thoughtson's posterous

The tragedy of calling things silicon blah @bfeld #silicon allee

I was in LA for the past three days hanging out at Oblong, meeting with a bunch of entrepreneurs I know, then spending time at MuckerLabs, giving a talk at SCVStartup, and finishing up my trip with a half day at LaunchPad LAfollowed by a dinner that LaunchPad LA and Mark Susterput on. Even though I still felt fried from my 50 mile run, I had a great time and I’m sure I fed off of the energy of all the people I spent time with.

At the dinner I gave a short talk on Startup Communities and then answered some questions. The first question was “what do I think of the phrase ‘Silicon Beach’ for the LA startup community.” I responded that I thought it was stupid. I hate Silicon Whatever. LA should be LA. When I was in downtown LA at Oblong I didn’t notice a beach. Before I could go on a rant about why you should not call things “Silicon Blah” I got a round of applause.

In the late 90′s a wave of “Silicon Blah” appeared. Silicon Alley, Silicon Mountain, Silicon Prairie, Silicon Slopes, Silicon Gulch, Silicon Bayou, and on, and on, and on. The rallying cry was “we are going to be the next Silicon Valley.” Whatever. At the time, my opinion as someone who disliked generic marketing was that this was the worst branding ever. I feel even more strongly about this today.

If you are going to create a startup community, build your own identity. People now talk about “New York” and “Boulder” as amazing startup communities. They don’t talk about Silicon Alley and Silicon Flatirons. Well – I suppose some do, but I don’t hear it anymore (or at least my brain doesn’t process it) – I just hear New York and Boulder. And when someone says “Do you like living in Denver?”, I say “I live and work in Boulder.” Sure – Denver has a startup community also, but it’s distinct from Boulder.

Even within a city like LA there are startup neighborhoods. I made this point when I spent a month in Cambridge, MA in January. Sure, you’ve got Cambridge, Boston, Waltham, and Hopkinton. But you’ve also got Kendall Square, Central Square, the Leather District, and the Innovation District. In New York you’ve got Union Square, and Brooklyn’s DUMBO. These are the “neighborhoods” – high density areas of entrepreneurs and their startups. And, in a small town like Boulder, you’ve got – well – Boulder.

LA is huge. The startup community in LA isn’t “Silicon Beach.” It’s downtown, Santa Monica, and I’m sure a few other neighborhoods that I don’t know the name of. Brand the neighborhoods locally so the entrepreneurs know where to go, since you want them clustered together. Then brand your city (LA) which should be an easy one. And dump the Silicon Blah.

via: Brad Feld  

Some Thoughts about Selling at Startups


by MARK SUSTER on MARCH 31, 2012

Many MBA programs still cater too much to the needs of large, corporate management jobs or prepare students to enter big consulting companies or investments banks.


If you haven’t read Adam Lashinsky’s awesome new book about Apple, you should. It takes on many of the lessons MBA programs and Corporate America have been teaching about business for the past 50+ years and questions whether lessons from Apple might be more applicable in thinking about the future.

It is with this backdrop that I was really happy to learn from my friend Ethan Anderson (HBS alum & founder of RedBeacon) about an awesome program at HBS run by Tom Eisenmann called Launching Technology Ventures. Here is a sample of the reading list for the course that gives you a flavor for just how modern and practical this course is.

And that leads me to today’s post.

I got an email recently from my friend & fellow VC, Jeff Bussgang from Flybridge Capital Partners in Boston. Jeff (also an HBS alum) co-teaches the LTV course with Professor Eisenmann. He wrote me about a student of theirs who had written a blog post as a class exercise in which she had challenged assertions that I had made in a previous blog post.

That student is Erin McCann, who formerly worked in sales at Google, so she had pretty strong ground to stand on in her sales arguments. Her post is short & well written so definitely worth a read if you’re a startup person and want to hear some sensible views on sales. It’s titled “When Managing Sales People, Stage Matters.”

The fact that the course asks students to write public blog posts is a testament to its more modern teaching style. It’s one thing to write a class paper – it’s another to write a public blog post in which the person you’re challenging gets to respond. Awesome!

