Below are excerpts and a summary taken from the book.
Summary and Key Takeaways of The Lean Startup by Eric Ries
Part Two: Steer
At its heart, a startup is a catalyst that transforms ideas into products. As customers interact with those products, they generate feedback and data. The feedback is both qualitative (such as what they like and don’t like) and quantitative (such as how many people use it and find it valuable). As we saw in Part One, the products a startup builds are really experiments; the learning about how to build a sustainable business is the outcome of those experiments. For startups, that information is much more important than dollars, awards, or mentions in the press, because it can influence and reshape the next set of ideas.
This is where the loop comes in; it is the core of the Lean Startup model.
To apply the scientific method to a startup, we need to identify which hypotheses to test. I call the riskiest elements of startup’s plan, the parts on which everything depends, leap-of-faith assumptions. Once clear on these assumptions, the first step is to enter the Build phase as quickly as possible with a minimum viable product (MVP). This product lacks many features that may prove essential later on; however, it enables a full turn of the Build-Measure-Learn loop. Then this prototype needs to be put in front of real consumers. Then comes the Measure phase. Remember, if you’re building something that nobody wants, it doesn’t matter much if you’re doing it on time and on budget.
The method that Ries recommends to use instead is called innovation accounting. Then there is the pivot. Upon completing the Build-Measure-Learn loop, you must confront the most difficult question any entrepreneur faces: whether to pivot or preserve the original strategy. If you’ve discovered that one of your hypotheses is false, it is time to make a major change to a new strategic hypothesis.
In order to talk about leap assumptions, let’s analyse the world famous story of Mark Zuckerberg, Dustin Moskovitz, and Chris Hughes of Facebook. More precisely, how were they able to raise so much money ($500,000 in venture capital and an additional $12.7 million a year later) when its actual usage was so small?
By all accounts, what impressed investors the most were two facts about Facebook’s early growth: 1. The raw amount of time active users spent on the side; more than half of the users came back to the site every single day (their value hypothesis was validated — customers find the product valuable) and 2. The rate at which it had taken over its first few college campuses; less than a month after Facebook’s launch, three-quarters of Harvard’s undergraduates were using it, without a dollar spent on advertising (their growth hypothesis was validated).
Strategy is Based on Assumptions
Every businesses’ plan lays out a strategy that takes certain assumptions as a given and proceeds to show how to achieve the company’s vision. The first challenge for an entrepreneur is to build an organization that can test its assumptions systematically. The second challenge is to perform that rigorous testing without losing sight of the company’s overall vision.
Analogs and Antilogs
There is nothing intrinsically wrong with basing strategy on comparisons to other companies and industries. In his book, a venture capitalist Randy Komisar uses a framework of analogs and antilogs to plot strategy. He uses the iPod as an example. Walkman would be considered the analog. While Steve Jobs never had to answer the fundamental question of “Will people listen to music in a public place using earphones?” Sony did. Jobs then had to face the fact that although people were willing to download music, they were not willing to pay for it. Napster was the antilog. That antilog had to lead him to address his business in a particular way. Of these analogs and antilogs came a series of unique, unanswered questions aka leaps of faith. In the iPod business, one of those leaps of faith was that people would pay for music. Of course, that leap of faith turned out to be correct.
Value and Growth
The first step in understanding a new product or service is to figure out if it is fundamentally value-creating or value-destroying. Similarly, with growth, it’s essential that entrepreneurs understand the reasons behind a startup’s growth. There are many value-destroying kinds of growth that do not develop a value-creating product.
In the Toyota Production System, genchi genbutsu is one of the most important phrases; it means “go and see for yourself.” You cannot be sure you really understand any part of any business problem unless you go and see for yourself firsthand. To demonstrate, let’s look at the development of Toyota’s Sienna minivan for the 2004 model year. Yuji Yokoya was appointed as the chief engineer for this model so he decided to take a road trip across the U.S. states, Canada, and Mexico while renting the current-model Sienna. He drove the car as well as talked to and observed real customers and their experiences with the car. From those firsthand observations, Yokoya was able to start testing his critical assumptions about what North American consumers wanted in a minivan.
