Tag Archives: retention

AI won’t fix your growth problem – it will expose it

Most SaaS companies have become very good at measuring what they sell.

Bookings.
ARR.
Pipeline.
Win rates.

Some are reasonably good at measuring what they deliver.

Implementation completed.
Training delivered.
Success plans created.
Support tickets resolved.

Far fewer can tell you whether customers are actually achieving the outcomes they bought the product to achieve in the first place.

That gap matters more than most leadership teams realise.

It’s the gap between what was sold, what was delivered and what was ultimately realised by the customer.

And it’s where churn begins.

The most dangerous metric nobody measures

Imagine a customer buys your platform to reduce onboarding time from 30 days to 10.

The sales team closes the deal.

The implementation team deploys the product.

The Customer Success team runs training sessions.

Usage looks healthy.

Everyone internally believes the account is a success.

But six months later the customer is still taking 28 days to onboard new users.

The software was deployed.

The customer logged in.

The project was completed.

Yet the outcome never happened.

From the customer’s perspective, the investment failed.

Most SaaS businesses have no systematic way of spotting this.

They track activity.

They track adoption.

They track engagement.

They rarely track whether the original business problem was solved.

Why AI is making this problem impossible to hide

Many SaaS leaders believe AI will help them improve retention.

They’re partly right.

AI can identify patterns humans miss.

It can analyse usage data at scale.

It can spot declining engagement.

It can flag customers who look likely to churn.

It can even suggest actions for Customer Success teams to take.

But there is a problem.

AI can only analyse the data it can see.

If your business isn’t measuring customer outcomes, AI cannot magically create them.

Instead, it exposes the weakness.

Faster.

More accurately.

And often more publicly.

AI might tell you a customer’s usage is falling.

What it cannot tell you is whether the customer ever achieved the outcome they bought your solution for.

Because most companies never captured that information in the first place.

The three gaps that quietly destroy growth

Gap 1: The sales reality gap

The customer buys based on one expectation.

The product delivers something slightly different.

Nobody notices until renewal.

The wider the gap between promise and reality, the greater the retention risk.

Gap 2: The delivery gap

The customer receives the software but never fully adopts it.

Features are available.

Processes remain unchanged.

People revert to old ways of working.

The implementation succeeds.

The transformation fails.

Gap 3: The value realisation gap

The customer uses the platform regularly.

Adoption metrics look healthy.

Yet the business outcome never materialises.

Usage exists.

Value does not.

This is often the most dangerous gap because traditional SaaS metrics make everything look healthy.

Why expansion revenue depends on realised value

Many leadership teams view renewals and expansion as separate motions.

Customers don’t.

A customer who achieves meaningful outcomes naturally becomes more open to expansion.

A customer who is still waiting to see value rarely wants to buy more.

This is why the strongest Net Revenue Retention figures usually come from organisations that obsess over customer outcomes rather than product usage.

Expansion is often a lagging indicator of realised value.

Customers buy more when they believe the first investment worked.

Simple.

The question every leadership team should ask

If I stopped one of your account teams in the corridor and asked:

“Why did this customer buy?”

Could they answer?

More importantly:

“What measurable business outcome has the customer achieved since buying?”

Could they answer that too?

If not, you may have a value realisation problem hiding behind healthy-looking adoption metrics.

What to do next

What business outcome were they trying to achieve when they purchased?
How will that outcome be measured?
Has it actually been achieved?

Not implemented.

Not trained.

Not onboarded.

Achieved.

Then build your customer operating model around those answers.

Track them during onboarding.

Review them in customer meetings.

Report them to executives.

Use AI to help analyse them.

But don’t expect AI to create them.

Because AI won’t fix your growth problem.

It will expose it.

And if your customers are not achieving value today, the evidence is about to become impossible to ignore.

Final thought

The next generation of SaaS winners won’t be the companies with the most AI.

They’ll be the companies with the clearest understanding of customer value.

AI will simply make that difference visible.

The AI Tourist Effect

There’s a number circulating right now that should make every SaaS founder and leader sit up.

AI-native SaaS products have a median gross revenue retention rate of 40%. For context, the B2B SaaS median is 82%. That’s not a rounding error – it’s a 42-point gap and it’s prompting a lot of people to conclude that AI-native products are fundamentally harder to retain.

