The Monetization Gap.

If you're on the vendor side, it's money left on the table. Underpricing. Revenue leakage. Value you created but didn't capture.

If you're the customer, the problem is the opposite: surprise fees. Forced bundles. The chasm between what you're paying and what you're actually getting.

What you see depends on which side of the table you sit on.

Here's what I've learned after two decades and hundreds of executive conversations: You're both right. And you're both losing.

At first glance, this looks like a pricing issue. It's not.

I know this because I've seen this movie before.

Back when SaaS was first emerging—before it was even called "SaaS"—I was an industry analyst trying to measure its impact. And I had a problem.

If I counted these new software companies the same way I counted legacy vendors, I'd be comparing apples to oranges. Legacy vendors recognized perpetual license revenue upfront. The new cloud companies recognized revenue over time. Measuring them the same way would dramatically understate the size and impact of what was happening.

So I created an entirely new forecast. A new way of measuring software.

Because you can't measure a new paradigm with an old scorecard.

We're at that moment again. 

Everyone's focused on pricing models, retention tactics, and bundling strategies. They're solving the wrong problem. 

The real issue isn't how you're pricing. It's how you're measuring. We're still using the same scorecard we used for the old model. Measurement problems create incentive problems. Incentive problems create trust problems.

Let me show you what I mean.

Last month, at a dinner for cross-industry finance leaders, we talked about the usual suspects. AI. Return-to-office mandates and the impact on pets. The macro outlook, tariffs, and crypto.

Then someone said, "Our (CRM) SaaS renewal came in at 6x,” and no one was shocked. People around the table nodded in resigned recognition. Same same. 

Everyone's being told we're at the beginning of an exciting new era. But finance leaders already feel like they're out of runway, and they are definitely out of patience.

The data confirms what they're feeling:

Innovation no longer creates excitement. It triggers anxiety.

AI features. Usage tiers. Complex bundles. SaaS-era companies announce these with excitement.

And to be clear, their customers are counting on their major platform vendors to deliver AI capabilities ASAP. They've already invested in these systems—sunk costs, compliance, risk management. They tell me they'd rather get modern tech from vendors they know than buy multiple point solutions from vendors they don't.

But the capabilities are taking too long to materialize from out of the marketecture. And when they do arrive, finance hears something else: unpredictable cost. Not just the price on the invoice, but the operational burden, the testing and change management, the budgeting nightmare.

Too slow to deliver. Too expensive when they do. That's the gap.

SaaS was supposed to fix this. Instead, history is repeating itself. We're at the beginning of a new platform shift, and finance leaders already know how this story ends. Pricing tactics have replaced new customer acquisition as the primary growth lever. It's the exact extortionary playbook that killed the late '90s software era and birthed SaaS in the first place.

When the pricing conversation starts, the spreadsheet takes over. Customer-obsession is pushed aside for the moment. The question shifts from "What's the best way to create value?" to "What's the maximum we can extract?"

That shift—that moment—is the Monetization Gap in action.

And here's what makes it so dangerous: both sides lose, but neither sees it coming.

Right now, vendors are bundling AI capabilities into across-the-board price increases. Charging customers whether they adopt the features or not. Justifying 50%, 100%, and sometimes 600% increases by pointing to "innovation" that customers haven't touched.

This violates a core tenet of SaaS: monetize the value delivered, not the value promised.

When you charge for capabilities customers don't use, you're not creating value. You're extracting value while eroding trust. And that erosion? It's accelerating.

For customers, it feels like confusion by design: pricing pages that obscure more than they reveal, renewal increases that blindside finance teams, bundles that force payment for features they'll never use, and constant anxiety about what the bill will look like next quarter.

For vendors, the damage shows up differently but just as painfully: customers churning silently without warning, sales cycles dragging on as procurement teams scrutinize every line item, discounts deployed as blunt instruments just to close deals, and growth that stalls at a ceiling nobody predicted.

When customers can't forecast their software spend, they can't plan their business. And that uncertainty accelerates the outcome vendors fear most: rigorous evaluation and aggressive replacement.

The Monetization Gap creates an environment where dark patterns thrive. 

Dark patterns are manipulative design elements that trick users into decisions they wouldn't otherwise make. Confusing interfaces. Hidden cancellation buttons. Forced bundles disguised as defaults.

The poster child? Amazon's "Iliad Flow."

In June 2023, the FTC sued Amazon for knowingly tricking millions of customers into signing up for Prime subscriptions and then making cancellation nearly impossible. The cancellation process—which Amazon internally named "Iliad Flow" after Homer's 16,000-line epic poem—required:

Meanwhile, signing up for Prime? One or two clicks.

