Product
Product might translate it into undo, preview, manual override, staged automation or reversible migration.
CUSTOMER EXPERIENCE
Your Journey Map Has a Plot Hole
A customer journey can be full of correct observations and still send an organization in five different directions. The missing layer is often the mechanism underneath the moments.
Why this matters more now
Customer journeys are starting to do more than sit nicely in Miro.
More of the customer context we collect can now travel into actual decisions — what gets prioritized, what message someone sees, what the product does next, what gets measured afterward.
Which is useful. It also means we can be wrong much faster.
Say a customer still hasn’t adopted a feature. We know what happened. We don’t know why.
Maybe the feature is missing something important. Maybe switching means giving up a workflow they already trust.
Same behavior. Different reason. Different move.
More context isn’t the hard part. Knowing what it means — and what should change because of it — is.
CONTEXT
↓
INTERPRETATION
↓
RESPONSE
Otherwise, we’re just getting better at acting on the wrong story.
What is the problem?
Five problems. Or one?
A customer journey can be well researched, carefully mapped, and full of real opportunities — and still lead to fragmented decisions. full of correct observations and still send an organization in five different directions.
When we organize the customer experience by moments, we naturally start solving it by moments too. We improve one step, redesign another, add education somewhere else, create a message for the next gap.
Sometimes those really are separate problems.
But sometimes we are looking at different expressions of the same underlying mechanism.
The missing layer is often the mechanism underneath the moments.
Friction point
Where something is happening.
Customer story
The context needed to understand it.
Mechanism
A hypothesis for why it is happening.
And when we miss that, the organization can spend a lot of energy fixing symptoms while repeating the same response across the journey.
SAAS EXAMPLE
An example
Imagine a B2B software company that has built a new analytics workspace. It is faster than the old process. It brings the data together. Customers know it exists. They have received onboarding and documentation.
And yet many teams still export their data to Excel.
The finding is easy to describe:
Low feature adoption.
The responses almost write themselves.
- More onboarding.
- More tutorials.
- More prompts.
- More education.
All reasonable responses — if the problem is that customers do not understand the new feature well enough.
But now zoom out.
The same customers are also slow to import their historical data. They hesitate to turn on automation. Admins avoid opening certain permissions to their teams.
Some customers use the new workflow but keep the old manual process running beside it for months. Others delay connecting another system even though the integration would save them time.
On the journey, these appear as different problems:
Migration friction.
Low feature adoption.
Automation resistance.
Permission concerns.
Integration hesitation.
Different stages. Different behaviors. Different teams responsible for them.But the same thing may be happening underneath.
What is producing the behavior?
Look underneath the behavior
This is where the journey becomes more useful.
Instead of stopping at:Where is the customer struggling?
we can ask:What is producing this behavior — and does the same dynamic appear somewhere else?
SAAS EXAMPLE
So in our example, Excel is inefficient, but familiar. Customers know how to manipulate the data. They know how to check it. They know what to do when something goes wrong. Years of habits, knowledge and workarounds live inside that workflow.
The old system has limitations, but its limitations are known. The new workflow may objectively be better. Yet moving to it asks customers to surrender some proven control before the replacement has earned equivalent trust.
It gives us a possible mechanism:
PROVEN CONTROL
NEW WORKFLOW HAS NOT YET EARNED TRUST
SWITCHING FEELS RISKIER
STAYING PUT FEELS SAFER
When adopting a new workflow requires people to give up control they already understand before they trust the replacement, staying with the old behavior feels safer than switching.
A mechanism does more than describe a state. It explains how one thing may be producing another. And it makes a prediction.
If this explanation is correct, simply explaining the new feature better should have limited effect. Reducing the risk of transition should matter more.
A caveat: The journey helps us form the hypothesis. Research and behavior help us validate it.
One customer may keep Excel because it gives them control. Another may depend on a capability the product still lacks. Another may be working around an internal compliance requirement.
That is what keeps mechanism work grounded in evidence rather than turning it into a more sophisticated label for an insight.
SAAS EXAMPLE
Then turn the lens on the company
Now another pattern appears.
This one belongs to the company.
the company’s response
Once a recurring customer mechanism becomes visible, there is another pattern worth looking for: What do we repeatedly do when this mechanism appears?
Go back through the SaaS journey.
Customer behavior
Company response
Customers delay migration.
→
We send reminders.
They continue exporting to Excel.
→
We offer training.
They avoid automation.
→
We explain its benefits.
They maintain a parallel manual process.
→
We encourage them to switch completely.
They hesitate to integrate another system.