They’re wrong. And the data proves it.

What “AI Tourist” Actually Means

ChartMogul coined the phrase “AI tourist” to describe what’s driving this churn pattern. Users sign up because the product looks interesting, the demo is slick and the pricing is low enough to not require a procurement conversation. They experiment for a few weeks, hit a wall – maybe the use case isn’t quite right, maybe a competitor ships something shinier – and they’re gone.

This isn’t a story about bad AI. It’s a story about how people buy things they don’t need to commit to.

When the barrier to entry is $19 or $29 a month, the barrier to exit is equally low. There’s no contract to navigate, no internal stakeholder who championed the purchase, no workflow that breaks if the tool disappears. The customer signed up alone and they’ll leave alone.

That’s the AI tourist. They came for a look around. They were never planning to stay.

The Price Point Data Is Striking

Here’s where the conventional narrative falls apart.

ChartMogul’s data segments AI-native SaaS products by price point, and the retention picture is almost entirely determined by what you charge – not what the AI does.

Products priced above £200 (roughly $250) per month see 70% gross revenue retention and 85% net revenue retention. That’s essentially the same as traditional B2B SaaS. Products priced between $50 and $249 per month see 45% GRR and 61% NRR. Products priced below $50 per month see 23% GRR – meaning nearly 80% of revenue churns out within a year.

Same technology. Same category. Entirely different retention profile.

The variable isn’t the product. It’s who’s buying it, how they’re buying it and what’s at stake if they stop.

Why This Matters for CS Teams

If you’re a leader at an AI-native company and your retention numbers look ugly, the temptation is to reach for customer success playbooks. More onboarding touchpoints. Better health score models. Proactive outreach at 30 days.

Some of that will help at the margins. But if your core problem is that you’re selling a $29/month tool to individuals who don’t have a business outcome attached to it, no customer success motion fixes that. You’re putting a retention programme on top of a churn machine.

The commercial diagnosis here is uncomfortable but important: the customer success function can’t compensate for a flawed go-to-market model.

What you can do – and this is where customer success genuinely earns its seat in the room – is make the business case for moving upmarket. The data is unambiguous. When AI-native products sell to genuine business buyers at $250+ per month, they retain like traditional SaaS. The AI isn’t the risk. The self-serve, consumer-grade pricing model is.

What Moving Upmarket Actually Looks Like

Moving upmarket isn’t just raising prices. It’s a set of decisions about who you sell to, how they buy and how deeply embedded your product becomes in their actual workflows.

A few things tend to shift when you go from self-serve consumer to B2B buyer.

There’s a procurement process, which means there’s an internal champion. That person’s reputation is partly tied to the tool working. They don’t cancel without a conversation first.

There’s an integration layer. Your product connects to their CRM, their Slack, their ticketing system. Ripping it out takes effort. Switching costs go up.

There’s an outcome attached. The business bought the tool to achieve something specific – reduce support volume, increase renewal rates, speed up onboarding. You can measure it. You can show progress. That’s the foundation of a genuine retention conversation.

None of this happens at $25 or £25 a month. It happens when the deal is significant enough that both sides take it seriously.

The Lesson That Extends Beyond AI-Native SaaS

The AI tourist effect isn’t just a problem for AI-native companies. It’s a warning signal for any SaaS business that’s drifted toward self-serve growth without examining the retention consequences.

The economics look attractive at first. Low CAC, fast time-to-revenue, no sales headcount required. But if you’re churning 60%-70% of revenue annually, you’re running a leaky bucket – and growth is just masking the leak.

The companies that will build durable NRR in this environment are the ones embedding themselves into critical business workflows, selling to buyers who have accountability for outcomes and building customer success motions that tie directly to measurable value.

That’s not a new idea. But the AI tourist data gives it a specificity that’s hard to argue with.

The Practical Takeaway

If you’re a customer focused leader in an AI-native company, here’s the question worth putting to your leadership team: are the customers we’re churning the ones who were ever going to stay?

If the answer is no – if you’re churning self-serve, low-ACV, low-commitment buyers at the bottom of your customer base – then the retention conversation needs to start with pricing and ICP, not onboarding sequences.

The AI isn’t the problem. The market motion is.

