Your Next Growth Lever Is Already Inside Your Customers

In a previous product leadership role, I once looked at our pricing split and something felt off.

We had two tiers.

One had around 20% of customers.

The other had around 80%.

And somehow we were calling that a packaging strategy.

I remember writing directly to the CEO and asking a simple question:

"Why?"

Not in a dramatic way.

More like:

Why is this the right structure for where the company wants to go?

Why do we believe these two tiers are enough?

Why are we treating customers with very different needs as if they belong in the same bucket?

He wanted to talk right away.

That conversation became the start of a bigger pricing, packaging, and expansion redesign. Over time, the model moved from two plans to a more scalable structure with Business and Enterprise tiers.

The important part was not the new names.

It was the shift in thinking.

We stopped looking at packaging as a static pricing table.

We started looking at it as a way to understand customer maturity.

Who is still simple?

Who has grown?

Who is already operating at a higher level of complexity?

Who needs a human conversation?

Who needs a product-led nudge?

Who should not be touched at all?

That last one matters more than people admit.

Not every customer is ready to expand.

And not every quiet customer is unhealthy.

The problem hiding in plain sight

I see this a lot in B2B SaaS: the next growth lever gets framed as a feature problem.

Sometimes it is.

But often, the growth lever is already inside the existing customer base.

The problem is that the company has no clear way to see it.

Customers grow quietly.

Their workflows become more complex.

Their usage patterns change.

Their number of entities, invoices, locations, users, approvals, or integrations starts to look very different from the customer they were when they first signed.

But the company keeps treating them the same way.

Same plan.

Same segment.

Same CSM motion.

Same upgrade logic.

The customer is no longer using the product like a basic customer.

But the business is still treating them like one.

That is an expansion bottleneck.

And it is easy to miss because nothing looks obviously broken.

The customer is paying.

The product is being used.

CS has some health score somewhere.

Sales has a revenue target.

Everything exists.

But it does not connect.

The usual response is more work

When growth slows or expansion feels unclear, the default response is usually to add more work.

More features.

More AI.

More onboarding.

More CS outreach.

More dashboards.

I understand the instinct.

When you are under pressure, building something new feels more productive than questioning the system you already have.

But in many B2B SaaS companies, the first question should not be:

"What should we build next?"

It should be:

"Which customers have already outgrown the way we package, understand, and serve them?"

That question changes the conversation.

Because suddenly the bottleneck may not be a missing feature.

It may be that higher-value customers are hidden inside lower-tier plans.

It may be that CS is spending time with low-value accounts while expansion-ready customers go unnoticed.

It may be that product usage data exists, but no one has turned it into an actionable commercial motion.

A healthy customer is not always ready to expand.

An inactive customer is not always at risk.

A high-usage customer is not always a good upsell candidate.

A low-touch customer may be getting exactly the value they need.

This is where B2B SaaS gets messy.

And interesting.

Expansion readiness is not just usage

One of the biggest mistakes I see is treating product activity as the full story.

Someone logged in.

Someone clicked a feature.

Someone exported a report.

Useful signals.

But not enough.

In B2B SaaS, especially in workflow-heavy products, value often lives outside the login.

A customer might barely use the interface because the product is doing its job in the background.

Another customer might log in every day because their setup is painful.

One customer might be quiet and healthy.

Another might be quiet and about to churn.

This is why expansion readiness needs more than product analytics.

It needs a model.

A way to connect product behavior, account structure, commercial value, and timing.

In my previous role, this became the real work.

The pricing and packaging change created the structure.

But the harder question was:

How do we know which existing customers are ready for a different conversation?

Not theoretically.

Actually.

The model that changed the work

We started by comparing customers across tiers.

Not by opinion.

By structure.

What actually separated the more advanced customers from the basic ones?

Number of assets? Users? Locations? Operational complexity? Reporting behavior?

Then we checked whether those structural differences showed up in behavior.

Were advanced customers using the product differently?

Were they touching different areas?