The post of mine that Erin took issue with was TEDIC – The Excuse Department is Closed in which I characterized typical sales reps as driven nearly exclusively by cash and very quick to find excuses for sales processes that aren’t working well. My list of excuses includes that seasoned sales people employ include: product, pricing, competition and lack of sales support.

Erin’s main points:

“As a former tech sales executive, I agree with many of [Mark's] lessons — when applied to later-stage, post-traction point startups . However, I advocate a more nuanced approach for early-stage startup teams” 

1. Feedback isn’t always an excuse – and often sales people can provide the best feedback to your product teams 

2. Sales people aren’t always motivated only by cash – especially in early-stage business you need to focus on equity because cash won’t be plentiful

3. Complaints about support may be real – it might actually be time to scale and give your sales people more leverage

I actually don’t disagree with Erin’s post, which is why I think it’s a great read for you.

That said, I think it is written without taking the full extent of my sales articles into account. My guess is that Erin hadn’t seen some of my earlier posts – so I could understand why she took the positions that she did. My guess is that we are likely in total agreement.

I thought it would be useful for others who maybe missed my sales series to have access to the main arguments in one central place with links out to the details.

Specifically,

1. I don’t recommend that you hire traditional sales people when your company is too early-stage. I wrote about that here – regarding “evangelical sales”. In your earliest stages the founders should do much of the selling precisely for the reasons Erin highlights: you need customer feedback to refine your product, your pricing and your differentiation versus the competition.

2. When you are ready to hire sales staff I don’t recommend bringing in people who are too senior. I wrote separately about that here – regarding “hiring people who punch above their weight class.”  People who have “done it all before” often need bag carriers to assist them, are often accustomed to earning to high of commissions relative to what you can afford and are more equipped to sell once you’re gotten product/market fit and are quick to leave if things take longer to develop than anticipated.

I like to hire (not just for sales, but for all roles) people who aspire to be at the next level and are out to prove they can step up if given the chance.

3. There are different types of sales people; mavericks often work best early on. I wrote about the four types of sales people here. Mavericks have innate sales talent but are not necessarily good at following a process.  This resonates strongly with me because I personally have almost no ability to follow a process.

I think it works well for startups because in startups there are inherently less rules and with customers there is less clarity about your product category. Mavericks thrive in environments like this whereas rule followers may be frustrated by your lack of process.

As you grow you definitely need process-driven people. The leaders will be “superstars” who are inherently great at selling and follow processes religiously. Journeymen don’t have natural sales DNA but can be very effective by following the processes you set up. All of this is covered in the article.

4. As your company starts to grow faster, if you don’t adopt processes you will limit your growth. I wrote about that in my post about scaling sales – arming & aiming: A, B, C’s. and in a related post about objection handling.  As your company grows you need to division of labor based upon different skill sets. Erin talks about that here:

“Sometimes reps just don’t like the grunt work – for me, creating proposals always felt like a huge waste of time compared to closing more deals.”

She’s right.

As your team grows you start to segment sales teams into account executives & sales engineers. The latter tend to be more technical and can do a better job at dealing with all of the technical objections that will come up in the sales process not to mention: Talking about integration, building customer-specific versions of your demo and addresses implementation requests.

You may also build “inside sales reps” that handle phone calls the might either be speculative leads to be qualified for the field sales teams or perhaps they handle lower-value deals.

When you’re even bigger you might build sales ops teams that handle: Territory segmentation, sales compensation planning, forecasting, RFP generation and the like.

It is also common to divide sales executives into two types: Hunters & farmers. The former focuses on winning new customers and the latter on growing revenue at existing ones.

In my posts also talked extensively about the integration of marketing and sales. Marketing’s job in working with sales people is twofold:

A. To arm – which means to give the reps all of the sales collateral they’ll need to effectively win sales campaigns. This includes presentations, ROI calculators, competitive analyses and so forth.

B. To aim – which means helping sales reps figure out which target customers to focus on. It’s about helping weed out the non-serious leads from the urgent ones.