Internal documents revealed that in 2017, the Iliad Flow led to a 14% drop in cancellations, as fewer members reached the final page. Amazon didn't accidentally make it hard to cancel. They engineered difficulty and measured its effectiveness.

The FTC's complaint was damning: "The primary purpose of its Prime cancellation process was not to enable subscribers to cancel, but to stop them."

In September 2025, Amazon settled for $2.5 billion—$1.5 billion in consumer refunds plus a $1 billion penalty.

We treat pricing and packaging as operational decisions.

They're actually trust signals. When companies lose trust in their value proposition, they compensate with friction. When you can't keep customers through value, you trap them through design.

In B2B SaaS, dark patterns show up as pricing pages that hide actual costs behind a "Contact Sales" button, cancellation flows requiring multiple retention conversations, downgrade options buried six levels deep, surprise renewal increases, and extraneous features you can't opt out.

Every one of these says the same thing the Iliad Flow did: "We don't trust our value to keep you here."

So what happens when companies go the other direction—when they make it easy to leave?

A 2025 study published in Advances in Consumer Research analyzed subscription psychology across digital entertainment, SaaS software, and retail services. The findings were clear: when you're trustworthy, people stay—not because they're trapped, but because they want to.:

When companies deployed high cancellation friction:

When companies used transparent cancellation processes:

The research revealed something critical: "Loyalty sustained through fairness and trust is qualitatively different from loyalty sustained through manipulation." Trust and fairness keep customers. Dark patterns just delay the inevitable.

You can't fix all of this with better pricing.

I know. We just spent 1200 words dissecting pricing problems. Dark patterns. Bundling strategies. Surprise renewals. Cancellation friction.

And yes—those are all real problems. But they're all symptoms of measuring the wrong things.

When SaaS first emerged, I had to create an entirely new way to size and forecast the market. Because if you measured cloud companies using perpetual license metrics, you'd completely miss what was happening. Revenue recognition was different. Business health was measured differently. The entire model was different.

Measuring them the same way would understate the size and impact of SaaS on the software industry.

We're at that moment again. We're measuring ARR growth, net retention, and bookings. These are metrics that made sense when SaaS was about predictable subscriptions to stable software.

But AI isn't stable software. Usage isn't predictable. Value isn't linear.

And the companies optimizing for the old metrics are making the same mistakes legacy perpetual vendors made when cloud emerged.

AI is fundamentally changing how software creates value. Usage is variable. Adoption is experimental. ROI is emergent, not immediate. But we're still measuring success using metrics designed for predictable, stable subscriptions. And when companies optimize for those metrics in an AI-driven world, extraction pricing makes perfect sense. They end up:

The pricing problems we're seeing aren't pricing problems. They are measurement problems that lead to incentive problems, which create the value misalignment we see manifest in the Monetization Gap.

A reliance on ARR pushes companies to make AI incremental and committed rather than allowing organic usage growth. Worse, companies are now combining usage metrics with ARR—because if it doesn't impact ARR, leadership doesn't see value. This is leading to increasingly nebulous definitions of ARR, a term that was already soft. It's not a true GAAP metric, and there's significant diversity in practice around what gets included. When your core measurement is this malleable, it's easy to optimize for the number rather than the outcome.

And it's not just SaaS-era incumbents. AI-native companies are being measured the same way—revenue, ARR, growth velocity—and the pressure to demonstrate traction early is pushing them to move fast at the expense of foundations.

Companies are building AI capabilities they don't fully understand. The bar for coding is so low that teams are shipping models without the internal expertise to debug them later. Governance, traceability, and explainability—these functions are being deprioritized in practice because they don't appear in the metrics that matter to investors.

This is how you build a house of cards. All it takes is one major blow-up for this to fall. A compliance failure. A privacy breach. A model that makes a consequential decision no one can explain. When that happens, the trust deficit won't just affect one company. It will ripple across the entire ecosystem.

We're building the wrong things—because of what we're measuring.

I've seen companies with beautiful usage-based models still bundle features customers don't want because their board cares about ARR growth, not usage growth.

I've seen companies with "cancel anytime" policies still make cancellation a nightmare, because their comp plans reward retention rate, not customer satisfaction.

The pricing model doesn't predict the behavior. The measurement system does.

Right now, SaaS companies are acting exactly like the legacy vendors they disrupted.

But if you're serious about building an AI-era company—one that prioritizes adoption velocity, value realization, and trust—then you need an entirely different scorecard.

