nonetheless, some marketers still make statistical significance mistakes in marketing that invalidate your test and contribute to poor decision-making. Some people skip adjustments for multiple testing. Analyzing your results using the best variant or testing only a single one against control can increase the chances of getting false-positive results. So you need to try more than one variant on a single standard comparison to improve efficiency.
Others may lack a fixed sample size. It mainly happens when you use a simple significance test to assess data daily and stop once you get a marginally statistically significant result. You can try fixing your sample size in advance and only view data once at a predetermined time. Others use a proper sequential trying method but register the wrong recordings to adjust the statistics accordingly.
Another widespread mistake that we see is failing to compare data from previous experiments, leaving them with a very narrow understanding. Nothing in marketing exists in isolation. Suppose you wish to determine what makes customers interact with your brand the way they do. In that case, you have to learn from multiple experiments to see it from different angles and understand better what pattern emerges.
Start by reviewing all the results from various tests to see the connections and visualize an ecosystem that they create. You might want to look into all touchpoints that a customer interacts with, from social media to the website. See how all these elements are intertwined and connected. Ask yourself how interacting with one of them might affect the others.
Don’t worry; making mistakes in marketing is completely fine, especially when you can recognize them and learn from them. And while you cannot avoid all mistakes, being aware of them will help you eliminate some. Next time you plan to conduct a marketing experiment, try thinking ahead and seeing what you can do to protect your results’ accuracy. ,