In Summary

I applaud Erin for speaking up against my TEDIC post because in isolation my post isn’t precise enough. I also applaud Tom Eisenmann & Jeff Bussgang for bringing this kind of discussion into an MBA program. Hopefully this post gives a more complete picture of my thoughts on selling at startups.

And finally a reminder: Selling is about listening & reacting and not “pitching.” I encapsulate that in an analogy I had heard years ago. Be careful not to be a crocodile sales person – you know, all mouth and no ears.

I’d be happy to discuss / debate further in the comments section. Happy selling.

 

 via: http://www.bothsidesofthetable.com/

 

How does an early-stage investor value a startup? @ceduardo

via   
English: Diagram of the typical financing cycl...

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One of the most frequently asked questions at any startup event or investor panel, is “how do investors value a startup?”. The unfortunate answer to the question is: it depends.
Startup valuation, as frustrating as this may be for anyone looking for a definitive answer, is, in fact, a relative science, and not an exact one.

For those of you that want to cut to the summary of this post (which is somewhat self-evident when you read it) here it is:

The biggest determinant of your startup’s value are the market forces of the industry & sector in which it plays, which include the balance (or imbalance) between demand and supply of money, the recency and size of recent exits, the willingness for an investor to pay a premium to get into a deal, and the level of desperation of the entrepreneur looking for money.

Whilst this statement may capture the bulk of how most early stage startups are valued, I appreciate that it lacks the specificity the reader would like to hear, and thus I will try and explore the details of valuation methods in the remainder of my post with the hopes of shedding some light on how you can try and value your startup.

As any newly minted MBA will tell you, there are many valuation tools & methods out there. They range in purpose for anything from the smallest of firms, all the way to large public companies, and they vary in the amount of assumptions you need to make about a company’s future relative to its past performance in order to get a ‘meaningful’ value for the company. For example, older and public companies are ‘easier’ to value, because there is historical data about them to ‘extrapolate’ their performance into the future. So knowing which ones are the best to use and for what circumstances (and their pitfalls) is just as important as knowing how to use them in the first place.

Some of the valuation methods you may have have heard about include (links temporarily down due to Wikipedia’s position on SOPA and PIPA):

While going into the details of how these methods work is outside of the scope of my post, I’ve added some links that hopefully explain what they are. Rather, let’s start tackling the issue of valuation by investigating what an investor is looking for when valuing a company, and then see which methods provide the best proxy for current value when they make their choices.

A startup company’s value, as I mentioned earlier, is largely dictated by the market forces in the industry in which it operates. Specifically, the current value is dictated by the market forces in play TODAY and TODAY’S perception of what the future will bring.

Effectively this means, on the downside, that if your company is operating in a space where the market for your industry is depressed and the outlook for the future isn’t any good either (regardless of what you are doing), then clearly what an investor is willing to pay for the company’s equity is going to be substantially reduced in spite of whatever successes the company is currently having (or will have) UNLESS the investor is either privy to information about a potential market shift in the future, or is just willing to take the risk that the company will be able to shift the market. I will explore the latter point on what can influence you attaining a better (or worse) valuation in greater detail later. Obviously if your company is in a hot market, the inverse will be the case.

Therefore, when an early stage investor is trying to determine whether to make an investment in a company (and as a result what the appropriate valuation should be), what he basically does is gauge what the likely exit size will be for a company of your type and within the industry in which it plays, and then judges how much equity his fund should have in the company to reach his return on investment goal, relative to the amount of money he put into the company throughout the company’s lifetime.

This may sound quite hard to do, when you don’t know how long it will take the company to exit, how many rounds of cash it will need, and how much equity the founders will let you have in order to meet your goals. However, through the variety of deals that investors hear about and see in seed, series A and onwards, they have a mental picture of what constitutes and ‘average’ size round, and ‘average’ price, and the ‘average’ amount of money your company will do relative to other in the space in which it plays. Effectively, VCs, in addition to having a pulse of what is going on in the market, have financial models which, like any other financial analyst trying to predict the future within the context of a portfolio, have margins of error but also assumptions of what will likely happen to any company they are considering for investment. Based on these assumptions, investors will decide how much equity they effectively need now, knowing that they may have to invest along the way (if they can) so that when your company reaches its point of most likely going to an exit, they will hit their return on investment goal. If they can’t make the numbers work for an investment either relative to what a founder is asking for, or relative to what the markets are telling them via their assumptions, then an investor will either pass, or wait around to see what happens (if they can).

