3 Thinking Errors That Hinder Business Growth and Validation-Based Solutions
Automatically avoiding competitive markets, dismissing success as luck, or trying to be completely different from the outset can reduce opportunities for validation. This explains how to validate ideas by confirming demand, reverse-engineering examples, and running small differentiation experiments.
- The presence of competition is a sign of demand, but it does not guarantee profitability.
- Rather than replicating outcomes, successful cases should be broken down by customer, offer, distribution, pricing, and retention factors.
- For early differentiation, it is more effective to solve one important customer problem better than to change every element.
- Figures such as “30 cases” or “80% imitation and 20% differentiation” are not universal laws, but empirical benchmarks that aid analysis.
- Explaining business performance through fixed mindsets attributed to the poor and the wealthy can overlook factors such as capital, information, environment, and luck.
In business and content, advice such as “the rich use proven formulas, while the poor try to invent from scratch” appears frequently. This contains a useful message about confirming demand in competitive markets and learning from successful cases, but it is inaccurate to explain economic circumstances solely through an individual’s mindset.
Income and asset accumulation are also affected by initial capital, education, health, social relationships, economic conditions, institutions, risk tolerance, and luck. Therefore, rather than dividing people into “the rich” and “the poor,” this article examines three decision-making errors that can hinder business growth and ways to test them.
Claims That Must First Be Corrected
“The rich do not invent” is not a universal fact
Entrepreneurs may improve existing markets or develop new technologies and business models. What matters is not whether they invent, but whether they can answer the following questions.
- Does the problem they are trying to solve actually exist?
- Can they specifically identify customers who experience that problem?
- Does it offer customers a meaningful advantage over current solutions?
- Are customers likely to pay or change their behavior?
- Can they create a system of activities that competitors cannot easily imitate?
A completely new idea can solve a validated problem, while a product that improves on an existing idea can fail if there is no demand.
Thinking Error 1: Looking Only for Markets Without Competition
Structure of the error
This error involves judging a market as attractive simply because it has little competition or giving up on entering because it has many competitors. The presence of competitors can indicate that customers are already spending money and time. However, intense competition does not automatically mean that a new entrant will earn a profit.
At a minimum, market attractiveness must be assessed by considering the following factors together.
| Item to check | Key question | Observable signals |
|---|---|---|
| Problem frequency | How often do customers experience the problem? | Repeated inquiries, negative reviews, manual work |
| Problem severity | Is the loss substantial if the problem remains unsolved? | Loss of time or money, operational disruption, increased risk |
| Willingness to pay | Do customers actually pay for a solution? | Existing purchases, subscriptions, use of paid alternatives |
| Competitive intensity | How much does customer acquisition and operation cost? | Advertising costs, price competition, switching costs |
| Feasibility of entry | Can legal, technological, and distribution barriers be overcome? | Need for permits, capital, data, or supply chains |
| Unit economics | Is the value generated by one customer greater than the cost of acquiring and serving that customer? | Gross profit, retention rate, refund rate |
Understanding red oceans and blue oceans accurately
A red ocean refers to a market in which existing demand and competitive rules are relatively clear. Blue ocean strategy is not simply about finding “something no one else is doing.” It is an approach that restructures the boundaries of an existing market to create new demand and combinations of value.
Therefore, a market without competition has both of the following possibilities.
- It may be a promising opportunity that has not yet been discovered.
- It may be a market with insufficient customer problems or willingness to pay.
The presence or absence of competition alone cannot distinguish between these two possibilities.
How to act: Friction-based market validation
- Narrow the customer segment. Instead of “office workers,” be specific, such as “retail store managers who manually consolidate schedules from multiple locations each month.”
- Investigate current behavior. Ask when customers last experienced the problem, what solution they used, and how much they spent.
- Define competitive alternatives broadly. Alternatives include not only similar products but also Excel, outsourcing, manual work, and leaving the problem unresolved.
- Record recurring friction. Categorize issues such as slowness, complexity, high prices, uncertainty, and lack of accessibility.
- Test a small offer. Measure actual behavior through a landing page, prototype, preregistration, paid pilot, or similar method.
Costly actions such as purchases, reservations, and repeat visits are stronger validation signals than the number of people who express interest.
