Wednesday, March 27, 2013

How to make better decisions

Those of you who have ever launched anything know just how precious the hours on launch day are. Yesterday, author Dan Heath took time out of his launch day to spend some time with me and answered some questions about his new book, Decisive: How to Make Better Choices in Life and Work, co-authored with his brother Chip.  

Dan also kindly asked his publisher to reserve 50 copies of the new book to give away to my readers. You can visit the Heath Brothers’ website and enter to win a free copy of Decisive. 

Dan is a Senior Fellow at Duke University’s CASE center, which supports social entrepreneurs. Before Decisive, Dan and Chip wrote two New York Times bestsellers, Switch and Made to Stick
I asked Dan a few questions about decision-making, including what decisions lead him and his brother to write this book.

ER: Thanks for taking the time out on launch day. What was the hardest decision you had to make while writing the book?

Dan Heath: I think the hardest decision was “which book to write,” honestly. After Chip and I finished up Switch, we had several ideas about what was next. Rather than agonizing over which was the right one to start with, we thought we’d just get started with all three and see, based on how the research developed, which one grabbed us. About six months in, we decided the decision-making topic was the one we were both most passionate about and the one where we could offer the most practical advice. And, actually, we talk about a similar concept in Decisive: the importance of “multitracking” your options when you have a difficult decision. We were inspired to do it ourselves because we’d just come across the research, which says that if you consider multiple options at once, it can yield better decisions. The reason is that you’re learning about multiple alternatives, along with their strengths and weaknesses, simultaneously. And not only that, but some researchers have found that it even speeds up your decision-making.

ER: That’s something I see a lot with startups and yet I think it’s actually sort of counterintuitive that doing this extra work—considering more options—will help you make a decision faster. How is that possible that you are doing more work, yet it’s faster?

DH: Exactly, we feel like we don’t have time to consider more options. It’s more work, and it’s slower. But the counterpoint comes from a researcher named Kathleen Eisenhardt at Stanford, who did a study of Silicon Valley firms and found that the firms that considered more options were actually faster for three basic reasons. The first is: When you consider multiple things simultaneously, you’re actually learning a lot about the shape of the problem—the important factors involved—and that knowledge makes you more confident and quicker to decide. The second piece is: When you consider multiple options, it depoliticizes the choice. When you have one option on the table, and the choice is “do we do this or not,” you get two camps fighting each other. You spend a lot of time bickering and arguing. Versus when you have multiple options, you can approach them more objectively and consider their strengths and weaknesses a bit more honestly. The third piece is maybe the most obvious: when you consider multiple options, you have a built-in fallback. So, if you go with Option A and for some reason it fizzles out, you’ve already got Option B teed up. But if Option A was the only thing you considered, you might exert a lot of energy trying to rescue or redeem it, and maybe try to make it work even to the point of absurdity. So that’s the case for considering multiple options.

ER: It sounds a lot like the decision that a lot of startups struggle with, which is trying to figure out if they should pivot to a new strategy or persevere with what they’re working on. I know you guys write about PayPal, for one, and there are really a lot of other companies that end up doing something quite different than what they started on. I was really struck thinking about the framework that you guys propose in the book, and that really is the psychological counterpart to this: the pivot decision is often one that has to be made by a team under a lot of stress, under adverse conditions where it seems like every psychological bias in the book seems to get in the way of doing it well. 

DH: Right. In fact, the story we cite in the book about PayPal speaks exactly to that. What some of your readers may not know is that PayPal originally was designed as a way to secure financial transactions between PalmPilot users. The founder of PayPal, Max Levchin, had created this brilliant program to do secure transactions through PalmPilots, which is a very difficult thing to do. In fact, their first venture capital investment was actually beamed from PalmPilot to PalmPilot live in a restaurant in the Bay Area. Later, they built a web demo to demonstrate the functionality of the PalmPilot product—the idea was to lure people to the PalmPilot project. Then, to their surprise, the web demo really started to take off, and they started to attract a lot more interest in the web product than the PalmPilot product. Initially, this really frustrated them, because the PalmPilot product was much more technologically advanced. Levchin has this great quote about getting emails from people from this place called “eBay” who wanted to put the PayPal logo on their online payment options, and at first PayPal’s reaction was “no, no, go away!” We don’t want our logo on your owl macramĂ© auction, thanks! But PayPal came to this point where they had to decide: okay, we have 12,000 users on our PalmPilot program and over a million on the online demo. What do we do? So they abandoned the Palm Pilot side.

