Reinventing Life Science Startups – Evidence-based Entrepreneurship

What if we could increase productivity and stave the capital flight by helping Life Sciences startups build their companies more efficiently?

We’re going to test this hypothesis by teaching a Lean LaunchPad class for Life Sciences and Health Care (therapeutics, diagnostics, devices and digital health) this October at UCSF with a team of veteran venture capitalists.

Part 1 of this post described the issues in the drug discovery. Part 2 covered medical devices and digital health. This post describes what we’re going to do about it.  And why you ought to take this class.

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When I wrote Four Steps to the Epiphany and the Startup Owners Manual, I believed that Life Sciences startups didn’t need Customer Discovery. Heck how hard could it be?  You invent a cure for cancer and then figure out where to put the bags of money. (In fact, for oncology, with a successful clinical trial, this is the case.)

Pivots in life sciences companies

But I’ve learned that’s not how it really works. For the last two and a half years, we’ve taught hundreds of teams how to commercialize their science with a version of the Lean LaunchPad class called the National Science Foundation Innovation Corps.  Quite a few of the teams were building biotech, devices or digital health products.  What we found is that during the class almost all of them pivoted - making substantive changes to one or more of their business model canvas components.

In the real world a big pivot in life sciences far down the road of development is a very bad sign due to huge sunk costs.  But pivoting early, before you raise and spend millions or tens of millions means potential disaster avoided.

Some of these pivots included changing their product/service once the team had a better of understanding of customer needs or changing their position in the value chain (became an OEM supplier to hospital suppliers rather than selling to doctors directly.) Other pivots involved moving from a platform technology to become a product supplier, moving from a therapeutic drug to a diagnostic or moving from a device that required a PMA to one that required a 510(k).

Some of these teams made even more radical changes.  For example when one team found the right customer, they changed the core technology (the basis of their original idea!) used to serve those customers. Another team reordered their device’s feature set based on customer needs.

These findings convinced me that the class could transform how we thought about building life science startups.  But there was one more piece of data that blew me away.

Control versus Experiment – 18% versus 60%
For the last two and a half years, the teams that were part of the National Science Foundation Innovation Corps were those who wanted to learn how to commercialize their science, applied to join the program, fought to get in and went through a grueling three month program.  Other scientists attempting to commercialize their science were free to pursue their startups without having to take the class.

Both of these groups, those who took the Innovation Corps class and those who didn’t, applied for government peer-reviewed funding through the SBIR program. The teams that skipped the class and pursued traditional methods of starting a company had an 18% success rate in receiving SBIR Phase I funding.

The teams that took the Lean Launchpad class  – get ready for this – had a 60% success rate. And yes, while funding does not equal a successful company, it does mean these teams knew something about building a business the other teams did not.

The 3-person teams consisted of Principal Investigators (PI’s), mostly tenured professors (average age of 45,) whose NSF research the project was based on. The PI’s in turn selected one of their graduate students (average age of 30,) as the entrepreneurial lead. The PI and Entrepreneurial Lead were supported by a mentor (average age of 50,) with industry/startup experience.

This was most definitely not the hoodie and flip-flop crowd.

Obviously there’s lots of bias built into the data – those who volunteered might be the better teams, the peer reviewers might be selecting for what we taught, funding is no metric for successful science let alone successful companies, etc.  – but the difference in funding success is over 300%.

The funding criteria for these new ventures wasn’t solely whether they had a innovative technology. It was whether the teams understood how to take that idea/invention/patent and transform it into a company. It was whether after meeting with partners and regulators, they had a plan to deal with the intensifying regulatory environment. It was whether after talking to manufacturing partners and clinicians, they understood how they were going to reduce technology risk. And It was after they talked to patients, providers and payers whether they understood the customer segments to reduce market risk by having found product/market fit.

Scientists and researchers have spent their careers testing hypotheses inside their labs. This class teaches them how to test the critical hypotheses that turn their idea into a business as they deal with the real world of regulation, customers and funding.

So after the team at UCSF said they’d like to prototype a class for Life Sciences, I agreed.

Here’s what we’re going to offer.

