In this episode, we focus on defining a clear business challenge—the crucial first step in journey management that ensures your CX efforts drive real, measurable outcomes.

In effective customer experience (CX) management, the most impactful starting point isn’t a polished journey map or sophisticated CX program. It’s a clearly defined business challenge.

By grounding your journey management strategy in a well-articulated business challenge, your CX initiatives align directly with meaningful business outcomes. Starting with the problem enables focused efforts, streamlined collaboration, and measurable results.

Starting from a clear business challenge ensures that CX activities directly relate to what matters most for your organization. This approach:

A well-articulated challenge statement typically includes:

Aligning stakeholders and scoping realistically

Clear challenges foster shared understanding and alignment, from senior executives to frontline employees. When everyone sees how their work supports business goals, momentum builds.

Make sure your challenges match your scope and influence. A product team might not own overall churn, but they can drive the adoption of specific features that reduce it. Realistic, well-scoped challenges build credibility, deliver quick wins, and lay the groundwork for broader improvements.

Identifying the challenge is only the beginning. To move from insights to results, take cues from others who’ve done it well.

One telecom company increased marketing spend, expecting more transactions, but saw no change. Only after analyzing qualitative feedback did they realize aggressive sales tactics were damaging trust. That insight led to a strategic pivot aligned with customer needs.

Start with one business challenge. Clearly articulate the problem you’re trying to solve and how it connects to your organization’s strategic goals. This focused starting point helps you prioritize, align stakeholders, and ensure your journey work delivers real impact.

This guide is part of a growing series of best practices for effective journey management, inspired by 

 to learn how starting with the right challenge sets the stage for better decisions and stronger CX outcomes: 

 See how combining data within journeys turns static maps into live tools for decision-making, in

EP01: Defining the business challenge

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 

 is happening. But it often misses the deeper context: 

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.

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

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

 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 

 to better understand the relationship between cause and effect over time.

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

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

Gather insights from both structured and organic sources:

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 

 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. 

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

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

—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. 

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.

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

: 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:

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 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:

 for your organization—one that reduces risk, increases clarity, and helps every team focus on what matters most.

 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. 

  to see how combining data within journeys unlocks clarity, confidence, and real impact:

 Tingting covers everything you need to know about building journeys, in 

EP02: Ground your journeys in evidence, not guesswork

In this episode, we focus on moving from raw data to structured journeys by collaborating with AI to build journeys that are grounded in evidence and shaped by your expertise.

A journey is more than a visual map. It’s a structured representation of the customer experience that:

But turning raw, messy data into a meaningful structure is no small feat. That’s where AI comes in, giving you a massive head start by processing unstructured data and proposing an initial journey. The magic happens when your human perspective completes the picture.

Before you open your journey platform, you need input. This input typically falls into two categories: raw data and interpreted content.

Raw data is unstructured, qualitative, and comes directly from the source. Journey AI performs well with this type of material. Examples include:

These inputs provide rich detail, grounding the journey in real-world experience. For a deeper look at working with raw data, see

our guide on grounding journeys in evidence

, by contrast, is the result of human analysis and synthesis. It includes:

This type of input provides early structure, fosters shared team understanding, and ensures alignment on language and themes.

A side-by-side layout comparing "Raw data" (e.g., survey text, transcripts) with "Interpreted content" (e.g., Miro boards, workshop outputs), with arrows leading into a unified journey framework.

Both input types are useful: Raw data grounds your journey in real evidence, while interpreted content adds structure and team alignment. Knowing how to balance and use both sets you up for a smoother AI-assisted journey build. 

With your input ready, you can begin building your journey.

AI reads your uploaded file (e.g., a CSV of open-ended responses or a TXT file of an interview transcript) and generates:

If you already have a journey with predefined phases, steps, and insights (e.g., from a Miro workshop), you can copy that structure into TheyDo first. 

To help AI perform better, enrich the existing journey by adding more context, such as descriptions for key insights or clearer step definitions. Then, AI can:

Both methods provide a fast, credible starting point, but your input remains crucial in shaping a journey that’s both accurate and actionable.

Step 3: Collaborate with AI like it’s a new colleague

Think of AI as a junior analyst. It will do what it can based on the information it has. However, it lacks context: It doesn’t know your business, your audience, or what steps like "actioned" or "observed" actually mean in your specific journey.

This collaboration ensures the journey isn’t just visual, it’s powered by your experience and shaped by the specific context you understand best.

With AI generation, you might end up with dozens (or hundreds) of insights to verify. Here, verification refers to the detailed review of AI-generated insights following your initial structure check in Step 3. 

This involves evaluating each insight for accuracy, refining its language, and repositioning it within the journey as needed. 

We'll explore this in greater depth in the next article, but first, it’s important to understand how to prioritize what to verify. Don’t try to verify everything at once; it’s rarely efficient or necessary. 

As you continue ingesting more data, the top insights will naturally evolve. Each new upload reshapes the landscape, which means insights are not static snapshots. Prioritization helps you focus on what’s most relevant right now.