On the one hand, it seems absurd to call this a “tough decision.” 12,000 users for one product versus over a million for the other! But it makes a ton of sense from the perspective of psychology. There are all these biases that were pulling PayPal toward the model that they started with. One of them is called mere exposure, which is the idea that we get more comfortable with things that we are more familiar with. The PayPal team grew up as a Palm Pilot company. The Palm Pilot was in their bones. It felt more familiar, more natural.

The second bias is loss-aversion. Losses are more painful to us than gains are pleasurable. So if you put yourself in the PayPal founders’ shoes, you start thinking “Yes, we could shift. Yes, there does seem to be a lot of enthusiasm on the web—but, what if we blow it? What if PalmPilots take over the world, and we threw away our advantage? What idiots we’ll look like to sacrifice our early lead for the sake of helping a bunch of flaky eBay sellers.” That IS a tough decision. And these forces are what complicate decisions like this, as you said, for entrepreneurs. 

ER: Unfortunately, in the business school cases that get written about these things, things wind up looking really easy in retrospect, so the narratives we have make it seem, sometimes, like the founders must just have been idiots. Why didn’t they just make the obviously correct decision? But when you’re in the moment, it’s a totally different cognitive experience. 

DH: Anyone who believes that business cases are the be-all and end-all of business education only needs to consider this fact: for years, Enron cases were a mainstay of the Harvard Business School curriculum. They were so innovative, you see! Then after the meltdown, those cases rather abruptly disappeared. 

ER: Ha! A very principled stance. I was thinking about, when you were telling the PayPal story, this great story about Starbucks. People forget that Starbucks began as an Italian copy-cat cafĂ© called Il Giornale—complete with Italian opera, and waiters with bow-ties. Howard Schultz wouldn’t even allow people to take the coffee to go! Because, you know, a porcelain cup is the only way you should be allowed to have real Italian espresso. But, eventually, they end up buying the Starbucks brand back from its owners  and have this company meeting where they have to decide if they should go forward under the brand name ‘Il Giornale’ or Starbucks. And I love the fact that they even had to have a meeting about it!

But when you look at it through the framework you just laid out, they’d been working for years already at the Italian, very differentiated coffee shop concept and they were very invested in it, and you can imagine them feeling like, “What if we end up just being another bland American coffee purveyor and we miss a chance?”

DH: That story is a perfect example of what mars our decision-making so often. You’ve got a bunch of smart, passionate people sitting in a conference room staging all these arguments in their head. But what entrepreneurs have to realize is that the answers are in the world, not in our heads. There are no points for predicting right from inside a conference room. There are only points for getting it right in the real world.

And this is a case where what we talk about in Decisive overlaps very nicely with The Lean Startup because this speaks directly to the philosophy you have. We both talk about Scott Cook at Intuit and his willingness to get out of his head and stop being in the business of trusting his gut. He embraces the philosophy of “leadership by experiment,” which is about giving ideas a chance to prove themselves in the world.

As you write, Intuit runs sometimes up to seventy tests per week. But we can do this same thing with our personal decisions!  I’ve talked to people who are agonizing, sometimes for weeks or months, over whether or not they should go back to school for social work or for counseling, and often these people haven’t spent a single day shadowing a social worker or counselor! I think one of the fundamental principles of decision making is that good decisions happen when we get out of our head and start taking information from the real world seriously. 

ER: Amen. That is a core belief of everyone in the Lean Startup movement. Another question along these lines that I get a lot is: how do you know when you’ve collected enough information to make a good decision? 

DH: That’s a great question. I think there are two gating factors. One is: have you considered enough options? One trap that people fall into is that they make “whether or not” decisions. They consider one alternative really seriously and the decision is “do we do that or not?” That’s a big problem, because adding incremental options really increases your chances for success. So one rule of thumb we talk about in the book is to fall in love twice—make sure you have at least two legitimate options before making your choice. (Don’t apply this to romantic decisions, though.) So, if you’re hiring someone, keep taking applications until you have at least two really good applicants. If you’re house shopping, make sure you keep looking until you have two really good options that you’d feel comfortable living in. That helps stop you from falling into the trap of rationalizing away the faults of your options.

