The Right It by Alberto Savoia

This book is fairly easy to summarise, and its central theses are easy to digest.

• The Law of Market Failure: the vast majority of new businesses fail.

• The Right It: These failures are because of many reasons, but a key one is because oftentimes markets don’t buy the business’s premise, i.e. businesses are often solving the Wrong It.

• Data Beats Opinions:

  • In order to predict the future success of a planned venture, data beats opinions.

  • Your own data is all that counts; other people’s data is meaningless.

  • People’s opinions do not matter since they have no skin in the game.

• Market Engagement Hypothesis: In order to gather data on whether one’s solving the Right It, one must seek to prove a hypothesis about the premise of the business, for instance, in case of the original idea for Netflix, quoting from the book’s Kindle edition.

  • Idea: By partnering with local universities, BusU will offer accredited college-level classes taught by top-rated professors while transporting professionals to and from work each day.

  • MEH: A lot of professionals with long commutes will pay university-level tuition to take buses with classes.

• XYZ Hypothesis: It’s important to convert everything from English to Math; so convert the MEH to a statement like so: at least X% of Y will Z. So, in the above case:

  • XYZ: 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.

• xyz Hypothesis: The central theme of the book is the importance of testing through something called pretotyping (introduced in the next bullet point). In order to test our market engagement hypothesis, we must convert the above XYZ statement into something we can test easily, quickly, and at the lowest possible cost. The idea is to convert the capital X, Y, and Z to small x, y, and z, by zooming into a local market where a quick and inexpensive test can be conducted. So, in the above case.

  • At least 40% of Google engineers commuting from San Francisco to Mountain View who hear about BusU will visit the BusU4Google.com website and submit their google.com email address to be informed of upcoming classes. (because the writer used that commute and worked at Google)

  • Personal note: Remember the Powai Principle – Powai is not Mumbai, Mumbai is not India. Sometimes what is local is not indicative of any market beyond that idiosyncratic market. Ensure your local market is representative of a larger market. This is particularly true for operations-heavy industries.

• Pretotyping: The central theme of the book. Before diving into running a venture or building expensive prototypes, make smaller pretotypes that help you get your own data to validate your xyz hypothesis, and by extension your XYZ hypothesis, and by extension, your market engagement hypothesis. The difference between a pretotype and a prototype is that pretotypes are quick, easy, inexpensive, and get you the data you need to know whether to solve the problem, whereas prototypes are expensive, time-consuming, and harder to make, and mainly serve the purpose of showing your ability to solve the problem. Some examples of pretotypes follow. These can be used in isolation or together.

  • The Mechanical Turk: Build something that pretends to do what your high-tech product would do, but with low to no tech. See how many people bite.

  • The Pinocchio: Make a zero-tech, zero-functionality doppelgänger to see if you / your market would use the product to begin with.

  • The Fake Door: Ask people to express interest. If your hypothesis is a certain percentage of people will express interest, this should prove/disprove it.

  • The Façade: Same as the Fake Door, except when people click the CTA, they get a lesser version of the final product for a (maybe discounted) price.

  • The YouTube: Show people a video showing the product in use and have them either express interest or pre-order.

  • The One-Night Stand: Use a limited-time, limited-space version of your final idea to test your initial hypothesis, e.g. Airbnb and only one apartment on Craigslist.

  • The Infiltrator: Sneak your product into an existing marketplace where similar products exist.

  • The Relabel: When people express interest to buy, take a competitive product and repackage it as yours to see if people buy its positioning.

  • In case of the BusU example, for instance:

    • After an email was sent introducing the idea, a guy responded saying this: I have a PhD in Artificial Intelligence from Berkeley, and I already have a ten-hour “Intro to Machine Learning” minicourse that I’ve taught several times at both Berkeley and Google (to great reviews, average 4.8/5.0). This would be a lot of fun for me, so you don’t even have to pay me. When can we start?

    • This meant the xyz statement could be tweaked to this: At least 10% of Google engineers commuting from San Francisco to Mountain View will pay $300 for a one-week “Introduction to Machine Learning” class on the bus taught by a fellow Google employee. If proven, this could further be iterated to finetune the business model and bring us closer to confidence on the overall XYZ statement.

• There is a diagrammatic representation of this this approach, which I don’t’ yet find as compelling as the approach itself. Attached, though.

That’s about it. Interesting book.