The Right It: Your Guide to Building What the Market Truly Wants

Ever wonder why brilliant ideas, backed by smart people and ample funding, still crash and burn in the market? Alberto Savoia, a seasoned entrepreneur and former Google engineering director, dives deep into this perplexing phenomenon in his book, The Right It. Drawing from his own painful experiences with market failure, Savoia presents a refreshingly practical framework designed to help innovators, entrepreneurs, and product managers drastically improve their odds of success. He argues that most failures stem not from poor execution, but from building the wrong product idea in the first place. This summary will unpack every essential idea, tool, and tactic from The Right It, offering a comprehensive guide to validating your ideas and building what the market genuinely wants, ensuring you’re always building The Right It before you build it right.

Prelude

The prelude sets a powerful, personal tone, revealing Alberto Savoia’s motivation for writing The Right It. After a devastating $25 million startup failure, despite a strong team and what seemed like a foolproof plan, Savoia was left reeling. He had a perfect record of successes at Sun Microsystems and Google, and his previous startup was a $100 million acquisition. Failure was something that happened to “less experienced, less competent” people. Yet, there he was, bitten by the Beast of Failure. This personal experience ignited an obsession: to understand why ideas fail, how to prevent it, and how to teach others to do the same. This book is the embodiment of that mission, offering a path to consistently align ideas with market demand and significantly increase the likelihood of success.

Book Overview

Savoia frames The Right It as a practical guide, structured around a powerful combination of facts, tools, and tactics. He promises to equip readers with the knowledge and methods to combat the high rate of market failure. The book is divided into three main parts:

• Part I: Hard Facts unflinchingly examines the reality of market failure. It confronts the pervasive myth that hard work and competence alone guarantee success, introducing the core concept of The Right It versus The Wrong It, and emphasizing the critical role of data over opinions.
• Part II: Sharp Tools unveils a suite of powerful methodologies. These include thinking tools for clarifying ideas, pretotyping tools for rapidly testing market engagement, and analysis tools for rigorously interpreting collected data.
• Part III: Plastic Tactics focuses on the practical application of these tools in real-world scenarios. It provides actionable strategies for organizing and executing market validation tests efficiently, demonstrating how to adapt plans in response to market feedback.

Throughout the book, Savoia integrates numerous real-world stories and case studies, acknowledging that these are often simplified or seen through different lenses (“Rashomon Effect”). He clarifies that while aiming for 100% accuracy in recounting complex failures is impossible, the goal is to extract valuable, actionable lessons. He also defines key terminology, using “product” or “It” broadly to refer to any new idea (product, service, business, organization), and “market” as any group intended to engage with that idea. He emphasizes that the book’s methods apply to any undertaking involving a non-trivial investment, a high probability of failure, and the desire to avoid that failure. Savoia also stresses that he practices what he teaches, even pretotyping this very book by first releasing a short, free booklet to gauge interest. He concludes by urging readers to use the newfound power responsibly, always considering ethical implications and seeking to build things that are not just successful, but also meaningful and beneficial to the world.

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Part I: Hard Facts

Part I lays the groundwork by forcing readers to confront uncomfortable truths about market failure, arguing that accepting these “hard facts” is crucial for building a solid foundation for success.

1: The Law of Market Failure

This chapter dismantles the pervasive myth of guaranteed success, introducing the Law of Market Failure as a foundational concept.

Failure Is Not an Option—Not!

Savoia directly challenges the popular motivational phrase, “Failure is not an option!” He asserts that when it comes to new product ideas, failure is always an option; in fact, it is the most likely outcome. This isn’t pessimism, but a crucial alignment with reality. Accepting this fact is the first step towards consistent market success, as it promotes a mindset of caution and rigorous testing rather than blind optimism.

The Law of Market Failure

The core “hard fact” of the book is presented: Most new products will fail in the market, even if competently executed. This is a two-punch realization. First, the majority of new ideas fail. Second, and more painfully, this failure often occurs despite the best efforts of skilled teams. Savoia elevates this consistent phenomenon to a “law” to emphasize its pervasiveness and necessity for respect.

Market Failure and Success Defined

To provide clarity, Savoia offers precise definitions:

• Market Failure: Any actual market result from an investment in a new product that is less than or the opposite of the expected result. This encompasses not just financial losses, but also unmet goals like market share, publicity, or customer acquisition. He provides examples such as a critically acclaimed movie that flops at the box office or a functional product that simply doesn’t sell enough to be profitable.
• Market Success: Any actual market result from an investment in a new product that matches or surpasses the expected result. He stresses the importance of defining explicit success criteria before beginning any project to avoid subjective post-hoc rationalizations.

Market Failure Statistics

Savoia reinforces the Law of Market Failure with concrete data. Citing Nielsen Research, he states that approximately 80% of new products fall short of their original expectations year after year. Across various industries—from consumer products and mobile apps to publishing and restaurants—the failure rate consistently hovers between 70% and 90%. He advises readers to conservatively assume a 90% chance of failure for any new product idea.

The Success Equation

This section introduces a simple but brutal logic explaining high failure rates: Right A × Right B × Right C × Right D × Right E, etc. = Success. This equation illustrates that for an idea to succeed, all key factors must be done right or turn out right. Conversely, failure requires only one key factor to go wrong: Right A × Right B × Right C × Wrong D × Right E, etc. = Failure. Just like multiplying any number by zero results in zero, a single critical misstep or unmet condition can doom an entire venture, regardless of how many other elements are perfected.

Too Smart to Fail?