Get that diagnosis right and the customer success playbook becomes much more straightforward.

Want to Talk Through What This Means for Your Business?

If you’re a SaaS leader wrestling with retention in an AI-native product – or advising a company that is – I work with customer success and revenue teams to build operating models that hold up under board scrutiny.

Book a 30-minute conversation and let’s look at the actual numbers together.

Or follow me on LinkedIn where I write about this kind of thing every week – commercial customer success, retention mechanics and what’s actually changing in the post-sale world.

Sources: ChartMogul SaaS Retention Report – The AI Churn Wave (2025/2026); ChartMogul The New Normal for SaaS Retention

Customer Retention vs. Acquisition in SaaS – What Actually Drives Growth

Most SaaS companies are built to acquire customers. Very few are built to keep and grow them.

That’s the problem.

Because in SaaS, growth doesn’t come from how many customers you win. It comes from what happens after the sale.

The traditional thinking

The default model is simple:

Invest in marketing
Drive pipeline
Close new customers
Repeat

It works. Until it doesn’t.

Because over time:

Customer acquisition gets more expensive
Sales cycles get longer
Conversion rates drop

And suddenly, growth slows.

The reality of SaaS growth

In SaaS, retention is not a support metric. It’s the foundation of growth.

If customers don’t stay, nothing compounds.

If they don’t expand, revenue stalls.

That’s why the strongest SaaS companies focus on:

High retention
Consistent expansion
Fast time to value

Not just new logo acquisition.

Why retention beats acquisition

Acquisition creates revenue. Retention protects it. Expansion multiplies it.

Without retention:

You’re constantly replacing lost revenue
Growth becomes fragile
Forecasting becomes unreliable

With strong retention:

Revenue compounds
Customer lifetime value increases
Growth becomes predictable

Where most SaaS companies get it wrong

They treat Customer Success as a support function.

Not a growth function.

So you see:

Reactive customer teams
No clear ownership of expansion
Metrics focused on activity, not outcomes
No alignment between sales and Customer Success

The result?

Retention underperforms and expansion never quite materialises.

The shift from Customer Success to Customer Growth

The best SaaS companies are making a shift.

From:

👉 Customer Success as support

To:

👉 Customer Success as a revenue engine

That means:

Defining customer value early
Aligning sales and post-sale around outcomes
Tracking leading indicators, not just churn
Building expansion into the lifecycle

What this means for founders and CEOs

If you’re leading a SaaS business, this is not a trade-off.

You need both, but the balance matters.

If your growth is driven only by acquisition:

You’ll always be under pressure to sell more
Your cost of growth will keep increasing

If your growth is driven by retention and expansion:

Revenue compounds
Growth becomes more efficient
The business becomes more valuable

Bringing it together

Customer acquisition gets you started. Customer retention and expansion are what scale the business.

That’s the difference between:

👉 chasing growth and…
👉 building a growth engine

CTA (link this properly)

If you’re thinking about how to improve retention, reduce churn or build a Customer Success function that actually drives growth:

👉 See how I work as a fractional Customer Success leader.

Why SaaS Growth Breaks After the Sale (and How to Fix It)

Introduction

Most SaaS companies think they have a pipeline problem.

They don’t.

They have a conversion problem after the deal.

Revenue doesn’t just come from winning customers. It comes from what happens next. – adoption, value realisation and expansion.

And this is where growth quietly breaks.

The Real Problem Isn’t Sales

SaaS leaders spend a lot of time optimising:

Lead generation
Conversion rates
Sales efficiency

But once the deal is signed, things become far less structured.

The assumption is simple:

“We’ve sold the value, now the customer will realise it.”

In reality, that rarely happens consistently.

Where Growth Actually Breaks

Growth doesn’t fail at the point of sale.

It breaks in the gap between:

What was sold
What is delivered
What the customer actually achieves

This shows up in familiar ways:

Slow or partial adoption
Customers using features but not achieving outcomes
Expansion opportunities that never materialise
Churn risk appearing late in the lifecycle

By the time it’s visible, the damage is already done.

The Hidden Revenue Leak

Every SaaS business has a flow:

Sale → Adoption → Value → Expansion

If that flow is not connected and managed:

Revenue is lost.

Not always immediately.