The Lean LaunchPad Life Sciences and Health Care class

The goal of the Lean LaunchPad Life Sciences class at UCSF is to teach researchers how to move their technology from an academic lab into the commercial world.UCSF Logo

We’re going to help teams:

  • assess regulatory risk before they design and build
  • gather data essential to customer purchases before doing the science
  • define clinical utility now, before spending millions of dollars
  • identify financing vehicles before you need them

We’ve segmented the class into four cohorts: therapeutics, diagnostics, devices and digital health.  And we recruited a team of world class Venture Capitalists and entrepreneurs to teach and mentor the class including Alan May, Karl Handelsman, Abhas Gupta, and Todd Morrill.

The course is free to UCSF, Berkeley, and Stanford students; $100 for pre-revenue startups; and $300 for industry. – See more here

The syllabus is here.

Class starts Oct 1st and runs through Dec 10th.

Download the all three parts of the Life Science series here.

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Reinventing Life Science Startups – Medical Devices and Digital Health

What if we could increase productivity and stave the capital flight by helping Life Sciences startups build their companies more efficiently?

We’re going to test this hypothesis by teaching a Lean LaunchPad class for Life Sciences and Healthcare (therapeutics, diagnostics, devices and digital health) this October at UCSF with a team of veteran venture capitalists.

In this three post series, Part 1 described the challenges Life Science companies face in Therapeutics and Diagnostics. This post describes the issues in Medical Devices and Digital Health.  Part 3 will offer our hypothesis about how to change the dynamics of the Life Sciences industry with a different approach to commercialization of research and innovation.  And why you ought to take this class.

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Medical devices prevent, treat, mitigate, or cure disease by physical, mechanical, or thermal means (in contrast to drugs, which act on the body through pharmacological, metabolic or immunological means). They span they gamut from tongue depressors and bedpans to complex programmable pacemakers and laser surgical devices. They also diagnostic products, test kits, ultrasound products, x-ray machines and medical lasers.

Incremental advances are driven by the existing medical device companies, while truly innovative devices often come from doctors and academia. One would think that designing a medical device would be a simple engineering problem, and startups would be emerging right and left. The truth is that today it’s tough to get a medical device startup funded.

Life Sciences II – Medical Devices

Regulatory Issues
In the U.S. the FDA Center for Devices and Radiological Health (CDRH) regulates medical devices and puts them into three “classes” based on their risks.

Class I devices are low risk and have the least regulatory controls. For example, dental floss, tongue depressors, arm slings, and hand-held surgical instruments are classified as Class I devices. Most Class I devices are exempt Premarket Notification 510(k) (see below.)

Class II devices are higher risk devices and have more regulations to prove the device’s safety and effectiveness. For example, condoms, x-ray systems, gas analyzers, pumps, and surgical drapes are classified as Class II devices.FDA approvals

Manufacturers introducing Class II medical devices must submit what’s called a 510(k) to the FDA. The 510(k) identifies your medical device and compares it to an existing medical device (which the FDA calls a “predicate” device) to demonstrate that your device is substantially equivalent and at least as safe and effective.

Class III devices are generally the highest risk devices and must be approved by the FDA before they are marketed. For example, implantable devices (devices made to replace/support or enhance part of your body) such as defibrillators, pacemakers, artificial hips, knees, and replacement heart valves are classified as Class III devices. Class III medical devices that are high risk or novel devices for which no “predicate device” exist require clinical trials of the medical device a PMA  (Pre-Market Approval).Life Science Decline

  • The FDA is tougher about approving innovative new medical devices. The number of 510(k)s being required to supply additional information has doubled in the last decade.
  • The number of PMA’s that have received a major deficiency letter has also doubled.
  • An FDA delay or clinical challenge is increasingly fatal to Life Science startups, where investors now choose to walk away rather than escalate the effort required to reach approval.

