How to gather qualitative data in a SaaS environment
Ok, first off, let’s establish that collecting qualitative (in addition to quantitative) data is key for creating a product that aligns with user needs.
For SaaS companies, gathering this kind of information can involve channels like user interviews, surveys, focus groups, product feedback, and even social media listening. For the purposes of our discussion, we’re going to talk about in-product data collection methods.
Unlike quantitative data, which gives you clear metrics that show what’s happening, qualitative research digs into the "why" behind the numbers. It helps inform deductive reasoning that can uncover the pain points causing a dip in engagement — or even leading to churn.
This is sometimes called phenomenology — an approach to understanding the subjective experience of using the product (rather than numerical data), often through open-ended questions or in-depth feedback. (Broadly speaking, this can be defined as customer behavior analysis.)
That’s why, when conducting research, the best way to get a holistic picture of user experience is with mixed methods and multiple data sources. When you gather unstructured data from open-ended questions alongside quantitative research, it’s possible to build a truly customer-centric product.
Identifying key touchpoints for data collection
So now it’s time to kick off your research process. To begin, there are some product moments are perfect for getting insights.
For SaaS research design, collecting data from the right product touchpoints is critical for understanding user experiences and making informed product decisions. These touchpoints give you direct access to contextual feedback, allowing you to identify patterns and trends that drive both product enhancements and overall customer satisfaction.
Which touchpoint (or touchpoints) you decide to focus on will depend on your research objectives — for example, if you’re trying to better understand the experience of new users, then you’ll want your respondents to be going through the onboarding experience.
Here’s our take on the most effective touchpoints for gathering user insights:
- Onboarding: The onboarding process is the first interaction many users have with your product. Analyzing qualitative research from this stage helps you understand how new users engage with the product, pinpointing usability challenges, unmet expectations, or areas where the workflow needs improvement. These research findings will provide insight into where users may struggle or abandon the product early on.
- Feature launches: New feature releases are a prime opportunity to collect feedback. Gathering data through triggered surveys or user interviews during feature launches reveals how users perceive the new functionality, whether it meets their needs, and if any adjustments are needed to enhance adoption. This type of data collection (alongside quantitative analysis) is essential to refining features post-launch.
- Customer support interactions: Customer support is a goldmine of qualitative research data. Analyzing the types of issues users bring up — such as technical problems or requests for help with certain features — provides insights into product gaps or pain points. And, when you use AI-powered qualitative data analysis software, you can (automatically) categorize these interactions and identify recurring themes that may indicate deeper product flaws.
- Product usage patterns: Monitoring how users interact with specific features or during certain stages of the buyer journey is a key method for uncovering insights. Through thematic analysis, you can group feedback to understand trends in feature engagement or why users drop off at certain points. For example, you might find that premium users regularly engage with advanced settings, while freemium users struggle with basic navigation. Triggered, in-product surveys are a great way to build a qualitative study to gather real-time feedback related to these patterns.
- Common churn points: Understanding why users leave the product is crucial for improving retention. Identifying common churn points — such as after a particular update or at the end of a trial period—provides data on where the product is failing to deliver value. Qualitative data analysis (including voice-of-customer analytics) can help you isolate the reasons for churn and inform strategies to mitigate it.
- Renewals and upgrades: The moments when users decide to renew or upgrade their subscription provide deep insights into long-term product satisfaction. By collecting feedback during renewals and upgrade periods, you can understand what factors drive users to stay or increase their commitment. Using AI for content analysis at these touchpoints can reveal whether users are upgrading for new features or sticking with the product due to overall satisfaction (not to mention scale your ability to track these trends).
Leveraging customer feedback tools and AI
Instead of relying on manual analysis of qualitative data, which can be time consuming and also prone to errors, it’s more efficient (and actually more effective) to use automated tools (like Sprig!). Using continuous feedback and in-product surveys, you can collect real-time qualitative data, and streamline your analysis — which means the insights from your research project will be available faster.
For example, Sprig allows you to set up triggered, in-product surveys that pop up when users hit a specific milestone or struggle with a particular feature. With built-in templates and AI-powered analysis, you can categorize feedback and even receive recommended product changes—without deep technical know-how.
Results page of a drop-off analysis Survey in Sprig — Sprig AI analyzes all responses to generate a study summary and key takeaways.But the most advanced qualitative analysis includes behavioral data, in addition to feedback and sentiment data. Behavioral data allows you to understand and visualize how users are interacting with your product — you can capture this type of qualitative data using session replays and heatmaps. Session replays allow you to watch recordings of actual user sessions, and heatmaps show you the areas where users are most engaged (or disengaged) in your app or website.
The playback page of a Replay study in Sprig, where you can watch all of the targeted session recording clips you’ve captured.The results page of a Heatmap study in Sprig — including the AI Summary and heatmap, scrollmap, and clickmap data.
