Pivoting
I was never much of a reader as a child. It wasn't until Freshman year of college that I started reading in any significant volume. My reading preference back then, and for a long time afterwards, have primarily been for escapism. It was only recently that I started reading more non-fiction books, focusing on a couple of topics. Thankfully, both of my kids enjoy reading voraciously - it's sometimes a challenge to get them to stop and put the book down!
Naturally, it was at our local library that I got the inspiration for this style of post. I was browsing the books in the business section when I saw a book spine that piqued my interest. The title of the book is The 100 Best Business Books of All Time. Granted, it was published in 2009, and many business books have been published since then, but the concept lodged in my brain. It's easy enough to ask an AI to summarize a book for you, but I've always learned and retained more when summarizing something myself.
Here's my planned reading list. If you have recommendations, please let me know.
+ about 30 more -?Not Started
Please note: My full book summaries will always be available on my website.
The first book summary:
The Lean Startup, by Eric Ries, Published 2011
Introduction
The Lean Startup Method
We have a century of management principles and practices, but they're not going to work with startups and innovation.?
Part 1: Vision
Chapter 1: Start
Entrepreneurial management is a form of management, but not the traditional style. Entrepreneurs trying to use a traditional style of management fear bureaucracy and stifling innovation. A new form of management needs to account for huge uncertainty in the market.
Lean thinking was pioneered in the auto industry (Toyota) decades ago, and emphasizes a few key differences from traditional manufacturing: shrinking batch sizes, just-in-time production and inventory control, and an acceleration of cycle times. The Lean Startup movement applies similar concepts to entrepreneurial management. The key measure for startups is validated learning, and shifts away from more traditional measurements of productivity. Shorter feedback loops are super important in measuring learning, and allow an entrepreneur to make quick adjustments to increase the likelihood of success.
Startups still need a vision, a destination in mind. That vision is converted into a strategy, a plan to get to the vision which may include a business model, product road map, customer analysis, partner and competitor investigation, etc. The product is the end result of the strategy. Products can change constantly, through the process of optimization. The strategy can also change, through the process of pivoting.
Chapter 2: Define
Definition of entrepreneur: someone who has the right personnel organized into a proper team structure, with a strong vision for the future and an appetite for risk-taking.
Definition of a startup: a human institution designed to create a new product or service under conditions of extreme uncertainty.
In most cases, the new product or service will be an innovation provided to customers. The innovation can be novel scientific discoveries, repurposing an existing technology for new use, devising new business models that unlock value that was hidden, or bringing a product or service to a new location or previously underserved set of customers.
Startups, and the entrepreneurs that run them, need nurturing and support, especially if they’re groups within a large enterprise. Large enterprises typically follow traditional management styles which can lead to sustaining innovation. But a faster feedback cycle and a higher risk appetite can lead to disruptive innovation, typically resulting in new sustainable sources of growth. The responsibility for nurturing and supporting entrepreneurs within an enterprise is full-stack, from senior leadership all the way down through the middle managers to the workers on the floor.
Chapter 3: Learn
Learning is often used as a justification for failure to deliver. Failures become “learning experiences”. Lean Startup distinguishes between learning and validated learning. The former is an after-the-fact rationalization to explain away failure. The latter is the process of demonstrating empirically that a team has discovered valuable truths about a startup’s present and future business prospects.
Author’s startup, IMVU, launched based on what they thought customers wanted. They built a product based on those assumptions, and launched, with no customers. They were able to get a very small number of customers using the product, but not nearly enough to call themselves a success. As soon as they started talking to their customers and understanding what the customers actually wanted (rather than assuming what the customers wanted, or assuming that the customers wanted what they said they wanted) they were able to see tremendous growth in their product.
Lean manufacturing’s first, and most important, question is “which of our efforts are value-creating and which are wasteful?” The goal is to see waste and eliminate it. As a result, it’s super important to understand what the customer actually wants as early in the process, with as little time / resources spent, to be able to eliminate the waste of building something that the customer doesn’t want. Experiment early and often, and learn from those experiments. Vanity metrics don’t get you to success, justifying your failure as a learning experiment doesn’t get you to success. Only the hard work of understanding what the customer actually needs / wants and is willing to pay for, gets you to success.