So once you’ve got two or three options, then the question is: Have you gathered enough information from the world to tip you one way or another? And this is where Scott Cook provides a good example. Just run a test of some kind. If you have two or three good options, what kind of experiments can you run to get good, determinative information? Alternatively, you might make a values-based decision.  You can ask yourself: which option is truest to your core priority?
For resolving personal decisions, we offer a useful question in the book: What would you tell your best friend to do in this situation?

ER: Yeah, project it onto somebody else.

DH: Exactly. I know that sounds simple. But I’ve had conversations with people who have reported agonizing about a decision for months, and then I’ve asked that question and they’ve had an answer in ten seconds. There’s something profound that happens when we create a little bit of distance by shifting perspectives.

ER: It’s almost ironic because a lot of us believe in a methodology called “net promoter score” in which you ask customers to recommend a product to a friend or colleague, and I’ve always thought of that as exploiting a bug in human psychology. It doesn’t actually predict peoples’ referral behavior very well—it actually predicts how much they like your product in the first place. So they’re projecting themselves on to their friends and colleagues and telling you what they feel. But if you ask them directly, they won’t tell you.

But I want to switch topics here, because one of the things I really like about all of your books is that you organize your ideas around a very simple mnemonic device. Here you have a really nice one for how to think through a decision: WRAP, which stands for:
  • Widen your options
  • Reality-test your assumptions
  • Attain distance before deciding, and 
  • Prepare to be wrong. 

I want to talk about my favorite of these, which is P for Prepare to be wrong. Because that’s the story of my life!

I think people think preparing to be wrong is setting themselves up for some sort of failure, or it’s some sort of self-sabotage. 

DH: Yeah it sounds like we’re pitching some kind of defeatism. It’s not that at all actually, though. Psychologists tell us that we tend to be overconfident with our decisions, meaning that we think we know how things are going to turn out, but we’re often wrong. Ask anyone who has filled out an NCAA bracket and they can probably identify with this right now.

ER: Ha! Yes. I even used Nate Silver’s bracket and that didn’t turn out very well.

DH: Yes—and that was a pretty wise decision-making strategy by the way!

But when we talk about “Preparing to be Wrong,” it doesn’t mean that we should be pessimistic or bummed out. What it means is that we need to do a better job of stretching our sense of how the future might unfold. We need to consider positive and negative outcomes. Our minds often know more than we think they do. For instance, there’s a study that asks people to estimate the average box office haul for movies in the 1990’s that featured Angelina Jolie. And they were told to specify a range of box office totals that you believe are 80% certain to contain the true value. So, you and I might think, okay, somewhere between fifty and a hundred million dollars is going to capture that to 80% certainty. 

But what they found is that the actual average fell outside of people’s ranges over 60% of the time, rather than the 20% you’d expect (since they were supposed to be 80% certain). People just did a horrible job estimating. But here’s the twist: The researchers did a separate study where they got people to probe for the extremes. They asked: what’s a high value for these Angelia Jolie movies that has only a 10% chance of being exceeded. Or, what’s a low boundary that has only a 10% chance of the number falling below it?

What happens when you stretch peoples’ thinking that way is that they start surfacing new knowledge. They  start thinking, “Well hell, Tomb Raider was in the 90’s, wasn’t it?, and that was a huge hit and so that means the average will be skewed up.” For the lower bound, they’ll think, “But wasn’t she in some really strange indie films, and I bet those barely cracked a million bucks and maybe that drags the average down.” And all of a sudden you’re accessing this information that before was ignored or flattened out because we came to this quick conclusion about the average.

And I think the significance of that for entrepreneurs is obvious. Rather than making a prediction, treating the future like it’s a single point, we need to get people to stretch their expectations and begin, as much as possible, to start planning for the spectrum of possible outcomes. That’s preparing to be wrong. We need to start treating our decisions as predictions that could be right or wrong, rather than as a firm conclusion.

ER: Right. In the Lean Startup movement, we’ve been trying to get people to think of something that used to be called “requirements” in business-speak as “hypotheses” instead. They’re beliefs about what might happen. Everything you do is an experiment, whether you admit it or not. You’ll always have—whether you call them this or not—predictions about how things will go, and that’s a great opportunity for learning.

