Customer insight tools: How to extract actionable data

Most product teams have plenty of customer data but struggle to extract actionable insights, such as customer personas creating customer personas.

They collect interview transcripts, survey responses, support tickets, usage analytics, NPS scores, feature requests, and sales call notes. However, turning this raw data into clear product decisions is challenging. Insights from this data are essential for analysis, trend identification, and guiding feature development, upselling, and customer journey mapping.

For example, Notion’s research team in 2021 faced this issue, they gathered extensive data but found it hard to produce actionable insights that informed product decisions. The problem wasn’t data collection but the insight extraction process. To make effective, data-driven decisions, teams must leverage customer insights rather than rely on assumptions.

What makes an insight actually actionable

Before diving into tools, clarify what you want to extract.

Bad insight: “Users want better collaboration features.” Too vague, what collaboration? How? Which users?

Good insight: “Teams of 5-10 struggle to track document ownership, causing duplicate work. They use Slack to coordinate and would pay for built-in presence indicators and real-time notifications when conducting product research.”

Actionable insights include:

• Specific user segment

• Clear problem description

• Evidence of impact

• Direction for solutions

• Context on current workarounds

Understanding customer behavior helps generate valuable insights that inform decisions and drive growth.

Slack’s research team uses this checklist to ensure insights are meaningful and actionable, improving product positioning, buyer personas, and emotional connections with customers.

Types of customer insight tools

Different tools extract different types of insights from different data sources. Top customer insight tools play a crucial role in helping businesses uncover customer insights and customer experience insights by analyzing feedback, conversations, and sentiment to reveal satisfaction levels, pain points, and brand perception. These tools enable organizations to analyze customer interaction and behavioral data across various touchpoints, helping identify friction points in digital experiences.

Behavioral analytics track real-time digital interactions on websites or apps, while heatmaps and session recordings visualize user behavior by showing where users click or scroll. Customer Data Platforms (CDPs) unify data from fragmented sources into a single 360-degree view of each customer, making it easier to understand the complete customer journey. By transforming raw data into actionable knowledge, these tools create detailed customer profiles and journey maps for personalized experiences.

Qualitative analysis tools

These help you make sense of interviews, open-ended survey responses, and customer feedback. Interviews can provide qualitative insights into customer experiences and motivations.

Dovetail organizes research data and helps identify themes across interviews. You upload interview transcripts, tag relevant quotes, and the tool surfaces patterns showing up repeatedly. Qualitative analysis tools like Dovetail help extract consumer insights from interviews and written feedback, enabling businesses to understand customer behaviors, preferences, and motivations. Analyzing written feedback for sentiment and recurring themes is crucial for uncovering deeper insights that drive data-driven decisions and improve customer engagement.

Figma’s research team uses Dovetail for all qualitative research. After running 15 interviews about mobile app usage, they tagged quotes with themes like “offline access,” “performance issues,” and “gesture controls.” Dovetail showed “offline access” mentioned in 12 of 15 interviews, with consistent pain points, making it an obvious priority.

Aurelius works similarly but focuses more on building research repositories over time. You’re not just analyzing one study, you’re building organizational knowledge.

Notion or Airtable work for teams wanting simple databases without specialized software. Linear’s research team uses Notion databases tagging insights with user segments, problem areas, and feature implications. It’s less sophisticated but more flexible.

Survey and customer feedback analysis tools

Survey and feedback analysis tools use collected data to uncover insights that inform business decisions and support sales efforts. Surveys are a common way to gain key insights into customers' thoughts, opinions, and sentiments toward a business. Tools like SurveyMonkey and Typeform are popular for creating engaging surveys and gathering high-quality data. These tools capture direct input from customers through surveys and structured forms to measure satisfaction and loyalty.

These turn survey responses and feedback into structured insights.

Qualtrics provides text analysis for open-ended responses, identifying themes and sentiment automatically. Enterprise research teams use it for large-scale surveys.

SurveyMonkey is a powerful online survey platform that helps gather data and derive meaningful insights. It has simpler analysis but works well for smaller teams. You can cross-tab responses to see how different segments answer differently.

Typeform excels in gathering high-quality data through its interactive chat-like format.

Sprig combines surveys with product usage data, showing you not just what users say but who they are based on behavior. You can trigger surveys after specific actions and analyze responses by user segment, which is essential in customer journey mapping.

