The Hidden Pattern: Why B2B SaaS Markets Are More Predictable Than You Think
Markets follow a predictable pattern of converging toward optimal solutions. Understanding this dynamic allows you to build a product that captures a dominant position years before your competitors.
👋 Hi, it’s Antti LK and this is the first part of my 4-article series “How to Predict Where Your B2B SaaS Market is Going Years In Advance.” Subscribe now to keep following the series!
The Unfair Advantage: How Market Leaders See Years Into the Future
You can predict market direction by asking “what is the best solution to this problem?”
That deceptively simple question – shared by a CTO whose eight-figure B2B SaaS company is growing 40% annually – reveals a hidden truth about B2B SaaS evolution. While markets often appear unpredictable, they actually tend to follow a consistent pattern: markets converge toward optimal solutions.
Most product teams operate in perpetual reaction mode, frantically implementing features, chasing feedback, and burning resources just to maintain their market position. Meanwhile, market leaders quietly position themselves at the optimal solution where the market will eventually arrive.
This pattern proves itself repeatedly. Consider smartphones: the market ultimately converged on large touchscreen devices, despite early resistance. Or video conferencing: Zoom had recognised that customers valued one-click simplicity and rock-solid reliability, and optimised its product accordingly. When the pandemic hit, it had the best solution in a fragmented market. Zoom zoomed past its competitors, leaving them far behind scrambling to copy its approach to the core video conferencing experience.
In both cases, companies that anticipated and built the best solution first gained a massive lead, and left competitors playing costly catch-up. Despite their efforts, Nokia and BlackBerry couldn’t recover. Microsoft had to abandon Skype for Business and urgently upgrade Teams’ video capabilities.
The Choice: Reactive Gambling vs. Predictive Confidence
Most B2B SaaS companies follow a reactive, iteration-heavy approach. But a different path is available for more ambitious and visionary ones:
A Conventional Reactive Path (Where Most Companies Are)
Invest in building something based on limited understanding
Gather customer feedback, complaints and feature requests
Iterate to fix issues and improve
Burn years and millions and gradually approach better solutions for complex B2B problems
A Predictive Path (That Creates Unfair Advantages)
Discover the customer problem space, independent of today’s solutions
Reason out what the optimal solution for these problems must be
Build directly toward that optimal solution
Gain years of advantage while competitors waste resources on trial and error
What if you could see your B2B SaaS market’s future with clarity? What if you could position your product at the destination where your market will inevitably arrive, years before your competitors even see the path?
The difference isn’t just theoretical. Consider how Zoom positioned itself at the intersection of reliability and simplicity years before competitors recognised these as the critical factors. When circumstances suddenly changed, they didn’t need to pivot. They were already where the market would inevitably converge.
Your Path to Market Domination Through Prediction
Over the next four articles, I’ll show you how to develop this predictive capability and create first-mover advantages your competitors cannot overcome:
Part 1 (this article) explores the fundamental pattern of market convergence toward optimal solutions and the five mental barriers that prevent most companies from taking advantage of it.
Part 2 introduces a systematic methodology for market prediction that overcomes the limitations of traditional product development approaches.
Part 3 demonstrates the effectiveness of this approach through real-world examples where I and others predicted major market shifts years before they happened.
Part 4 provides a practical framework you can apply to your own business to predict and lead your market’s evolution.
I understand this challenges today’s best practices. The software industry has embraced iteration so completely that the idea of predicting optimal solutions may seem impossible. But conventional wisdom conflates two distinct challenges: predicting technological breakthroughs (which is indeed impossible) and predicting how existing technologies will be optimally applied to solve stable customer problems (which is remarkably predictable).
This predictive capability isn’t reserved for rare visionaries or lucky guessers. It’s a systematic skill you can develop and leverage by understanding the hidden pattern driving market evolution.
The Convergence Law: How Markets Inevitably Flow Toward Optimal Solutions
If you examine the history of many successful product categories, you will notice something remarkable: markets eventually converge toward optimality.