DH: That’s a beautiful sentiment and it dovetails so nicely with what we talk about in the book. People have such a sense of false permanence about decisions. There are some decisions, of course, where commitment is baked-in—marriage, say, or commitment to the armed forces, but those are the exceptions that prove the rule. The vast majority of decisions are nothing more than hypotheses. It’s: I think this going to be right job for me, the right place to live. We’ve got to stop treating our decisions as permanent and instead think of them as provisional. And of course, if you’re willing to make that leap, then it demands something of us. It means that when you make a decision, you’ve got to think about the circumstances under which you’d reconsider. What could you learn in 6 months that would convince you to go a different direction—or, conversely, convince you that it’s a great decision and worth doubling-down on?

ER: Well this sets me up perfectly to take advantage of your expertise and ask a question I’ve been wanting to ask you. One of the recommendations I make in The Lean Startup is that since you know you’re going to have to pivot, you’re gonna fail, you’re gonna make mistakes, you should schedule the pivot meeting in advance. Say, start the company today but in six weeks from today, let’s have a meeting to see if our strategy is still working, so that way it’s not a crisis that we’re having this meeting and in fact it’s perfectly normal. Is that a psychologically-sound recommendation?

DH: Yes. Absolutely. It sounds to me like we sort of wrote the same book with different terminology. We call that same idea in our book a ‘tripwire’—the notion that there’s something in the future that will snap us awake and force us to re-consider the decisions we’re making. The tripwire might be a metric we’re following, or a budget, or a date. So I’m 100% with you on that one.

ER: You have a notion in the book that you should not only have tripwires for problems in the book but also for unexpected successes. 

DH: Yeah. There’s this great quote from Peter Drucker that I think sums it up really well [we looked up the full quote]:
When a new venture does succeed, more often than not it is in a market other than the one it was originally intended to serve, with products or services not quite those with which it had set out, bought in large part by customers it did not even think of when it started, and used for a host of purposes besides the ones for which the products were first designed. If a new venture does not anticipate this, organizing itself to take advantage of the unexpected and unseen markets…then it will succeed only in creating an opportunity for a competitor. 
So part of what it means to be a great entrepreneur is to begin to sensitize your teams to the signs of unexpected threats or opportunities. This allows you to spot unexpected opportunities—novel ways that customers use your product, for instance. You must see this kind of thing a lot, given the work you do with entrepreneurs…

ER: Yeah. That really is the principle advantage to being in-market and doing experiments rather than any other type of market research. It really allows you to be truly surprised by what customers actually do. You see that in many of the stories we talked about today—Starbucks or PayPal or even you guys with the three books you were considering writing. I remember in a business that I was in, I was constantly targeting the wrong demographic—in that case I was going after older users who were casual gamers. But teenagers kept using my product, and it was a communication product, and I’d be like “Dammit! Another teenager! Stop clogging up my access to my target customer!” 

The question I really want to ask is: if you’re opportunistic, and willing to change directions, how do you deal with the fear that you will inevitably compromise on your vision or abandon your core principles because you’re willing to do what’s popular? How do you reconcile the need for adaptability with this original goal?

DH: I think this gets down to needing our decisions to align with our core priorities. For entrepreneurs, that answer is going to differ based on what you consider your core priority. A lot of great entrepreneurs start out with the goal to serve a certain audience. If serving that audience well is your core priority, then you might burn through a dozen product/service ideas before you finally find one that succeeds. In other cases, your core priority might be to introduce some new idea to the world—some clever way to help people collaborate better, for example. And if that’s your anchor, then you might be willing to discard one audience for another if they embrace it more quickly. But it’s hard to make good decisions without an anchor, or if your only anchor is “I want to make as much money as humanly possible.”

ER: One last question. I was just thinking about—probably because I live in the Bay Area, and so this might come off as a little woo-woo—but I’m curious if this resonates with you. To me, the process of entrepreneurship is partly about the external world, but partly about self-discovery. In making the decision and putting it into practice, you actually discover something about yourself and what your core priorities really are. Does that resonate with you?

DH: Yeah—it does. It’s almost like the notion you talk about in your book called Minimum Viable Product, which is similar to what we talked about earlier: the importance of conducting small tests. (In the book, we call that “ooching”—to ooch is to test something or sample it.)

Let’s say someone wants to quit their job and go sell cupcakes. Rather than quit their job today, why not start a catering business or set up a booth on the weekend at a farmer’s market? Test it out a little. I think there’s something similar that happens for entrepreneurs, where you learn so much about who you are and what makes you tick just by doing stuff. And these are things you could not anticipate in advance. It’s only by virtue of running the experiment or conducting the ooch that you learn what’s important. And that brings us back to the idea that good decisions don’t start in our head; we find the answers in the world.