Calendly uses Sprig to survey users right after they schedule their first meeting. By connecting survey responses to usage patterns, they identify which onboarding flows lead to specific types of feedback, an approach closely tied to market segmentation.

Analytics and customer behavior tools

Analytics and behavioral tools track and analyze behavioral data to uncover insights about consumer behavior, helping businesses understand how users interact with their products and services. These extract insights from what users actually do in your product, making it easier to tailor offerings to meet customer needs.

Amplitude helps identify behavioral patterns distinguishing different user segments. You can see which actions correlate with retention, activation, or churning.

Mixpanel does similar analysis but excels at tracking conversion funnels showing where users drop off.

Kissmetrics simplifies tracking customer behavior through your web offering, making it easier to analyze consumer behavior and uncover actionable insights.

PostHog includes session recording so you can watch what users do when analytics show interesting patterns.

Miro uses Amplitude to identify “collaboration catalysts” - the specific actions that predict whether a team becomes highly active. They discovered teams that invite 3+ members within 24 hours have 10x higher retention. This behavioral insight drove their onboarding redesign.

Understanding customer behavior is crucial because it reveals patterns of interaction with your business and enables you to tailor products and services to better meet customer needs.

Voice of customer aggregation tools

These tools aggregate feedback from your customer base and analyze various customer interactions across multiple touchpoints, providing a comprehensive view of customer needs, preferences, and satisfaction. They capture direct input from customers through surveys and structured forms to measure satisfaction and loyalty, helping businesses develop better products that fulfill customer needs and eliminate friction points.

Enterpret uses AI to analyze feedback from support tickets, app reviews, surveys, and sales calls, automatically categorizing themes and tracking trends over time.

Thematic does similar analysis focused on finding patterns in qualitative feedback at scale.

Productboard collects feature requests and customer feedback, organizing it by user segments and connecting it to your product roadmap.

Asana uses Productboard to aggregate feedback from support, sales, customer success, and research. When considering new features, they can see which segments have requested it, how often it comes up, and the business impact.

Customer conversation intelligence

These tools analyze sales calls and customer success conversations, extracting customer experience insights and using sentiment analysis to uncover customer preferences, satisfaction, and pain points often missed in manual reviews. AI-powered solutions quickly surface key data, enabling prompt, proactive decision-making and improving customer support interactions.

Gong records and analyzes sales calls and customer interactions to pinpoint successful behaviors and identify deal risks, as well as identifying common objections, questions, and competitor mentions.

Chorus (now part of ZoomInfo) does similar analysis focusing on what differentiates winning versus losing deals.

HubSpot’s product team uses Gong to understand why prospects don’t convert. They discovered objections about integrations were 3x more common in lost deals than won deals, revealing a critical product gap.

Market research techniques for deeper insights

Market research is essential for gathering customer insights that drive business success. Combining traditional methods like focus groups, interviews, and with modern techniques such as social media listening and AI-powered analytics offers a comprehensive understanding of customer behavior and needs.

Social media listening tools help monitor real-time customer conversations, uncover pain points, and track sentiment trends. Focus groups and interviews provide deep qualitative insights into customer motivations and unmet needs.

AI-powered analytics quickly identify patterns in user interactions, predict churn, and highlight drop-off points, enabling faster, data-driven decisions. Tools like Google Analytics offer quantitative data on user behavior and demographics, supporting customer journey optimization.

Online surveys and feedback forms gather direct input from existing customers, measuring satisfaction and revealing trends. When combined, these methods provide a holistic view of the customer experience, empowering teams to make informed decisions and enhance engagement.

The insight extraction process

Having tools isn’t enough. You need a process turning data into insights. To maximize value, it’s essential to align your insight extraction process with your business goals and business performance objectives. Customer insights analysis requires a standardized process to ensure accurate and actionable insights. Understanding customer insights can help businesses improve customer experience and drive better decision-making.

1. Collect with intention

Don’t just accumulate data. Collect with specific questions in mind.

Bad approach: “Let’s do user interviews to understand users better.” Good approach: “Let’s interview teams that invited members but those members didn’t activate to understand adoption barriers.”

The second approach yields specific actionable insights. The first creates transcript piles you don’t know what to do with.