5 advanced qualitative data analysis methods for product teams
Once you’ve collected qualitative data, the next step is to make sense of it and translate insights into actionable product improvements. These five advanced (but practical!) qualitative data analysis methods are built to help SaaS product teams turn user feedback into meaningful changes quickly.
With the power of AI and automated tools, these analysis techniques are easier than ever to implement and can significantly enhance your product strategy.
1. Content analysis for product decisions
Content analysis is all about categorizing feedback into distinct groups, such as feature requests, pain points, or workflow issues, so you can prioritize product changes.
Here's how to execute this method effectively:
- Categorize feedback automatically: Use AI-driven tools (like Sprig’s AI Recommendations) to automatically sort feedback into relevant categories. For example, when multiple users mention difficulty navigating a particular feature, this can be tagged as a "usability issue."
- Prioritize based on volume: If “feature requests” consistently appear in the feedback, product teams can allocate resources to enhance those areas. For instance, if users repeatedly ask for a search function within your app, this feedback can be shared with key stakeholders and prioritized over less frequently mentioned suggestions.
- Monitor changes over time: Track how often certain categories (e.g., “workflow issues” or “bug reports”) appear after a product update to assess whether improvements have made a difference.
By organizing qualitative data into structured categories, teams can quickly pinpoint where to focus their efforts.
2. Thematic analysis to identify user trends
Thematic analysis goes a step further by uncovering broader patterns and trends across different user segments. Here’s how you can apply it:
- Segment feedback by user type: Use AI tools to automatically group research question responses from different customer segments (e.g., freemium vs. premium users). If both groups express similar frustrations, like onboarding complexity, that signals a widespread issue.
- Spot unique trends: If only one segment consistently mentions an issue — such as freemium users having trouble with navigation — focus on what’s unique about their experience to make targeted adjustments.
- Identify cross-product themes: AI can help identify recurring themes across your entire user base, such as a common complaint about feature discoverability, prompting you to improve your product's UX.
This method helps product teams prioritize changes that will have the biggest impact across user segments, minimizing churn.
3. Grounded theory in product innovation
Grounded theory is a powerful tool for uncovering user needs that aren’t explicitly stated. It involves analyzing qualitative data to infer what users want — but haven’t clearly articulated. Here’s how you can leverage it:
- Look for underlying patterns: Use AI to detect recurring phrases or feedback like “ease of use” or “too complex.” While users may not directly request a change, grounded theory might suggest simplifying your product’s interface thanks to this textual data.
- Develop new feature ideas: If users frequently mention how “time-consuming” certain tasks are, this could imply a need for automation or shortcuts in your product.
- Predict future needs: When users reference upcoming trends or evolving needs, grounded theory can help teams develop features ahead of time, ensuring the product remains relevant.
When they use AI to analyze patterns in user behavior and feedback, product teams can drive innovation without relying solely on direct requests.
4. Narrative analysis to understand customer journeys
Narrative analysis takes a storytelling approach, mapping out the user’s journey through your product. Here’s how to implement it:
- Create user stories from feedback: Take multiple pieces of feedback and create a timeline or "story" of how users interact with your product. For example, if users report getting lost after onboarding, this suggests a need to improve feature discovery or revise the onboarding process.
- Identify key journey moments: Look for common issues that arise during specific stages—such as onboarding or feature exploration—and use those insights to adjust your product roadmap.
- Visualize user flow: Using AI tools, you can generate customer journey maps based on feedback to visually track where users experience friction, guiding decisions like which features need better positioning.
When you focus your research study on the qualitative research gathered from user stories, you gain a clearer picture of the overall user experience and the key points that drive success — or frustration.
5. Discourse analysis to improve communication features
Discourse analysis again leverages textual data to examine how language, tone, and sentiment in user feedback reveal deeper insights about their feelings toward the product. Here’s how to use it:
- Analyze sentiment in feedback: AI tools can gauge whether users are frustrated, satisfied, or indifferent by analyzing the language they use in surveys or support tickets. For instance, if users repeatedly express frustration around complex features, that’s a signal to revisit your UX.
- Improve communication clarity: If the tone of feedback suggests confusion, you might need to simplify instructions, onboarding guides, or product messaging to make the experience more user-friendly.
- Refine product updates: After a feature launch, if users’ feedback is filled with negative sentiments like “too difficult” or “overwhelming,” this can prompt revisions to your communication style or even the feature itself.
Using discourse analysis, product teams can interpret not just what users say, but how they say it, giving you a clearer understanding of user sentiment and where improvements are needed.
Using AI-enhanced qualitative analysis tools for product experience insights
Qualitative data analysis doesn’t have to be overwhelming. With Sprig’s automation and AI, you can analyze feedback at scale and turn raw data into actionable insights without having to become a data expert.