ER: Amen. I could not agree more. Thank you for taking the time.

Don't forget to click here to visit the Heath Brothers’ website and receive a free copy of Decisive

Thursday, March 21, 2013

Lean Analytics

Lean Analytics is the latest addition to the Lean Series. The book has been a year in the making, and authors Ben Yoskovitz and Alistair Croll—themselves successful founders with several exits under their belts—spent much of that time speaking with founders, investors, and analysts to understand a really basic, but seldom-asked, question: What's normal? As it turns out, normal is a hard question. Normal depends on what kind of business you're in, and what stage of that business you're at. If you're working on the Sticky Engine of Growth, you're focused on very different metrics from those that you care about in the Viral Engine of Growth. Similarly, a two-sided marketplace cares about different things from a traditional e-commerce product.

Start with metrics in mind

To help with this, the book looks at dozens of metrics—such as churn, customer lifetime value, viral coefficient, acquisition cost, uptime, and engagement—and suggests where that metric should be before you can move on to the next stage of your business. Here's what they have to say about churn rates in SaaS businesses:
The best SaaS sites or applications usually have churn ranging from 1.5% to 3% a month. For other sites, it’ll vary depending on how you define “disengaged.” Mark MacLeod, Chief Corporate Development Officer at Freshbooks, says that you need to get below a 5% monthly churn rate before you know you’ve got a business that’s ready to scale. Remember, though, that if you’re surprising your subscribers in a bad way (i.e. billing them for something they didn’t know they’d ordered) then churn will spike during your first billing period, sometimes to 50%, so you should factor this into your calculations.
Matrix Partners' David Skok agrees with the 5% churn threshold, but only for early stage companies, and says that you have to see a clear path to getting churn below 2% if you want to scale significantly.
“In the early days of a SaaS business, churn really doesn’t matter that much. Let’s say you lose 3% of your customers every month. When you only have a hundred customers, losing three of them is not that terrible. You can easily go and find another three to replace them. However as your business grows in size, the problem becomes different. Imagine that you have become really big, and now have a million customers. Three percent churn means that you are losing 30,000 customers every month. That turns out to be a much harder number to replace.”
Not all SaaS companies are the same, of course. Certain products or services are very sticky, in part because of the lock-in users experience. Photo upload sites and online backup services, for example, are hard to leave—because there’s a lot of data in place. So churn for those product categories may be lower. On the other hand, in an industry with relatively low switching costs, churn will be substantially higher.
Social sites may have some tricks at their disposal, too. If users try to leave Facebook, they’re reminded that some of their close friends will miss them—along with pictures of those friends. This is an example of how an emotional tweak was supported later by the data: once implemented, this last-ditch guilt trip reduced deactivations by 7%, which at the time meant millions of users stayed on Facebook.
If you’re going to offer users an incentive to stick around—such as a free month or an upgrade to a new phone—you’ll have to weigh the cost of doing so against the cost of acquiring another customer. Of course, if word gets out that you’re incenting disgruntled users to stick around then many customers may threaten to leave just to receive the discount. And getting the word out is what the Internet is for.
Bottom line: Try to get down to 5% churn a month before looking at other things to optimize. If churn is higher than that, chances are you’re not sticky enough. If you can get churn to around 2.5% you’re doing exceptionally well.
Knowing what normal looks like is essential. If you don't know what normal is, you can't tell if your efforts are paying off. You don't know if you're at a point of diminishing returns and should focus on something else.

Finding your One Metric That Matters

But it's not enough just to know "normal". It's also vital to know what the most important metric is to your business right now. That's because one of the most precious resources a startup has is focus, and spreading your attention across dozens of metrics gets in the way of learning. Ben and Alistair call this focusing on the One Metric That Matters (OMTM), and it's a core theme of the book. The following table shows some examples of an OMTM based on stage and model: Lean Analytics Stages