But over time:

Deals don’t expand
Renewals become harder
Growth becomes unpredictable

This is why many companies feel like they are working harder for the same results.

Because they are.

Why Customer Success Alone Doesn’t Fix It

This is often labelled as a Customer Success issue.

It isn’t. It’s a system issue.

Common problems include:

Weak handover between sales and post-sale teams
No clear definition of what “value” actually means for the customer
Lack of ownership for outcomes
Misalignment between sales, CS and product

Customer Success can’t fix this in isolation. Because the problem doesn’t sit in one team.

What High-Growth SaaS Companies Do Differently

Companies that scale efficiently do one thing well:

They control what happens after the sale.

That means:

Clear alignment between sales, CS and product
Defined customer outcomes from day one
Strong ownership of value realisation
Early visibility of risk and opportunity
A structured path to expansion

In these organisations, growth feels more predictable. Because it is.

How to Fix Where Growth Breaks

If you want to improve growth, start here:

1. Define Value Clearly:

What does success look like for the customer? Not usage. Not features. Outcomes.

2. Strengthen the Handover:

Make sure context, expectations and goals carry through from sales into delivery.

3. Track Leading Indicators:

Don’t wait for churn. Track adoption, engagement and value signals early.

4. Align Teams Around Outcomes:

Sales, customer success and product need to be aligned around customer success, not just their own metrics.

5. Build a Path to Expansion:

Expansion should not be opportunistic. It should be designed into the lifecycle.

Final Thought

Most SaaS companies don’t struggle to win customers. They struggle to turn those customers into long-term, expanding revenue.

That’s where growth really happens. And that’s where it often breaks.

Call to Action

If growth feels harder than it should, it’s worth asking a simple question:

Where does it break after the sale?

AI in Customer Success: A Practical Guide for SaaS Leaders

AI is reshaping customer success. Not by replacing people, but by changing how decisions are made.

For SaaS companies, growth no longer depends on sales alone. It depends on what happens after the deal. This is exactly how I approach customer-led growth.

Retention, expansion and customer value are now the real drivers of revenue.

AI is accelerating that shift.

What AI In Customer Success Actually Means

AI in customer success is not about automation for its own sake.

It’s about improving how teams:

Identify risk earlier
Spot expansion opportunities
Guide customers towards value
Focus on what actually matters

The goal is not more activity. It’s better decisions.

Why AI Is Changing SaaS Retention & Growth

Traditional customer success models rely on:

health scores
manual check-ins
reactive engagement

These approaches are limited.

They depend on lagging indicators and human bandwidth.

AI changes this by:

analysing usage data in real time
identifying patterns across customers
surfacing signals before problems are visible

This shifts customer success from reactive to proactive and is already visible in how teams are evolving, as outlined in how AI will impact customer success teams.

Where AI Really Creates Value

The real impact of AI in customer success comes from a few specific areas.

Risk detection:

AI can identify churn risk before the customer feels it. Changes in behaviour, usage drops or engagement patterns can be flagged early, giving teams time to act.

Expansion signals:

AI can highlight where value is already being created. This allows teams to focus expansion conversations on real outcomes, not assumptions.

Time to value:

AI can guide the next best action for customers. This reduces friction, shortens onboarding and helps customers reach value faster.

Focus & prioritisation:

Customer success teams often manage too many accounts with too little time.

AI helps prioritise where attention is needed most, improving both efficiency and impact.

What Most Companies Get Wrong

Adding AI on top of a weak customer success strategy does not fix the problem.

It amplifies it.

Common mistakes include:

unclear definition of customer value
misalignment between sales, product and customer teams
over-reliance on generic health scores
focusing on activity instead of outcomes

If you don’t know what value looks like for your customer, AI won’t solve that.

How To Implement AI In Customer Success

The starting point is not technology. It’s clarity.

1. Define customer value clearly

What outcomes does your customer care about? Revenue, efficiency, risk reduction, growth. Be specific.

2. Align teams around those outcomes

Sales, product and customer success need to agree on what success looks like. Otherwise, AI will surface data but not meaning.

3. Use AI for signal, not noise

Focus on:

risk indicators
expansion triggers
usage patterns

Avoid drowning teams in dashboards.

4. Combine AI with human judgement

AI prepares. Humans decide. The best teams use AI to reduce uncertainty, not replace thinking.