Savoia directly confronts the common misconception that experience and competence are sufficient antidotes to market failure. He provides compelling examples of industry giants who have failed spectacularly despite their expertise:

• Coca-Cola’s New Coke and PepsiCo’s Crystal Pepsi demonstrated that even leading beverage companies could misread consumer preferences.
• Disney’s John Carter and George Lucas’s Howard the Duck showed that Hollywood’s most successful studios and filmmakers are not immune to box office bombs.
• Google Wave, Google Buzz, and Google Glass are examples of how Google, a world leader in internet products, consistently launched high-profile failures.

He argues that while competence is necessary for lasting success with a product the market wants, it is powerless when applied to a product the market is not interested in. In fact, expertise often leads to bigger, more public failures due to larger investments and inflated expectations.

Failophobia

Savoia introduces failophobia—the deeply human desire to be associated with success and avoid failure. He observed this phenomenon at Google, where individual employees often shied away from risky, unproven projects, preferring to work on established successes, even if it meant taking a lesser role. While companies might tolerate failure as a by-product of innovation, individual fear of failure can stifle internal innovation. He notes that while people avoid new failures, they love to recount past ones, turning them into “battle scars.”

FLOP

Through interviews with countless individuals about their failed projects, Savoia identified a clear pattern for the reasons behind market failure, summarized by the acronym FLOP:

• Failure due to Launch: Occurs when sales, marketing, or distribution efforts fail to reach the intended market. The product might be great, but people don’t know it exists or can’t get it.
• Failure due to Operations: Happens when the product’s design, functionality, or reliability fails to meet minimum user expectations. The product simply isn’t good enough in its implementation.
• Failure due to Premise: The most common and insidious reason. This occurs when people are simply not interested in the idea, even if they understand it, believe it works, and can easily access it.

Savoia emphasizes that after getting past the initial blame game, most interviewees realized their projects failed primarily due to a wrong Premise. This led to his core mantra: Make sure you are building The Right It before you build It right. Competence in execution is wasted on a product the market doesn’t want.

Part II: Sharp Tools

Part II transitions from understanding why ideas fail to providing the “sharp tools” necessary to significantly increase the chances of building The Right It.

2: The Right It

This chapter defines the book’s central concept and its insidious counterpart, explaining why so many well-intentioned efforts go awry.

The Wrong It

The Wrong It is defined as an idea for a new product that, even if competently executed, will fail in the market. This is the “evil twin” of The Right It. Savoia stresses that no amount of brilliant design, clever engineering, or superior salesmanship can save a product based on a fundamentally wrong Premise. The more time and effort invested in The Wrong It, the more painful the inevitable failure.

Thoughtland

Savoia introduces Thoughtland as an imaginary place where new ideas are hatched and spend too much time. In Thoughtland, ideas attract opinions – subjective, biased judgments without evidence or “skin in the game.” This is where most traditional market research, such as focus groups, operates, leading to unreliable results. He emphasizes that The Right It cannot be deduced through thinking alone; it must be discovered through real-world experimentation.

Hocus-Pocus Focus Groups

Savoia vividly illustrates the dangers of Thoughtland-based market research using the example of focus groups, which he renames “hocus-pocus groups” due to their misleading nature. He recounts a hypothetical scenario where “Alberto’s Beer Company” (ABC) uses focus groups to develop “LadyLike Beer” for women. Initial surveys show high interest in a “feminine” beer. After tasting, participants express strong likelihood of buying it. However, upon launch, LadyLike flops. This demonstrates that what people say they’ll do in a controlled, risk-free environment often doesn’t translate to real-world purchasing behavior.

The Four Trolls of Thoughtland

Savoia identifies four “trolls” that infest Thoughtland-based market research, leading to unreliable data:

• The Lost-in-Translation Problem: An idea, when communicated, is imagined differently by each person, distorted by their unique beliefs, preferences, and prejudices. What the presenter envisions may be vastly different from what the audience perceives.
• The Prediction Problem: People are notoriously bad at predicting their future behavior regarding new products or services. They may say they’ll use something often but rarely do, or dismiss an idea they’ll later embrace (e.g., Savoia’s initial skepticism about Uber and Airbnb).
• The No-Skin-in-the-Game Problem: Opinions are freely given when there’s nothing to lose or gain from the outcome. Focus group participants have no stake in the product’s success or failure, leading to unreliable feedback. This is a central concept in the book.
• The Confirmation-Bias Problem: People tend to seek out and interpret information in a way that confirms their existing beliefs, dismissing contradictory evidence. This skews research design and interpretation of results, leading to self-deception.

When these trolls combine, they produce “fur balls of subjective, biased, misguided, and misleading opinions” instead of dependable data, resulting in both false positives and false negatives.

Thoughtland and False Positives

False positives occur when Thoughtland-based research convinces you an idea is worth pursuing, only for it to fail miserably in the market. Savoia uses the spectacular failure of Webvan as a prime example. Convinced by market analysis and customer opinions that online grocery delivery was a “sure bet,” Webvan raised and spent over $800 million building infrastructure, only to find that consumers weren’t interested in buying groceries online at the scale predicted. The initial buzz and promises, generated in Thoughtland, never materialized into real demand.

Thoughtland and False Negatives

False negatives are the opposite: Thoughtland-based feedback convinces you to abandon an idea that would have been The Right It. Savoia shares personal anecdotes: his friends dismissed Google as a “minor internet player” compared to Yahoo! and laughed at his proposal to work on Google’s ad team (which became a multi-billion dollar business). He himself was a “false negative” generator for now-successful companies like Twitter, Uber, Airbnb, and Tesla’s Roadster. He also highlights Ring, the video doorbell company, which was rejected by investors on Shark Tank but later sold to Amazon for nearly a billion dollars, after selling millions of units. This section underscores how often initial opinions, even from experts, miss the mark.

Escape from Thoughtland

Savoia concludes that given the pervasive “trolls,” it’s impossible to trust opinions—your own, others’, or experts’. Relying on Thoughtland leads to two detrimental scenarios: overinvesting in The Wrong It (false positive) or abandoning The Right It (false negative). The only way to escape this trap is to move beyond opinions and collect data.