Many Mores

The book goes into detail about how founders can move the needle a different stages of growth. For example, in the revenue stage—where the company is busy growing revenues and pouring a percentage of them back into user acquisition—there are several places where analytics and iteration can help increase revenues.
Sergio Zyman, Coca-Cola’s CMO, once said that marketing is about selling more stuff to more people more often for more money more efficiently.
Business growth comes from improving one of these five “knobs”:
More stuff means adding products or services, preferably those you know your customers want so you don’t waste time building things they won’t use or buy. For intrapreneurs, this means applying Lean methods to new product development, rather than starting an entirely new company.
More people means adding users, ideally through virality or word of mouth, but also through paid advertising. The best way to add users is when it’s an integral part of product use—such as Dropbox, Hotmail , or a project management tool that invites outside users—since this happens automatically and implies an endorsement from the inviting user.
More often means stickiness (so people come back), reduced churn (so they don’t leave) and repeated use (so they use it more frequently). Early on, stickiness tends to be a key knob on which to focus, because until your core early adopters find your product superb, it’s unlikely you can achieve good viral marketing.
More money means upselling and maximizing the price users will pay, or the revenue from ad clicks, or the amount of content they create, or the number of in-game purchases they make.
More efficiently means reducing the cost of delivering and supporting your service, but also lowering the cost of customer acquisition by doing less paid advertising and more word of mouth.
In the Revenue Stage, you need to figure out which “more” increases your revenues per engaged customer the most:
If you’re dependent on physical, per-transaction costs (like direct sales, or shipping products to a buyer, or signing up merchants) then more efficiently will figure prominently on either the supply or demand side of your business model.
If you’ve found a high viral coefficient, then more people makes sense, because you’ve got a strong force multiplier added to every dollar you pour into customer acquisition.
If you’ve got a loyal, returning set of customers who buy from you every time, then more often makes sense, and you’re going to emphasize getting them to come back more frequently.
If you’ve got a one-time, big-ticket transaction, then more money will help a lot, because you’ve only got one chance to extract revenue from the customer and need to leave as little money as possible on the table.
If you’re a subscription model, and you’re fighting churn, then upselling customers to higher-capacity packages with broader features is your best way of growing existing revenues, so you’ll spend a lot of time on more stuff.
Any founder knows that it's really, really hard work to identify the riskiest part of the business, then find the simplest way to validate or repudiate your business model with that risk in mind. To do this, you need metrics, and a mindset that turns everything into a learning an experience. As I said in the foreword to the book,
Ben and Alistair have done the incredibly hard work of surveying the best thinking on the metrics and analytics, gathering in-depth examples, and breaking new ground in presenting their own frameworks for figuring out what metrics matter, and when. Their work collecting industry-wide benchmarks to use for a variety of key metrics is worth the price of admission all by itself.
While Lean Analytics applies to startups, it's also valuable for companies selling to business customers, and to intrapreneurs within large organizations trying to change the status quo. That's because the cycle of learning and measurement is universal. But unlike startups, Intrapreneurs have to work within existing systems and ultimate hand over their successful new products to the host organization, which presents some unique challenges.
If you work in a company of any significant size, you owe your org chart to an enterprising General Superintendent of the railroad era named Daniel C. McCallum. In the 1850s, railroads were a booming business. Unfortunately for investors, they didn’t scale well. Small railroads turned a profit; big ones didn’t.
McCallum noticed this, and divided his railroad into smaller sections, each run by subordinates who reported back a standard set of information he defined. McCallum’s line—as well as other lines that copied this approach—thrived. McCallum’s model, inspired by his time as a soldier and the regimented hierarchies he had learned there, was then applied to other fields.
McCallum was the first management scientist, introducing controls, structure, and regulations in order to reduce risk and increase predictability at scale. Companies like Google and Apple know this, creating their own advanced research groups such as the Google X Lab.
Intrapreneurs aren’t trying to solve for safety and predictability. Their job is to take risks, and to uncover the non-obvious and the unpredictable. If you’re trying to provoke change and disrupt the status quo, then the organizations McCallum introduced are your kryptonite. You need to shield yourself, just as the engineers within the Skunk Works did decades ago. But you also need to coexist with the organization, because unlike an independent startup, the fruits of your labors must integrate with your host company.
What you make may cannibalize the existing business, or threaten employees’ jobs. People will behave irrationally. When Marc Andreesen famously said “software eats everything,” one of the things in its diet was jobs. When a software company introduces a SaaS version of their application, salespeople who make a living selling enterprise licenses get angry.
Inertia is real. If you’re asking people to change how they work, you’ll need to give them reason to do so. Consider an Apple store: there’s no central cash register, and they’ll e-mail you a receipt. It takes a fraction of the time to purchase something; but convincing an existing retailer to change to this model will require retraining and modifying store layout.
If you do your job well, you’ll disrupt the ecosystem. A traditional music label has relationships with distributors and stores. That made it hard to move into online music distribution, leaving the opportunity open for online retailers.
Your innovation will live or die in the hands of others. While it’s easy to be myopic about your work—and disdainful of what the rest of the company is doing—the two are one and the same. “When problems crop up it is easy to see things from your own point of view,” says Richard Templar in The Rules Of Work, “Once you make the leap to corporate speak it gets easier to stop doing this and start seeing problems from the company’s point of view.”
In their book Confronting Reality, Larry Bossidy and Ram Charan list the six habits of highly unrealistic leaders: Filtered information; selective hearing; wishful thinking; fear; emotional overinvestment; and unrealistic expectations from capital markets.
Intrapreneurs need the opposite attributes to thrive—and many of those attributes are driven by data and iteration. You need access to the real information, and you need to go where the data takes you, avoiding confirmation bias. You need to set aside your own assumptions and preconceived notions, and you need to combine high standards with low expectations.