Thinking Error 2: Explaining Success Only Through Luck or Brand
The problem with uncritical admiration and dismissal
If you see a best-selling product or highly viewed content and conclude only that “it was lucky” or “it was possible because they were famous,” you cannot identify variables from which you can learn. Conversely, directly copying the outward appearance of successful cases is also risky. Success includes conditions that are not visible from the outside, such as brand recognition, an existing customer base, distribution networks, timing, and budget.
The purpose of reverse engineering is not to replicate the output but to extract the hypotheses and structures that produced the outcome.
Five steps for reverse engineering successful cases
1. Gather comparable cases
Collect both successful and ordinary cases that share the same customers, price range, distribution channels, or usage situations. Looking only at successful cases can create survivorship bias, making every characteristic appear to be a cause of success.
“30 cases” can be a practical starting point for identifying diverse patterns, but it is not a statistically guaranteed minimum sample. The required number varies depending on the size of the market and the similarity among cases.
2. Record them in a common format
| Analysis factor | What to record |
|---|---|
| Target customers | Whose situation is it aimed at, and what is that situation? |
| Problem | What is the customer trying to avoid or gain? |
| Value proposition | Does it emphasize being faster, easier, cheaper, or safer? |
| Evidence | What support does it provide, such as reviews, performance records, demonstrations, or guarantees? |
| Price | What is the price level, and how is the cost justified? |
| Distribution | Where does it reach customers—through search, referrals, advertising, partners, or elsewhere? |
| Conversion | What next action does it ask the customer to take? |
| Retention | What drives repeat purchases or continued use? |
3. Distinguish success factors from mere correlation
Even if all successful content uses short titles, you cannot conclude that short titles caused the success. Title length, publication timing, topic demand, distribution channels, and other factors must be tested separately.
4. Test key variables one at a time
If you change the price, title, and design simultaneously, it is difficult to determine what improved the result. Where possible, change one important variable at a time and compare predefined metrics such as conversion rate, completion rate, and repeat purchase rate.
5. Redesign it to fit your circumstances
You cannot directly copy a large company’s discount policy or a celebrity’s content format. You must adapt the underlying principles to the capital, credibility, channels, technology, and customer base available to you.
Thinking Error 3: Making Everything Different From the Beginning
The difference between novelty and value
A product’s uniqueness and its value to customers are not the same thing. Customers must understand new features, learn how to use them, and move away from existing alternatives. The more differences there are, the greater these switching costs may become.
Conversely, if there is no difference at all from existing products, customers have little reason to choose it. The key is balancing familiarity and differentiation.
The proper use of “80% validated, 20% differentiated”
The advice to “follow a proven formula for 80% and make only 20% different” can be useful as a rule of thumb for limiting the scope of experimentation. However, this is not a ratio proven across all industries. Regulated industries, advanced technology, art, and platform businesses may require different levels of innovation.
The following questions matter more than the ratio.
- What familiar elements do customers want to retain?
- What is the single greatest source of friction for customers?
- Can that friction be improved more clearly than competitors can?
- Can customers quickly understand and directly verify the difference?
Single-axis differentiation methods
Instead of changing every feature, you can select one axis with high customer value.
- Speed: Reduce the time required for ordering, installation, processing, or reaching a result.
- Convenience: Reduce signup steps, required fields, and learning burden.
- Pricing structure: Reduce upfront costs, payment risk, or unpredictable fees.
- Trust: Strengthen validation materials, transparent standards, guarantees, and support.
- Target specialization: Tailor the offering to a specific role, industry, language, or usage situation.
- Connectivity: Make integration with existing tools or data transfers easier.
From Michael Porter’s strategic perspective, differentiation does not stop at adding a single feature. Activities that support a value proposition distinct from competitors must reinforce one another to create a sustainable difference.
Imitation, Reverse Engineering, and Plagiarism Are Different
Analyzing the principles behind success does not grant the right to reproduce an output without permission. The following boundaries must be observed in business analysis and creative work.
What can be analyzed
- Publicly available prices and product configurations
- Customer journeys and purchasing procedures
- General business methods and problem-solving principles
- Recurring needs and complaints in public reviews
- Performance data obtained through direct observation or lawful means
What may cause problems if copied
- Copyright-protected expressions such as text, images, videos, music, and code
- Names and designs that create trademark confusion
- Patent-protected inventions
- Trade secrets or materials acquired without access authorization
- Data collected in violation of terms of service or privacy principles
Learning from ideas and general principles must be distinguished from copying another person’s specific expression. Before applying them in practice, review the relevant country’s intellectual property laws and contractual terms.
