Data Analysis in UX Research and Design
Implementing data analysis for research or UI UX design services can be an advantage when creating wonderful products. In this article we will talk about the relevance of quantitative and qualitative research to achieve a better digital product.
Big Data
Focusing on particular questions when collecting and synthesizing data will help convey what we want to tell. First of all, you must evaluate where the data comes from, to delve into its quality and know how it was defined.
Don't forget to ask the right questions so as not to confuse the person in charge of analyzing the information obtained. Some specific questions could be:
Where does this data come from? (here you can talk about the sources of information)
Does it represent our expected population?
What are your assumptions behind this analysis? Could it be that some conditions invalidate the assumptions and your analysis model?
What alternatives did you consider for this analytical approach?
Are the independent variables likely to be causing changes in the dependent variable?
Do not forget! Being clear about what we need can help us mitigate risks.
Keep in mind that when we conduct research , the data is not always perfect, so we do not have to get into a total controversy. The questions we listed above will help you avoid erroneous conclusions and extract information to make important decisions with more confidence.
It doesn't matter if it's qualitative or quantitative, they are biased
As UXers , we spend a lot of time exploring and visualizing data to drive the generation of a hypothesis. It is common for most of the work to end up in presentations to communicate the results clearly and concisely. However, there will always be two ways to know the user better: qualitatively and quantitatively.
These two ways, although they do bring us results, have similar problems and points in common. However, mixing them is very necessary when doing research. Let's visualize that all types of data can enhance your design process and help validate ideas and design decisions, preventing you from going blind.
Human-Centered VS Business-Centered
Knowledge of metrics and Analytics is essential to create better digital products and increase their value. Therefore, understanding users, their experiences, motivations, and how a product becomes involved in their lives is extremely important.
Qualitative research in UX can offer us more information about people, you can even address questions such as:
What motivates a person to use this product?
What is this person's perception of the product?
What was your emotional and physical reaction like?
What features do you like and what don't you like?
What role can the product play in your daily life?
On the contrary, when a data analyst intervenes, he or she may ask:
How does the behavior of a product change in relation to data analysis?
Is this change reflected in the clicks or the time they are using the product?
What functions do they use?
At what point do they abandon a stock?
User intent VS user action
When we refer to user actions, we talk about how many times they clicked a button or how often they use the product. Unlike intentions, where we talk about the relationship between people and the product, for example, out of every 5 times they used the product, how many times did they buy an item that they searched for a while and couldn't find easily? How much time did they spend inside our product and if it took them, why did they do it?
When someone who is an expert in digital analytics rates the product, they don't go into those questions as much. On the contrary, he evaluates the moment and behavior of the user more, but in a concrete way; measuring the number of visits, clicks made or time spent.
When you conduct UX research, you focus on quantitative topics. The goal is to review and understand how people use products, what their intent is, and what their experience is like (to rate quality), supported by other behavioral methods.
Inference vs. Prediction Accuracy
Both a UX researcher and someone dedicated to data analysis use tools to collect all types of information. However, they use them differently to reach the intended objective.
Someone who is dedicated to doing UI UX design services is more motivated by inference. On the contrary, it does not focus on predicting future phenomena or understanding factors hidden from a user's experience and behavior.
On the contrary, the person who is an expert in data analysis has as his main motivation to improve the predictive accuracy of his models.
Analysis of survey data or recorded behavioral data
When you conduct qualitative research, the information you obtain is highly variable. This is because it is entirely dependent on the questions you ask. For this, there are multiple methods you can use for data collection
When you conduct quantitative research (as a data analyst does), you must understand the data collection process very precisely. You also need to prepare to handle problems that arise given the size of the data obtained.
Correlation does not mean causation
Both could seem like two different worlds, however they share the same intention. Both quantitative and qualitative research can be very complementary when designing a product. Even Facebook relies heavily on implementing both techniques to obtain the best results.