From this, Yokoya learned one very important discovery: “The parents and grandparents may own the minivan. But it’s the kids who rule it. It’s the kids who occupy the rear two-thirds of the vehicle. And it’s the kids who are most critical — and the most appreciative of their environment. If I learned anything in my travels, it was the new Sienna would need kid appeal.” Identifying these assumptions helped guide the car’s development. Yokoya spent an unusual amount of the Sienna’s development budget on internal comfort features, which are critical to a long-distance family road trip. The results boosted Sienna’s market share dramatically. The 2004 model’s sales were 60 percent higher than those in 2003.
Get out of the Building
Entrepreneurs have to always be reminded that metrics are people too. No matter how many intermediaries lie between a company and its customers, at the end of the day, customers are breathing, thinking individuals whose behaviour is measurable and changeable. All successful sales models depend on breaking down the monolithic view of organizations into the disparate people that make them up.
Startups need extensive contact with potential customers to understand them, so get out of your chair and get to know them.
The first step; however is to confirm that the customer has a significant problem worth solving.
Design and the Customer Archetype
The goal of such early contact with customers is not to gain definitive answers. Instead, it is to craft a customer archetype, a set of guidelines aligned with proposed target customer profile, as well as the problems they may have. This archetype will ensure that every decision your team makes is aligned with the customer it is trying to appeal and thus, guide product development.
It is important to recognize that no amount of design can anticipate the many complexities of bringing a product to life in the real world. A customer profile is a hypothesis, not a fact, and should, therefore, be considered provisional until the strategy has shown via validated learning.
Too much analysis is dangerous, reading research reports, and endless whiteboard strategizing can lead to analysis paralysis since most of the subtle errors cannot be detected because they depend on actual interactions with the product. No analysis is dangerous too, the just-do-it entrepreneurs delude themselves that they are on the right path because customers don’t really know what they want. Therefore, the best option is the middle: a concept called minimum viable product.
Groupon today is known to be one of the fastest-growing companies of all time. However, it didn’t start out that way. It wasn’t originally meant to be a commerce site; the founder, Andrew Mason, intended his company to become a “collective activism platform” that would bring people together to fundraise for a cause or boycott a certain retailer. The results were disappointing so they decided to try something new. They built a minimum viable product: a WordPress Blog selling coupons. Even though the coupons were handmade PDFs, this simple arrangement was enough to prove the concept at the time and eventually become the fastest company in history to achieve $1 billion in sales.
The beauty of MVP is that it helps entrepreneurs start the process of learning as quickly as possible. It does not strive for product perfection but a mean to begin the Build-Measure-Learn feedback loop with a minimum amount of effort. Unlike a prototype or concept test, an MVP is designed not just to answer product design or technical questions. Its goal is to test fundamental business hypotheses.
Why First Products Aren’t Meant to be Perfect
Before new products can be sold successfully to mass market, they have to be sold to early adopters. These people are a special breed of customers that accept or even prefer an 80 percent solution. You don’t need a perfect solution to capture their interest. They use their imagination to fill in what a product is missing. What they do care about is being the first to use or adopt a new product.
This is hard for entrepreneurs to accept, as their vision dictates a high-quality mainstream product that will change the world and not one used by a small niche of people who are willing to give it a shot before it’s ready. After all, we were always taught to put our best work forward.
How complex an MVP need to be, though, requires judgment. Would it be a little more than an advertisement or an actual early prototype? When in doubt, simplify.
For example, consider a service sold with a one-month free trial. Before the customer can use the service, he or she has to sign up for the trial. One obvious assumption, then, of the business model is that customers will sign up for a free trial once they have a certain about of information about the service. This is your value hypothesis: whether they will, in fact, sign up. And to support this assumption, your business model should present the following leap-of-faith question: “We assume 10 percent of customers will sign up,” in giant bold letters.
