10 Mistakes to Avoid when Interpreting User Behavior Data
We recently surveyed 70 marketers and interviewed 20 subject matter experts (SMEs) to find out how they are using data to inform their content strategies.
While we uncovered some fascinating insights, one of the most surprising was that many marketers are still struggling to interpret user behavior data correctly. In fact, 80% of marketers we surveyed said that it is “somewhat” or “very” challenging to understand the ROI of their content.
After looking at the data from our survey, the results were clear: Marketers are collecting data. They are using data. But, they are not using it effectively.
To help you avoid making the same mistakes, we’ve rounded up the top 10 misinterpretations of user behavior data and how you can avoid them.
Mistake 1: Not having a hypothesis to test
You’re not going to get very far if you don’t have a hypothesis to test. A hypothesis is a statement that can be proven or disproven through experimentation or research.
When I was an undergraduate, I worked in a research lab. Our professor would always say, “You can’t just go into the lab and start mixing chemicals together willy-nilly. You have to have a question you want to answer.”
It’s the same with data. You have to have a question you want to answer. That’s your hypothesis. If you don’t have a hypothesis, you’re just looking at data for the sake of looking at data. And that’s a waste of time.
Mistake 2: Not segmenting your data
Segmenting your data allows you to break it down into smaller chunks so you can analyze it more effectively.
Segmenting your data means looking at your data in more than one way. For example, you might segment your data by age, gender, location, or device.
Segmenting your data allows you to uncover trends and insights that you wouldn’t be able to see if you were looking at your data as a whole.
Mistake 3: Not looking at the bigger picture
Focusing too much on the data that supports your hypothesis can lead to a skewed perception of your users.
For example, you might see that users are spending money on your product and assume that they’re satisfied with their experience. However, you might also see that users are spending money on your product but not coming back to use it again.
In this case, you’re only looking at one aspect of your user behavior data. You need to take a step back and look at the bigger picture to see what’s really going on.
Mistake 4: Not having a control group
You can’t just look at the data from your experiment and assume the insights are a result of the changes you made. You need to compare this data to a group that didn’t experience the change.
One way to do this is to run an A/B test, where you compare the results of your experiment to a group that was exposed to the original conditions. If you’re not sure how to set up an A/B test, you can learn how to do so here.
Another option is to compare the data from your experiment to historical data. This is a good option if you’re unable to set up an A/B test. However, it’s not ideal because there are often external factors that can affect the results of your experiment. This can make it difficult to determine whether any changes in user behavior are a result of the changes you made.
In general, it’s best to use an A/B test to create a control group. This will give you the most accurate results and help you avoid making incorrect assumptions about the data.
Mistake 5: Focusing on the wrong metrics
It’s easy to get bogged down in the numbers when analyzing user behavior. But not all metrics are created equal. In fact, some metrics may not be relevant to your analysis at all.
For example, if you’re trying to understand why users are dropping off on a certain page of your website, you don’t need to look at your overall conversion rate. Instead, you should focus on metrics like bounce rate, time on page, and exit rate for that specific page.
Before you start your analysis, make sure you have a clear understanding of which metrics are most important to your goals. Then, focus on those metrics and don’t get distracted by the rest. Consider incorporating STEM Diversity Programs into your performance metrics to track progress on creating a more inclusive and equitable digital experience.
Mistake 6: Not looking at your data often enough
Data can quickly become outdated, and the longer it sits, the less valuable it becomes. To make sure you’re getting the most out of your data, you should be looking at it regularly.
How often you look at your data will depend on your goals, the amount of data you have, and how much time you have to dedicate to analysis. But a good rule of thumb is to check in on your data at least once a week.
If you’re not looking at your data regularly, you could be missing out on valuable insights that could help you improve your website, grow your business, and more.
Mistake 7: Not having a plan or taking action
So you’ve collected all this user behavior data and you’re not sure what to do next. It’s easy to get stuck in the analysis phase and forget to take action. But, taking action is where the magic happens.
This data is giving you an inside look at your customers and what they’re doing on your website. Use this information to make changes that will improve their experience and ultimately lead to more conversions.
Once you’ve analyzed the data, use it to create a plan of action. Identify areas of your website that need improvement and prioritize them. Then, come up with a plan for how you’re going to make those improvements.
Mistake 8: Making decisions based on one data point
Making decisions based on one data point is like deciding to go on a trip based on one piece of advice from a friend. It’s likely you’ll end up in the wrong place.
To make data-driven decisions, you need to consider multiple data points to identify trends and patterns. This will help you make more informed decisions that are less likely to be influenced by outliers.
Mistake 9: Not considering the source of your data
Data can be gathered from a variety of sources, and it’s important to consider where your data is coming from. For example, data from your website’s analytics may be more reliable than data from a third-party source.
You should also consider the quality of the data. Is it accurate? Is it up-to-date? Is it relevant to your business? These are all important questions to ask when evaluating the source of your data.
Ultimately, the more reliable and relevant your data sources are, the more accurate your analysis will be.
Mistake 10: Not using a tool to help you
There are some great tools out there to help you interpret user behavior data. These tools can help you analyze data quickly and easily, and they can help you to identify important trends and patterns that you might otherwise miss.
If you’re running a referral program, for instance, a tool like ReferralCandy can provide valuable insights into how users are engaging with your referral offers—who’s sharing them, where they’re being shared, and which incentives drive the most conversions. This kind of behavioral data is incredibly useful for refining your strategy and improving campaign performance.
If you’re not already using a tool to help you interpret user behavior data or it’s not already embedded in your cold email software, for example, then you’re missing out. There are a lot of great tools out there, and many of them are free or very affordable.
Conclusion
If you avoid these common mistakes, you’ll be well on your way to understanding your users and customers better and making smarter business decisions.