What does “optimal” really mean though?
True optimality isn’t about having the most features or the sleekest UI. Instead, it emerges when a product perfectly solves the whole underlying customer problem with minimal complexity.
The companies that lead markets don’t chase competitors or feature requests. They first identify what the best solution must be like, then relentlessly execute toward it, and will arrive at the optimal Problem-Solution Fit first.
Before we go further, we need to acknowledge one important exception to this pattern of convergence toward optimality.
The Network Effect Exception: When Being First Beats Being Best
While markets generally converge toward optimal solutions over time, there’s one significant exception: products with strong network effects. In these cases, a product’s value depends primarily on how many others use it, creating a barrier to displacement even by superior solutions.
Network effects exist on a spectrum from minimal to overwhelming. The stronger the network effects, the less Problem-Solution Fit determines market convergence. If network effects are dominant, timing and aggressive scaling often become more important than solution quality.
In markets with overwhelming network effects, like social media platforms or messaging apps, even solutions with significantly better Problem-Solution Fit struggle to displace established players. The switching costs become prohibitively high as users would lose connection to valuable networks by moving to an otherwise better alternative.
For most B2B SaaS product categories, however, Problem-Solution Fit remains the primary driver of long-term success, with network effects playing a secondary role. Business intelligence tools like Tableau, accounting software like Xero, and HR systems like Workday create value primarily through their functionality rather than their user networks. Asking the question “what is the best solution to this problem?” provides powerful predictive capabilities and helps achieve market leadership.
Market Friction: Why Some Industries Resist Optimal Solutions Longer
Competition is the engine that drives markets toward optimal solutions. Without it, there’s little incentive to improve. Several factors may reduce competitive pressure:
Monopolies: In monopolistic or highly concentrated markets, whether created by regulation or market consolidation, convergence slows down or even stalls.
High switching costs: B2B software often creates substantial switching costs. While these don’t prevent convergence, they slow it down compared to B2C software.
Market opacity: When customers lack information about alternatives or cannot easily evaluate solution quality, inferior solutions persist longer. This is often the case in B2B software and especially enterprise software where assessment requires deep domain expertise.
Two out of these three forces slow down convergence specifically in B2B SaaS. In typical B2B SaaS categories, meaningful convergence might take:
2-4 years for simple tools with low switching costs
4-8 years for departmental solutions with moderate complexity
8-15+ years for complex enterprise-wide systems
Slow convergence is an advantage if you are seeking the best B2B SaaS solution. It gives you more time to research customer problems and figure out what the best solution must be like, and then build it incrementally without a panic or rush. And when the optimal solution has a 10x better Problem-Solution Fit than existing solutions, you can much more easily overcome high switching costs and demonstrate your solution’s superiority over existing solutions in an opaque market.
Despite the advantages of slower convergence, many B2B software companies fail to capitalise on the opportunity to identify optimal solutions. I have identified five common mental barriers that seem to prevent product teams from even asking what the best solution might be.
Five Mental Barriers Blocking Your Path to Market Dominance
If finding the best solution is so powerful, why do most product teams remain trapped in cycles of iteration and incremental improvement? Why do they continue suffering through painful quarter-ends, explaining missed targets, and hoping that the next release will finally deliver breakthrough results? Five specific mental barriers prevent even experienced product teams from escaping this reactive cycle and claiming the advantages of identifying optimal solutions.
1) The Relativism Trap: How “Different” Sabotages “Better” Solutions
Modern culture is permeated by relativism, the misguided idea that all beliefs and interpretations are equally valid. As David Deutsch argues, this mindset stifles progress by discouraging the comparison of ideas and the search for better solutions.1 Anyone heavily invested in relativism rejects the concept of a “best” solution because it doesn’t fit their worldview.
In product teams this manifests as a reluctance to make definitive judgements about the quality of solutions. Instead people discuss opinions: “I don’t like that” or “this feels right to me”. Or they hide behind phrases like “it depends on the user” or “different customers have different preferences.”