→
We add more onboarding.
It is the way the organization repeatedly interprets and responds to the same underlying dynamic:
When customers persist with an old behavior, we tend to interpret persistence as an understanding or adoption problem — and respond with more education or more pressure to move.
Put the two patterns beside each other.
The two patterns tell different stories
Customer mechanism
Moving forward requires surrendering proven control before the new system has earned enough trust.
Company response pattern
Explain the new way more clearly and encourage faster adoption.
Now the mismatch becomes visible.
We may be treating a transition-risk problem as an education problem.
Turn the mechanism into decision logic
Once the mechanism is clearer, the goal is not necessarily to produce five separate recommendations. A more useful output can be shared decision logic:
When an old behavior protects control, reduce the risk of switching before increasing pressure to adopt. Let the new workflow earn trust progressively.
That is not a solution.That is why it can travel.
UX
UX might make the consequences of a change clear before a customer commits to it.
Customer Success
Customer Success might stop asking only, “Do they understand the feature?” and start asking, “What does the current workflow allow them to control that they are afraid of losing?”
Lifecycle communication
Lifecycle communication might encourage a small, low-risk first use instead of reminding customers that they still have not adopted the feature.
Implementation teams
Implementation teams might allow old and new workflows to coexist for a period rather than treating parallel behavior as failure.
Analytics
Analytics might look beyond adoption rate and ask who tries the new workflow, who reverts, who keeps a backup process, and at what point that backup finally disappears.
Different functions make different decisions that make sense for the same reason.That is coherence.
The journey starts to reveal a system
That wider view lets us move from:“Here are five things customers struggle with.”
to:“Here is a mechanism that may be producing several of them — and here is the pattern in how we keep responding.”
This adds a layer to journey work rather than replacing what is already useful. Sequence still matters. Customer goals still matter. Behavior, friction, emotion and opportunity still matter.
But another line of analysis becomes possible:
Observed behavior
mechanism hypothesis
other manifestations
company response pattern
response mismatch
shared decision logic
Now the journey can reveal more than where problems occur. It can reveal recurring dynamics across the experience.
Find where the pattern matters most
Finding the priority intervention points
Seeing a mechanism everywhere does not mean fixing it everywhere. A systemic view should help focus investment, not create a more complicated map.
The better question is: Where does this mechanism matter enough that changing the response could materially change the outcome?
Three things become especially useful.
Consequence
Does this mechanism meaningfully affect a customer or business outcome?
Mismatch
Is the company’s current response poorly suited to what is actually producing the behavior?
Leverage
Do we have a meaningful ability to change what happens here?
Priority intervention point
Where those overlap, we have a priority intervention point
SAAS EXAMPLE
In the SaaS example, keeping Excel beside the product may create very little harm.
But refusing to enable automation may prevent the customer from receiving much of the product’s value.
Or perhaps the real leverage point comes earlier: customers never fully migrate their data, so everything downstream remains fragile.
It also changes what we test
Test the explanation, not just the solution
Different explanations produce different tests.
INSUFFICIENT EDUCATION EXPLANATION
If we believe the problem is insufficient education, we test education.
- Does a better tutorial increase adoption?
- Does a clearer message increase feature use?
- Does another onboarding step help?
CONTROL / TRANSITION-RISK MECHANISM
But if the mechanism is about control and transition risk, the important assumptions change.
- Does making the change reversible increase willingness to try?
- Does temporary manual oversight increase adoption of automation?
- Does staged migration reduce the need for parallel workflows?
- When do customers stop maintaining their Excel backup?
- What happens when customers can see exactly how to recover if something goes wrong?
Now we are testing more than the solution.We are testing our explanation of the behavior.
Then the issue may not be control at all. Perhaps customers need functionality that is still missing. Perhaps the real constraint is organizational. Perhaps different customer groups are being held back by different mechanisms.
That gives us an observed effect and an updated understanding. The loop becomes:
Evidence
mechanism hypothesis
decision logic
intervention
observed effect
updated understanding
Looking across the journey shows us where that mechanism may be appearing in other forms. Looking back at the organization shows us how we repeatedly respond when it does.
From there, we can identify the moments where the pattern matters most, create shared decision logic, intervene, and learn from what happens next.
At the beginning, we had five customer problems. After looking underneath the moments, we may discover something else:
one mechanism appearing in five places, and one organizational habit shaping how we respond to it.
That changes what we fix. It changes where we intervene. It changes what different teams do. And it changes what we test next.
Sometimes the biggest opportunity in a customer journey is not inside one troublesome moment.It is in the pattern connecting several of them.