Data Yields Insights

In many of the book's case studies—there are thirty in all—analysis of the data yields an insight that unlocks product-market fit, such as the discovery of an adjacent market or the effectiveness of a different pricing model. Consider what happened to ClearFit, a SaaS recruiting software company, as described in the book.
ClearFit is a SaaS provider of recruitment software aimed at helping small businesses find job candidates and predict their success. When they started, founders Ben Baldwin and Jamie Schneiderman offered a $99/month (per job posting) package. “We kept hearing over and over that monthly subscriptions was the key to growing a successful SaaS business,” says Ben. “So that’s the direction we took, but it didn’t work as planned.”
Two things confused ClearFit’s customers: the price point and the monthly subscription. Ben and Jamie wanted to price ClearFit below what customers paid for job boards (typically $300+ per job posting), but customers were so used to that price point they were skeptical of ClearFit’s value at $99/month.
Ben says, “We don’t compete with job boards, we partner with them, but at the time it seemed reasonable to have a lower price point to garner attention.” Customers didn’t understand why they would pay a subscription fee for something that they would most likely use sporadically. “When a company needs to hire, they want to do it fast and they’re willing to invest at that moment in time,” says Ben. “Our customers are too small to have dedicated HR staff or recruiters that are constantly looking for talent, and their hiring needs go up and down frequently."
Ben and Jamie decided to abandon their low monthly subscription and switch to a model that their customers understood: a per job fee. ClearFit launched its new price point at $350 for a single job (for 30 days) and almost immediately saw three times the sales. The increase in volume and the higher price point improved revenue 10x.
“When we increased the price,” Ben says, “it was an important signal to our customers. They understood the model and could more easily compare the value against other solutions they use. Even though what we do is different than a job board, we wanted our customers to feel comfortable with purchasing from us, and we wanted to fit into how they budget for recruiting.”
In ClearFit’s case, innovating on the business model didn’t make sense. Ben says, “People don’t do subscriptions for haircuts, hamburgers and hiring. You have to understand your customer, who they are, how and why they buy, and how they value your product or service.”
ClearFit’s switch to a per job posting model may go against the popular grain of subscription-based SaaS businesses, but the company continues to see great success with 30% month-over-month revenue growth.
ClearFit initially focused on a subscription model for revenue, but customers misinterpreted their low pricing as a sign of a weak offering. They switched to a paid listing model, and tripled sales while improving revenue tenfold. Ultimately, the problem wasn’t the business model, it was the pricing and the messages it sent to prospects.
The company learned that just because SaaS is a recurring service doesn’t mean it needs to be priced that way. If your product is ephemeral—like a transient job posting—it might be better to offer more transactional pricing. Pricing is a tricky beast. You need to test different price points qualitatively (by getting feedback from customers) and quantitatively. Don’t assume a low price is the answer; customers might not attribute enough value to your offering.
This prescriptive, data-informed approach shows founders what to do, when. While every business is unique, the book provides a starting point for building your own model, creating your own metrics, and deciding when it's time to grow, when it's time to pivot, and when it's time to quit.