This mattered because structural size alone can lie.

A large account that barely uses the product is not the same as a large account with active teams, repeated workflows, and clear complexity.

So we layered the signals.

First structure.

Then behavior.

Then readiness.

Then motion.

That last step is where the work became commercially useful.

Because a segment is not a motion.

A list of accounts is not a strategy.

A dashboard is not revenue.

Once we had the signals, we could ask:

Which accounts should CS contact directly?

Which ones need sales involvement?

Which ones are better suited for an in-product nudge?

Which ones should be left alone for now?

This is the part many teams skip.

They identify an opportunity, but not the right motion.

Then everything becomes generic outreach.

Or worse, a generic in-app prompt.

The four layers of expansion readiness

I now think about expansion readiness in four layers.

Structural fit

Has the customer outgrown the plan based on how their account is configured?

This depends on the product. For one company, it might be locations. For another, invoices, seats, workflows, or integrations.

The point is not the metric itself.

The point is to identify what "more mature" actually looks like in your product. Not in a pricing deck. In the customer base.

Behavioral evidence

Is the customer actually behaving like a more advanced customer?

This is where product data matters. Not as a vanity dashboard. As a validation layer.

If higher-tier customers use certain workflows more heavily, and lower-tier customers start showing the same pattern, that is meaningful.

If they do not, maybe the opportunity is only theoretical.

Commercial readiness

What motion does this account deserve?

Some accounts deserve a human conversation. Some should be routed to sales. Some can be handled through product-led education. Some are not worth touching yet.

That does not mean they are bad customers. It means the timing is wrong.

Timing trigger

What customer behavior tells us this is the right moment?

A good expansion motion does not interrupt randomly. It appears when the customer is already feeling the edge of their current plan.

Maybe they explore a premium feature. Maybe they hit a usage threshold. Maybe they start building workarounds.

The timing matters because the same message can feel helpful or annoying depending on when it appears.

This is also where product-led growth becomes real.

Not PLG as a slogan.

PLG as a well-timed intervention based on actual customer readiness.

Why this matters before adding AI

Many product teams are under pressure to build AI features right now. Some of that pressure is real. Some of it is investor theatre.

AI can help with this kind of work: summarizing account context, connecting signals across systems, preparing CS for conversations.

But AI is not the starting point.

If the underlying expansion model is unclear, AI just accelerates confusion.

You can automate outreach to the wrong accounts.

You can generate beautiful summaries of weak signals.

You can ship AI features while the packaging problem remains untouched.

AI makes a broken motion faster.

It does not automatically make it smarter.

Before adding intelligence to the workflow, the team needs to know what the workflow is trying to decide.

Who is ready?

What signal matters?

What motion fits?

That is the bottleneck.

A practical question for product leaders

If you lead product in a B2B SaaS company, especially one with a complex workflow product, I would start here:

Take your current customer base and ask:

Who is already operating above the plan they are on?

Not who could pay more.

Not who sales wants to call.

Not who logged in last week.

Who has actually grown into more complexity?

Then ask:

Can we prove it with behavior?

Can we separate expansion-ready accounts from healthy low-touch accounts?

Can we identify the right motion for each group?

Can CS act on this without adding more manual work?

If the answer is no, the opportunity may still exist.

You just cannot see it clearly yet.

This is where I help

This is the kind of problem I now help B2B SaaS teams diagnose in a focused product growth diagnostic.

The goal is not to create another dashboard.

The goal is to find the actual expansion bottleneck.

Which customers are ready for expansion?

Which ones are not?

Which product signals matter?

Which commercial motion fits?

Is the blocker packaging, pricing, product behavior, CS capacity, or timing?

Sometimes the answer becomes a product-led nudge.

Sometimes a CSM priority list.

Sometimes a packaging redesign.

Sometimes a decision to stop bothering customers who are perfectly healthy where they are.

That is still a good outcome.

Because the point is not to push every customer upward.

The point is to understand where expansion is earned.

Before adding more roadmap work.