Collecting and analyzing data regarding users allows us to improve our products and marketing strategies. What’s more, thanks to the proper analysis and interpretation of user activities, we can better meet their needs and also change user behavior.
In our previous article, “User behavior analysis: pitfalls and inference types,” we focused on discussing what mistakes data analysts make during data interpretation and what inference types can help us verify hypotheses.
In this article, we’ll delve a little deeper into how user behavior is perceived. We’ll discuss why we should see user interactions with a product more as a process than a problem and how perceiving behavior in such a way brings us closer to better understanding user motivation and the variables that influence it.
Key information
- We should see user behavior as a process consisting of various factors and variables.
- Discovering users' goals and motivations allows us to understand their behavior better and adapt products to their needs.
- While working, analysts encounter challenges that influence the analysis of user behavior.
Do you want to perform UX Research?
A problem and a social process — how do they differ?
Understanding the difference between a problem and a social process will allow us to analyze user behavior accurately and help us link product behavior with a concrete result.
What is a problem?
In dictionaries, we can find the following definitions:
- “A situation, person, or thing that needs attention and needs to be dealt with or solved.”
- “Something that causes difficulty or that is hard to deal with.”
- “A question raised for inquiry, consideration, or solution.”
While analyzing a problem, we usually try to find a straightforward solution that will leave no doubt that we’ve solved the problem. Often, the process of solving a problem leads us to one particular path, which crystalizes and becomes the solution to the problem, a concrete result.
What is a social process?
Compared to a problem, a social process consists of several to a dozen seperate events that occur over a given period. Analyzing a social process doesn’t lead to clearly defined solutions or results. A social process is composed of various behaviors that can change during the process, rarely remaining the same.
User behavior: a problem or a process?
Can we perceive user behavior as a problem that needs to be solved?
We might want to answer yes instinctively. After all, the task of data analysts is to improve the functionality of digital products based on the user analysis and actions they take during product use. So, in theory, data analysts solve a particular problem—a product doesn’t work optimally, so they optimize it based on data.
However, if we remember the above definitions, the situation is no longer so clear. User behavior is more like a social process than a problem because different variables can influence and change it constantly.
We can’t accurately indicate one cause for user behavior and one solution that will change it. We need to analyze variables in user behavior and discover their mechanism for decision-making if we want to draw correct conclusions that will allow us to design an effective intervention that will change the behavior.
Goals and motivations of users
Users interact with digital products in various ways. They can read articles, fill out forms, leave opinions and comments, take part in discussions on forums, etc. Behind all of these are goals and motivations that influence users’ choices.
This leads us to another matter: the goals and motivations behind user behavior. Many methods allow us to discover them, from quantitative to qualitative research or even techniques from psychology and behavioral economics. However, it’s worth remembering that we won’t always be able to determine why users behave in a certain way.
Goals and norms are examples of factors that affect how users behave at a given moment. Goals determine what users want to achieve with our product, and norms prevailing in a society influence whether users feel comfortable taking a given action.
For example, norms condition whether a user will feel brave enough to send a friend request to somebody on a social media platform. If they decide that they’re too shy to do it because their level of familiarity is too low, then this affects the user’s behavior, and the interaction doesn’t happen.
Are social systems closed systems?
A closed system consists of elements that aren’t subject to external factors. Processes occurring within such systems don’t change.
Social systems aren’t among such systems; as we mentioned above, social systems and user behavior are influenced by many factors.
Does that mean we can’t treat analytical models built based on behavior as closed systems? We can and, in fact, we must.
When an analyst creates an analytical model, they need to assume that they’re working on a closed system. The catch is that they still need to identify factors that can have the strongest impact on the model (and change it) as well as those that they won’t take into account but still have a chance to influence the system.
In other words, the analyst needs to simplify the complex process of human behavior and define it within the closed system. However, they should be aware that external factors still affect it. The reason for this simplifaction is the potentially infinite number of factors the analyst must analyze.
Challenges related to the analysis of social processes
As mentioned, social processes and user behavior are exceptionally difficult to analyze. The main reason is the lack of clear causes and solutions that would be easy to implement. There are also others that are more related to collecting and analyzing data.
A lack of clearly defined results
A black-and-white perception of user behavior will get us nowhere. Every user will behave differently because different factors will influence them. Since social processes don’t have clearly defined results, how can we analyze them?
The most straightforward method is to create an initial hypothesis, select appropriate and measurable metrics, and conduct research to help us understand various aspects of a given process. If we look at user behavior from different perspectives, we’ll be able to piece them together, and although it won’t be a complete picture, it will give us a good foundation on which we can base the decision-making process.
The problem of incomplete data
As we mentioned, analysts might not have all the information about user behavior; however, this issue can work both ways. Users might have incomplete information about the offered product, which can directly influence their behavior.
User testing allows us to collect a huge amount of valuable data, but there is always a risk of omitting some motivations or variables.
A large number of variables
Analysts deal with a large amount of data and variables influencing user behavior, and analyzing them all at once is simply impossible.
Developing an analytical model can make analysts’ work easier because they can segregate variables according to the relevancy criterion. They can divide the variables into those with the smallest influence on the user and those with the biggest impact. Thanks to this, analysts are able to create an accurate process description.
Confirmation bias
During research, analysts should be wary of confirmation bias. They shouldn’t only consider data that will confirm their hypothesis. This approach leads to creating an incorrect outline of the process and user situation and making the introduced solutions less effective or completely ineffective.
To analyze the data correctly, analysts can divide it into smaller groups, which will be easier to study and allow them to discover factors they might have missed. They can divide data according to the type of interaction. For example, data regarding contact form interactions, clicks on the CTA button, forum, or social media interactions.
Why should we look for causes of user behavior?
Asking questions about user behavior while designing various products allows us to discover users' goals and motivations. By discovering them, we can introduce effective changes and improvements to our products to better meet users' needs and expectations.
For example, our application may be designed to help users introduce healthy habits. The key to success is understanding user motivations, what prompts them to interact with the app, and what functionalities they expect.
In other words, understanding user behavior provides practical conclusions we can translate into specific actions.
By finding variables influencing user behavior, we can isolate those with the biggest impact, allowing us to change and adapt user behavior to our business goals.
Summary
User behavior shouldn’t be seen as a problem but as a process influenced by many variables and factors. Treating user behavior as a social process will allow us to minimize the probability of omitting important factors impacting user behavior.
If we try to discover the thought process of a user using our product, we’ll be able to meet their needs more accurately. Drawing the correct conclusions from user data can help us take specific actions to improve our products' quality. Additionally, those actions will help us change user behavior and acquire loyal customers.
Frequently asked questions
Why should we treat user behavior as a process and not a problem?
User behavior consists of various interactions and variables that influence it—seeing it as a seperate problem that needs solving leads to trying to define concrete solutions that might not exist. To develop a new product and describe user behavior, we need to analyze many variables to create a cohesive and comprehensive picture of the process.
What are the benefits of discovering the goals and motivations of users?
By discovering user goals and motivations, we can learn more about them and understand what influences their behavior. We can also define their expectations and needs, thanks to which we can improve the product and marketing strategies by adding desired features and creating messages aligning with user preferences.
What challenges do analysts face during data analysis?
Data analysts must deal with problems such as incomplete data, a lack of clearly defined results, a huge amount of variables, and confirmation bias. Those issues make it difficult for analysts to draw correct conclusions, and they should effectively combat them.