Final Thought

AI does not fix customer success. It exposes whether you understand customer value. The gap between average and high-performing teams is about to get much wider.

Want To Go deeper?

If you’re thinking about retention, expansion or how AI fits into your strategy, I work with a small number of SaaS companies on exactly this.

👉 If you’re rethinking your approach, let’s talk.

Why SaaS Companies Outgrow Their Customer Success Model

Customer success rarely breaks overnight. What usually happens is much quieter.

The company grows
The customer base expands
Contract values increase

And the customer success model that worked in the early years slowly stops working.

Most leadership teams don’t notice this immediately.

The team still looks busy
Customers are still renewing
The product still works

But under the surface the system starts to drift.

The Moment SaaS Companies Start Feeling It

There are a few signals that usually appear around the same time.

The team feels constantly busy but mostly reactive.

Customer conversations revolve around product usage rather than business outcomes.

Renewals start to feel less predictable than they used to.

Expansion tends to happen through sales rather than emerging naturally from customer adoption.

None of these issues look dramatic in isolation but together they signal something more structural. The company has outgrown the way customer success operates.

What Worked Lower ACV Customers Doesn’t Work For Bigger Ones

In the early stages of a SaaS company the Customer Success model is usually informal. Customer relationships are close and founders are involved in the customer journey.

Customers are often smaller and more forgiving, but as the business scales two things change.

Contract values increase
Customer expectations increase with them

What worked when customers were small rarely works when customers are larger, more strategic and expecting measurable outcomes.

At that point customer success needs to shift from relationship management to value leadership.

When the Team Looks Busy but Value Feels Blurry

One of the most common symptoms is that customer success teams become highly active but struggle to consistently land the value story with customers.

They have data
Product usage dashboards
Activity reports
Feature updates

But customers are not buying activity. They are buying outcomes.

If the link between product adoption and business impact is not clear, renewal conversations become much harder than they should be.

This is rarely a capability problem inside the customer success team. It is usually a missing framework.

The organisation has never clearly defined how customer value should be articulated and reinforced across the customer journey.

Why Hiring Another Customer Success Leader Doesn’t Always Solve It

A pattern I see quite often is companies trying to fix these issues by hiring new customer success leaders. But if the underlying operating model has never been redesigned, leadership changes alone rarely fix the problem.

Customer success is operating inside the same system. The same segmentation. The same customer journey. And the same expectations placed on the team.

Without addressing those foundations the organisation simply repeats the same challenges with different people.

The Real Work: Designing the Post-Sales Engine

The real question leadership teams eventually face is this:

How should the post-sales engine actually work?

That normally means stepping back and looking at three things.

How customers move through the lifecycle from onboarding to renewal
How Customer Success is structured to support different customer segments
How the organisation consistently connects product usage to measurable business value

When those foundations are clear, customer success teams can focus on proactive work that drives adoption, retention and expansion.

When they are not, the team inevitably ends up reacting to problems rather than leading customer outcomes.

Customer Success as a Growth Function

The most successful SaaS companies eventually recognise something important.

Customer success is not simply about protecting revenue. It is about creating the conditions where revenue can grow.

When the post-sales model is designed well:

customers adopt the product more deeply
retention becomes predictable
expansion becomes natural

And the company stops relying solely on new sales to drive growth. This is when customer success truly becomes a strategic function.

Helping SaaS Companies Reset Customer Success

At certain points in a company’s growth it helps to step back and look at the whole post-sales system. Not just the customer success team itself, but how the organisation connects product adoption, customer outcomes, retention and expansion.

That usually means examining the customer journey, the way the customer success organisation is structured and how value is articulated with customers.

Sometimes those changes are driven internally.

Other times leadership teams bring in external experience to help diagnose the challenges and reshape the operating model.

Either way, when the post-sales engine is designed deliberately, customer success stops being reactive and becomes one of the most powerful drivers of long-term SaaS growth.

Want To Go Deeper?

If you’re thinking about retention, expansion or how AI fits into your strategy, I work with a small number of SaaS companies on exactly this.

👉 If you’re rethinking your approach, let’s talk.

Customer Success Trends That Matter in 2025

Everyone’s talking about AI, automation, and customer data. But here’s the reality: it’s no longer about data – it’s about decisions.

