From Data to Decisions: How to Analyze User Research Results

Understanding user research

The goal of user research is to inform the design process from the perspective of the end user. It involves understanding user behaviors, needs, and motivations through various user research methods such as observation techniques, task analysis, and feedback collection.

User research is not simply about asking users what they want but rather about understanding how they use the product, their pain points, their goals, and their overall behavior when interacting with a product or service.

There are various types of user research methods, each suited for specific goals and scenarios. They can be broadly categorized into two types: qualitative and quantitative.

Qualitative research provides in-depth insights into user behavior and preferences, and some of the common methods include:

user interviews
focus groups
field studies
contextual inquiry
diary studies
card sorting
competitive analysis

Quantitative research, on the other hand, gives us statistical data about user interactions and patterns. Some of the methods used include:

user surveys
A/B testing
eye tracking
clickstream analysis
heatmaps
time-on-task analysis
task success rate

Surveys are great for collecting large amounts of user data quickly and cost-effectively:

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Conducting user research is integral to creating a user-centered design as it reduces the risk of failures by revealing what users want, need, or expect.

Beyond informing design, user research can also guide your business strategy. It can reveal new market opportunities, inform marketing and sales approaches, and even influence business decisions. Additionally, conducting user research can foster a user-centric culture within your organization, ensuring all teams understand and prioritize the user experience.

Analyzing quantitative user research data

Quantitative analysis refers to dealing with numerical data obtained from methods such as surveys, usability tests, or analytics.

Through this analysis, you can identify significant trends, patterns, and correlations, allowing you to make data-driven decisions with a high degree of certainty.

The process of analyzing quantitative data can be broadly divided into three key steps: descriptive statistics, correlation analysis, and regression analysis.

Descriptive statistics provide a summary to help you understand the central tendency of your data (mean, median, mode) and its dispersion or variability (range, standard deviation). How long, on average, do users spend on your website? What's the standard deviation in the time spent? These are some of the questions you might get answers to with descriptive statistics.

Correlation analysis is about exploring the relationship between two or more variables. For example, is there correlation between the age of a user and the type of tasks they perform on your app? Correlation analysis can reveal if and how variables influence each other, but it's important to remember that correlation does not imply causation.

Regression analysis takes correlation a step further by predicting the outcome of a variable based on the value of another variable. For instance, can we predict a user's satisfaction level based on the number of clicks they need to complete a task? Regression models can help you understand which factors are significant predictors of your outcome of interest.

In essence, quantitative analysis provides objective insights and patterns that, when combined with qualitative analysis, lead to a comprehensive understanding of your users.

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Analyzing qualitative user research data

Qualitative analysis helps you interpret and make sense of non-numerical data collected from a variety of user research methods, including interviews, focus groups, and usability tests.

The first step in analyzing qualitative data is often transcription. This is done to preserve the original context of the conversation and set the stage for further analysis.

Once you have the transcriptions, the next step is coding. This process involves assigning labels or tags to segments of your data to denote ideas or themes they represent. Coding helps categorize and segment the data into manageable bits of information, enabling easier analysis.

In the next step, you can perform a thematic analysis to identify common patterns across the coded data to form overarching themes. These themes help tell a holistic story about your users' experiences, attitudes, and motivations. For instance, themes like "difficulty navigating the menu" or "need for a search feature" might emerge from your analysis.

Finally, interpret your findings by considering what the themes mean in the context of your research objectives. How do they answer your research questions? What insights do they provide about user behavior, attitudes, or preferences? Summarize these interpretations in a clear and understandable user research report.

Common mistakes made when analyzing user research data

Analyzing user research data can be a complex task—after all, you might be looking at lots of different types of data gathered using a variety of tools and methods. Here are some common pitfalls you should be aware of.

Ignoring context

User research doesn't occur in a vacuum—it's deeply influenced by the context in which the data was gathered. For instance, a product tested in a quiet usability lab might get rave reviews but perform poorly in a real-world setting. Hence, failing to account for factors such as the user's environment, mood, cultural background, or even the time of day can lead to inaccurate interpretations.

Confirmation bias

Confirmation bias occurs when researchers subconsciously favor data that supports their existing beliefs or hypotheses and ignore data that contradicts them. For example, if a researcher believes that a new feature will be well received, they might overlook feedback highlighting potential issues with the feature.

The short video below explains how to recognize and avoid bias in user research:

Ignoring outliers

Outliers are data points that significantly differ from others in the same set. While it's tempting to disregard them as anomalies, outliers can sometimes highlight unexpected user behavior or hidden issues that can lead to valuable insights.

Relying solely on quantitative or qualitative data

Quantitative data offers hard numbers on user behaviors, while qualitative data provides insights into user motivations and feelings. Relying on only one type can lead to a lopsided understanding of your users. A mixed-methods approach provides a more holistic view.

Misinterpreting correlation as causation

A classic mistake in data analysis is confusing correlation with causation. For instance, if data shows that users who watch a tutorial video are more successful in completing tasks, it doesn't necessarily mean the video led to their success. There could be other factors at play, such as these users being more motivated or diligent.

Failing to communicate findings

Research findings must be communicated clearly, succinctly, and in a manner that resonates with your audience, which often includes non-researchers. Findings that are buried in technical jargon or complex charts may fail to inform decision-making or convince stakeholders of the need for action.

How to share your user research data effectively

But what good is data analysis if it doesn't translate into actions that drive product development in the right direction?

Once you’ve analyzed your results, it’s time to translate the findings into actionable recommendations, for instance in the form of a research report.

Here are some factors to keep in mind when sharing your results with stakeholders.

Understand your audience

Every stakeholder has unique concerns and areas of interest. A product manager might be interested in feature usability, while a developer might want to know about bugs. Tailor your data and findings to the audience's needs. Before presenting, clarify who will be present and what they hope to gain from the research.

Tell a story

Instead of merely presenting facts and figures, weave them into a compelling story. Start by setting the context and detailing the problem you aimed to solve. Then, discuss your research methodology and the insights you gathered. Stories create emotional connections and make data more memorable.

Use clear, simple language

Don't alienate your audience with complex jargon or technical terms. Keep it simple, engaging, and accessible to all, regardless of their expertise level. If specialized terminology is unavoidable, make sure to provide definitions or explanations.

Visualize your data

Visuals can clarify complex concepts and highlight trends in a way that's easily digestible. Visualize your quantitative data with charts, graphs, and infographics. For qualitative data, use quotes, personas, or user journey maps. Be mindful of color choice, scale, and layout to ensure visuals accurately represent your data.

Get better user insights with surveys

Analyzing user research is an ongoing process that helps inform your decisions at every step of your product's life cycle.

Having access to a tool that analyzes chunks of the data for you automatically can seriously speed up the process so you can focus on improving your product.

Survicate not only allows you to collect a wealth of user data with surveys but it also analyzes the feedback as it comes in. Plus, with a host of native integrations, it’s super easy to connect with third-party tools, such as Slack, HubSpot, and Intercom.

To get started, simply sign up and pick from over 125 survey templates or create your own from scratch.