med device pipeline

Business Model Issues

  • Cost pressures are unrelenting in every sector, with pressure on prices and margins continuing to increase.
  • Devices are a five-sided market: patient, physician, provider, payer and regulator. Startups need to understand all sides of the market long before they ever consider selling a product.
  • In the last decade, most device startups took their devices overseas for clinical trials and first getting EU versus FDA approval
  • Recently, the financing of innovation in medical devices has collapsed even further with most Class III devices simply unfundable.
  • Companies must pay a  medical device excise tax of 2.3% on medical device revenues, regardless of profitability delays or cash-flow breakeven.
  • The U.S. government is the leading payer for most of health care, and under ObamaCare the government’s role in reimbursing for medical technology will increase. Yet two-thirds of all requests for reimbursement are denied today, and what gets reimbursed, for how much, and in what timeframe, are big unknowns for new device companies.

Venture Capital Issues

  • Early stage Venture Capital for medical device startups has dried up. The amount of capital being invested in new device companies is at an 11 year low.
  • Because device IPOs are rare, and M&A is much tougher, liquidity for investors is hard to find.
  • Exits have remained within about the same, while the cost and time to exit have doubled.

Life Sciences III – The Rise of Digital Health
Over the last five years a series of applications that fall under the category of “Digital Health” has emerged. Examples of these applications include: remote patient monitoring, analytics/big data (aggregation and analysis of clinical, administrative or economic data), hospital administration (software tools to run a hospital), electronic health records (clinical data capture), and wellness (improve/monitor health of individuals). A good number of these applications are using Smartphones as their platform.digital health flow

Business Model Issues

  • A good percentage of these startups are founded by teams with strong technical experience but without healthcare experience. Yet healthcare has its own unique regulatory and reimbursement issues and business model issues that must be understood
  • Most of these startups are in a multisided market, and many have the same five-sided complexity as medical devices: patient, physician, provider, payer and regulator.  (Some are even more complex in an outpatient / nurse / physical therapy setting.)
  • Reimbursement for digital health interventions is still a work in progress
  • Some startups in this field are actually beginning with Customer Development while others struggle with the classic execution versus search problem

Regulatory Issues

  • Digital Health covers a broad spectrum of products, unless the founders have domain experience startups in this area usually discover the FDA and the 510(k) process later than they should. 

Venture Capital

  • Seed funding is still scarce for Digital Health, but a number of startups (particularly those making physical personal heath tracking devices) are turning to crowdfunding.
  • Moreover, the absence of recent IPOs and public companies benchmarks creates uncertainty for VCs evaluating later investments too

Try Something New
The fact that the status quo for Life Sciences is not working is not a new revelation. Lots of smart people are running experiments in search of ways to commercialize basic research  more efficiently.

Universities have set up translational R&D centers; (basically university/company partnerships to commercialize research).  The National Institute of Health (NIH) is also setting up translational centers through its NCATS program.  Drug companies have tried to take research directly out of university labs by licensing patents, but once inside Pharma’s research labs, these projects get lost in the bureaucracy.  Realizing that this is not optimal, drug companies are trying to incubate projects directly with universities and the researchers who invented the technology, such as the recent Janssen Labs program.

But while these are all great programs, they are likely to fail to deliver on their promise. The assumption that the pursuit of drugs, diagnostics, devices and digital health is all about the execution of the science is in most cases a mistake.

The gap between the development of intriguing but unproven innovations, and the investment to commercialize those innovations is characterized as “the Valley of Death.”valley of death

We believe we need a new model to attract private investment capital to fuel the commercialization of clinical solutions to todays major healthcare problems that is in many ways technology agnostic. We need a “Needs Driven/Business Model Driven” approach to solving the problems facing all  the stakeholders in the vast healthcare system.

We believe we can reduce the technological, regulatory and market risks for early-stage life science and healthcare ventures, and we can do it by teaching founding teams how to build new ventures with Evidence-Based Entrepreneurship.

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Part 3 in the next post will offer our hypothesis how to change the dynamics of the Life Sciences industry with a different approach to commercialization of research and innovation. And why you ought to take this class.

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Reinventing Life Science Startups–Therapeutics and Diagnostics

It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity, it was the season of Light, it was the season of Darkness, it was the spring of hope, it was the winter of despair, we had everything before us, we had nothing before us, we were all going direct to Heaven, we were all going direct the other way.