3: Data Beats Opinions

This chapter introduces the fundamental principle that guides all market validation: data reigns supreme over subjective opinions.

Other People’s Data

Savoia introduces the concept of Other People’s Data (OPD), defined as any market data collected and compiled by others, for other projects, at other times, in other places, with other methods, and for other purposes. He argues that relying on OPD is a tempting, but lazy and dangerous shortcut because it often violates key criteria for reliable data:

• Freshness: Data quickly spoils, especially in fast-changing markets. An “eight-second rule” for web page loading in the 90s is now a “two-second rule.”
• Strong Relevance: Data must directly apply to your specific product and decision. McDonald’s onion ring data isn’t relevant for a new burger food truck.
• Known Provenance: You must know who collected the data, how, and for what purpose, as biases can easily creep in.
• Statistical Significance: Data must be based on a large enough sample size to be meaningful, not just anecdotal experience.

He dissects five scenarios where people might think OPD is useful, concluding that in almost all cases, it’s either nonexistent, insufficient, or misleading. The success or failure of similar ideas by others (Scenario 2c and 2d) doesn’t guarantee your outcome, as contexts, execution, and timing differ. Ultimately, OPD can inform but should not be depended upon for crucial decisions about your idea’s market potential.

You Must Get Your Own DAta

The antidote to unreliable OPD is Your Own DAta (YODA). YODA is defined as market data collected firsthand by your own team to validate your own idea. To qualify as YODA, it must be fresh, relevant, trustworthy, and statistically significant. Savoia asserts that an ounce of YODA is worth a ton of OPD, and crucially, collecting YODA is “neither difficult nor time consuming nor expensive.” This sets the stage for the pretotyping tools that will enable this direct data collection.

Quick Recap

Part I concludes with a concise summary of the core lessons, emphasizing the critical mindset shift required:

• The Law of Market Failure: Most new ideas fail, even with competent execution.
• Most failures are due to building The Wrong It.
• Success requires combining competent execution with The Right It.
• Intuition, opinions, and OPD are unreliable; only YODA can determine if an idea is The Right It.
• Proper YODA must always involve skin in the game. These principles form the foundation for the practical tools and tactics explored in the subsequent sections, promising to flip the odds of market success in the reader’s favor.

Part II: Sharp Tools

Part II introduces the practical instruments necessary to move beyond assumptions and gather concrete evidence about an idea’s market viability.

4: Thinking Tools

This chapter introduces foundational “thinking tools” designed to bring clarity and precision to your ideas, transforming vague concepts into testable hypotheses.

Market Engagement Hypothesis

Savoia introduces the Market Engagement Hypothesis (MEH) as a crucial first step for any new idea. This is a clear articulation of how you envision the market engaging with your product. It’s the core belief or assumption about customer behavior. For example, for a “Cheapo Sushi” food truck, the MEH might be: “If we make sushi as fast and inexpensive as other fast food, many sushi lovers will choose it over burgers, tacos, or less healthy options.” The MEH helps you focus on the most critical assumption: market demand. Savoia contrasts this with mere “hope” or “hallucination,” emphasizing that while the MEH is an initial vision, it must be testable. The guiding principle here is: “If there’s no market, there’s no way.” Conversely, “If there’s a market, there’s a way.” If real demand exists, a solution can usually be found.

Say It with Numbers

To make the MEH testable, Savoia advocates the “Say It with Numbers” principle, learned from his time at Google. This means transforming vague terms into specific, quantifiable metrics. Instead of “a few more clicks,” specify “at least 10% more subscribers.” Numbers, even initial rough estimates, eliminate fuzziness and allow for objective testing. He uses the example of A/B testing a “Subscribe” button’s width: changing “a little wider” to “20% wider” makes the hypothesis testable and the results measurable. This approach helps to surface implicit assumptions and disagreements within a team.

Hypozooming

Hypozooming is presented as a method to take a broad, specific hypothesis and zoom in to a version that is immediately actionable and testable. The goal is to move from a large, ultimate target market (Y) to a small, manageable, and easy-to-reach initial test market (y). This minimizes initial investment and speeds up data collection. Savoia emphasizes aggressively zooming in, but ensuring the sample size is statistically significant (e.g., 100-1,000 people representative of the target audience).

He illustrates this with an idea for a portable air-pollution monitor. The initial XYZ hypothesis was:
“At least 10% of people living in cities with an AQI above 100 will buy a $120 portable pollution sensor.”
However, this hypothesis is too broad for immediate testing.

Hypozooming helps narrow down the market (from Y → y)—starting with “all polluted cities worldwide,” then focusing on “Beijing, China,” and eventually narrowing further to “parents at Beijing Tot Academy,” a specific preschool. This targeted segment enables practical and realistic testing.

5: Pretotyping Tools

This chapter introduces the core methodology of the book: pretotyping, explaining its distinction from traditional prototyping and showcasing various pretotyping techniques.

The IBM Speech-to-Text Example

Savoia introduces the concept of pretotyping with a pivotal story: in the early days of computing, IBM wanted to test market demand for a speech-to-text machine. Lacking the technology for a functional prototype, they created a “Mechanical Turk” setup. Users spoke into a microphone, believing a computer was transcribing their words, but a hidden typist in the next room was doing the work. This allowed IBM to discover that even with perfect speech-to-text, businesspeople found dictating clumsy and problematic. This example perfectly illustrates the essence of pretotyping: pretending to have a product to validate market interest before building it.

Pretotyping

Savoia formally defines pretotyping as a word he coined, derived from “pretending” and “pre-,” meaning something that comes before. He distinguishes it from prototyping:

• Prototypes primarily answer: “Can we build it? How should it be built? Will it work as intended?” They are about technical feasibility.
• Pretotypes primarily answer: “Should we build it? Would people use it? How often? Would they pay for it?” They are about market desirability.

