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How to synthesize user research data for more actionable insights

Last updated

12 October 2023

Author

Chloe Garnham

Your organization likely gains user research data from many sources, from social media to usability testing results. 

Data may be in the form of images, text, numbers, or checkboxes. The question is, how can you organize that valuable data to be useful and actionable? 

Once you’ve collected user research data, the next critical step is synthesizing the data. This can help validate data from multiple sources, making it more reliable and actionable. 

It’s only when you’ve organized the data in meaningful ways that real change can occur and boost decision-making. That’s all to provide better experiences for the end user. 

Let’s learn more about how to synthesize data and why it’s important. 

What is data synthesis, and why does it matter?

Data synthesis is a step in the data review process. Once you’ve collected data, it needs analyzing for insights. Synthesis is an essential part of doing this accurately and effectively. 

When you’re gathering data from multiple sources, you need to combine, integrate, and evaluate it. And it may come in many forms, like:

  • Surveys

  • Focus groups

  • A/B testing

  • Call center notes

  • Social media comments

  • Usability testing

The process involves merging mixed data into a uniform sequence for simpler analysis. 

Imagine several social media comments mention sluggish website processes. The call center may have also received this feedback. But which part of the website is sluggish? And where is critical investment needed? 

That’s where combining data comes into play. Checking website analytics alongside this user feedback can help you validate this data. It also ensures your company can take relevant actions to radically change the user experience

Data synthesis is essential for a few core reasons: 

Holistic overview

Synthesizing data into one dataset means any insights are more holistic across different data collection methods. This provides a more comprehensive view of your users and more accurate insights. 

Correlations

Combining data from multiple sources provides opportunities to see relationships between seemingly disparate items. This can boost your understanding of users and highlight issues you might not have noticed.

Hidden patterns

Patterns and trends may become clearer when you synthesize data. This can lead teams to draw deeper insights about users and continually stay ahead of the curve. 

Boosted decision-making

Integrating data from multiple sources can unveil insights and boost decision-making across the business. This ensures that any business actions are data-backed and not based on assumptions.

Improved data quality

Synthesizing data can ensure high data quality. Data from multiple sources may be more robust, provide a greater degree of evidence, and show whether findings are consistent. Any outliers will also become more obvious and easier to exclude. 

What are valuable research insights?

Insights are key takeaways from rigorous data analysis. They provide a rich understanding of users, facilitating the design and development of better user experiences. 

An insight may encompass a user’s pain points, motivations, preferences, or interests. It can also delve into specific metrics, such as wait times in a shopping cart, conversion rates, and overall customer sentiment

However, an insight goes beyond merely what happened. It informs us about the underlying reasons driving particular behaviors in a specific situation.  We can also uncover the subsequent implications. 

Essentially, an insight reflects a user's pain point, motivation, preference, or interest, providing a comprehensive understanding of their experiences. 

For insights to be actionable and valuable, they should be: 

Reliable

Your team’s insights need to be grounded in real, reliable data. Acting on assumptions can lead businesses in unhelpful directions. Synthesizing data can help boost the validity and reliability of data for decisions. 

Understandable

All team members and stakeholders should be able to understand the insights and act on them accordingly. That means using simple sentences to ensure all teams can interpret insights, recognize their importance, and take action. 

User-focused

To create user-centric experiences, consider your users at every step. User-centricity is essential for deeply understanding users and developing the best possible experience. 

It can also boost customer satisfaction, loyalty, and retention. 

Any insights should speak directly to your users’ wants, needs, and pain points. If you’re not solving a specific problem, perhaps it’s time to relook at what the data is telling you. 

Communication

Insights are only useful with clear communication. Teams shouldn’t act in silos; instead, it’s crucial to communicate insights across the business. Taking ownership of the process can ensure teams act upon insights.

Retaining your customer data in a platform that acts as a single source of truth can improve collaboration. It also ensures that critical insights don’t go missing amid lots of data.

Actionable

Actionable insights are findings that an organization can use to make positive changes. If insights are not actionable, they may be pretty useless. 

Insights should not be broad statements or insufficient takeaways. They should be short sentences that speak directly to a problem and a solution. Actionability allows your team to act on the findings to improve user experience. Some examples of actionable insights could include: 

Insight #1: 35% of shoppers are abandoning the shopping cart before payment because of the long, complex checkout process. 