Charles Dickens

Life Science (therapeutics- drugs to cure or manage diseases, diagnostics- tests and devices to find diseases, devices to cure and monitor diseases; and digital health –health care hardware, software and mobile devices and applications streamline and democratize the healthcare delivery system) is in the midst of a perfect storm of decreasing productivity, increasing regulation and the flight of venture capital.

But what if we could increase productivity and stave the capital flight by helping Life Sciences startups build their companies more efficiently?

We’re going to test this hypothesis by teaching a Lean LaunchPad class for Life Sciences and Healthcare (therapeutics, diagnostics, devices and digital health) this October at UCSF with a team of veteran venture capitalists and angels.

It was the best of times and the worst of times
The last 60 years has seen remarkable breakthroughs in what we know about the biology underlying diseases and the science and engineering of developing commercial drug development and medical devices that improve and save lives. Turning basic science discoveries into drugs and devices seemed to be occurring at an ever increasing rate.

Yet during those same 60 years, rather than decreasing, the cost of getting a new drug approved by the FDA has increased 80 fold.  Yep, it cost 80 times more to get a successful drug developed and approved today than it did 60 years ago.Overall efficiency

75% or more of all the funds needed by a Life Science startup will be spent on clinical trials and regulatory approval. Pharma companies are staggering under the costs.  And medical device innovation in the U.S. has gone offshore primarily due to the toughened regulatory environment.

At the same time, Venture Capital, which had viewed therapeutics, diagnostics and medical devices as hot places to invest, is fleeing the field. In the last six years half the VC’s in the space have disappeared, unable to raise new funds, and the number of biotech and device startups getting first round financing has dropped by half. For exits, acquisitions are the rule and IPOs the exception.

While the time, expense and difficulty to exit has soared in Life Sciences, all three critical factors have been cut by orders of magnitude in other investment sectors such as internet or social-local-mobile.  And while the vast majority of Life Science exits remain below $125M, other sectors have seen exit valuations soar.  It has gotten so bad that pension funds and other institutional investors in venture capital funds have told these funds to stay away from Life Science – or at the least, early stage Life Science.

WTF is going on?  And how can we change those numbers and reverse those trends?

We believe we have a small part of the answer.  And we are going to run an experiment to test it this fall at UCSF.

In this three post series, the first two posts are a short summary of the complex challenges Life Science companies face; in Therapeutics and Diagnostics in this post and in Medical Devices and Digital Health in Part 2.  Part 3 explains our hypothesis about how to change the dynamics of the Life Sciences industry with a different approach to commercialization of research and innovation.  And why you ought to take this class.

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Life Sciences I—Therapeutics and Diagnostics

It was the Age of Wisdom – Drug Discovery
There are two types of drugs. The first, called small molecules (also referred to as New Molecular Entities or NMEs), are the bases for classic drugs such as aspirin, statins or high blood pressure medicines. Small molecules are made by reactions between different organic and/or inorganic chemicals. In the last decade computers and synthesis methods in research laboratories enable chemists to test a series of reaction mixtures in parallel (with wet lab analyses still the gold standard.) Using high-throughput screening to search for small molecules, which can be a starting point (or lead compound) for a new drug, scientists can test thousands of candidate molecules against a database of millions in their libraries.

Ultimately the FDA Center for Drug Evaluation and Research (CDER) is responsible for the approval of small molecules drugs.Drug discovery pipeline

The second class of drugs created by biotechnology is called biologics (also referred to as New Biological Entities or NBEs.) In contrast to small molecule drugs that are chemically synthesized, most biologics are proteins, nucleic acids or cells and tissues. Biologics can be made from human, animal, or microorganisms – or produced by recombinant DNA technology. Examples of biologics include: vaccines, cell or gene therapies, therapeutic protein hormones, cytokines, tissue growth factors, and monoclonal antibodies.

The FDA Center for Biologics Evaluation and Research (CBER) is responsible for the approval of biologicals.