Pretotyping aims to validate an idea quickly and cheaply, often in hours or days for a few hundred dollars, contrasting with prototypes that can take months/years and millions. He argues that having distinct terms helps set proper expectations and encourages the right mindset.

In Search of Pretotypes

After discovering the IBM story, Savoia actively sought out other examples of pretotyping techniques and analyzed past market failures. He found that most costly failures (The Wrong It) could have been prevented by well-planned pretotyping experiments. This reinforced his conviction that pretotyping could dramatically improve success rates. He encourages readers to “Don’t trust me. Test me!” by applying these techniques themselves to collect YODA.

The Mechanical Turk Pretotype

This technique involves replacing costly, complex, or undeveloped technology with a concealed human being performing the functions of that supposedly advanced technology. The IBM speech-to-text example is the classic case.

Savoia then applies it to “Fold4U,” an idea for an automatic clothes-folding machine for laundromats. Instead of building the machine (costing $50,000 and six months), the inventor, Ivan, gets an angel investor, Angela, to agree to a small test. They use a modified broken dryer (a dummy machine) where Ivan hides inside, manually folding clothes when customers insert money. The goal is to see if customers will actually pay for the service (“At least 50% of Lenny’s coin laundry customers will put their clothes in a Fold4U machine and pay $2 to have them folded”).

• Failure Scenario: The pretotype reveals low uptake (12% of customers, even at $1). Ivan learns cheaply that his market assumption was wrong.
• Success Scenario: High uptake (78% of customers initially, stabilizing at 62%) validates the idea. Ivan gets strong YODA to secure further investment and proves a compelling business case for laundromat owners.

This highlights the win-win nature of pretotyping: prevent likely failures or validate promising ideas with concrete data.

The Pinocchio Pretotype

Named after the wooden puppet who wanted to be a real boy, the Pinocchio pretotype involves creating a non-functional mock-up of a product and pretending it works to gain insights into usage and desirability.

The classic example is Jeff Hawkins’ PalmPilot pretotype. Before committing to building the PDA, he carved a block of wood the size of the intended device and carried it for weeks, using a chopstick as a stylus, simulating checking his calendar or looking up phone numbers. This allowed him to gather YODA on whether he would actually carry and use such a device, and for what primary functions (address book, calendar, memo, to-do lists). This informed the actual product’s design, leading to the highly successful PalmPilot and influencing future smartphones.

Other examples include:

• Smart Horn: Sticking labels on a steering wheel to simulate different horn sounds to see how often a driver would “use” specific messages.
• Smart Speaker (HAL): Savoia himself used a pinto bean can wrapped in tape as a dummy smart speaker (“HAL”) for a week. He pretended to ask it questions or give commands, learning he’d want multiple units, a “stop listening” button, and whisper mode. This validated the concept’s desirability for him long before Amazon Echo launched.

The key is to use the mock-up to actively pretend it works, collecting YODA on potential usage patterns and needs.

The Fake Door Pretotype

The Fake Door pretotype involves setting up a “front door” (an ad, website, physical sign) for a product or service that doesn’t yet exist, to gauge interest. If people don’t “knock” (show interest), there’s no market.

• Kevin Kelly’s travel guide catalog: He bought an ad in Rolling Stone for a catalog that didn’t exist. Sufficient orders proved market interest, enabling him to bootstrap the business.
• Savoia’s video games: He wishes he had used this technique by creating “coming soon” ads for game concepts, directing people to send a self-addressed stamped envelope to receive a discount coupon, then developing only the games that generated the most interest.
• Antonia’s Antique Bookstore: Antonia put a “coming soon” sign on a vacant storefront. She sat across the street, counting how many people stopped and knocked. Low numbers indicated insufficient foot traffic interest, saving her from a major investment.
• Sandy’s Guide to Squirrel Watching: Sandy created a basic website for her unwritten book, with a “Buy Now” button that led to a message explaining the book wasn’t ready and asking for email sign-ups. By analyzing ad clicks and email submissions, she could determine customer acquisition cost and genuine interest before writing the book.

Savoia acknowledges the ethical implications of deception with Fake Door pretotypes but argues the minor inconvenience to interested individuals is far outweighed by preventing massive waste of time and money on doomed ventures. He suggests being transparent and rewarding “knockers” (e.g., a gift certificate or free sample) to turn it into a win-win.

The Facade Pretotype

The Facade pretotype is a variation of the Fake Door, where when potential customers “knock,” something actually happens, and they might even get what they’re looking for. It requires more investment but yields richer data.

• CarsDirect: At the dawn of the internet, Bill Gross put up a fully functional-looking website for online car sales. When four customers ordered cars, he quickly shut the site down (as he had no inventory), but the actual sales validated the demand. He then built the real company. This provided strong YODA (actual money exchanged) and revealed insights into back-end processes.
• Antonia’s Antique Bookstore (revisited): Instead of just a sign, Antonia could rent the storefront for a few days, placing a few books she owned, and personally engaging customers who entered. She could then gauge specific interests (e.g., types of books, price points) and even take preorders, gaining richer data than just counting knocks.

Facade pretotypes offer more in-depth learning about customer interaction and operational challenges, providing stronger evidence for investors.

The YouTube Pretotype

The YouTube pretotype leverages video to simulate a product that doesn’t yet exist, to gauge market interest. The “magic of movies” helps people imagine and experience the future product.