The question needs to shift from “Can we build this product?” to “Should we build this product?” and “Can we build a viable business around this set of products and services?” To answer those questions, we need a method for systematically breaking down a business plan into component parts and testing each part empirically.
Chapter 4: Experiment
Think big, capture your assumptions, then start with a small scale experiment to test those assumptions. If we’re assuming that X number of people will buy our product nationally, test it on a much smaller scale like a single metro or a neighborhood within a city. The cost will be much smaller than a big rollout associated with a big vision, but the feedback will validate the assumptions and confirm the big vision. Experimenting with real people using a (stripped down version of your) real product will give you the opportunity to see how your customers will use your product - spoiler: it may not be the way you intended the product to be used.
Big visions can be broken down into a value hypothesis and a growth hypothesis. The value hypothesis tests whether or not a product or service really delivers value to customers once they start using it. The growth hypothesis tests how new customers will discover the product or service. The goal is to target your experiments to early adopter customers who will give you insights on your value hypothesis, and can then turn around and become your champions for the growth hypothesis. In order to maximize the chances of your early adopters to spread the word, deliver the best possible minimum viable product experience you can to those early adopters.
Shipping an initial version of a product that has a much smaller feature set from your roadmap will invariably lead to your test users complaining about a lack of features. But the set of features they’re requesting can confirm their validity on your roadmap, and conversely, the set of features on your roadmap that the customers are not requesting can be deferred or deleted.
Part 2: Steer
The Build-Measure-Learn loop is at the core of the Lean Startup model. Many people have professional training in one element or another of the loop. Engineers learn how to build well. Data scientists learn how to measure well. But the power of the loop is in minimizing the time it takes to go through one turn of the loop, using the value and growth hypotheses. Based on the learnings out of the turn, e.g. we’ve discovered that one of our hypotheses is false, we may want to pivot. Getting to the decision on whether or not to pivot is the key to Lean Startup. But to determine the hypothesis, we may need to plan backwards through the loop, i.e. we need to pin down what we want to learn, plan our measurement criteria for that learning, and then plan the product build.
Chapter 5: Leap
The core value and growth hypotheses of any business are called its leap-of-faith hypothesis, and the business should strive to determine their validity as soon as practical. The key is to get out of your chair, get out of your office, and talk to customers. But don’t blindly accept what they tell you they want. Ask them about pain points rather than solutions to their pain points. Don’t try to sell them your vision or strategy - you shouldn’t have one yet. Your conversations can help you create a customer archetype, a brief document that seeks to humanize the proposed target customer. This customer archetype should always be kept in mind when making decisions about the product.
Basing your business strategy on another business that has worked in the past is not always wrong, but you have to identify the similarities and differences between your business and the predecessor. The exercise of finding those similarities and differences will help uncover assumptions. Those assumptions, then, become your leap of faith hypotheses.
Value is not the same as profit. A business can be value-creating without being profitable - see Amazon. But a business that is profitable without being value-creating will eventually crumble - see all the busted companies from the dotcom era.
Chapter 6: Test
Test your leap of faith assumptions using an MVP approach. MVP can be created in many different ways, but the goal should be to minimize the amount of effort in building it so that you incur the lowest amount of wasted effort.
One example for MVP mentioned in the book is a screen capture video of the product being used.
One example for a concierge MVP mentioned in the book is to have a fully manual process on a very small scale, of what would eventually become automated. The very small number of customers get special treatment from the startup founder. Once you confirm that customers are willing to pay for the product, you start building the automations to scale up.
The concept of releasing an MVP goes against a sense of pride in quality for engineers and product people. But the central question is around testing assumptions and learning. Releasing an MVP also allows the customer to imagine the features that the product could have in the future, and may even get early adopters more engaged in the feature definition cycle. One caveat is to ensure that the MVP has high quality in the form of no defects. Defects make it difficult to iterate the Build-Measure-Learn loop.
And if the MVP proves the leap of faith hypotheses incorrect, then it’s time to pivot.
领英推荐
Chapter 7: Measure
Standard accounting doesn’t work for startups because the main unit of progress is validated learning. Innovation accounting is a three-step approach to measure the success of the startup.?
