It’s no secret that customer relationships play an important role in marketing and web analytics. But how can you ensure your customers are happy with the service you provide? Correlation analysis can help with that!
In a world where User Experience (UX) is a constantly changing phenomenon, not all users behave the same way! Even the same user can behave differently at different times!
You see? It’s a whole world! And how do you measure it? Through correlation. Correlations measure whether or not there’s a relationship between two variables. For example, whether an increase in product supply can be linked to a lull in customer demand.
And another important factor you need to know about this is that a correlation can be positive, meaning both variables increase together, or negative, meaning when one increases, the other decreases:
- Positive: When one variable increases, so does the other. For example, if your website sells flowers online, you determine that sales increase on specific days (Valentine’s Day, Mother’s Day).
- Negative: When one variable increases, the other decreases, and vice versa. For example, if we increase the supply of flowers on your website and demand decreases.
But wait! It’s a common mistake to expect correlation to tell you what you need to fix. After all, if you’re going to the trouble of collecting UX metrics, don’t you want the reward of gaining some diagnostic information?
In fact, once you’ve identified statistical relationships and can demonstrate results, they help you predict your business’s sales and target your web campaigns.
Importance of correlation in web page testing
As mentioned, correlation metrics help you make more informed decisions about your website and your audience’s receptivity to it.
To evaluate the effectiveness of your campaigns, it’s important to track key performance indicators (KPIs) and marketing metrics.
The main goal of these checks is to maximize return on investment (ROI) and efficiency while maintaining the quality of the customer experience. Correlation analysis can therefore reveal significant relationships on your page between different metrics or groups of metrics.
Keep in mind that information about these relationships can provide new insights and reveal interdependencies in how your users behave.
It’s important to understand how your target audience digests the media you put in front of them. This will undoubtedly help you find better ways to analyze your data and, in the long run, allow you to understand what works and what doesn’t for your business.
That’s why it’s so important to measure. Apply it and start seeing the benefits it brings you!
Correlation variables
It sounds simple enough, but there are actually different types of variables aligned with correlation. Understanding the differences is one of the keys to properly interpreting your findings.
- Dependent (outcome) variables: These are what you expect to change when you modify your interface, whether through a new design or through troubleshooting. For example, they are based on people’s broader attitudes. Branding, ease of use, trust, and appearance are also examples of dependent variables.
- Independent variables: These are things you manipulate or change. These can be specific design changes within an interface or application, such as a redesigned form or new navigation labels.
- Latent vs. observed variables: Trust, such as usability or satisfaction, cannot be directly observed. These variables cannot be seen; they are hidden, or, to use a more elegant term, latent.
- Extraneous (annoying) variables: Extraneous variables aren’t things you’re manipulating, but they can distort your dependent variables. Prior experience is a common extraneous variable in UX research.
