A later analysis from CB Insights, based on 111 post mortems, again identifies problems with demand and product market fit among the leading reasons for failure. Even as other issues such as running out of cash also feature prominently, the theme of misjudged market need persists.
In parallel, the Lean Startup framework developed by Eric Ries presents the minimum viable product, or MVP, as a way for teams to learn quickly whether a product concept matches real customer needs. This idea, described in detail by Lean Startup Co., advocates for testing core assumptions before committing to full-scale development.
Startup Failure Analysis and MVP Discipline
- CB Insights data on 101 post-mortems show no market need cited in 42 percent of failures.
- A 2021 CB Insights update based on 111 post-mortems again highlights demand and product market fit issues.
- In Lean Startup, the MVP is a minimal product built to maximize validated learning with limited effort.
- The build-measure-learn loop enables earlier testing of demand and incremental changes before full launches.
- Overbuilt products without validation risk wasted capital, time, and missed timing in competitive markets.
- MVPs are not formulaic and require judgment about scope, quality, and continued iteration.
No Market Need as a Leading Failure Pattern
The CB Insights brief reproduced by Stanford aggregates reasons founders gave in 101 post mortems. It shows no market need at the top of a list of 20 distinct causes, cited in 42 percent of cases.
Because founders could list more than one cause, the percentages exceed 100 percent. However, the position of no market need at 42 percent indicates that many teams built products customers did not consider necessary or valuable enough to adopt.
In the same chart, other frequent reasons such as running out of cash, having the wrong team, or being outcompeted appear below no market need. This suggests that demand risk is central even when financial or competitive pressures are also present in a failure story.
When startups misjudge demand, they often lock resources into features, infrastructure, and marketing for a solution that never secures a stable base of paying users. This outcome is consistent with the patterns summarized in the CB Insights research brief.
The 2021 CB Insights update covering 111 post mortems reinforces this view. It again lists weak demand and product market fit issues among the top reasons for failure, indicating the underlying problem did not disappear as the sample size grew.
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Minimum Viable Product as a Tool for Validated Learning
Within the Lean Startup Co. approach, Eric Ries defines the minimum viable product as a version of a new product that allows a team to collect the maximum amount of validated learning about customers with the least effort.
Validated learning is a specific type of learning backed by data from real customer behavior rather than opinion or internal speculation. It is treated as the primary measure of progress under conditions of uncertainty.
An MVP is built to test concrete hypotheses about who the customer is, what problem matters most, and which solution elements they will actually use or pay for. It is not an attempt to implement a complete feature set from the outset.
This framing shifts the goal of early product work from delivering a polished release to designing experiments. The aim is to reveal whether a proposed value proposition has a real market, so teams can stop or adjust before large sunk costs accumulate.
By treating each MVP as an experiment, founders can iteratively refine their understanding of demand, pricing, and positioning. This process limits exposure if early assumptions prove wrong.
The Build-Measure-Learn Loop and Speed to Insight
The Lean Startup principles described on The Lean Startup site emphasize a build-measure-learn loop. In this loop, teams quickly build an MVP, measure how customers respond using qualitative feedback and quantitative metrics, and then learn whether to persevere, refine, or pivot.
In this model, building is not an end in itself but a way to expose a specific hypothesis to real world use. For example, it tests whether a particular segment will sign up, return, or pay under defined conditions.
The same principles state that entrepreneurs do not need to spend months waiting for a product beta launch before changing direction. They can instead adapt plans incrementally as data arrives, which reduces the delay between discovering a mismatch and acting on it.
Using smaller, faster cycles of building, measuring, and learning allows teams to test multiple versions of a product, pricing model, or channel before committing to any single option. This is especially valuable when markets are shifting or poorly understood.
Over time, repeated build-measure-learn cycles can converge toward product market fit. This fit is understood as alignment between the product, a defined customer group, and a sustainable way of capturing value.
Risks of Overinvesting Before Product Market Fit
Building a full featured product before any meaningful validation may appear attractive because it promises a complete offering. However, the CB Insights data suggest that doing so often results in teams perfecting a solution for which there is little or no market.
When teams commit major engineering effort, design work, and go-to-market spending to an untested concept, they reduce the remaining runway available to adjust if customers do not respond as expected. This increases the chance that a cash shortage or competitive pressure ends the attempt.
The top reasons summarized across 101 post mortems show how these factors can compound. No market need, running out of cash, and being outcompeted all appear frequently in the same set of failures.
In dynamic or highly competitive markets, long build cycles also create timing risk. Customer preferences and rival offerings may shift while a team is still refining features that have not yet been tested with real users.
An MVP driven approach does not remove these risks, but it attempts to surface lack of demand or weak differentiation earlier and at lower cost. This allows teams to either adjust their concept or reallocate their efforts to a different opportunity.
Judgment and Limitations in Applying the MVP Concept
Eric Ries notes in his discussion of MVPs that the concept is not a rigid formula. Teams must exercise judgment in deciding what qualifies as a meaningful minimum product in their situation. Ries describes the MVP approach as "not formulaic" and dependent on context-specific judgment.
If a team interprets "minimum" too literally and releases something that does not solve a real problem even for early adopters, the feedback collected may say more about execution gaps than the underlying idea. This makes decisions based on that data unreliable.
On the other hand, if they insist on including every feature before any release, they lose the learning advantage the MVP method is designed to create. This increases the risk of ending up among the cases where no market need is identified only after heavy spending.
Careful MVP design therefore balances the need to limit upfront investment with the need to provide a coherent experience. The experience must allow customers to demonstrate real interest, such as signing up, using the product repeatedly, or paying for access.
Ries also emphasizes that MVPs are part of an ongoing process rather than a single event. Multiple rounds of iteration are usually needed before a team can claim to have strong evidence of product market fit.
Using MVP Discipline to Address the 42 Percent Risk
Taken together, the CB Insights post mortem data and Lean Startup guidance point to the same operational lesson. Treating early product work as a search process, anchored in MVP experiments and build-measure-learn loops, is a practical way to reduce the likelihood of devoting years to a product the market does not want.
By defining explicit hypotheses about customers and value, teams can translate these into minimal yet functional products. Collecting evidence on actual usage and willingness to pay helps identify lack of demand while there is still capital and time to make meaningful changes.
In uncertain, dynamic, or highly contested markets, this discipline also helps founders respond to new information. Their organization becomes structured around short learning cycles rather than long, fixed roadmaps.
The 42 percent figure attached to no market need is not a guarantee that any specific venture will fail for that reason. However, it underlines how common it is for teams to overestimate demand. This is why methods that prioritize early learning over comprehensive builds remain central to startup practice.
When startups use MVPs thoughtfully, combine them with clear metrics, and continue iterating beyond the first version, they improve their chances of finding product market fit before scale. They avoid discovering a lack of demand only when resources and flexibility are exhausted.