Intercom’s research team always starts with a clear research question before collecting data. They might be investigating why mobile users churn faster, or understanding enterprise admin needs. The question shapes what data they collect and how they analyze it. Collecting data with intention ensures that the collected data directly supports business goals, enabling more effective statistical analysis, trend identification, and informed decision-making.

2. Tag and categorize as you go

Don’t wait until you have all your data to start analyzing. Tag interviews, survey responses, and observations during data collection.

Create a tagging taxonomy upfront:

• User segments (small team, enterprise, power user, etc.)

• Problem areas (onboarding, collaboration, performance, etc.)

• Feature categories (mobile, integrations, admin controls, etc.)

• Sentiment (pain point, delight, confusion, workaround, etc.)

• Behavioral data (click patterns, feature usage, drop-off points, etc.) to identify patterns in user actions and engagement.

Notion’s research team maintains a standard taxonomy all researchers use. When analyzing interviews, everyone uses the same tags, making it easy to search across studies for patterns.

They use tags like:

• Segment: freelancer, small_team, enterprise

• Problem: cant_find_content, slow_loading, missing_feature

• Feature_area: database, sharing, mobile, api

• Urgency: critical, important, nice_to_have

3. Look for patterns, not single mentions

One person mentioning something doesn’t make it an insight. Patterns make insights.

Look for:

• Problems mentioned by multiple users

• Similar language describing needs across interviews

• Behavioral patterns in analytics appearing across user segments

• Feedback themes appearing across different data sources (interviews, support, surveys)

Slack’s research team uses the “rule of three” - a theme needs to appear in at least three different sources before they consider it a validated insight. By analyzing behavioral data and leveraging customer insight tools with automated analysis features, teams can efficiently uncover insights and validate patterns across multiple sources. One interview mention might be interesting but isn’t actionable yet.

4. Quantify qualitative themes

Once you identify qualitative themes, measure them to understand scope. Use collected data from surveys, interviews, and support tickets to quantify how widespread each theme is among your users.

If interviews reveal “offline access” as a pain point, check:

User research can help answer questions like:

• How many support tickets mention offline issues?

• What percentage of survey respondents request it?

• How many users lose connectivity during typical sessions?

• How does this vary by user segment?

User research for product managers: A complete guide

Quantitative data analysis is the process of interpreting numerical data to identify patterns, trends, correlations, and relationships within your responses, while qualitative data analysis focuses on interpreting non-numerical data to understand customer experiences, motivations, and opinions. Quantifying tells you whether this affects 5% of users or 50%. That changes prioritization dramatically.

Dropbox quantifies every major qualitative insight. When research revealed confusion about file syncing status, they measured how many users contacted support about it (thousands monthly), checked how many checked sync status repeatedly (behavior indicating confusion), and surveyed to estimate total impact. This business case justified investing in clearer sync status indicators.

5. Connect insights to product decisions

The final step is explicitly linking insights to potential actions.

For each insight, document:

• What problem exists

• Who experiences it (segment, volume)

• Why it matters (business impact, user pain level)

• Potential solutions (what could address it)

• Evidence strength (how confident are you)

Figma uses a template for every documented insight including these fields. Product managers can review insights and understand what to build without re-reading all the source data. Connecting customer insights to product decisions ensures alignment with business goals and directly impacts business performance by driving growth, engagement, and improved success metrics. Additionally, customer insights can inform product positioning, helping you understand who your customer is and what problem you are solving.

Practical workflows for different data types

How you extract insights depends on the data type. Today, customer insights platforms and AI tools streamline workflows to extract actionable insights from diverse sources. For example, Maze offers automated analysis to identify patterns and opportunities quickly. AI and machine learning analyze large datasets to predict future trends, supporting better decisions across sales, support, and product development.

From user interviews

Step 1: Record and transcribe all interviews. Tools like , Rev, or Grain handle this automatically.

Step 2: Read through transcripts highlighting key quotes revealing needs, problems, or behaviors. Pay special attention to written feedback from participants, as analyzing this feedback can provide qualitative insights into customer experiences and motivations.

Step 3: Tag highlights with your taxonomy in tools like Dovetail or Notion.

Step 4: After 8-12 interviews, review all tags looking for patterns. What themes appear repeatedly? Which user segments mention which issues? This process is a fundamental part of market research.

Step 5: For major themes, create insight documents with representative quotes, frequency counts, and product implications.