So, the next logical question is, how does an investor size the ‘likely’ maximum value (at exit) of my company in order to do their calculations?

Well, there are several methods, but mainly “instinctual” ones and quantitative ones. The instinctual ones are used more in the early-stage type of deals and as the maturity of the company grows, along with its financial information, quantitative methods are increasingly used. Instinctual ones are not entirely devoid of quantitative analysis, however, it is just that this “method” of valuation is driven mostly by an investor’s sector experience about what the average type of deal is priced at both at entry (when they invest) and at exit. The quantitative methods are not that different, but incorporate more figures (some from the valuation methods outlined) to extrapolate a series of potential exit scenarios for your company. For these types of calculations, the market and transaction comparables method is the favored approach. As I mentioned, it isn’t the intent of this post to show how to do these, but, in summary, comparables tell an investor how other companies in the market are being valued on some basis (be it as a multiple of Revenues or EBITDA, for example, but can be other things like user base, etc) which in turn can be applied to your company as a proxy for your value today. If you want to see what a professionally prepared comps table looks like (totally unrelated sector, but same idea), go here.

Going back to the valuation toolset for one moment… most of the tools on the list I’ve mentioned include a market influence factor , meaning they have a part of the calculation that is determined by how the market(s) are doing, be it the market/industry your company operates in, or the larger S&P 500 stock index (as a proxy of a large pool of companies). This makes it hard, for example to use tools (such as the DCF) that try and use the past performance of a startup (particularly when there is hardly a track record that is highly reliable as an indicator of future performance) as a means by which to extrapolate future performance. This is why comparables, particularly transaction comparables are favored for early stage startups as they are better indicators of what the market is willing to pay for the startups ‘most like’ the one an investor is considering.

But by knowing (within some degree of instinctual or calculated certainty) what the likely exit value of my company will be in the future, how does an investor then decide what my value should be now?

Again, knowing what the exit price will be, or having an idea of what it will be, means that an investor can calculate what their returns will be on any valuation relative to the amount of money they put in, or alternatively what their percentage will be in an exit (money they put in, divided by the post-money valuation of your company = their percentage).  Before we proceed, just a quick glossary:

Pre-Money = the value of your company now
Post-Money = the value of your company after the investor put the money in
Cash on Cash Multiple = the multiple of money returned to an investor on exit divided by the amount they put in throughout the lifetime of the company

So, if an investor knows how much % they own after they put their money in, and they can guess the exit value of your company, they can divide the latter from the former and get a cash-on-cash multiple of what their investment will give them (some investors use IRR values as well of course, but most investors tend to think in terms of cash-on-cash returns because of the nature of how VC funds work). Assume a 10x multiple for cash-on-cash returns is what every investor wants from an early stage venture deal, but of course reality is more complex as different levels of risk (investors are happy with lower returns on lower risk and later stage deals, for example) will have different returns on expectations, but let’s use 10x as an example however, because it is easy, and because I have ten fingers. However, this is still incomplete, because investors know that it is a rare case where they put money in and there is no requirement for a follow-on investment. As such, investors need to incorporate assumptions about how much more money your company will require, and thus how much dilution they will (as well as you) take provided they do (or don’t ) follow their money up to a point (not every investor can follow-on in every round until the very end, as many times they reach a maximum amount of money invested in one company as is allowed by the structure of their fund).