One of the best uses of AI is to process large amounts of qualitative data, helping turn it into actionable recommendations at scale. By triggering targeted studies, Sprig helps you gather, analyze, and make sense of data from various touchpoints without manually sorting through endless feedback.
Core benefits of all-in-one qualitative data analysis with automation and AI
QDA can feel overwhelming — especially when you're managing multiple feedback sources and trying to make sense of it all. But with automation and AI, you can transform this traditionally time-consuming process into an efficient, streamlined one.
Here are the key benefits of using an all-in-one tool that leverages AI to handle your QDA (argh that’s a lot of abbreviations, we know):
Automate the collection of user feedback at key touchpoints
One of the biggest challenges in qualitative research is consistently gathering feedback across the entire user journey.
With automated tools like Sprig, you can set up surveys and prompts at crucial moments, such as onboarding, after a feature launch, or during renewal periods. Instead of relying on manual outreach or combing through endless support tickets, automation ensures you’re getting relevant insights right when users are most engaged — or when issues arise.
Use AI to process and synthesize large datasets
Qualitative data can come in many forms, from survey responses to interview transcripts to sessions recordings to session heatmaps. Manually organizing and interpreting this large amount of data takes significant time (and expertise). AI changes the game by quickly processing and categorizing feedback and behavior data at scale.
It not only organizes data but highlights patterns that would be difficult for a human team to detect — speeding up the analysis process and ensuring nothing important gets overlooked.
Many leading companies use Sprig to analyze user experience data from Surveys, Replays, and Heatmaps at scale. Here’s an example of what the AI Analysis page of a Sprig Replay looks like:
Replays results page in Sprig — Sprig AI instantly analyzes and groups session clips into themes to uncover patterns in your users' behavior.Discover hidden trends and insights from qualitative data
It’s easy to spot obvious feedback, like recurring feature requests or common complaints. However, qualitative data analysis software powered by AI can help you discover subtle trends that might otherwise go unnoticed.
So, for example, if users consistently use certain phrases to describe a challenge — across different segments or demographics —A I (like Sprig’s AI Explorer feature) can flag these patterns as potential areas of opportunity or concern. By revealing these hidden insights, you can better understand underlying issues and make informed decisions on where to focus your efforts.
Capture experience data at scale via Replays, Heatmaps, Surveys, and Feedback — Sprig AI instantly analyzes all data to uncover trends in your product experience.Receive prioritized product recommendations based on analysis
AI doesn’t just categorize feedback; it provides actionable recommendations based on the data. Instead of simply presenting you with a list of user comments, tools like Sprig’s AI Recommendations can rank issues by priority, helping product teams focus on the most impactful changes.
This means less time spent figuring out what needs attention, and more time actually implementing meaningful improvements that make things better.
Bypass the need to manually choose or apply specific analysis methods
Ok, so we’ve gone over a series of analysis techniques... but, here’s the thing: If you leverage an AI-driven platform for your research, then you can effectively bypass having to choose just one technique up front.
Backing up a bit — qualitative data analysis often requires selecting the right methodology — whether it's content analysis, thematic analysis, or something more advanced like grounded theory.
But with AI, you don’t need to worry about picking the right approach. The software applies the best-fit techniques for the data at hand, ensuring a thorough analysis without needing in-depth expertise in each method. This simplifies the process and makes qualitative analysis more accessible to non-experts.
Streamline feedback into actionable tasks for product teams
AI not only helps with analysis but also translates findings into clear, actionable tasks. Once the data is processed and synthesized, it can be directly linked to product roadmap updates, backlog items, or sprint tasks.
This automation reduces the need for manual handoffs between teams and ensures that insights are turned into actions without delay. Your product team can focus more on the coding process — building and improving, not deciphering feedback.
Enhance efficiency by reducing time spent on data interpretation
Time is one of the most valuable resources for product teams, and manual data interpretation can drain it quickly. Since AI allows you to automate much of the analysis and synthesis process, teams can spend less time figuring out what the data means and more time executing on the insights (kind of like our last point).
That means you can react to user needs more quickly, iterate faster, and deliver updates that have a meaningful impact — all without getting stuck in analysis paralysis.
In short, integrating AI into your qualitative data analysis workflow ensures you can handle large amounts of data quickly, discover critical insights that drive product improvements, and keep your team focused on building a product that your users love.
Make qualitative data analysis work for you
Gone are the days of manually sifting through feedback to find actionable insights. When you leverage AI-driven tools like Sprig, you can easily analyze large sets of qualitative data efficiently and strategically.
These advanced methods (whether it’s content analysis, thematic analysis, or grounded theory) don’t just reveal problems — they help you uncover opportunities to innovate faster.
The result? Quicker (and more data-driven) decision-making, better user experiences, and a product that truly reflects customer needs.
Get a demo today to see how Sprig can enhance your qualitative data analysis process, and empower your team to stay focused on what matters — creating a product that keeps users engaged and satisfied.