Superhuman runs weekly user interviews. After each batch of 5 interviews, researchers spend an hour tagging and identifying patterns. Every Friday, they review the week’s findings with product teams, sharing 2-3 top insights with clear product implications.

From survey responses

Step 1: Export raw survey data including all open-ended responses. Note: Collected data from surveys is essential for gaining key insights into customer thoughts, opinions, and sentiments toward your business.

Step 2: For quantitative questions, create cross-tabs showing how different segments answered. Compare power users vs. casual users, different company sizes, different use cases.

Step 3: For open-ended responses, use text analysis tools or manual coding to categorize themes.

Step 4: Look for correlations between quantitative patterns and qualitative themes. If you’ve ensured you are recruiting the right participants for your user research studies, users rating feature satisfaction low also mention specific pain points in open responses.

Step 5: Segment analysis by user characteristics. Enterprise customers might have different needs than small teams.

Airtable surveys users quarterly about feature priorities. They segment responses by plan type, team size, and usage patterns. This reveals that different segments need different things - what enterprise customers want differs from startup users.

From product analytics

Step 1: Define questions before diving into data. “Why do mobile users have lower retention?” not “let’s explore the data.”

Step 2: Create relevant cohorts or segments. Compare retained vs. churned users, activated vs. not activated, different acquisition sources.

Step 3: Look for behavioral differences between cohorts. What do retained users do that churned users don’t? Analyzing behavioral data at this stage is essential for understanding consumer behavior, as it reveals how users interact with your product and what drives their engagement or churn.

Step 4: Watch session recordings of interesting behavioral patterns to understand the “why.” Understanding customer behavior is crucial because it tells you about their patterns of interacting with your business, and helps tailor products and services to meet their needs.

Step 5: Validate behavioral insights with qualitative research. Interview users exhibiting specific patterns to understand their thinking.

Linear uses Amplitude to identify power user behaviors, then interviews power users to understand what drives those behaviors. They discovered power users extensively use keyboard shortcuts not because the UI is bad, but because they value speed. This insight shifted their product strategy toward optimizing for efficiency rather than simplifying for new users.

From support tickets and feedback

Step 1: Export ticket data including categories, tags, resolution notes, and any written feedback provided by customers.

Step 2: Use text analysis tools or manual review to identify common issues, questions, requests, and analyze customer interaction details from support tickets. Written feedback and customer interaction data are crucial for extracting actionable insights.

Step 3: Quantify theme frequency and trend over time. Are certain issues increasing?

Step 4: Connect support themes to analytics. Users contacting support about feature X probably struggled using it. Check usage data.

Step 5: Cross-reference support insights with research. Validate that support issues reflect real user problems, not just vocal minorities.

Customer feedback is one of the simplest ways to gather insights, helping you assess your internal practices while understanding your customers.

Webflow’s research team reviews support ticket themes monthly. When they see spikes in tickets about specific features, they dig deeper with interviews and analytics to understand root causes. A spike in “how do I publish my site” tickets led to discovering onboarding gaps, not problems with the publish feature itself.

Making insights accessible to your team

Insights hidden in researcher heads or buried in documents don’t drive decisions. Customer insights platforms make it easy for teams to access, share, and leverage customer insights, enabling better collaboration and more informed decision-making. By leveraging customer insights, your business can transform and deliver specific solutions tailored to your target market.

Build a research repository

Create a centralized place where anyone can search past insights.

Notion’s research team maintains an Airtable database with:

• Insight summary (one sentence)

• User segment affected

• Problem description

• Evidence (links to studies, data sources)

• Product implications

• Status (validated, hypothesis, needs more research)

By organizing collected data and insights about your customer base in a centralized repository, teams can easily analyze trends, understand customer needs, and make informed decisions. Product managers search this before planning new features, finding relevant insights without asking researchers to repeat past work.

Create insight summaries

Not everyone will read 30-page research reports. Create one-page insight summaries with:

• The insight (2-3 sentences)

• Key evidence (top quotes, data points)

• Affected users (segment, volume)

• Product implications (see our market entry guide powered by AI for 2024)

• Recommended next steps

Slack’s research team creates one-pagers for every major insight, posting them in a dedicated Slack channel. These summaries are designed to highlight meaningful insights and deep insights, making it easy for product teams to quickly grasp comprehensive findings and actionable intelligence. Product teams see insights as they emerge rather than waiting for quarterly reports.