To overcome this trap, we need objective criteria for measuring and evaluating solutions based on how well they address customer problems.
2) The Measurement Challenge: Why Empirical Testing Keeps You Playing Catch-Up
If we accept that some solutions can be better than others, we still need to be able to measure them objectively to see which is better.
The only generally known approach to measuring Problem-Solution Fit objectively is empirical testing with customers. For enterprise software products, valid results about the overall fit would require designing, building, deploying, and having a few target customers use the whole solution in real-life conditions. To compare alternatives, we would need to develop multiple solutions and collect significant data to overcome measurement noise2, which is clearly infeasible for all but the smallest pieces of software.
Testing with customers works well enough with small products that are used for relatively small tasks if testing can be done with prototypes rather than production code. But we cannot test user performance in many important scenarios, like complex long-lasting planning work. We can test at most small pieces of the overall process.
While we can measure the overall Problem-Solution Fit of a large enterprise software product in principle, we can’t in practice. And what we can’t measure, we can’t improve. People lose sight of the possibility of radical differences in Problem-Solution Fit, and stop asking what the best solution would be.
What instead gets measured is the cost of building the solution, which is easy, but not the result of that investment. This leads to short-term thinking and concentration on inexpensive incremental improvements, but eventually to an unnecessarily complex product that’s at best a local maximum in the solution space.
3) The Kilimanjaro Effect: Why All Current Solutions Look Equally Mediocre
When one solution outperforms another by an order of magnitude, precise objective measurements become unnecessary. The difference is so blatantly obvious that even relativists will acknowledge it.
However, in B2B and enterprise software, alternative solutions to the same customer problem are often similarly mediocre. One solution has certain strengths and weaknesses; another has different ones. When all solutions are very suboptimal, just in different ways, determining which is best is practically impossible.
Think of it like this altitude map of Mount Kilimanjaro where each point represents a different solution. The altitude corresponds to Problem-Solution Fit. The vast surrounding plains (colour-coded in green) vary in height by only 400 metres or so.
But the peak rises 4500 metres above the plains – 10x difference. This dramatic elevation gain is typical of the improvement potential in the B2B SaaS space, and Enterprise SaaS in particular.
When all available solutions are far from optimal, meaningful differences become hard to detect. People begin to believe there are no better or worse solutions, just different ones, and stop seeking for the best solution.
4) The Cost Fallacy: Why Market-Dominating Solutions Can Cost Less to Build
Product leaders often believe that better solutions demand proportionately higher investments. It’s the “Good, Fast, Cheap: Pick Two” fallacy applied to product development.
This belief is understandable. In any area of life where existing solutions are close to optimal, all choices require making trade-offs, so the fallacy likely holds true. In physical manufacturing, for example, 10x better products might require 10x more expensive materials or manufacturing processes.
But when existing software solutions are far from optimal, typically overly complex both internally and externally, building the best solution can actually cost less, not more.
Consider these examples:
Zoom vs. WebEx: Cisco’s WebEx was encumbered by years of accumulated complexity. Zoom focused on what really mattered, reliability and simplicity, and built a superior solution that required fewer developers and less time.
Salesforce vs. early CRM systems: Salesforce didn’t outspend Siebel to build their cloud CRM. They simply recognised the optimal solution, cloud-based subscription model, when others were stuck in on-premise thinking.
Slack vs. Skype for Business: Skype for Business was burdened with legacy infrastructure, complex integrations, and bloated features. Slack built a more elegant, focused solution with a smaller development team and budget.
The examples illustrate the premise of this article series: Markets inevitably move toward optimal solutions that address customer problems with minimal complexity. When existing solutions are complex, bloated, and far from optimal, there’s an opportunity to build something both superior and less expensive.
