In this episode, we’re focusing on how to combine quantitative metrics and qualitative insights to turn static maps into live journeys—and make smarter decisions with more confidence.

Most journey maps start with good intentions. However, they’re often created in isolation—through lengthy workshops, guided by assumptions—and quickly become outdated. The result? A static snapshot that might look good on the wall, but fails to reflect the real customer experience.

To drive meaningful change, journey management must be data-driven, powered by both quantitative metrics and qualitative insights.

Whether you’re a CX professional looking to demonstrate impact, a business leader aiming to prioritize with confidence, a researcher identifying gaps, or a product team refining backlogs, the key is bringing data together in context, through journeys.

This approach transforms journeys into dynamic, living tools that remain aligned with customer realities and business priorities.

In this guide, we’ll explore why combining numbers and narratives is essential and how to do it effectively.

The power of qualitative and quantitative data—together

Relying solely on quantitative data, like churn rate, revenue, or customer lifetime value, tells you what is happening. But it often misses the deeper context: why it’s happening.

That’s where qualitative data comes in. Interviews, support calls, surveys, and spontaneous feedback help reveal the motivations, emotions, and friction points behind behavior.

Together, these two data types paint a full picture—one that’s not only accurate but also actionable. When combined and structured in journeys, they enable better decisions, stronger alignment, and more strategic CX investments.

How to integrate data into your journeys

Here’s a practical, step-by-step guide for combining qualitative and quantitative data in your journey management practice: 

1. Define the right metrics

Start by identifying metrics tied to your business challenge. These typically fall into three categories:

• Business performance metrics:

  Churn rate, revenue, cost to serve (e.g., monthly subscription cancellations)

• Customer experience (CX) metrics:
  NPS, CSAT, CES

• Behavioral metrics:
  Conversion rates, website visits, session attendance (e.g., community event participation frequency)

Pro tip: Focus on metrics that directly relate to your business challenge, and ensure the data you use is both high-quality and relevant. When possible, track both leading and lagging indicators to better understand the relationship between cause and effect over time.

• Lagging indicators reflect outcomes that have already occurred.

  Example: Churn rate—how many customers have already left the community.

  These are often measured at the journey level, helping you evaluate overall performance.

• Leading indicators are predictive. They highlight early signs that suggest potential future issues or performance problems.

  Example: Attendance rate—declines here might signal early disengagement that could lead to churn.

  These are often mapped to specific journey steps, making it easier to spot where problems may begin.

Combining both types of metrics gives you a more complete picture—what’s happening, why, and where to act next.

2. Collect diverse qualitative insights

Quantitative trends tell you what’s going on—qualitative insights explain why.

Gather insights from both structured and organic sources:

• Researcher-generated data – Interviews, focus groups, surveys

• Naturally occurring data – Social media mentions, support transcripts, online reviews, forum posts, and spontaneous customer conversations

Naturally occurring data is especially powerful. It often reveals issues that formal research might miss. For example, if churn is rising, a scan of community forums might uncover frustration with unclear onboarding emails—something that may not show up in structured surveys.

Unstructured data is a rich source of insight, but it requires a different approach to unlock its value. With Journey AI, you can quickly process spontaneous, unfiltered input like support tickets, forum posts, or open-ended feedback. It surfaces emerging themes and trends, helping you ask better questions and uncover deeper insights. 

3. Contextualize data within journeys

Journeys are the connective tissue that binds your data points together. Use them to organize and interpret what you’re seeing:

• Pinpoint where metrics matter most (e.g., churn spikes during onboarding)

• Spot relationships between behaviors (e.g., low session attendance linked to rising cancellations)

• Highlight critical touchpoints (e.g., time constraints or tech issues that lead to drop-off)

Without journey context, data remains fragmented. With it, your insights become integrated, visual, and easier to act on.

Journey context also reveals what’s missing—whether that’s a lack of leading indicators, inconsistent tracking, or disconnected ownership across teams. This is especially important in large organizations where data is often scattered or siloed. 

Turning insight into opportunity

Data alone doesn’t drive change—clear opportunities do.

Use your journey structure to frame what you’re learning into “How might we…” statements that invite creative problem-solving.

Example:

• Quantitative insight: Low attendance during onboarding

• Qualitative insight: Feedback points to scheduling conflicts

• Opportunity: How might we offer more flexible participation options, such as asynchronous sessions or varied time slots, to increase engagement?

This framing prevents premature solutioning and helps your teams stay focused on what truly matters.

Bonus: Triangulating data (e.g., pairing declining attendance rates with negative sentiment in feedback) increases confidence in your next step.

Data-driven decisions reduce risk and increase impact

When you combine qualitative and quantitative data within journeys, your organization can:

• Predict outcomes with greater confidence
  → Understand behavior drivers and reduce uncertainty

• Demonstrate the value of CX initiatives
  → Tie improvements to measurable business impact

• Make better strategic decisions
  → Align actions with both customer needs and business goals

Better data leads to smarter priorities—and stronger results.

Use AI to accelerate qualitative analysis

One of the biggest barriers to utilizing qualitative data is the time required. Reviewing feedback manually is labor-intensive and slows down decision-making.

AI-driven tools help by:

• Analyzing large volumes of unstructured input

• Identifying recurring patterns and themes

• Surfacing actionable insights in minutes, not weeks

AI works especially well on unstructured qualitative data—freeing up your team to do higher-value work like validating insights, curating opportunities, and designing solutions.

Your data-powered journey management toolkit

When you combine the scale of quantitative data with the depth of qualitative insight—and structure them within journeys—you get more than a better map. You get a high-impact, decision-making system that’s:

• Customer-informed

• Business-aligned

• Continuously evolving

This is the foundation of a decision support system for your organization—one that reduces risk, increases clarity, and helps every team focus on what matters most.

Ready to put it into practice?

Start with one journey. Gather all the qualitative and quantitative data you already have that’s relevant to that journey, ready to be explored, visualized, and layered into context.

The goal isn’t to map everything at once, but to anchor your insights in the flow of real customer experiences.

This guide is part of a growing series of best practices for effective journey management, inspired by The Journey Management Playbook series, featuring Tingting Lin. 

Watch Episode 2 of the Journey Management Playbook to see how combining data within journeys unlocks clarity, confidence, and real impact:

Next Up: Tingting covers everything you need to know about building journeys, in Episode 3.