Many would approach this by building the product and then checking to see how customers react to it. But that is a backwards method because it can lead to a lot of waste. If it turns out that the customers don’t actually want the product, the whole exercise is an avoidable waste of time and money. If your customers didn’t sign up for the trial, they’re likely to not use the product.
And even if your customers did sign up, there are still many other opportunities for waste. For example, how many features do we really need to include to appeal to early adopters? Every extra feature is a form of waste, and if you delay the test for these extra features, it comes with a tremendous potential cost in terms of learning and cycle time.
The lesson of the MVP is that any additional work beyond what was required to start learning is waste, no matter how important it might have seemed at the time. The following real life examples show entrepreneurs avoiding the temptation to overbuild and overpromise.
The Video Minimum Viable Product
The Dropbox founding team did something different. In parallel with their product development efforts (and it was a lot, a seamless file-sharing tool that works across all operating systems is incredibly hard to build), they also wanted feedback from customers about what really mattered to them. The first test answered the leap-of-faith question: if we can provide a superior customer experience, will people give our product a try? They believed — rightly, as it turned out — that file synchronization was a problem that most people didn’t know they had. Once you experience the solution, you can’t imagine how you ever lived without it!
Drew Houston, CEO of Dropbox, believed that if the software “just worked like magic,” customers would flock to it. However, this was very hard to explain to investors. The challenge was that it was impossible to demonstrate the working software in a prototype form. To avoid the risk of waking up after years of development with a product nobody wanted, Drew did something unexpectedly easy: he made a video. A three-minute demonstration, narrated by Drew himself, describing the kinds of files he’d synchronize as well as jokes and humorous references, was targeted at technology early adopters. This video, or MVP, was so well appreciated that the beta waiting list went from 5,000 to 75,000 overnight. It proved that customers wanted the product he was developing because they actually signed up. Today, Dropbox is worth more than $1 billion.
The Concierge Minimum Viable Product
Consider the case of Food on the Table, a Texas-based startup that searches through recipes that match your meal needs, prices out the cost of the meal for you, and lets you print out your shopping list. As you can imagine, this requires elaborate work of chefs, algorithms, all of the grocery store’s databases in the area as well as information about which items are on discount on a particular day. In order to avoid wasting time on building a platform that may potentially prove to be useless, Manuel Rosso (CEO) and Steve Sanderson (VP of product) started out with one customer. They went to a local grocery store and found someone who was eager to sign up for their service.
That early adopter got the concierge treatment. She got an in-person visit each week from Manuel and Steve together, the three of them would discuss her preferences on grocery stores and recipes. You might think that this is incredibly inefficient — an entirely non-scalable system that doesn’t market to millions and sells to one. They have no product, no meaningful revenue, no database of recipes, not even a lasting organization.
However, viewed through the lens of the Lean Startup, they were making monumental progress. Each week, they were learning more and more about what was required to make their product a success. Each new customer provided useful insights on products, stores, and customer behavior. After a while, the founders found themselves too busy to bring on additional customers. That is when they decided to invest in automation in the form of product development. Along the way, their product development team was always focused on scaling something that was working rather than trying to invent something that might work in the future.
In this case, a concierge MVP is not the product but a learning activity designed to test leap-of-faith assumptions in the company’s growth model. In fact, a common outcome of concierge MVP is to invalidate the company’s proposed growth model, making it clear that a different approach is needed.
Pay No Attention to the Eight People Behind the Curtain
Technologists Meet Max Ventilla and Damon Horowitz had the vision to build a new type of search software designed to answer the kinds of non-factual, subjective, experience based questions that Google can’t such as “what’s a good place to go out for a drink in my city?” So they created a product called Aardvark.