By identifying what the optimal solution must look like, as Zoom, Salesforce, and Slack did, you can anticipate market evolution and position your product at the destination where the market will eventually arrive. Companies that predict this movement gain an enormous first-mover advantage that’s difficult for competitors to overcome.
The optimal solution is always the simplest one that fully addresses the complete customer problem. Complex solutions to the same problem require more code, more maintenance, more documentation, more testing, and more customer support. By deeply understanding customer problems and pursuing optimality, you can simultaneously reduce costs and increase value.
Sometimes building the optimal 10x solution might require 2x the development resources of an incrementally better solution, but not 10x more. While it costs more, the return on this investment is exponential: premium pricing, dominant market share, reduced support costs, faster sales cycles, and customer loyalty.
The real cost isn’t building the optimal solution – it’s the years spent iterating, extending, maintaining, and supporting suboptimal ones. Many product teams could save enormous resources by figuring out the best solution upfront, rather than paying the perpetual tax of complexity.
5) The Impermanence Fallacy: The Dangerous Myth of “Rapidly Changing” Customer Needs
A widespread belief in product development is that customer needs are constantly changing, necessitating continuous iteration and pivoting. This misconception leads teams to conclude that predicting optimal solutions is futile. Why bother mapping the complete problem space if it will be different tomorrow?
This fallacy stems from confusing solutions with underlying problems. Solutions indeed evolve rapidly with technology, but the customer problems they address remain remarkably stable. When correctly understood, customer problems are situations that people and businesses have come across for years, decades and in some cases even centuries or millennia.
Consider some examples:
People have been making tea to relax for hundreds of years (the example I used in my earlier article)
Surgeons have been trying to repair damaged tissue for centuries
Accountants have been trying to track and report financial transactions for generations
Shopkeepers have been trying to match the supply in their stores with customer demand for millennia
Telecom operators have been trying to balance network capacity with demand for decades
The underlying customer problems, understood independently of solutions, change remarkably slowly. What changes rapidly over time are the tools, methods, technologies, products and services that companies offer to help solve those problems.
This stability is what makes market prediction possible. By understanding your customers’ enduring problems in depth and detail, you can deduce characteristics of optimal solutions that will eventually dominate.
Coming Next: The Blueprint for Seeing Your Market’s Future Before Competitors Do
In this article, we have explored the hidden pattern of market evolution toward optimality and the five mental barriers that prevent many people from seeing this pattern. We have discovered that the stability of customer problems provides the foundation for predicting market direction years in advance.
But a critical question remains: If traditional product development approaches like agile, lean startup, and design thinking weren’t designed for prediction, what methodology can fill this gap?
In our next article, we will explore why traditional product development approaches fail to leverage these stable patterns, and introduce a systematic methodology for predicting market evolution years before your competitors. You will discover an inherently different approach to product development that doesn’t rely on luck, genius, or slow iteration, but on a structured process for identifying optimal solutions before the market discovers them.
The companies that master this approach will shape markets rather than follow them, and create sustainable competitive advantages that last many years rather than a few quarters. They will build products that seem to anticipate customer needs before any customer ever asked for such solutions.
I have spent over 20 years developing and refining this approach, and I’m convinced it represents a fundamental advancement in how we create B2B software products. Join me in part 2 of the series as we explore this methodology in detail.
This is the first part of a 4-part series on market evolution and how to predict it using Deductive Innovation. Continue to part 2 of the series:
How to Predict B2B SaaS Market Evolution Before Your Competitors
👋 Hi, it’s Antti LK and this is the second article in a 4-part series on predicting market evolution in B2B SaaS. Read Part 1 here. Subscribe now to keep following the series!
David Deutsch is a philosopher and the physicist who invented quantum computing. For his argument against relativism, read his book “The Beginning of Infinity: Explanations that Transform the World”, Allen Lane, 2011.
Measurements of e.g. learning time or task performance heavily depend on individual users, their circumstances and all kinds of factors beyond the solution itself. Distinguishing this noise from the effect of the solution makes it difficult to measure the signal.