It was the Season of Light
The drug development pipeline for both small molecules and biologics can take 10-15 years and cost a billion dollars. The current process starts with testing thousands of compounds which will in the end, produce a single drug.

In the last few decades scientists searching for new drugs have had the benefit of new tools — DNA sequencing, 3D protein database for structure data, high throughput screening for “hits”, computational drug design, etc. — which have sped up their search dramatically.Drug funnel

The problem is that the probability that a small molecule drug gets through clinical trials is unchanged after 50 years. In spite of the substantial scientific advances and increased investment, over the last 20 years the FDA has approved an average of 23 new drugs a year. (To be fair, this is indication-dependent. For example, in oncology, things have gotten significantly better. In most other areas, particularly drugs for the central nervous system and metabolism, they have not.)

drugs approved

It was the Season of Despair
With the exception of targeted therapies, the science and tools haven’t made the drug discovery pipeline more efficient. Oops.

There are lots of reasons why this has happened.

Regulatory and Reimbursement Issues

  • Drug safety is a high priority for the FDA. To avoid problems like Vioxx, Bexxar etc., the regulatory barriers (i.e. proof of safety) are huge, expensive, and take lots of time. That means the FDA has gotten tougher, requiring more clinical trials, and the stack of regulatory paperwork has gotten higher.
  • Additional trials to demonstrate both clinical efficacy (if not superiority) and cost outcomes effectiveness are further driving up the cost, time and complexity of clinical trials.

Drug Discovery Pipeline Issues

Drug target Issues

  • In a perfect world the goal is to develop a drug that will go after a single target (a protein, enzyme, DNA/RNA, etc. that will undergo a specific interaction with chemicals or biological drugs) that is linked to a disease.
  • Unfortunately most diseases don’t work that simply. There are a few diseases that do, (i.e. insulin and diabetes, Gleevec -Philadelphia Chromosome and chronic myeloid leukemia), but most small molecule drugs rarely act on a single target (target-based therapy in oncology being the bright spot.)
  • To get FDA approval new drugs have to be proven better than existing ones.  Most of the low-hanging fruit of easy drugs to develop are already on the market.

Venture Capital Issues

  • For the last two decades, biotech venture capital and corporate R&D threw dollars into interesting science (find a new target, publish a paper in Science, Nature or Cell, get funded.) The belief was that once a new target was found, finding a drug was a technology execution problem.  And all the new tools would accelerate the process.  It often didn’t turn out that way, although there are important exceptions.
  • Moreover, the prospect of the FDA also evaluating drugs for their cost-effectiveness is adding another dimension of uncertainty as the market opportunity at the end of the funnel needs to be large enough to justify venture investment

drug dev pipeline fundedIn Part 2 of this series, we describe the challenges new Medical Device and Digital Health companies face.  Part 3 will offer our hypothesis how to change the dynamics of the Life Sciences industry with a different approach to commercialization of research and innovation in this sector.  And why you ought to take this class.
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How to get meetings with people too busy to see you

Asking, “Can I have coffee with you to pick your brain?” is probably the worst possible way to get a meeting with someone with a busy schedule.  Here’s a better approach.

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Jason, an entrepreneur I’ve known for over a decade, came out to the ranch today. He was celebrating selling his company and just beginning to think through his next moves. Since he wasn’t from Silicon Valley, he decided to use his time up here networking with other meetings with VC’s and company executives.

I get several hundred emails a day, and a good number of them are “I want to have coffee with you to bounce an idea off.” Or, “I just want to pick your brain.” I now have a filter for which emails get my attention, so I was curious in hearing what Jason, who I think of as pretty good at networking, was asking for when he was trying to set up meetings.

“Oh, I ask them if I can have coffee to bounce an idea off of them.”…Sigh.foot in the door

I realized most entrepreneurs don’t know how to get meetings with people too busy to see you.