Max and Damon spent their first six months building a series of functioning products, each designed to test a way of solving this problem Each product was then offered to beta testers, whose behavior was used to validate or refute each specific hypothesis. Here are examples of some of those products: Rekkit, a service to collect ratings from across the web and give better recommendations to you; The Webb, a central number that you could call and talk to a person who could do anything for you that you could do online, and the like.
Each prototype was very cheap and required about a two- to four-week effort. All of the early prototypes described above failed to engage the customers. What became Aardvark was the sixth prototype! This approach helped them prove that they were building the stuff people would respond to.
Of course, a product like that requires a very important algorithm so Max and Damon used Wizad of Oz testing to fake it. In a Wizard of Oz test, customers believe they are interacting with the actual product, but behind the scenes are humans doing the work.
Like the concierge approach, it is extremely inefficient; however, it allowed Max and Damon to answer all-important questions: if we can solve the tough technical problems behind artificial intelligence product, will people use it? This allowed for a lot of pivots in strategy and rejecting of concepts that seemed promising but would have been viable. Aardvark was acquired for a reported $50 million by Google.
The Role of Quality and Design in an MVP
The most vexing aspect of the minimum viable product is the challenge it poses to traditional notions of quality. Most modern business and engineering philosophies focus on producing high-quality experiences for customers. The best professionals and craftspersons alike aspire to build quality products; it is a point of pride.
These discussions of quality presuppose that the company already knows what attributes of the product the customer will perceive as worthwhile. If we do not know who the customer is, we do not know what quality is.
And when MVPs are considered low quality, you should use this opportunity to learn what attributes customers care about. Sometimes customers love your product in spite of its “low quality,” such as Craigslist and Groupon.
Customers don’t care how much time something takes to build, they care only if it serves their needs. And you must always ask: what if customers don’t care about design in the same way we do? This method is not opposed to building high-quality products, but only in service of the goal of winning over customers.
You must set aside traditional professional standards. And this does not mean operating in a sloppy or undisciplined way. This is an important caveat. There is a category of quality problems that have the net effect of slowing down the Build-Measure-Learn feedback loop. Defects make it more difficult to evolve the product. They actually interfere with our ability to learn and so are dangerous to tolerate in any production process.
As you consider building your own minimum viable product, let the simple rule suffice: remove any feature, process, or effort that does not contribute directly to the learning you seek.
Speed Bumps in Building an MVP
Unless understood ahead of time, building an MVP can derail a startup effort. The most common speed bumps are legal issues, fears about competitors, branding risks, and the impact on morale.
Another common issue is patenting. For startups that rely on patent protection, there are special challenges with releasing an early product. Those kinds of startups should seek legal counsel to ensure that they understand the risks fully.
The most common objection to the Lean Startup method that Ries has heard over the years is fear of competitors stealing startup’s ideas. If only it were so easy to have a good idea stolen! Ries jokes that he often gives such fearful entrepreneurs an assignment: take one of your ideas, find the name of the relevant product manager at an established company who has responsibility for that area, and try to get that company to steal your idea. Call them up, write them a memo, send them a press release — go ahead, try it. The truth is that most managers in most companies are already overwhelmed with good ideas.
In addition, sooner or later, a successful startup will face competition from fast followers. The only way to win is to learn faster than anyone else.
Many startups plan to invest in building a great brand and an MVP can seem like a dangerous branding risk. Here’s an easy solution, launch under a different brand name. As a startup, use your advantage of being obscure and experiment under the radar, then do a public marketing launch once the product has proved itself with real customers.
Finally, it helps to prepare for the fact that MVPs often result in bad news and thus provide a needed dose of reality.
From the MVP to Innovation Accounting
If an MVP fails, teams are prone to giving up and abandoning the project altogether. But the right solution is commitment. You have to promise that you will not give up hope. Successful entrepreneurs do not give up at the first sign of trouble, nor do they persevere the plan right into the ground. Instead, they possess a unique combination of perseverance and flexibility.