The first step is to release a minimum viable product to establish a real baseline based on real customers. The MVP will also define and start testing the leap of faith assumptions.
The second step is to tune the engine. Tuning the engine requires a small batch size and a crucial validation step at the end of the development and launch process to measure how much the feature moved the needle. A/B testing is the main strategy to measure the impact of each feature.
The third step is to pivot or persevere based on the outcome of the A/B test.
Vanity metrics are metrics used to tell a positive story. They’re usually summary-level and don’t give us a clear indication of why the numbers are moving the way that they are. Cohort analysis is a better approach because it measures a different set of users (cohort) on a periodic basis, rather than aggregate numbers.
In order for metrics to be useful, they must follow the three A’s:
Chapter 8: Pivot (or Persevere)
Every entrepreneur eventually faces an overriding challenge in developing a successful product: deciding when to pivot and when to persevere. The decision criteria are not set in stone, they do not follow a formula. Each pivot or persevere decision is unique and dependent on the company, industry, inflection point, etc.
One major pitfall is the company that tests a hypothesis and concludes that the hypothesis is partially true. This company will generate enough growth to keep the lights on, but not as much as expected based on the company’s vision. Even in these situations, perhaps especially in these situations, it’s important to have an unbiased, scientific approach to determining if the company should pivot to a new fundamental hypothesis.
Pivoting takes courage. Courage to admit that your hypothesis was wrong. Courage to declare your work not as value-achieving as hoped-for. Pivoting can also demoralize a company’s staff.
The pivot or persevere decision should be made periodically (every few weeks to every few months), and requires the participation of the product development team and the business leadership team. Product dev should bring the results of the A/B tests for more than just the past period, and business leadership should bring the customer interactions for more than just the past period. Based on the evidence, the joint team should make a decision to stay the course (persevere) or adjust their fundamental hypothesis (pivot).
Catalog of pivots:
Part 3: Accelerate
Develop the techniques that allow a Lean Startup to grow and mature into a Lean Enterprise that maintains its learning orientation, agility, and innovative culture.
Chapter 9: Batch
Large batch sizes may seem more efficient. In the manufacturing industry, the goal is to make sure that all machines are operating 24x7 at peak efficiency, which leads to specialization. Specialized machines make specific parts of your product. Unfortunately, if there’s a defect in one of the machines, the entire (large) batch of parts that was produced by that machine are now considered waste. And even if there’s no defect, all the parts take up physical space on the warehouse floor, without a finished product.
The same can apply to a non-physical production process like software. Specialization in roles (developer, tester, designer, product manager, etc.) leads to large batch sizes. The product manager comes up with a block of work in the form of a new feature, who hands off to the designer, who hands off to the developers, who hands off to the tester. The expectation is that, if the process is running smoothly, we have a pipeline of efficiency. If there’s any ambiguity or lack of understanding, the whole pipeline comes to a halt. If the development team has a question on functionality that they need clarification from design or product on, those other teams cannot work on the next large batch. The amount of rework is high, which reduces the overall throughput of the pipeline. And even if there’s no ambiguity, the quantity of incomplete deliverables (requirements docs, designs, untested code, undeployed code, etc) exceeds the amount of deliverables.
To solve this issue, a Lean Startup needs to implement smaller batch sizes, sometimes as small as a single feature or product, and see the smaller batch through to the customer. Only then can the hypothesis be validated, and the company achieves their learning.
There are many automation tools to help a company achieve smaller batches, regardless of industry or product. Software can obviously benefit from cloud infrastructure and CICD pipelines. Manufacturing can use the assembly line method similar to auto manufacturers. 3D printing also allows for a much faster turnaround than traditional mold injection for production.
Smaller batches, combined with a pull-based, WIP-limiting approach like Kanban, can supercharge the lean company.
Chapter 10: Grow
Sustainable growth is characterized by one simple rule: New customers come from the actions of past customers. There are four primary ways that past customers drive sustainable growth:
These four mechanisms power feedback loops that are termed “engines of growth”. Each engine of growth has specific metrics that allow a startup to measure their progress. These metrics may seem counterintuitive, but are fundamental to drive sustainable growth.