Depending on your needs, you can prioritize using one or more of the following approaches:

These approaches help you make smarter use of your time and highlight what matters most. Among them, the insight score is often the most efficient way to prioritize, particularly when working with large datasets.

Once verified, your insights become more than sticky notes:

Structure makes your insights portable, allowing them to be reused and scaled across various journeys. It also makes your journeys operational, turning them into live tools for decision-making and collaboration, where opportunities surfaced from insights can be systematically acted upon.

Let your data lead the way, but don’t forget to steer.

EP03: From data to structure: AI sensemaking for your journeys

In Episode 4, we focus on a critical but often overlooked part of the journey-building process: verifying and tagging insights to ensure they drive meaningful action.

Turning data into structure is just the beginning

AI can help you organize unstructured data into structured journeys in seconds, but that’s just the start. Once AI generates insights, human input becomes critical. To fully unlock their value, insights need to be verified and strategically tagged.

In this guide, we’ll break down how to verify and tag insights effectively, so your journeys stay credible, clear, and actionable.

Part 1: Verification: ensuring credibility and clarity

Verification involves thoroughly reviewing AI-generated insights to confirm their accuracy and clarity. This ensures that stakeholders receive reliable and actionable information.

How to prioritize which insights to verify (JM Playbook, Ep. 3 recap)

Your step-by-step verification workflow 

Start with the prioritized insights and follow a consistent review process:

 Don’t verify alone. Work with team members who know the customer context best. They’ll help validate what the AI surfaced, spot gaps, and make sure each insight reflects the reality behind the data.

Collaborative verification leads to more relevant, reliable journeys.

Part 2: Tagging: Unlocking strategic value

Tagging is what transforms a verified insight into a strategic asset. Tags help you organize and retrieve insights quickly, and ensure teams can act on them when it counts.

Admins can configure available statuses, groups, and global tags in 

Part 3: Continuous refinement and scaling

Verification and tagging aren’t one-and-done tasks. As new source data is added, AI will continue to update and enrich your insights, so it’s essential that your verification and tagging practices keep pace.

To keep journeys relevant and actionable over time:

This continuous refinement transforms your journeys into sustainable, strategic assets that grow in tandem with your organization.

Here’s how to prepare your journeys for action with verification and tagging:

This foundation sets the stage for building more effective, action-ready journeys. We’ll dive deeper into other aspects of the journey in upcoming episodes.

 for practical demonstrations of verification and tagging, or revisit earlier topics on 

Start transforming your journeys into powerful, actionable tools today.

EP04: Validating your journeys for action

In Episode 5, we explore the power of simplification in journey work, not as a reduction, but as a refinement. It’s about distilling your journey to the essential elements that drive action.

Simplifying structure for stakeholder action

 remains one of the biggest blockers in journey management. When everything feels equally important, nothing gets prioritized.

Many journey maps are rich in detail and visually impressive. They shine in workshops and presentations, sparking those all-important “aha!” moments. But if they don’t lead to real decisions or change, what’s the point?

Even if you’ve defined your business challenge, grounded your journey in evidence, used AI to surface insights, and refined your draft, your structure still needs to support follow-through.

In this guide, you’ll learn how to structure journeys with purpose, so they:

Part 1: Building your structural foundation

The most effective journeys follow a deceptively simple structure. Start with a minimal foundation and only add complexity when it directly serves stakeholder needs.

Begin with these four lanes, and expand only when needed:

Quantitative data showing current performance and targets

Key customer experience findings from your research

Problem statements derived from insights, reframed as positive opportunities

Specific actions and initiatives addressing opportunities

 Because they map directly to your workflow needs:

Each lane has clear ownership and purpose. So, there are no debates about what goes where, or endless categorization discussions that slow progress to a crawl.

When designing your journey structure, think of your internal stakeholders as customers too. Structure should serve the people who act on the journey, not just describe the people in the journey.

If you can’t answer clearly, you probably don’t need that additional complexity. Leave it out.

Step 3: Focus on outcome-relevant phases and steps

Instead of mapping every micro-interaction, focus on meaningful outcomes that require cross-functional support.

Each phase should be a prompt for action. If needed, you can open a phase into a few concise steps to add context. Even a single step can be more descriptive than the phase name.

I discuss with other members in the community forum

These steps don’t have to be in strict sequence; they can also run in parallel.

Once you’ve built the foundation, these techniques help you manage complexity without losing clarity.

What about edge cases, branching paths, or granular steps? This is where nested journeys become your most effective tool.

Your main journey offers strategic visibility. Nested journeys handle details without cluttering the big picture.

Start with the journey that addresses your current business challenge. Then expand:

In TheyDo, insights, opportunities, and solutions roll up in different ways to help you keep structure lean while still seeing the bigger picture. From 

progress tracking via nested building blocks

, you can explore how each of these works in more detail through the linked guides.

These scenarios come up in every journey management implementation. Here’s how to handle them while keeping your structure simple and actionable.

Use your high-level journey as an aspirational “to-be” vision—broad enough to stay relevant, specific enough to guide decisions. Nest your current-state “as-is” journeys beneath it.