In traditional management, a manager who promises to deliver something and fails to do so is in trouble. He/she either failed to execute or failed to plan. Entrepreneurial managers have to plan and project based on uncertainty. So how does a CFO or VC know you’re failing because you learned something critical and not because you were goofing off or misguided. This is where innovation accounting comes in.
A startup’s job is to 1. Rigorously measure where it is right now, confronting the hard truths that assessments reveal, and then 2. Devise experiments to learn how to move the real numbers closer to the ideal reflected in the business plan.
Most products, even the ones that fail, do not have zero traction. Most products have some customers, some growth, and some positive results. One of the most dangerous outcomes for a startup is to bumble along in the land of living dead. This is why the myth of perseverance is dangerous. We don’t hear enough stories about the countless nameless others who persevered too long, leading their companies to failure.
Why Something as Seemingly Dull as Accounting Will Change Your Life
Accounting is the key to success, even though perceived as dry and boring by many. It is important to know that when you’re improving the product, your customers are actually responding well to these improvements. Moreover, that the changes you’ve made are actually related to the results you’re seeing and that you’re drawing the right lessons from these changes.
An Accountability Framework That Works Across Industries
Innovation accounting begins by turning the leap-of-faith assumptions discussed earlier into a quantitative financial model. Every business model provides assumptions about what the business will look like at a successful point in the future.
How Innovation Accounting Works — Three Learning Milestones
Innovation accounting works in three steps: first, use a minimum viable product to establish real data on where the company is right now. Second, startups attempt to tune the engine from the baseline toward the ideal. Third point: pivot or persevere. Pivoting means restarting the process all over again. The sign of successful pivot is that these engine-turning activities are more productive after the pivot than before.
Establish the Baseline
A startup might create a complete prototype of its product and offer to sell it to real customers through its main marketing channel. This would establish baseline metrics for each assumption simultaneously. Alternatively, a startup might prefer to build separate MVPs that are aimed at getting feedback on one assumption at a time. Before building the prototype, the company might perform a smoke test to measure whether customers are interested in trying a product. By itself, this is insufficient to validate an entire growth model. Nonetheless, it can be very useful to get feedback on this assumption before committing more money and other resources to the product.
These MVPs provide the first example of a learning milestone. An MVP allows a startup to fill in real baseline data in its growth model. You should always test the riskiest assumptions first because if you can’t find a way to mitigate these risks, there’s no point in testing the others.
Tuning the Engine
Once the baseline has been established, the startup can work toward the second learning milestone: tuning the engine. Every product development, marketing, or other initiatives that a startup undertakes should be targeted at improving one of the drivers of its growth model. For example, the design changes must improve the activation rate of new customers. If they do not, the new design should be judged a failure. Good design is one that changes customer behavior for the better.
Pivot or Persevere
If the numbers in your model rise from the baseline (MVP) to ideal (business plan), you’re making progress. If you’re not moving the drivers or your business model, it’s a sure sign that it’s time to pivot.
Innovation Accounting at IMVU
IMVU team struggled with seeing that their quality improvements were not yielding any change in customer behavior. So they started tracking “funnel metrics” like customer registration, application download, repeat usage, etc. Spending five dollars a day on Google AdWords gave them enough clicks per day to get enough data. Every single day the team were able to measure their performance with a brand new set of customers.
The key is to ask whether the early success is related to the daily work of the product development team. In most cases, the answer is no; that success is driven by decisions the team had made in the past. The most common danger is not making sure that all of the current initiatives have an impact.
Traditional numbers used to judge startups are called “vanity metrics,” they give the rosiest possible picture. For example, a graph of total paying customers. The graph is most likely to look like a hockey stick which makes it look ideal. A set of data presented cohort style, on the other hand, allows you to look at each group separately. From the traditional graph alone, you can’t tell whether your company is on pace to build a sustainable business and you certainly can’t tell anything about the efficacy of the entrepreneurial team behind it.