Here's what that new scorecard looks like:

1. Measure adoption rate, not just ARR. Measure adoption rate for every new capability, especially AI features. If you’re charging for functionality with 10% adoption, your pricing is ahead of your value delivery. Look at usage-adjusted ARR—the ARR you’d have if you only counted what customers are actually using. That’s your real business. Everything else is a liability waiting to churn.

2. Measure realized value relative to what customers are paying. Retention is meaningless if customers aren’t realizing measurable outcomes. Can they articulate ROI in their own words? Are they achieving results that matter to them? If they’re renewing but not realizing value, you’re living on borrowed time.

To take this a step further:

3. Build a price-to-value metric specific to your business. For an ecommerce platform, it might be ARR per transaction volume. For a data platform, it might be cost per insight generated. Whatever the formula, it should tell you: Are we delivering commensurate value for what we charge? That ratio is often a leading indicator of expansion—or churn.

4. Measure time-to-value, not just time-to-close. Time-to-close tells you how fast you can sell; time-to-value tells you how fast customers can succeed. If it takes months for customers to activate or see results, your renewal risk clock starts ticking the moment the contract is signed. Designing smart expansion paths and measuring the time it takes for customers to reach their first “aha moment” is one of the strongest leading indicators of retention—not renewal rate.

5. Run regression analysis on what actually drives retention. Stop guessing. Cassie Young’s work on “sticky drivers” demonstrates that the most straightforward way to understand retention is to run statistical analysis on which customer attributes correlate with higher net dollar retention. The analysis should consider both product and customer performance metrics—usage patterns, feature adoption, and critically, your price-to-value ratio. Don’t just look at who churned; analyze who contracted, who expanded, and why. That’s where the gold is.

None of this is a "pricing strategy." It's a measurement transformation. And incentives make it real. If sales and success teams earn based on value realization or first renewal, forcing bad-fit deals becomes economically irrational. Behavior follows incentives; make sure yours support the metrics that matter.

Now, here's why most companies won't do it: if you manage to this scorecard, you’ll need to take steps that require boards to accept short-term pain. Investors to value durability over velocity. Executives to report metrics that make growth look slower, and less durable, than it is.

The shift to cloud only worked because a generation of companies dared to be measured differently. They had to do the difficult work of educating the investment community, customers, and partners. They walked away from perpetual license revenue and took a dip in valuation. Wall Street punished them. Analysts said the unit economics didn't work.

Adobe famously embraced upfront losses for ratable recognition and took three years to recover from the dip before they began to grow again. They bet their business on a new scorecard: predictability, flexibility, transparency. Because they knew you couldn't build a new model while being measured by the old scorecard.

AI is forcing the same question the cloud did 15 years ago: Will you be measured differently, or keep optimizing for metrics designed for a different era?

During the cloud transition, the companies that couldn't stomach being measured differently died. They tried to keep perpetual license economics while offering SaaS products. They wanted credit for both upfront revenue and recurring revenue. They wanted it both ways.

You can't have it both ways here either.

When I created that first SaaS forecast, I wasn't just making a measurement decision. I was making a philosophical one. "This deserves to be measured differently." 

The companies that won the cloud transition understood this. They weren't just better at building software. They were willing to be measured by a new scorecard—even when it made their growth look slower, their margins look worse, and their economics look broken.

You're being asked to make that choice again.

The Monetization Gap doesn't close because you find the right pricing model. It closes when you prioritize customer outcomes over revenue capture. When you measure value realized, not just value promised. When you remember why SaaS was supposed to be better in the first place.

Twenty years ago, I had to create a new category because the old measurements didn't fit the new reality.

The new reality is here again.

Will you have the courage to be measured by it?

Gore, Anand Rajaram, et al. "Understanding Subscription Models: How Psychology Shapes Customer Loyalty, Value Perception, and Cancellation Patterns." Advances in Consumer Research, vol. 4, 2025, pp. 3794-3801.

Last night in London, a LinkedIn post I’d written came up twice independently.  The post is about that gut feeling shift between a CEO and an executive that typically signals a forthcoming end to the relationship.  That moment when the winds seem to change direction.  When the feeling isn’t as warm.  When the CEO stops calling after hours just to check in.  Two different people were experiencing it at that moment.  The market provides a clue: a 20% increase in the number of tech companies with no increase in tech employment and 500K-1M layoffs projected for next year.

So who’s going to make it out of this intact? The executives that are still willing to get their hands dirty.  Get tactical.  There is no appetite for the huge team and a CMO or CRO that sits behind their proverbial desk reviewing spreadsheets or making powerpoint slides.  You’re either in it every day grinding or you’re mercilessly pushed out.  It’s going to take resilience that’s for sure. 

Become a Topline insider by joining our Slack channel.

We want to hear your feedback! Let us know your thoughts.