This approach works especially well for established organizations with ongoing research and live customer data. As-is journeys stay updated in real time, while progress toward the future state is reflected through data roll-up into the high-level view.

Start with an assumption-based “to-be” journey and continuously enrich it with real data as it becomes available. Instead of managing separate views, you evolve a single journey from assumption to reality.

Many organizations combine both approaches, depending on team maturity and the complexity of their operations. 

 Capturing every path quickly becomes overwhelming. For example: 

Customer pays by credit card, invoice, or bank transfer, then receives a confirmation email or SMS, and logs in via desktop or mobile...

 Focus on high-impact, actionable insights instead:

Each insight reflects a general area of friction your team can address. You can always drill into the underlying data and quotes later if more detail is needed.

This approach keeps your journey structure clear and actionable—while still offering traceability back to the supporting research when decisions require deeper context.

At the heart of simplification lies one critical skill: 

For every piece of information you're considering including, ask:

Be ruthlessly honest. That detailed customer sentiment analysis might be fascinating, but if it doesn't change how customer service trains its team or how product prioritizes features, it's noise.

The most impactful journey maps aren't the most comprehensive; they're the most useful. Here's your action plan:

You can always add complexity later. You can rarely subtract it once stakeholders are used to it.

Take a hard look at your current journey structures. Ask yourself: are they built for admiration, or action? The answer will determine whether your journey management efforts become transformational tools or expensive wall art.

EP05: Structuring journeys that drive action

In Episode 6, we tackle the critical "so what?" question. You've mapped the experience and verified insights. Now, how do you turn that work into tangible business results?

Up to this point, you have a documentation map. It shows what's happening, but not what to do about it.

 are the building blocks that transform journeys from documentation into decision-making tools. They enable you to:

This isn't about customer empathy alone. It's about helping organizations make profitable decisions on resource allocation.

Opportunities are problem statements derived from insights, reframed positively to guide solution discovery. They sit at the critical junction between understanding problems and implementing solutions.

Why separate opportunities from solutions?

Jumping straight from insight to solution often means solving the wrong problem. By defining opportunities first, you:

A practical example: The mutual fit problem

Marc's Circle community showed high cancellation rates. The highest-scored insight? Members canceled due to time constraints, workload changes, and personal circumstances, not service quality issues.

"Make membership cheaper" or "Improve video quality."

"How might we find out if there is a mutual fit between the community and prospective members?"

This opportunity opens different solution possibilities:

By understanding the root problem (mutual fit, not price or quality), solutions become targeted and effective.

Part 2: Creating meaningful opportunities

Bottom-up: From insights to opportunities

Opportunities should synthesize patterns across multiple insights, not just rephrase single pain points.

In TheyDo, opportunity review analyzes insights from journey steps and suggests opportunity statements. 

 Don't limit analysis to single steps. The most valuable opportunities emerge from looking across:

Opportunities from research must align with organizational goals, annual strategic plans, quarterly priorities, and OKRs.

When research shows major issues not covered in strategic goals, you have a data-driven rationale to inform strategy, transforming CX from order-taker to strategic contributor.

 "How might we..." works well, but don't be dogmatic about format. Principles matter more than exact wording.

Solutions turn opportunity understanding into concrete action. But not all solutions look the same.

Solutions fall into different types, each owned by different teams:

 For smooth registration, you might need all three—digital interface, approval workflow, and welcome emails. Each requires different expertise and timelines.

Start by documenting what teams are already working on. Map their existing solutions back to the journey. This helps stakeholders quickly see:

This isn't cheating—it's effective engagement that demonstrates value before asking for behavior change.

Different stakeholders need different things:

Dashboard view of progress, resource allocation decisions, and ROI understanding

Collaborative opportunity definition, prioritization, and trade-off decisions

Operational level (developers, designers): 

Context for why work matters, connection to customer impact, motivation through meaningful work

A practical example: The developer value proposition

When delivery teams see how their solutions improve customer lives, they:

 If your product owner can't explain how work connects to a journey opportunity, pause and ask why. This ensures effective resource allocation.

The opposite. Understanding opportunities first prevents wasted months building solutions that miss the mark. Time invested upfront saves far more than it costs.

"We already have OKRs and roadmaps. Why add this?"

Journey management enhances existing systems with customer context. It ensures OKRs and roadmaps actually address the most important customer problems.

"How do we handle stakeholders who want immediate solutions?"

Start where they are. Document their solutions, map them to the journey, then ask: "What problem does this solve? Are there other ways? How will we measure success?" Use their solutions as conversation starters.

"What if opportunities don't align with strategic goals?"

Start by acknowledging that not every opportunity can, or should, be prioritized. If an opportunity falls outside the current strategic focus, it may be appropriate to deprioritize it. But when research consistently shows a significant impact on key business or customer metrics, it may point to a strategic gap rather than a tactical misalignment. 

In those cases, bring the evidence to leadership:

This shifts CX from executing against strategy to helping shape it. Journey management then becomes a strategic partner in decision-making.

Opportunities and solutions transform journeys from documentation to decision-making tools that drive business results.