Cohort analysis is one of the most important tools of startup analytics. Instead of looking at cumulative tools, one looks at the performance of each group of customers that comes into contact with the product independently. One example of a cohort would be a percentage of customers who registered in a particular month who subsequently went on to take the indicated action. Such funnel analysis should be used in product development as it allows to understand a business quantitatively and have much more predictive power than do traditional gross metrics.
Actionable Metrics Versus Vanity Metrics
Actionable metrics are the alternative to vanity metrics, ones that you should use to judge your business.
Two approaches should be changed when evaluating your data: instead of looking at gross metrics, look at cohort-based metrics, and instead of looking for cause-and-effect relationships after the fact, launch a new feature as a true split-test experiment.
A split-test experiment is one in which different versions of a product are offered to customers at the same time. By observing the changes in behavior between the two groups, one can make inferences about the impact of the different variations. This technique is also sometimes called A/B testing.
This kind of testing uncovers many surprising things. For example, many features that make the product better in the eyes of engineers and designers have no impact on customer behavior. While split tests seem to be more difficult because of extra accounting and metrics to keep track of but it almost always saves tremendous amounts of time in the long run by eliminating work that doesn’t matter to customers.
A good way to see if your service is worth the investment (time, money, efforts) is by running a lazy registration. In this system, customers do not have to register for the service up front. Instead, they immediately begin using the service and are asked to register only after they have had a chance to experience the service’s benefit.
Let’s take a look at Grockit, an online teaching platform that integrates three approaches: teacher-led lectures, individual homework, group study; it applies technology and algorithms to optimize those three forms. What they did was a simple split-test. They took one cohort of customers and required that they register immediately based on nothing more than Grockit’s marketing materials. The results were surprising: the two groups exhibited the same rate of registration, activation, and subsequent retention. This gave insight into a very crucial conclusion that customers were basing their decision about Grockit on something other than their use of the product. This means that improving marketing and positioning might have a more significant impact on attracting new customers than would adding new features.
The Value of The Three A’s
There are three A’s of metrics:
For a report to be considered actionable, it must demonstrate clear cause and effect. Otherwise, it is a vanity metric. When the number go up, it is common to think that improvement was caused by their actions, by whatever they were working on at the time. Actionable metrics are the antidote to this problem. When cause and effect is clearly understood, people are better able to learn from their actions.
All too many reports are not understood by the employees and managers who are supposed to use them to guide their decision making. First, make the reports as simple as possible as metrics are people too. Use tangible, concrete units. For example, use “website visitor” instead of website hit.
This is why cohort-based analysis is the gold standard of learning metrics: they turn complex actions into people-based reports. Each report says: among the people who used our product in this period, here’s how many of them exhibited each of the behaviors we care about.
As the gross numbers get larger, accessibility becomes more and more important. For example, let’s say IMVU has 10,000 conversations in a period. Is that good? Is that one person being very, very social or are 10,000 people each trying the product one time and giving up?
This is how IMVU made their reports accessible: instead of housing analytics or data in a separate system, the reporting data and its infrastructure were considered part of the product itself and were owned by the product development team. The reports were available to anyone with an employee account.
When informed that their pet project is a failure, most of us are tempted to blame the messenger, the data, the manager, the gods, or anything else you can think of. That is why you must ensure that the data is credible to employees. Most often, the data reporting systems are not built by product development teams, whose job is to prioritize and build product features. They are built by business managers and analysts. Managers who must use these systems can only lack a way to test if the data is consistent with reality.
One solution to this is to allow managers to spot check the data with real customers. Second is to make sure the mechanisms that generate the reports are not too complex. Whenever possible, reports should be drawn directly from the master data, rather than from an intermediate system, which reduces opportunities for error.
- PIVOT (or PERSEVERE)
Everything that has been discussed so far is a prelude to a seemingly simple question: are we making sufficient progress to believe that our original strategic hypothesis is correct, or do we need to make a major change (pivot)?
There is no bigger destroyer of creative potential than the misguided decision to persevere. Companies that cannot bring themselves to pivot to a new direction are stuck in the land of living dead.