• Google Glass Explorer Edition: Before Glass was ready, Google created a compelling video showing how it would work. To turn views into YODA, they offered an “Explorer program” requiring a $1,500 payment and travel expenses (significant skin in the game). While initial interest was high, tracking long-term usage revealed that most “Explorers” stopped wearing them after the novelty wore off. This showed that while initial buzz was strong, the long-term desirability was not.
• Smart Horn (revisited): A video could simulate the different horn sounds and their usage, with an option for viewers to preorder the non-existent product.
• Portable Pollution Sensor (revisited): A video could tell a story of parents using the device to protect their child, to appeal to the target market.
• FeeBird (mobile app): Savoia suggests creating a video using presentation software (like Keynote) to simulate the app’s functionality (e.g., finding rare birds, paying for locations). This creates a realistic demo without writing any code, allowing for rapid testing of market interest through sign-ups or preorders, demonstrating a high Return on Pretotyping Investment (e.g., $100 and 10 hours to discover zero interest vs. thousands of dollars and weeks for a real app).

The key is to combine the visual demonstration with a call to action that requires skin in the game, not just views or likes.

The One-Night Stand Pretotype

This pretotype involves testing an idea with a short-term, minimal commitment or investment. It emphasizes testing a core hypothesis for a limited duration (e.g., a few hours, a day, or weeks) to see if it warrants a longer-term commitment.

• Virgin Airlines: Richard Branson, stranded after a canceled flight, wrote “Virgin Airlines / $39 one-way ticket to BVI” on a blackboard and sold enough tickets to charter a plane. This single successful “one-night stand” experiment encouraged him to start Virgin Airlines.
• Airbnb: Cofounders Joe Gebbia and Brian Chesky, facing rent issues, rented out three air mattresses in their apartment for a single night, including breakfast, at $80 each. This “one-night stand” yielded three paying customers, validating the core idea of strangers paying to sleep in a stranger’s home.
• Tesla’s Pop-Up Showroom: Tesla uses portable shipping containers that transform into showrooms. These “pop-up” locations are quickly set up in different areas to gauge local market interest and collect deposits, avoiding long-term, expensive dealership commitments until demand is proven.

The principle is to validate a long-term XYZ hypothesis with a short-term xyz experiment, applying the “test a little before you invest a lot” mantra to the dimension of time.

The Infiltrator Pretotype

The Infiltrator pretotype involves sneaking your product (or a sample of it) into someone else’s existing sales environment (physical store or online platform) where similar products are typically purchased, to see if people will buy it.

• Walhub: Justin Porcano, who designed an innovative switch plate (Walhub), bought a used IKEA employee shirt, created fake IKEA labels, and with accomplices, placed his Wälhub prototypes in IKEA stores. Customers picked them up, and some even tried to buy them. This provided YODA on interest, pricing, and optimal placement, all captured on video, demonstrating a strong return on a small marketing budget.
• Online Infiltrator: The same principle applies online by contacting existing online retailers to display your product on a trial basis, leveraging their established traffic to collect sales data.

Savoia acknowledges the ethical and legal risks of physical infiltration but suggests partnering with smaller businesses as a safer alternative. The goal is to get customers to demonstrate “skin in the game” by attempting to purchase the product.

The Relabel Pretotype

The Relabel pretotype leverages an existing product or service by changing its external appearance (label, branding) to pretend it’s a new or different offering, and then observing market response.

• Second-Day Sushi: During a discussion about expensive sushi, Savoia’s students debated if there was a market for “cheap sushi.” They relabeled half the packaged sushi boxes in a campus café with “Second-Day Sushi: 1/2 Off!” The experiment showed very low interest, indicating that cheap, less-fresh sushi was not The Right It.
• Book Covers: To test interest in a book of programming jokes, the idea was to relabel an existing book with a mock-up cover, place it in a bookstore’s computer section, and count how many programmers picked it up. This would indicate initial engagement before the actual book was written.

This technique is often combined with the Infiltrator pretotype to use existing sales environments. It highlights how minor changes in presentation can test core assumptions about a product’s appeal.

Pretotyping Variations and Combinations

Savoia emphasizes that the listed pretotyping techniques are not exhaustive but serve as inspiration for developing new variations and combinations. He encourages creativity in modifying, adapting, and combining techniques to fit specific ideas.

• Live Demo Pretotype: Instead of a video, demonstrate a non-functional product live to an audience, then offer a chance to sign up or make a small commitment. An example is an app to help students relax and focus: a live demo showing a partner’s heart rate drop while using a mock-up, then asking for email sign-ups or small payments.
• Morsel Pretotype: For creative works like novels, offer a “morsel” (e.g., a few free sample chapters) to gauge interest before completing the full work. Andy Weir, author of The Martian, initially posted chapters of his novel as a free serial online. The strong engagement (thousands of readers, volunteer editors) and subsequent paid e-book sales (30,000 copies at 99¢) provided the YODA needed to attract an agent, publisher, and movie deal.

These examples underscore the flexibility and power of pretotyping to test complex ideas and iterate based on market feedback.

What Makes a Pretotype a Pretotype

Savoia distills the essence of a true pretotype into three core requirements:

1. Produces YODA with skin in the game: The data collected must come from genuine market actions that involve some form of commitment or risk (e.g., money, time, personal information) from potential customers.
2. Can be implemented quickly: Pretotyping experiments should yield data in hours or days, not weeks or months. This allows for rapid iteration and minimizes wasted time.
3. Can be implemented cheaply: The cost of a pretotype should be minimal, ideally a few dollars or hundreds, not thousands or millions. This reduces financial risk and allows for more experiments.

These criteria guide the selection and design of effective pretotyping experiments, ensuring that the collected data is truly valuable for making informed decisions.

6: Analysis Tools

This chapter introduces the “analysis tools” necessary to interpret the collected YODA objectively, making the crucial transition from raw data to actionable decisions.