Now, armed with assumptions about the value of your company at exit, how much money it may require along the way, and what the founding team (and their current investors) may be willing to accept in terms of dilution, they will determine a ‘range’ of acceptable valuations that will allow them, to some extent, to meet their returns expectations (or not, in which case they will pass on the investment for ‘economics’ reasons). This method is what I call the ‘top-down’ approach…

Naturally, if there is a ‘top-down’, there must be a ‘bottom-up’ approach, which although is based on the ‘top-down’ assumptions, basically just takes the average entry valuation for companies of a certain type and stage an investor typically sees and values a company relative to that entry average. The reason why I say this is based on the ‘top-down’ is because that entry average used by the bottom-up approach, if you back-track the calculations, is based on a figure that will likely give investors a meaningful return on an exit for the industry in question. Additionally, you wouldn’t, for example, use the bottom-up average from one industry for another as the results would end up being different. This bottom-up approach could yield an investor saying the following to you when offering you a termsheet:

“a company of your stage will probably require x millions to grow for the next 18 months, and therefore based on your current stage, you are worth (money to be raised divided by % ownership the investor wants – money to be raised) the following pre-money”.

One topic that I’m also skipping as part of this discussion, largely because it is a post of its own, is “how much money should I raise?”. I will only say that you will likely have a discussion with your potential investor on this amount when you discuss your business plan or financial model, and if you both agree on it, it will be part of the determinant of your valuation. Clearly a business where an investor agrees that 10m is needed and is willing to put it down right now, is one that has been de-risked to some point and thus will have a valuation that reflects that.

So being that we’ve now established how much the market and industry in which you company plays in can dictate the ultimate value of your company, lets look at what other factors can contribute to an investor asking for a discount in value or an investor being willing to pay a premium over the average entry price for your company’s stage and sector. In summary:

An investor is willing to pay more for your company if:

  • It is in a hot sector:investors that come late into a sector may also be willing to pay more as one sees in public stock markets of later entrants into a hot stock.
  • If your management team is shit hot: serial entrepreneurs can command a better valuation (read my post of what an investor looks for in a management team). A good team gives investors faith that you can execute.
  • You have a functioning product (more for early stage companies)
  • You have traction: nothing shows value like customers telling the investor you have value.

An investor is less likely to pay a premium over the average for your company (or may even pass on the investment) if:

  • It is in a sector that has shown poor performance.
  • It is in a sector that is highly commoditized, with little margins to be made.
  • It is in a sector that has a large set of competitors and with little differentiation between them (picking a winner is hard in this case).
  • Your management team has no track record and/or may be missing key people for you to execute the plan (and you have no one lined up). Take a look at my post on ‘do I need a technical founder?‘.
  • Your product is not working and/or you have no customer validation.
  • You are going to shortly run out of cash

In conclusion, market forces right now greatly affect the value of your company. These market forces are both what similar deals are being priced at (bottom-up) and the amounts of recent exits (top-down) which can affect the value of a company in your specific sector. The best thing you can do to arm yourself with a feeling of what values are in the market before you speak to an investor is by speaking to other startups like yours (effectively making your own mental comparables table) that have raised money and see if they’ll share with you what they were valued and how much they raised when they were at your stage. Also, read the tech news as sometimes they’ll print information which can help you back track into the values. However, all is not lost. As I mentioned, there are factors you can influence to increase the value of your startup, and nothing increases your company’s value more than showing an investor that people out there want your product and are even willing to pay for it.

Please find Carlos Eduardos Blog here.

 

You are too serious - Or why Germany still sucks for startups

After a wild startup ride with Liquid Labs in Boston last year, I moved to Berlin recently and have joined ezeep as CTO. Right before I left Cambridge I gave a little seminar about the differences in European and US startup culture at MIT E-Club. An interesting discussion evolved in the seminar and finally I found some time to write a little post about the question why European startups (I will focus on Germany) still face trouble to form global market leaders like Facebook or Google.

There are many reasons, why Germany still isn't a good environment for innovation driven startups. I will just talk about some of them in this post. The most obvious one is probably that Germans tend to take everything (including themselves) way too serious. But more about that later.

The Importance of Innovation Clusters

Naturally, Cambridge/Boston is one of the most vibrant innovation driving communities you can find in the whole world. With more than 100 colleges/universities and more than 250,000 students in the greater Boston area, the city has one of the highest densities of higher education institutions. In 2010, Boston was ranked as the #1 city for innovation worldwide in the Innovation Cities Top 100 survey. Institutions like Harvard, Massachusetts Institute of Technology and Boston University contribute a lot to the local innovation economy with their research but also by linking higher ed and research with the entrepreneurial community.