Perfect World
Silicon Valley has a “pay-it-forward” culture where we try to help each other without asking for anything in return. It’s a culture that emerged in the 60’s semiconductor business when competitors would help each other solve bugs in their chip fabrication process. It continued in the 1970’s with the emergence of the Homebrew Computer Club, and it continues today.  Since I teach, I tend to prioritize my list of meetings with first my current students, then ex-students, then referrals from VC firms I’ve invested in, and then others.  But still with that list, and now with a thousand plus ex-students, I have more meeting requests than I possibly can handle. (One of the filters I thought would keep down the meetings is have meetings at the ranch; an hour from Stanford on the coast, but that hasn’t helped.)

So I’ve come up with is a method to sort out who I take meetings with.

What are you offering?
I’m not an investor, and I’m really not looking for meetings with entrepreneurs for deal flow. I’m having these meetings because someone is asking for something from me – my time – and they think I can offer them advice.

If I’d had infinite time I’d take every one of these “can I have coffee” meetings. But I don’t.  So I now prioritize meetings with a new filter: Who is offering me something in return.

No, not offering me money.  Not for stock.  But who is offering to teach me something I don’t know.

The meeting requests that now jump to the top of my list are the few, very smart entrepreneurs who say, “I’d like to have coffee to bounce an idea off of you and in exchange I’ll tell you all about what we learned about xx.”

get into my head

This offer of teaching me something changes the agenda of the meeting from a one-way, you’re learning from me, to a two-way, we’re learning from each other.

It has another interesting consequence for those who are asking for the meeting – it forces them to think about what is it they know and what is it they have learned – and whether they can explain it to others in a way that’s both coherent and compelling.

Irony – it’s Customer Discovery
While this might sound like a, “how to get a meeting with Steve” post, the irony is that this “ask for a two-way meeting” is how we teach entrepreneurs to get their first customer discovery meetings; don’t just ask for a potential customers time, instead offer to share what you’ve learned about a technology, market or industry.

It will increase your odds in any situation you’re asking for time from very busy people – whether they are VC’s, company executives or retired entrepreneurs.

  • Lessons Learned
  • Wanting to have coffee is an ask for a favor
  • Offering to share knowledge is a different game
  • Try it, your odds of getting a meeting will increase
  • And the meetings will be more productive

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How Kevin O’Connor, and FindTheBest Got Lean

When we started E.piphany there was an equally scrappy startup called DoubleClick (later acquired by Google for $3.1 billon). Over the years Kevin O’Connor, former CEO and founder of DoubleClick and I got to know each other.  It’s been fun watching a 20th Century entrepreneur learn new tricks as he builds his next startup, FindTheBest using Lean Methodology.  Here’s Kevin’s story to date.kevin_oconnor_headshot

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You might say Steve and I have lived parallel lives. We’re both serial entrepreneurs. We’ve both used a combination of luck, hard work, and mild insanity to get where we are today. We’ve both published bestselling books.*map_of_innovation_kevin_oconnor

* Okay: my book sold way less copies than Steve’s.

Steve and I have long held similar beliefs about how to run start-ups, even if we’ve used different names to describe the key principles. He came down a few weeks ago to visit our company, FindTheBest, where we chatted about his lean start-up concept and held a Q&A for the FindTheBest team and others in the Santa Barbara tech community. Here’s how our company is following the Steve Blank blueprint.

1. An Untested Hypothesis
Lean Start-up Connection: Business Model Canvas

By 2009, I was fed up. I remember trying to search for the best college for my son and the top ski resort for a family vacation, only to find scam sites promoting a “top 10″ list or “featured” options, meaning they were getting paid to promote them. Visiting each official site took too long, and the information wasn’t always easy to compare (ex: “Net Cost of Attendance” vs. “Resident Tuition per Semester”).

That’s when it hit me: what if there was a site where you get all the best information in one place, and have access to great research tools to help make a decision? What if you could think like an expert on any topic in a matter of minutes, instead of after hours of inefficient web browsing? Granted, the idea was a little crazy. To really be a game-changer, FindTheBest would have to compete against the thousands of niche sites that focus exclusively on a single market.

When we started building the site, we had to assume a lot. We figured our key partners would include consumers (to contribute data much like Wikipedia’s users), manufacturers (to keep their product information updated) and the US government (to supply datasets to power our content). We assumed our customers would be any smart Internet users looking to make a decision—granted, an extremely broad customer segment. We guessed that these customers would find value in the ability to make quick decisions on complicated topics. We hoped to start acquiring customers mostly through responsible, focused SEO, where we would target a variety of high-value search terms and provide relevant, useful content to users.