The best Customer Success teams in 2025 aren’t drowning in dashboards. They’re using insight to make faster, smarter moves that drive measurable value.

Let’s break down the three trends that really matter this year…

AI-driven Onboarding

AI isn’t replacing onboarding – it’s personalising and simplifying it. The new wave of CS platforms can map customer intent, predict setup friction and automate the right nudges before humans even step in.

The impact?

Faster time to value
Lower early-life churn
CSMs free to focus on higher-value conversations

Companies getting this right blend automation with empathy – AI handles scale, humans deliver trust.

Predictive Churn Models

Predictive analytics have matured. In 2025, they’re no longer just nice-to-have. CS teams now combine usage data, sentiment, and behavioural signals to spot churn before it happens and to decide which interventions matter most.

The shift isn’t technical. It’s cultural.

It’s about moving from “why did we lose them?” to “how do we keep them?”.

Value-Based Renewal Plays

Renewals aren’t transactions anymore – they’re validations of value. Leaders are tying renewal motions to quantified customer outcomes, not generic success stories.

That means:

Shared success plans
ROI tracking baked into customer value reviews
Cross-functional alignment with sales, engineering, marketing and product

It’s how SaaS companies turn retention into their growth engine.

The Bottom Line

Customer Success in 2025 is about decision intelligence – using the right signals to act faster, retain longer and grow smarter.

It’s not about having more data. It’s about making better decisions with it.

Want To Go deeper?

If you’re thinking about retention, expansion or how AI fits into your strategy, I work with a small number of SaaS companies on exactly this.

👉 If you’re rethinking your approach, let’s talk.

How AI Can Boost Retention & Drive Value for Customers

Retention has always been the real growth engine. Expansion doesn’t happen if customers are quietly slipping away. But in an era where churn can happen with a single click, traditional approaches to customer success aren’t enough. This is where AI steps in.

Why Retention Is The Battleground

Winning a new logo is expensive. Retaining one is cheaper, faster and more profitable – retention drives:

Higher lifetime value
Lower acquisition costs
Predictable growth and renewals

But the challenge isn’t knowing retention is important – it’s how to actively protect and grow it – and that’s where AI creates an edge.

From Signals To Foresight

Most customer success teams already track adoption, logins and usage. The problem is, those signals are backward-looking. By the time a red flag shows up, it’s often too late.

AI changes that by spotting patterns humans miss:

Early churn indicators: predicting which customers are at risk before they disengage
Engagement scoring: weighting behaviours across usage, support and sentiment analysis
Value forecasting: modelling ROI scenarios to show customers potential not yet unlocked

Instead of reacting to churn, CS leaders can use AI to anticipate it – and act early.

Driving More Customer Value With AI

AI doesn’t just keep customers – it helps them get more from your solution. Think of it as an accelerator for outcomes:

Personalised recommendations: AI can suggest best-fit features or workflows tailored to each customer’s context.
Proactive optimisation: AI can highlight inefficiencies and recommend fixes before customers notice problems.
Executive-ready insights: Automatically generated reports show cost savings, productivity gains or risk reduction in business terms.

This is where the conversation shifts from “Here’s how you’re using the product” to “Here’s how your business is stronger because of it.”

Building AI Into Your Playbooks

The most effective teams are weaving AI into everyday motions:

Onboarding: AI-driven guidance to shorten time-to-value
Adoption: predictive nudges that keep customers engaged with the right features
Health monitoring: dynamic scoring that updates as customer priorities shift
Renewals and expansion: data-backed ROI stories that win executive confidence

These build trust, reduce risk and prove value in ways that human effort alone can’t scale.

The Competitive Advantage

Retention isn’t about keeping the lights on. It’s about turning customers into advocates who expand, renew and drive referral business. AI helps CS leaders do this at scale – not by replacing the human element, but by enhancing it with sharper insight and timing.

In a market where choice is abundant and patience is short, the companies that master AI-powered retention will set the pace. They’ll move from firefighting churn to consistently proving and expanding value.

Want To Go deeper?

If you’re thinking about retention, expansion or how AI fits into your strategy, I work with a small number of SaaS companies on exactly this.

👉 If you’re rethinking your approach, let’s talk.