The heart of the scientific method is the realization that although human judgment may be faulty, we can improve our judgment by subjecting our theories on repeated testing. Successful pivots put us on a path toward growing sustainable business.
Innovation Accounting Leads to Faster Pivots
One of the hardest decisions entrepreneurs face is whether to pivot or persevere. When a company has achieved a modicum of causes — just enough to stay alive — but is not living up to the expectations of its founder and investors is called stuck in the living dead.
Sometimes, what is needed is a zoom-in pivot or the refocusing of the product on what previously had been considered just one feature of a larger whole. This requires not working harder but working smarter. Taking a product development resource and applying it to a new and different product.
Once the pivot succeeds, another age-old entrepreneurship trap emerges: the metrics and product are improving, just not fast enough.
This means time to pivot again. Try customer segment pivot, keep the functionality of the product the same but changing the audience of focus (e.g.organizations vs individuals). Or try a platform pivot, when, for example, instead of selling an application to one customer at a time, you use a self-serve sales platform growth model (anyone could become a customer with a credit card, e.g. Google AdWords).
Another important note is the acceleration of MVPs. Each time, you should be able to validate or refute your next hypothesis faster than before. Nine months, then four, then three, then one. Every time you pivot, you shouldn’t start from scratch. But also because with each step, you should learn critical things about your company’s customers, market, and strategy.
A Startup’s runway is the Number of Pivots it Can Still Make
Every startup has a runway, a time in which it has to either lift-off or frail, defined as the remaining cash in the bank divided by the monthly burn rate. As the startup runs low on cash, it can either raise additional funds or cut costs. But when entrepreneurs cut costs indiscriminately, they are as liable to cut the costs that are allowing the company to get through its Build-Measure-Learn feedback loop as they are to cut waste. If the cut results in a slowdown to this feedback loop, all they have accomplished is to help the startup go out of business more slowly.
A true measure of a runway rate is how many pivots a startup has left. A startup has to find ways to achieve the same amount of validated learning at lower cost or in a shorter time. All the techniques in the lean Startup model that have been discussed so far have this as their overarching goal.
Pivots Require Courage
There are several reasons why entrepreneurs don’t pivot sooner. First, vanity metrics can lead to the formation of false conclusions. This robs them of the belief that it is necessary to change. Second is an unclear hypothesis. In this case, it’s almost impossible to experience complete failure. And without failure, there is no impetus to embark on the radical change a pivot requires. Third, many are afraid. Especially when you feel like your vision might be deemed wrong without having been given a real chance to prove itself. Acknowledging failure can lead to dangerously low morale. However, entrepreneurs should need to facer their fears and be willing to fail, often in a public way.
The Pivot or Persevere Meeting
The decision to pivot requires a clear-eyed and objective mindset. Telltale signs of the need to pivot include decreasing effectiveness of product experiments and the general feeling that product development should be more productive.
Pivoting is definitely an emotionally charged decision. That’s why every startup should have a pre-scheduled “pivot or persevere” meeting on the regular basis. While every startup has its own pace, it is advised that the meeting should be about every 4-8 weeks. And it should be attended by both product development and business leadership teams. Product development side should bring a complete report of the results of its product optimization efforts over time (not just the past period) as well as the comparison of how those results stack up against expectations (again, over time). The business leadership team should bring detailed accounts of their conversations with current and potential customers.
Failure to Pivot
The decision to pivot is so difficult that many companies fail to make it. A few years after IMVU’s founding, the company was having tremendous success. The business had grown to over $1 million per month in revenue with more than twenty million avatars for the customers. The team had raised significant new rounds of financing and were riding high.
Unfortunately, this was only a trap commonly faced by startups. The team was so successful with early efforts that they ignored the principles behind them. This caused them to miss a need to pivot.
The whole point of building low-quality MVPs is that developing any features beyond what early adopters require is a form of waste. But this logic only takes you so far. Once you have found success with early adopters, you want to sell to mainstream customers. Mainstream customers have different requirements and are much more demanding.