From day one, we knew our biggest cost would be hiring more employees, but we didn’t know exactly how much it would cost to enter each new market (ex: smartphones, then mountain bikes, then business schools, etc.). By leaning on our technology, we knew we could build cheaply, we just didn’t know how cheaply. We resolved to build out the first dozen comparisons before we started calculating an exact cost per new market.

Regarding revenue, we made a big bet on “purchase intent.” We looked at social sites—like Facebook and Twitter—with huge user bases, but comparably small revenue. We then looked at wildly profitable ventures—like Google or Kayak—noting that their users were much more likely to make purchases. When you enter a search query for “car insurance,” or submit details for a trip to London, you’re much more likely to end up spending money. We hypothesized that FindTheBest, with its focus on making big decisions, would attract the same purchase-minded users found on Google or Kayak.

We didn’t need to spend months researching; we just needed to create a viable product to test these hypotheses against. As we went out and started building, many people thought we were insane, stupid, or both. In fact, some probably still do.

Our First FIndtheBest Wireframe

A very early FindTheBest screenshot

2. Validation
Lean Start-up Connection: Customer Development

Over the next couple of years, several of our hypotheses were confirmed. Thousands, then millions of new customers were coming to the site through SEO, just like we’d guessed with our customer acquisition hypothesis. Our assumptions about cost also proved correct—we were entering new markets incredibly cheaply. Here, we even beat our most optimistic assumptions.

Once we built out a sales team, revenue also started to grow nicely. We’ve confirmed our ability to harness purchase intent, like Google and Kayak before us, verifying our revenue stream hypothesis.

That said, a few of our hypotheses were proven at least partially wrong. First, our assumption that consumers would be a key supplier of content (like Wikipedia contributors) was wrong. While user adds and edits grew at a small rate, it wasn’t nearly enough to support the hundreds of topics on the site. Visitors loved using the site to research new topics, but were less likely to add their own listings or consistently update old content. Our customers had identified a flaw in our original plan. We realized our internal team would need to be bigger, and adjusted our business model canvas accordingly .

Additionally, our value proposition needed adjustment. We had focused on quick decisions, when really, users wanted a sophisticated research tool for making carefully considered decisions. As a result, we’ve had to adjust our positioning and messaging to better capture the value users see in our product. We’re now promoting FindTheBest as a research hub that helps you think like an expert, much closer to what users were telling us in feedback surveys.

3. Staying Agile
Lean Start-up Connection: Agile Development

At FindTheBest, we constantly practice a “test-fail-learn-test-succeed-scale” approach, which is simply another way of describing the philosophy of “agile development.” We’re happy to fail, as long as we do so quickly, learn the appropriate lesson, and move to a new hypothesis. Once we find one that works, we scale the hell out of it.

For example, we tested out two features early on that didn’t end up as popular as we’d wanted: video guides (a how-to video about researching the topic) and green guides (an environmental report on which products and services were most eco-friendly). Each time, we hoped to capture a new audience by appealing to a specific subset of Internet users. We rolled them out on a limited number of pages to test. Once we saw that these new guides weren’t attracting significant visits or creating increased interaction, we quickly ended the projects.

On the flip side, we’ve had many major successes that have helped inform our product’s direction. Sister sites FindTheData (a site for researching huge datasets on topics like crime, salaries, and government spending) and FindTheCompany (a tool for finding key information on millions of companies and organizations) have grown user bases of several million visitors per month. We’ve found that people love our platform and use our technology for doing research in a variety of different ways.

* * *

At just 3 years in, FindTheBest is real-time proof of Steve Blank’s lean start-up blueprint. Even as we make the transition from startup to established company (which I call “company puberty”), we’ll continue to test our hypotheses, seek customer feedback, and test before we scale.

Hopefully we’ll find that FindTheBest will have a bigger exit than DoubleClick – this time as a Lean Startup.
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