The Skin-in-the-Game Caliper

Savoia provides a formal definition of “skin in the game” as having a vested interest in an outcome, something to lose or gain. For new products, it means potential customers demonstrating commitment. He stresses that opinions and promises without skin in the game are worthless. The Skin-in-the-Game Caliper is a tool to assign “points” to different types of market responses, reflecting the level of commitment:

• 0 points: Opinions (expert or non-expert), encouragement, throwaway email addresses, social media comments/likes/shares, surveys, polls. These are unreliable indicators.
• 1 point: A validated email address (given knowingly for product updates).
• 10 points: A validated phone number (due to higher personal value).
• 30 points: A 30-minute time commitment (e.g., attending a demo), valued at 1 point per minute.
• 50 points: A $50 cash deposit (valued at 1 point per dollar).
• 250 points: Placing a $250 order (1 point per dollar).

He uses the example of the “Tortell-o-matic” (an automated tortellini maker) to illustrate the caliper’s application. The caliper brutalizes subjective responses, valuing only actions that demonstrate real commitment. This helps differentiate genuine interest from mere “spectator” engagement. He presents a “Tale of Two Teams,” where Team A boasts high “likes” and “views” (0 points), while Team B shows actual email sign-ups and preorders (high points), highlighting that quality of data beats quantity.

The TRI Meter

The Right It Meter (TRI Meter) is a visual analysis tool developed to help objectively interpret collected YODA and estimate the likelihood of an idea’s market success. It’s a gauge with five categories: Very Unlikely (10% chance), Unlikely, 50/50, Likely, and Very Likely (90% chance).

• The “Law of Market Failure” (big black arrow) perpetually points to “Very Unlikely,” serving as a constant reminder that most new ideas fail and requiring substantial evidence to shift the needle.
• Smaller, light-colored arrows represent individual pretotyping experiments. After each experiment, you ask: “If this idea is destined to succeed, how likely is it that this experiment would have produced this result?”
  • Data significantly exceeding predictions: Very Likely
  • Data meeting or slightly exceeding predictions: Likely
  • Data falling slightly short: Unlikely
  • Data falling really short: Very Unlikely
  • Ambiguous/corrupted data: 50/50 or discard.

Savoia emphasizes that a single experiment is never enough, just as one date isn’t enough to decide on marriage. Multiple experiments are needed to build confidence, especially when significant investment or risk is involved. The number of experiments should be commensurate with the investment and consequences of failure. He illustrates this using the “Second-Day Sushi” example, showing how different sales outcomes (0% to 75%) would map to the TRI Meter, and how to identify and discount tainted data (e.g., a news article skewing results, or an unusual demographic present during testing). The goal is to achieve a “preponderance of positive evidence” to outweigh the initial odds of failure.

Part III: Plastic Tactics

Part III focuses on the “plastic tactics”—the flexible and adaptable strategies for effectively applying the tools and interpreting their results in the real world.

7: Tactics Toolkit

This chapter introduces four key tactics for organizing and executing market validation tests efficiently and effectively, emphasizing adaptability in the face of market realities.

Tactic 1: Think Globally, Test Locally

Inspired by environmental slogans, this tactic advises to have global aspirations but validate ideas on a small, accessible, and representative subset of your target market first. This minimizes initial investment and speeds up data collection. “Locally” can mean geographic proximity (e.g., your town, neighborhood, or specific community) or conceptual proximity (e.g., one specific smartphone platform if you’re a mobile app developer, rather than launching on all platforms simultaneously).

Savoia introduces Distance to Data (DTD) as a metric to quantify and minimize the “distance” (physical or conceptual) to your test market. He uses the example of “LaundroDone” (a device that texts when laundry is done). Instead of driving 120 miles to Los Angeles for tests, the inventor should find a coin laundry in a nearby town, saving time and money for more tests. This tactic encourages rapid entry into the real market to collect YODA.

Tactic 2: Testing Now Beats Testing Later

This tactic is straightforward: don’t delay testing. Get your idea out of Thoughtland and into contact with the market as soon as possible. Savoia attributes delays to fear of rejection—fear of finding out the beloved idea isn’t wanted. He stresses that rejection is inevitable, and it’s always better to discover it quickly and cheaply than after a massive investment.

He introduces Hours to Data (HTD), a metric measuring how many hours it takes to execute a pretotyping experiment and collect high-quality YODA. He aims for an HTD of under 48 hours, sharing an anecdote where a student achieved a “3 minutes to data” record for a bicycle cleaning service. This tactic emphasizes urgency and lean experimentation over prolonged planning.

Tactic 3: Think Cheap, Cheaper, Cheapest

This tactic encourages minimizing the financial investment in pretotyping. Savoia challenges the common tendency to overspend on market research. He advocates for continually asking, “Is this the best that we can do?” to find increasingly cheaper ways to test an idea without sacrificing YODA quality. He recounts the Henry Kissinger story about demanding multiple revisions until the best possible output is achieved.

He introduces Dollars to Data ($TD) as a metric to quantify and minimize the cost of collecting market data. He emphasizes that “creativity loves constraints,” suggesting that if you initially budget $1,000, challenge yourself to get the same data for $100, or even $10, or zero. The goal is maximum insights for minimal expenditure.

Tactic 4: Tweak It and Flip It Before You Quit It

This tactic advises against abandoning an idea prematurely if initial YODA is disappointing. Instead, the Right It might be just a few tweaks away. Savoia illustrates this with a visual of “The Right It zone” (light gray) within a broader market opportunity, surrounded by “The Wrong It zone” (dark gray). Your initial idea (It1) might land in the Wrong It zone due to minor misalignments (too expensive, wrong color, bad name), even if the core problem or opportunity is real.

Each pretotyping experiment, even those yielding negative results, provides valuable YODA about market preferences. This data should inform subsequent tweaks to the idea. He suggests trying various iterations (It2, It3, etc.) until one lands in The Right It zone. If, after many tweaks, the idea consistently fails, it might mean The Right It for that specific problem does not exist.