Host insight sharing sessions

Schedule regular meetings where researchers share recent insights with product teams.

Figma runs bi-weekly “Research Showcase” meetings. Researchers present 2-3 recent insights in 5 minutes each, leaving time for questions and discussion. These sessions are an opportunity to share valuable customer insights and discuss how to leverage customer insights in team discussions to drive action. Product teams hear findings while they’re fresh and can ask clarifying questions immediately.

Integrate insights into planning

Don’t make research something separate from product planning. Build insight review into roadmap planning processes.

Miro requires product managers to reference relevant research insights when proposing features. This forces teams to consider existing customer knowledge before making decisions. Integrating these insights into planning helps ensure that product decisions are aligned with overall business goals and directly support improved business performance.

Common mistakes extracting insights

Confusing data with insights

“50% of users clicked the new button” is data. “Users clicked because the old button was hard to find, but they’re not completing the flow, indicating the new button solved one problem but created another” is an insight.

Data shows what happened; insights explain why it matters and what to do. Insights from data provide valuable customer insights that guide decision-making and product optimization.

Cherry-picking evidence

Avoid only sharing data that supports your beliefs. Seek disconfirming evidence too.

If you think users want feature X, look for evidence they don’t—low engagement, no requests, or effective workarounds.

Balanced insight extraction uses all collected data and behavioral information to validate findings.

Stopping at surface-level patterns

“Users want better search” isn’t enough. Ask why, what’s wrong, and who cares most.

Deeper insights reveal true needs, leading to impactful solutions.

Treating all insights equally

Prioritize insights by user impact, problem severity, strategic fit, feasibility, differentiation, and business goals.

Not connecting insights to business impact

Translate user insights into business terms.

Instead of “Users struggle with onboarding,” say “30% abandon onboarding, costing $2M annually due to confusion about setup.”

Leveraging customer insights improves business performance by identifying actionable opportunities to optimize experiences and drive success.

Tools for different team sizes

Your tool needs to scale with team size and maturity. Whether you’re a startup or an enterprise, choosing the right customer insight tools and platforms is essential for understanding customer behavior and making informed decisions. In 2025, AI and Natural Language Processing (NLP) enhance these tools, enabling teams to uncover deeper insights and drive customer-centric strategies.

Small teams (1-2 researchers, pre-product-market fit)

Recommended tools:

• Notion or Google Sheets for organizing insights

• Otter.ai for interview transcription

• Amplitude or Mixpanel free tier for analytics

• Hotjar or Maze for usability feedback

Focus on simple, usable tools within a $0-200/month budget.

Medium teams (3-10 researchers, growing product)

Recommended tools:

• Dovetail or Aurelius for qualitative analysis

• Productboard or Aha! for feedback aggregation

• Amplitude or Mixpanel Growth plan for analytics

• Sprig or Pendo for in-product surveys

Use tools that analyze behavioral data across touchpoints to personalize experiences and improve satisfaction. Budget: $1,000-5,000/month.

Large teams (10+ researchers, mature product)

Recommended tools:

• Dovetail Enterprise for qualitative analysis

• Enterpret or Thematic for large-scale feedback analysis

• Amplitude or Mixpanel Enterprise for analytics

• Qualtrics for surveys

• Gong or Chorus for conversation intelligence

• Talkwalker for sentiment and social media monitoring

• Brandwatch and Meltwater for brand mentions and trend detection

Require enterprise features and AI tools to analyze vast data and uncover pain points. Budget: $10,000-50,000+/month.

The truth about tools

Tools help, but they’re not magic. The best customer insight tools are those your team uses consistently. Choosing tools that fit your workflow matters most.

Notion’s research team abandoned complex enterprise software for a simple Airtable database and Google Docs. Less advanced but actually used, it generated more insights than the unused fancy platform.

Focus on building good insight extraction habits:

• Ask clear research questions upfront

• Tag and categorize data consistently

• Look for patterns across sources

• Quantify qualitative themes

• Document insights with product implications

• Make insights accessible to product teams

The tools just enable these practices. Pick simple tools matching your team’s workflow rather than trying to match your workflow to sophisticated tools. Remember, leveraging customer insights is more important than having the most advanced software.

Ready to act on your research goals?