The kind of pivot IMVU needed is called customer segment pivot. In this pivot, the company realizes that the product it’s building solves a real problem for real customers but that they are not the customers it originally planned to serve.
This kind of pivot is very hard to execute because the very actions that make a pilot success with early adopters are diametrically opposed to the actions a startup has to master to be successful with mainstream customers.
It took the IMVU team far too long to make the changes necessary to fix this situation. As with all pivots, they had to get back to basics and start the innovation accounting cycle over. They’ve gotten really good at optimizing, tuning, and iterating, but in the process, they had lost sight of the purpose of those activities: testing a clear hypothesis in the service of the company’s vision. Instead, they were chasing growth, revenue, and profits wherever they could find them.
What was needed was a reacquaintance with the new mainstream customer. So IMVU’s interaction designers developed a clear customer archetype based on extensive in-person conversations and observation. Next, they needed to invest heavily in major product overhaul designed to make the product dramatically easier to use. Then, the team created a sandbox experimentation (described later) and had a cross-functional team work exclusively on this major redesign. As they built, they continuously tested their new design head to head against the old one. This foundation has paid off handsomely.
Pivots come in different flavors; they’re all outlined below:
In this case, what previously was considered a single feature in a product becomes the whole product. An example would be Votizen moving away from a full social network and toward a simple voter contact product.
In the reverse situation, sometimes a single feature is insufficient to support a whole product. In this type of pivot, what was considered the whole product, becomes a single feature of a much larger product.
Customer Segment Pivot
When the team is building something that solves a real problem for real customers but they are not the type of customers it originally planned to serve. Product hypothesis is partially confirmed, solving the right problem, but for a different customer than originally anticipated.
Customer Need Pivot
When the product hypothesis is partially confirmed; the target customer has a problem worth solving, just not the one that was originally anticipated. Sometimes, the problem you’re trying to solve for the customers is not very important. However, there are related problems that are. And those are the ones that require repositioning of the product or a completely new product.
This kind of pivot refers to a change from an application to a platform or vice versa. Most commonly, startups that aspire to create a new platform begin life by selling a single application, the so-called killer app, for their platform. Only later does the platform emerge as a vehicle for third parties to leverage as a way to create their own related products. However, this order is not always set in stone, and some companies have to execute this pivot multiple times.
Business Architecture Pivot
Companies generally follow one of two major business architectures: high margin, low volume (complex systems model) or low margin, high volume (volume operations model). In this pivot, a company can change from high margin, low volume by going mass market (e.g., Google’s search “appliance”); or, originally designed for the mass market, can switch to something that requires long and expensive sales cycles.
Value Capture Pivot
There are many types of monetization or revenue models. Often, changes to the way a company captures value can have far-reaching consequences for the rest of the business, product, and marketing strategies.
Engine of Growth Pivot
There are three primary engines of growth that power startups: the viral, sticky, and paid growth models. In this type of pivot, the company changes its growth strategy to seek faster or more profitable growth.
The mechanism by which a company delivers its product to customers is called the sales channel or distribution channel. Often, the requirements of the channel determine the price, features, and competitive landscape of a product. An example of a channel pivot would be abandoning a complex sales process to “sell direct” to its end users (proven to be more effective).
Occasionally, a company discovers a way to achieve the same solution by using a completely different technology. In this case, the company is a sustaining innovation and an incremental improvement is designed to appeal to and retain an existing customer base.
A Pivot is a Strategic Hypothesis
Pivot is better understood as a new strategic hypothesis that will require a new minimum viable product to test. Pivots are a permanent fact of life for any growing business. Even after a company, achieves initial success, it must continue to pivot.
A pivot is the heart of the Lean Startup method. It is what makes the companies that follow Lean Startup resilient in the face of mistakes: if we take a wrong turn, we have the tools we need to realize it and the agility to find another path.
Compiled by Aliya Serikpayeva