Savoia also introduces “flipping” assumptions (inspired by Tina Seelig’s creativity exercises). Instead of cheap sushi, explore “Superior Sushi” with premium pricing and packaging. This systematic challenge of assumptions can reveal entirely new “Right It” opportunities. He contrasts “tweaks” with “pivots”: pivots often occur after significant investment in The Wrong It, born of desperation. Tweaking early and often prevents traumatic, costly pivots, dramatically increasing chances of landing The Right It.

8: Complete Example: BusU

This chapter provides a comprehensive, realistic example of how all the tools and tactics learned throughout the book are applied in sequence, from initial idea to market decision.

Thinking Clearly About Our Idea

The example starts with the author’s idea for BusU: turning commuter buses into classrooms, offering accredited college-level classes to professionals during their San Francisco-to-Silicon Valley commute. The vision includes customized buses, top-rated professors, and a fee of $3,000 for a ten-week course, assuming companies would subsidize tuition.

The initial, vague Market Engagement Hypothesis (MEH) is:
“A lot of professionals with long commutes will pay university-level tuition to take buses with classes.”

This is then refined into a more specific XYZ Hypothesis:
“At least 2% of working professionals with daily commutes of one hour or longer each way will pay $3,000 to take an accredited ten-week class on BusU at least once a year.”

Next, hypozooming is applied to narrow the target market (from Y → $y). Leveraging his personal connections, the author focuses on a specific segment: Google engineers commuting from San Francisco to Mountain View.

This leads to the creation of three distinct XYZ hypotheses, each representing a different level of “skin in the game” and expected engagement.

• xyz1: At least 40% will visit BusU4Google.com and submit their google.com email for info. (Low skin: 1 point)
• xyz2: At least 20% will attend a one-hour lunchtime presentation. (Medium skin: 60 points)
• xyz3: At least 10% will pay $300 for a one-week “Intro to AI” class on the bus. (High skin: 900 points)

The different percentages reflect the “conversion funnel” – fewer people will commit to higher levels of “skin in the game.”

Time to Test

The selection of the first pretotype is guided by “Testing now beats testing later” and “Think cheap, cheaper, cheapest.”

• xyz1 is chosen as the fastest and cheapest to test (HTD: $\sim$48 hours, $TD: <$100), requiring only a simple website and access to a Google mailing list (MTVCarPoolers).
• xyz2 would take longer (HTD: $\sim$336 hours/2 weeks) due to scheduling presentations.
• xyz3 would be the most expensive and time-consuming (HTD: $\sim$672 hours/4 weeks, $TD: >$5,000) due to needing a professor and bus rental.

The first experiment involves sending an email about BusU (linking to the website) to 100 Google employees on the MTVCarPoolers list. Initial results are promising: 62% submission rate, far exceeding the 40% target.

Analyzing and Iterating

Initial analysis with the TRI Meter for xyz1 would show “Likely” or “Very Likely.” However, the author applies an objective lens, noting that many respondents asked about the service being free or company-subsidized. This leads to a tweak: the email and website are updated to explicitly state the $3,000 tuition and that it’s not eligible for Google reimbursement.

A second batch of 100 emails is sent. The response drops to 22% (22 forms submitted). While disappointing compared to the first round, the author interprets this as still “quite good” because these 22 people are now aware of the full cost and lack of subsidy, showing more genuine interest (higher skin in the game). This leads to a revised xyz1 (xyz1A): “At least 20% of Google engineers… who hear about BusU’s $3,000 (not eligible for company reimbursement) courses will visit… and submit their email…” This demonstrates the “Tweak it and flip it before you quit it” tactic, adjusting the hypothesis based on real market feedback.

A Lucky Break

During this iteration, a Google employee named Bob, with a PhD in AI, offers to teach a one-week “Intro to Machine Learning” class for free. This is a lucky break that allows for a much cheaper test of xyz3 (the high-skin class model). The author tweaks xyz3 to xyz3A: “At least 10% of Google engineers… will pay $300 for a one-week ‘Introduction to Machine Learning’ class on the bus taught by a fellow Google employee.”

The author secures a bus for $1,000/day ($5,000/week) and calculates that 20 sign-ups would cover costs. Beth sends an email to 200 Google employees, explicitly mentioning the $300 cost and no reimbursement. Forty-eight people register and pay in less than two days, selling out the first class and exceeding the 10% target with a 24% response rate. This generates $14,400 in “skin in the game.”

The first class runs with 35 students (some cancellations/no-shows provide further real-world data). Subsequent feedback reveals students preferred morning-only classes and were less interested in the $3,000 ten-week courses, but 48% signed up for another $300 one-week class. This confirms that the original BusU idea ($3,000 ten-week classes) was The Wrong It, but the tweaked version ($300 one-week classes) is The Right It.

A Few Notes About the BusU Example

Savoia concludes the example with key takeaways:

• Strong Business Case for Investors: The process generates powerful YODA with skin in the game (actual revenue, repeat customers) and demonstrates the team’s commitment, resilience, and flexibility—all highly attractive to investors.
• Realistic Scenario: The example, while fictional, is based on a plausible real-world idea and realistic challenges, demonstrating the practical application of the methods.
• Iterative Success: The first version of BusU (expensive, long classes) was The Wrong It; The Right It was discovered through testing and tweaking.
• Dynamic Process: The steps are not rigid; the plan evolves as new data and opportunities emerge, highlighting the “Plastic Tactics.”

9: Final Words

The concluding chapter summarizes the book’s core teachings and offers final, profound advice on the responsible and meaningful application of these powerful tools.