The sticky engine of growth attracts and retains customers for the long term. It aligns with the “repeated use” mechanism for how past customers drive sustainable growth. The key metric for this engine of growth is retention rate. A high retention rate validates the fundamental assumption that once a customer starts using your product they want to continue using it. Tracking retention (and its counterpart: attrition) is as important as tracking new customers. If the attrition rate is lower than the new customer rate, then the company will grow.
The viral engine of growth relies on customers doing the majority of your marketing. Awareness of the product spreads virally from one customer to a set of additional customers. Growth happens automatically as a side effect of using the product. The key metric for the viral engine of growth is the viral coefficient, which measures how? many new customers will use a product as a consequence of each new customer who signs up. How many friends will each new customer bring with him/her? A viral coefficient of >1 will result in exponential growth, versus a viral coefficient of <1 will cause growth to stagnate. The key is to minimize the resistance to using the product. Many viral companies give away their product for free, and rely on advertising revenue.
The paid engine of growth relies on a sales process or sales team to acquire new customers, and will almost definitely rely on advertising. The two key metrics are the cost per acquisition (CPA) and the lifetime value (LTV). The cost per acquisition is self-explanatory: it’s the amount of money that was required to gain a new customer. This could be an ad campaign that generates new B2C customers, or a funded sales team that works on winning a B2B contract. The lifetime value is the projected amount of money that the customer will generate for you over the course of their lifetime using the product, whether in dollars spent or advertising revenue generated. As long as the LTV is greater than the CPA, then the company will grow. The key is to increase LTV while decreasing CPA.
All growth engines eventually run out. The market gets saturated. The lean startups find additional growth engines in order to continue growing.
Chapter 11: Adapt
Startups must adapt, but do so in a just-in-time way. For example, does it make sense for a startup to put together onboarding material for new hires? Yes, but it doesn’t make sense to invest a ton of time into an initial version. The startup must become an adaptive organization, willing to adjust its processes and performance to current conditions. One key item to note is that the startup must not sacrifice quality for speed. It is possible to go too fast, if the consequence is poor quality. Poor quality now reduces speed later
When an issue arises, use the Five Whys approach to figure out the root cause, and identify incremental improvements to the process along the way. This is particularly useful because most technical issues have a human root cause. Fixing the human root cause must be in two parts: first, accept that people make mistakes, and second, do everything you can to prevent yourself from getting into that position again. And in many cases, the incremental improvements to avoid the human mistakes ends up becoming your onboarding material.
Immature organizations turn the Five Whys into the Five Blames, where different groups and teams end up finger-pointing when an issue arises. Ensuring that this doesn’t happen requires a well trained Why Master to drive the Five Whys discussion, prevent tangents, and shut down the blame game. Additionally, the Five Whys meeting should be held when a new issue arises, and for a limited scope. Don’t bring the entire laundry list of current issues to the Five Whys meeting, the result will not be good. It may also benefit to start your first Five Whys meeting on a non-critical issue, in order to build up muscle memory on the lower risk issues.
The second major adaptation is smaller batches, which has already been covered earlier in the book.
Chapter 12: Innovate
Startup teams need three things in order to create disruptive innovation, whether they’re startup companies or divisions within a larger enterprise: 1) Scarce but secure resources, 2) Independent authority to develop their business, and 3) A personal stake in the outcome. Having these three things is necessary but not sufficient to achieve successful disruptive innovation.
In a startup division within an enterprise, once you have these three things, you need to create a safe place for the division to operate. An experimentation sandbox allows you to buffer the startup efforts while still creating the transparency that this division exists. Operating the startup division in isolation is setting the other division leaders for defensiveness when the new idea is sprung on them.
The experimentation sandbox is a place where any team can run A/B tests on new features for a subset of the audience (either a customer segment or a feature set within a product), with certain rules.
Innovation also requires a certain type of leader. Those innovation-focused leaders shouldn’t have to stick with their products after they’re out of the innovation phase and into a maintenance phase. It may be better to hand off the scaling and maintenance phase of ideas to leaders who are more comfortable in that space.
Having fun while learning to build exceptional products
2 个月3 non-fiction books that I really enjoyed reading are Never Split the Difference by Christopher Voss, How Will You Measure Your Life by Prof. Clayton Christensen, and Four Thousand Weeks by Oliver Burkeman