Thanks to New Atlantic Ventures, we were able to work from the Cambridge Innovation Center for the last few months. CIC is one of the largest co-working and startup spaces on the east coast and is located on the MIT campus. Right next to CIC you will find Dogpatch Labs and Tech Stars Boston just across the street. Can you find any comparable innovation cluster in Germany? No, you can't. Berlin, which is currently praised as startup city #1 in Germany still hasn't started any larger scale efforts to fuel innovation and technology entrepreneurship. Individuals have started many interesting initiatives, meetups, startup programs etc., which is great but can't replace institutional efforts. Berlin's institutions, including research centers and universities, should invest in creating a dense and powerful innovation cluster.

When I'm talking about density I mean geographic density. Right now all startups are spread across Berlin which is a problem since the probability to just randomly meet interesting people is quite low. With our last startup we've built an incredible network in Cambridge and most of our connections started by randomly running into the most interesting people in research and entrepreneurship. We literally met everyday at least one new person who was working on something interesting. The value of this serendipity factor is higher than most people think because your startup evolves so much faster when discussing it with smart and experienced people.

 

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Education and Entrepreneurship

Yes, there are some positive examples of German universities that start to get into supporting and teaching entrepreneurship. Amongst others, the Center for Innovation and Entrepreneurship at Karlsruhe Institute of Technology and the LMU and TU in Munich are doing a great job. But there are still too many universities that offer barely anything. The college I went to, the University of Bayreuth, doesn't offer any startup support at all.

It is also interesting to see that many entrepreneurship professors in Germany have never started an own company. You can't do entrepreneurship by the book, so it would be important to attract successful entrepreneurs to give seminars and to help universities and students navigating the opportunities and risks of entrepreneurship.

At this point I have to refer to the MIT again, which is doing a great job in preparing the next generation of successful tech entrepreneurs. The MIT Trust Entrepreneurship Center and MIT StartLabs, founded last year by Chris Benson and his team are just two examples from a wide variety of great campus startup initiatives and seminars.

Tying up Smart People in the Copy Cat Machine

You might have heard of StudiVZ, Wimdu, Simfy or CityDeal. All these companies have something in common: They are all copy cats. In Germany many investors tend to put their money in 'proven' business models. This means they tend to invest in companies that copy promising products and business models of startups from the US and apply them to the European market, before the original US company has the resources to expand to Europe on their own. The goal is to sell back these copy cats to the original US company after while (e.g CityDeal got acquired by Groupon for more than $100 million).

Don't misunderstand me, starting copy cats is a legitimate business model and I respect how well for example the Samwer brothers have perfectioned their execution strategy. But the tragic consequence of the copy cat economy is that many smart people spend all their time and effort in making the quick buck. I know of more than one case when some smart founders approached German VCs with an innovative and promising business idea and the investors refused to invest but asked the team to work on a copy cat instead. How can Germany possibly form an innovation economy when so many smart people are chasing the quick $100 million exit?

Germans don't like to fail

You might ask yourself why so many German founders decide to start a copy cat. I think the reason is deeply rooted in our culture. German people are usually relatively risk averse compared to for example the US. We like to make the 'safe bet'. Copy cats are a pretty safe bet compared to hacking something completely new without any monetization model behind right out of your dorm room. Failure in Germany is still perceived as something negative which is ridiculous since failing is usually the best learning experience you can get in life. Many people are stuck in their risk aversity and try to navigate through life without collecting a track record of epic fails. 

Let's build the European startup community!

Germany and the European startup community has a vast potential that desperately needs the right environment to flourish. We won't be able to overcome cultural boundaries like risk aversity very soon, but we should better support innovation by better connecting research to entrepreneurship and embedd entrepreneuship in the education system. European business angels and college administrations should visit innovation clusters like Cambridge or Silicon Valley to learn how these places have become what they are today. We should not try to copy them though. We should learn from the US and build our own European startup environment. Last but not least we should make people in Germany aware that failure is usually a positive learning experience and should be embraced instead of rejected.

Let's fail, learn, communicate, built great things together, be successful and have an impact!

via: http://iopanic.com/what-going-on-here