Action: Simplify the payment process to ensure users can quickly confirm payments.

Insight #2: Longer-form blog posts are performing 46% better than short-form. Google is favoring long-form content, which targets a large number of keywords and more backlinks.

Action: Assign more long-form blog content for the website.

Insight #3: Social media posts with video content receive 3x the engagement. They capture viewers’ interest in the first few seconds and are more likely to be shared online.

Action: Develop more video-based social posts.

6 common challenges with synthesizing UX research data 

While synthesizing data is a cornerstone of accurate and reliable insights, it still has hurdles.  

These are some of the most common challenges in data synthesis. 

Large data sets

Synthesizing large datasets can be time-consuming. That’s why it’s crucial to manage data effectively. Prioritize the most critical research questions and synthesize data that speaks to those goals. This can significantly cut back on management time. 

The right tool can also make all the difference. An all-in-one platform can house all your data to help you gain insights and act on them faster. And Dovetail is the perfect solution.

Nuanced interpretation of qualitative data

In user research, data synthesis is an art and a science. One of the recurrent challenges in this domain is the nuanced interpretation of qualitative data

Qualitative insights from user interviews, observations, social media, and surveys often come in diverse forms. This requires a solid approach to categorization, labeling, and interpretation. 

Striking a balance between preserving the richness of individual responses while identifying overarching patterns demands careful consideration. 

Dovetail can come in handy for this. The platform uses natural language processing (NLP) to analyze and automatically uncover themes in your text.

Unclear goals

Synthesizing data can become time-consuming and problematic when your user experience (UX) research doesn’t have defined goals. 

Data should link back to core goals to continually problem-solve for users. This ensures you create user-centric products and limit the data you collect to the most crucial areas. 

Bias and assumptions

Teams may bring biases or rely on assumptions when synthesizing data. To avoid this, all team members must stay objective to ensure any insights are evidence-based. 

Contradictory insights

Sometimes, throughout the synthesis process, you may discover conflicting findings. It’s helpful to cross-validate the data sources to discover why these contradictions may have occurred. 

A deeper analysis may explain the discrepancies. Considering the context of different data sources can be insightful. 

Insufficient reviews

When making investment decisions, it’s essential to review the data thoroughly. Mistakes can understandably occur when handling large amounts of data. 

It’s helpful to use advanced tools, use AI to boost accuracy (but be wary of relying on it entirely), and have multiple people review the findings.

How to synthesize data, analyze it, and gain insights 

To get started on synthesizing your user research data, follow these best practice steps:

1. Define the research goals

All UX research projects should have clearly defined goals. This ensures data is relevant to improving your products’ UX and isn’t speaking to issues your users don’t have. 

In UX, all goals should keep the user front-of-mind, so any insights speak directly to their experiences.  

Remember also that all goals should be SMART (specific, measurable, achievable, relevant, and time-based) to be effective. 

Some examples of research goals could be: 

  1. Identify the pain points associated with the shopping cart to streamline the process and prevent drop-offs. 

  2. Understand user privacy concerns and define better ways of managing user data securely and communicating those measures to customers. 

  3. Identify core issues in customer sentiment to highlight how the business can improve across key areas. 

2. Collect and organize data 

Once you’ve defined your goals, collect data from a range of sources like:

  • Surveys

  • Interviews 

  • Focus groups 

  • Usability testing

  • Website analytics

  • A/B testing 

  • Diary studies

  • Social media comments

  • Call center notes

  • Chatbot conversations 

Ideally, gather quantitative and qualitative data to gain a full understanding. These research types help you see how users think, feel, and behave in certain situations. 

Researchers should collect all of the data with the project's core goals in mind. 

3. Use a UX research repository 

Gaining data from diverse sources is important, but it can make management tricky. To simplify things, gather and store data in one platform where all relevant team members can manage it. This will also allow your team to group the data in meaningful ways. 

An all-in-one platform to store, manage, and analyze data is critical. Dovetail allows teams to bring all customer data into one streamlined platform to collaborate, discover insights, and act on them quickly. This can vastly speed up the process of managing disparate data sets.