The Right It: A Recap

Savoia begins by reiterating his guiding principle: “Make sure you are building The Right It before you build It right.” He then succinctly reviews the hard facts:

• The Law of Market Failure: Most new products fail, even with competent execution.
• Most failures are due to building The Wrong It, which no amount of brilliance can save.
• Success comes from combining competent execution with The Right It.
• Data beats opinions; YODA beats OPD.
• YODA must come with skin in the game.

He then summarizes the sharp tools:

• Thinking Tools: Use Market Engagement Hypothesis (MEH), Say It with Numbers (XYZ Hypothesis), and Hypozooming (xyz hypotheses) to clarify ideas and create testable assumptions.
• Pretotyping Tools: Rapidly test market interest using techniques like Mechanical Turk, Pinocchio, Fake Door, Facade, YouTube, One-Night Stand, Infiltrator, and Relabel. These answer “Should we build it?” quickly and cheaply.
• Analysis Tools: Interpret YODA objectively using the Skin-in-the-Game Caliper (to assign value to market commitment) and the TRI Meter (to visualize the likelihood of success against the Law of Market Failure).

Finally, he recaps the plastic tactics:

• Think globally, test locally: Minimize Distance to Data (DTD).
• Testing now beats testing later: Minimize Hours to Data (HTD).
• Think cheap, cheaper, cheapest: Minimize Dollars to Data ($TD).
• Tweak it and flip it before you quit it: Iterate based on YODA, adjusting the idea or hypotheses.

He emphasizes that this iterative process helps readers go from rough idea to testable hypotheses, to experiments, to data-driven decisions, highlighting that it’s okay to tweak plans based on new learnings.

What to Build?

This final section poses two crucial existential questions about the purpose and ethical implications of using the book’s powerful tools.

Make Sure It’s The Right It for You

Savoia highlights that finding The Right It (for the market) is only half the battle. The other half is ensuring it’s The Right It for you. Success can lead to “success disasters” if you’re not fully committed, as seen with Elon Musk’s “production hell” with the Tesla Model 3. He stresses that you must truly care about what you’re doing to overcome inevitable problems and challenges. The pretotyping process not only validates market desirability but also reveals your own “enjoyability” of working on the idea. If you don’t enjoy the initial stages, the long-term grind will be unsustainable. It’s crucial that the idea is not just a good match for the market, but also a good match for your passion and long-term commitment.

Make Sure It’s The Right It for the World

Savoia closes by urging readers to think beyond mere financial success and consider the broader impact of their ideas. He warns against “Breaking Bad ideas” (like crack cocaine or meth) that are financially successful but harmful. He encourages a personal ethical compass (“What would my grandma think?”). He also pushes readers to look beyond “quick-buck ideas” (like silly phone apps) that make no meaningful difference to the world.

He emphasizes that the tools apply beyond business to nonprofit organizations and social ventures, where success is measured by social impact rather than profit. Ultimately, he challenges readers to ask: “What would you build if you knew that it would succeed?” His hope is that the book’s teachings empower readers to pursue “the right Right It” – ideas that are not only successful in the market but also meaningful to them and beneficial to the world. Such ideas not only provide intrinsic motivation to overcome obstacles but also attract unexpected support, increasing the odds of overall success.

Key Takeaways

The core lessons from The Right It revolve around a fundamental shift in how we approach new ideas, prioritizing rigorous market validation over intuition and assumption.

• Accept the Law of Market Failure: Understand that most new ideas, even well-executed ones, will fail. This realism fuels the need for diligent market testing.
• Data Beats Opinions (and OPD): Never rely on personal opinions, expert opinions, or Other People’s Data (OPD). These are unreliable and often misleading.
• Get Your Own DAta (YODA) with Skin in the Game: The only reliable way to validate an idea is to collect firsthand data from your target market, where they demonstrate genuine interest through commitment (money, time, personal information).
• Pretotyping is the Key: Use quick, cheap, and creative pretotyping experiments (like the Mechanical Turk, Fake Door, or Pinocchio) to answer the critical question: “Should we build it?” before investing heavily in “building it right.”
• Think Clearly and Iterate: Sharpen your fuzzy ideas into quantifiable XYZ Hypotheses, hypozoom to easily testable xyz hypotheses, and continuously tweak and adapt your idea based on market feedback.
• Be Fast and Frugal: Design experiments that minimize Hours to Data (HTD) and Dollars to Data ($TD) by thinking locally and choosing the cheapest, fastest tests.
• Don’t Quit Too Soon, But Know When to Stop: If initial results are discouraging, tweak your idea and retest. The Right It might be just a few iterations away. However, if consistent YODA indicates no market interest despite tweaks, be prepared to abandon the idea gracefully.
• The Right Idea for the Market AND for You: Ensure your idea aligns not only with market demand but also with your passion, values, and long-term commitment. Don’t build something you don’t care about, even if it’s profitable.
• Aim for the “Right Right It”: Use your newfound power to build ideas that are meaningful to you and beneficial to the world, not just financially successful. This increases motivation and attracts support.

Next Actions:

• Identify your current core idea. Write down its Market Engagement Hypothesis.
• Transform your MEH into an XYZ Hypothesis, saying it with numbers (At least X% of Y will Z).
• Hypozoom into 2-3 small, local, testable xyz hypotheses.
• Brainstorm 2-3 pretotyping experiments for your first xyz hypothesis that can be executed in under 48 hours and for less than $100.
• Commit to running one experiment this week. Don’t overthink it; just do it.

Reflection Prompts:

• What assumptions am I making about my idea that I haven’t tested?
• What’s the absolute fastest and cheapest way I could get some skin-in-the-game YODA for my idea right now?
• If my current idea proves to be The Wrong It, what variations or “flips” could I explore based on early market feedback?
• Beyond market success, is this idea truly The Right It for me, and for the world?