Example of a 7-Day Validation Plan
| Timing | Task | Output |
|---|---|---|
| Day 1 | Define the target customer and problem in one sentence | Problem hypothesis |
| Day 2 | Research competing products and substitutes | Competitive alternatives table |
| Day 3 | Conduct customer interviews or observations | Record of actual behavior and friction |
| Day 4 | Analyze successful and ordinary cases using the same criteria | Pattern list |
| Day 5 | Select one value on which to differentiate | Value proposition statement |
| Day 6 | Release a minimum offer or prototype | Landing page or demo |
| Day 7 | Review behavioral metrics and decide whether to retain, revise, or stop | Next experiment plan |
The purpose of short-term validation is not to determine business viability conclusively within one week. It is to test the riskiest assumptions before committing to costly development or inventory.
When Using AI as an Analysis Support Tool
Generative AI can assist with categorizing reviews, creating comparison tables of competing products, drafting value propositions, and generating experimental ideas. However, AI-generated analysis requires fact-checking, and input data must not include customers’ personal information or a company’s nonpublic information.
The recommended process is as follows.
- Enter only publicly and lawfully obtained materials.
- Ask AI to distinguish observed facts from inferences in its output.
- Compare the output with the original sources to verify prices, features, figures, and quotations.
- Treat commonalities identified by AI as hypotheses and validate them through actual customer behavior.
- Humans retain final judgment and responsibility.
Conclusion
Business growth is not a matter of choosing between “completely new thinking” and “copying successful outcomes.” It is closer to an iterative process that begins with validated demand, structurally analyzes competitive cases, and solves one important customer problem better.
Competition is only a signal of demand, not a guarantee of success, and commonalities among successful cases remain hypotheses until validated. Rather than attributing people’s economic circumstances to their mindset, it is more accurate and practical to improve decision-making based on observable customer behavior, costs, conversion, retention, and profitability data.
FAQ
Is entering a highly competitive red ocean more likely to lead to success?
No. Competition can be a sign that existing demand and willingness to pay exist, but it can also entail high customer acquisition costs, price competition, and low profit margins. You need to validate the severity of the problem, willingness to pay, barriers to entry, unit economics, and potential for differentiation together.
Does a market with no competitors necessarily have no demand?
Not necessarily. It may be a market that has not yet been discovered or has newly opened up due to technological or regulatory changes. Conversely, there may be no competitors because the customer problem is not significant or there is no willingness to pay, so this should be validated through interviews, pre-orders, prototypes, and paid pilots.
Do I have to analyze exactly 30 success stories?
Thirty is not a universal minimum sample size, but a rule of thumb for identifying patterns. Fewer may be sufficient in specialized markets with few comparable cases, while more may be needed in markets where cases vary widely. Including ordinary and failed cases as well as successful ones helps reduce survivorship bias.
Is it enough to imitate 80% and differentiate only 20%?
This ratio is not a scientific law that applies to every business. It is more of a practical analogy suggesting that you retain familiar usage patterns while improving one or two elements that matter to customers. The degree of differentiation needed varies depending on the industry, regulations, technological changes, and customers' switching costs.
How is reverse-engineering a successful product different from plagiarism?
Reverse engineering involves analyzing a publicly available product's customers, value proposition, pricing, distribution, and user flow to learn general principles. Reproducing someone else's text, images, code, trademarks, patented technology, or trade secrets without permission may constitute a separate intellectual property issue.
What is the most important metric when validating an idea?
It depends on the stage of the business, but actions that require customers to bear a cost or make an effort—such as reservations, purchases, repeat usage, repurchases, and referrals—are stronger signals than simple expressions of interest. At the same time, you should also assess customer acquisition costs, service costs, refund rates, and profitability potential.
Can generative AI automatically extract a formula for success?
AI can quickly classify reviews and summarize common patterns, but it cannot automatically prove causal relationships behind success. AI outputs should be treated as hypotheses and verified by checking the original sources, conducting customer research, and running real-world experiments; personal information and trade secrets must not be entered.
Sources
Images