4. Develop a UX research taxonomy 

To synthesize data effectively, develop a categorization system known as a research taxonomy. This coding system can help your team quickly identify data trends and patterns.

A taxonomy ensures that you can organize and classify data from many sources. Creating a taxonomy means you can analyze the data as one group set to boost efficiency and draw deeper insights. 

To develop a UX research taxonomy, keep these three best practice steps in mind:

Define clear terminology to understand how disparate pieces of data can logically and accurately fit together. This will keep the information uniform and legible to all team members.

Make it relevant: The best taxonomy for your organization’s project may differ from another’s. Ensure your taxonomy is relevant to the project’s aims.

Provide documentation to ensure all core stakeholders can access and understand the terminology. Documenting the process also enables teams to review and reuse the research in the future. 

Group similar pieces of information into distinct categories or levels based on common characteristics, themes, or patterns. Use codes/tags that can be inductive (data-driven codes) or deductive (predefined codes). 

Once you’ve synthesized the data into one coded set, it’s significantly easier for your team to discover patterns and trends. Common and specific pain points for customers will become more evident. And you’ll likely discover trends that you didn’t expect. 

As you analyze the data, keep the core goals and end users in mind. The insights you gather throughout this process should link to the goals, which should always benefit the end user.

6. Share your findings 

Once you’ve gathered insights, it’s essential to communicate them. Insights are wasted if core stakeholders can’t act on them. After all, they’re the ones who will drive change. 

Collate all findings into an easy-to-understand report with critical insights, actions, and project owners highlighted. Consider different learning styles by adding color, images, and graphs to communicate the information simply and clearly. 

Creating timelines can ensure that change happens in a timely way. 

7. Ask “how might we” questions 

Once you’ve shared your insights, it’s helpful to organize a design workshop to consider potential solutions. As part of this, how might we questions can help you ideate solutions to better deliver for customers. 

These questions relate to the insights you’ve gathered. 

Let’s take these examples:

  • Customers are dropping off at the shopping cart

  • Customers are avoiding sign-ups due to data privacy concerns

  • Customers are reviewing the business badly

Some “how might we” questions to address these problems could be:

  • How might we ensure users spend only a few minutes in the shopping cart? 

  • How might we communicate data privacy when customers are signing up?

  • How might we ensure our customers have a better sentiment towards the company?

These questions can then lead to critical actions across the business. 

Gain actionable insights with data synthesizing 

Data from multiple sources is more reliable and powerful. But organizing that data into a uniform pattern is key to making the most of it. That’s where data synthesis comes in.

Synthesizing data requires: 

  • Bringing various sources and various formats into one platform

  • Creating a UX research taxonomy to make the data uniform

  • Drawing insights for action across the business

Deep analysis can take time, but it’s a critical aspect of user-centered design and development. Once you’ve organized the data meaningfully, you can draw vital insights to drive positive change for users. 

Ultimately, synthesizing data can boost customer satisfaction, loyalty, and retention. And those can seriously boost your bottom line.

FAQs

What is the difference between data analysis and data synthesis?

Synthesizing data is where you collate various data types and systematically categorize them. 

User research data may come from: 

  • Surveys

  • Social media comments

  • Call center notes

  • Focus groups

  • A/B testing

  • Website analytics

  • Usability testing

These all deliver different data, so synthesis is crucial to make the most of it. 

In contrast, data analysis examines data to uncover trends, patterns, and insights. You can turn these insights into actions, which can guide thoughtful design and development for better UX.

These complementary processes generate a comprehensive understanding of user behaviors, preferences, and needs.

How do you synthesize qualitative data? 

It’s essential to bring qualitative data into one streamlined platform to synthesize it. You can organize the data by theme, sentiment, or trend. Multiple iterations and reviews may be necessary to ensure you’ve labeled and categorized all data pieces. 

A taxonomy can help you organize the data into a uniform, easy-to-read dataset. It ensures researchers can more easily and accurately analyze the data. 

Synthesize data meaning

Synthesizing data is bringing data from multiple sources into a meaningful and uniform pattern. This ensures researchers can review, evaluate, and analyze data for deeper insights and faster action. 

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