Ask Good Questions

As Ben and Alistair say in the conclusion:
"There’s never been a better time to know your market. Your customers leave a trail of digital breadcrumbs with every click, tweet, vote, like, share, check-in, and purchase, from the first time they hear about you until the day they leave you forever, whether they’re online or off. If you know how to collect those breadcrumbs, you have unprecedented insight into their needs, their quirks, and their lives.
This insight is forever changing what it means to be a business leader. Once, a leader convinced others to act in the absence of information. But today, there’s simply too much information available. We don’t need to guess—we need to know where to focus. We need a disciplined approach to growth that identifies, quantifies, and overcomes risk every step of the way. Today’s leader doesn’t have all the answers. Instead, today’s leader knows what questions to ask.
Go and ask good questions."
You can order Lean Analytics today.

Wednesday, March 6, 2013

The Lean Startup SXSW 2013

We're back! Once again, along with my partners at 500 Startups, we are proud to present the most substantive track at SXSW:

There was a running joke last year that "the Lean Startup track was the only place at SXSW you couldn't get out of the building." That's because the room we were in was so packed, the only way to stay for the next session was to refuse to give up your seat. Luckily, in a room full of entrepreneurs, plenty of new businesses popped up to bring people food and water.

(If you want to see what all the fuss was about, you can watch complete video of last year's track courtesy of my friends at Udemy.)

Because of your tremendous enthusiasm (and possibly a few words from the fire marshall), SXSW has granted us a much bigger space this year to put on what may be the best lineup we've ever had. I've embedded the flyer for the event below, but the best way to see what we're doing is on the conference website - which we try to keep up-to-date with the inevitable flurry of last-minute changes. (This year, we're battling both the flu and the Congressional sequester.)

If you're planning to be in Austin, please come say hello. See you there!

The Lean Startup SXSW


 Etsy AirbnbUber Github Hotel Tonight

 Google ModCloth HubSpotWufoo

     500 Startups Khosla Ventures meeboSharethrough

 + 1,000 startup founders, investors, and press!

SATURDAY, MARCH 9TH - Hilton Austin Downtown

Vinod Khosla

Vinod Khosla


Khosla Ventures
Dave McClure

Dave McClure
Founding Partner 

500 Startups
Eric Ries

 Eric Ries

The Lean Startup
Steve Blank

Steve Blank 
Serial Entrepreneur
 & Professor
Stanford University
Todd Park

Todd Park 

Executive Office

Travis Kalanick

Travis Kalanick
CEO & Co-Founder 
Joe Zadeh

Joe Zadeh
Director of Product

Sam Shank

Sam Shank
Co-Founder & CEO

Hotel Tonight
Nicole Lazzaro

NEW!  Nicole Lazzaro
Udi Nir
Udi Nir

Seth Sternberg

NEW! Seth Sternberg

Scott Chacon

Scott Chacon

Kevin Hale

Kevin Hale
Senior Product Manager

Juan Diego Calle

NEW! Juan Diego Calle 
Dharmesh Shah

Dharmesh Shah
Co-founder & CTO
Ross Snyder

Ross Snyder
Sr. Software Engineer

Steven VanRoekel

Steven VanRoekel 
Executive Office
Dan Greenberg

 Dan Greenberg
Co-Founder & CEO



Uber's "Reality Therapy 4 Startups": What U Need 2 Know NOW 
Steve Blank on why NOT to be an Entrepreneur
Airbnb's Globetrotting Lessons: Building 4 a Global Market
Vinod Khosla - The VC Legend: Interview w/ Dave McClure
Eric Ries on Entrepreneurship: What He's Learned Along the Way
> GitHub's Secret 2 Success: Rethinking The Way We Work
The Metrics Behind Hotel Tonight: Sam Shank Reveals It All
The CTO & CIO of the USA: Innovating our Government
Wufoo's 29,561% Return: Kevin Hale on Customer Development
> Etsy's [Backend] Transition: From Startup to Superpower
Meebo's Pivot to Acquisition: Seth Sternberg's Pivot Metrics
Under the Hood of ModCloth: CTO on Agile Development
David BEATS the Goliath: The true story of .CO's rise to fame
> Tips from an Anti-Social Founder: Dharmesh Shah on culture

> 8:30am Mimosa Meetup - 1st come, 1st served, hosted by GitHib
Grilled Cheese + Beer (not lean, but awesome) - hosted by Neo
 Elon Musk + Popcorn - hosted by DealFlicks, during SXSW keynote
.CO hookups 4 startups: A SXSW favorite is back again!
Mentor Sessions - Featuring experts in building + running startups

Patrick Vlaskovits

Patrick Vlaskovits


The Lean Entrepreneur
Alistair Croll

Alistair Croll
Lean Analytics

REGISTER for SXSWi to Join



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