Monthly Archives: January 2013

Entrepreneurs aren’t under NDA’s, investors are

I’ve recently been meeting with many startups attacking one specific marketplace vertical. I deliberately reached out to each of the players in this specific vertical as I like the space and believe that it holds the potential for a very big business. When meeting with so many startups attempting to solve the same problem, you gain a deep appreciation for how important it is to sweat the details while executing. It is often the little things that matter.

For example, in the context of this marketplace vertical, some very important details are how many steps it takes for a buyer to reach sellers, how many sellers the buyer is allowed to reach so that the buyer does not feel overwhelmed, and which information is revealed about each seller. As a direct consequence of diving into the details of each startup, an investor gains a lot of information about what differentiates them and which specific approaches may be better conducive to success. The information provided by one entrepreneur is very valuable for a competing entrepreneur.

Non-disclosure agreements, or NDA’s, emerged exactly for this reason. Since investors gain such valuable information during the course of these meetings, it is very important that they treat all proprietary information gained from one startup as confidential when meeting competing startups. Since NDA’s are a hassle to sign and explicitly signal a lack of trust which the entrepreneur has for the investor, they are very rarely used. However, although NDA’s are rarely used, there is a common understanding between entrepreneurs and investors that what is discussed in a meeting stays in that meeting. This is why I was surprised when, in one of the meetings, an entrepreneur asked several sensitive questions about what I had learned in meetings with competitors.
I declined the repeated requests for competitor information because, although there may not be a formal NDA in place, I have the responsibility to keep private all information which entrepreneurs have trusted me with. However, although the right course of action for an investor in this situation is clear, what about for the entrepreneur? What does the search for competitor information from a potential investor say about the entrepreneur? 
On one hand, it shows that the entrepreneur is proactively looking for opportunities to improve his business by learning from what his competitors are doing. Replicating what works elsewhere, avoiding what doesn’t, and knowing what your competitors plan to do in the future, make you more likely to succeed. This is a good thing. 
On the other hand, it shows that the entrepreneur is willing to use information gained from someone else violating their informal NDA. While some may say that this is an ethical breach on the part of the entrepreneur, I don’t agree. Formal or informal, the investor is under NDA, not the entrepreneur. If the investor divulges sensitive information, it is the investor’s fault, not the entrepreneur’s. The investor may be unethical or simply not the brightest tool in the shed, but the entrepreneur is not to blame for this. On the contrary, he is to be praised for his opportunistic attitude. He might get no information nine out of ten times, but the one lapse may be a critical source of insight.
So, although taken aback at first, upon greater reflection I discovered that I actually liked the entrepreneur’s approach. It was quite entrepreneurial.

Why your startup might not be suitable for VC funding

There are many reasons why VC’s pass on investment opportunities. Two common ones are that they don’t gel with the team or don’t have sufficient expertise in the target market. Today I’m going to focus on another reason for passing on a deal: market size. Even if a VC likes your team and understands the value that your startup brings to its target market, the market may simply be too small for your startup to become large enough to produce the returns which the VC needs.

Let’s go through some numbers to make things clearer. I will perform the analysis for a seed stage investor but you can repeat the exercise for later stage investors by changing the relevant variables. Let’s say a seed stage VC makes 10 investments at $1M each from a $10M fund. Common wisdom suggests that 1 out of 10 seed stage investments will succeed, but let’s say that this is a talented VC so he invests in 2 winners. Let’s also assume that 3 startups return the capital invested in them, and that the remaining 5 fail. If the VC holds the average investment for 5 years, in order to meet his 20% IRR target, the $10M fund size needs to grow to $25M. Since 3 investments at $1M each returned the capital invested in them and 5 failed, the 2 successful startups need to produce a combined return of $22M. This is equivalent to $11M per startup. Since the VC does not know which of his investments will succeed in advance, he needs to invest in companies which can realistically generate an 11X return on his capital. 
In addition, the VC needs to account for the impact of dilution in future funding rounds. If we assume that the average seed startup will need two more funding rounds, each diluting existing investors by 20%, the VC investor will need the valuation of his successful investments to increase 17X from the time of his original investment for his fund to be successful. Although the best seed stage startups currently command valuations north of $5M, a more comprehensive data set shows that the average seed investment takes place at a $2.5M valuation. This means that a startup’s target market needs to be large enough to accommodate at least a $40M business in order for it to attract VC interest. 
This does not mean that a startup can’t be successful otherwise. There are plenty of businesses with valuations south of $40M that are very successful and produce great personal rewards for their founders. They just might not be suitable for VC funding.

When allocating equity among founders, equality is rarely fair

A critical input into the pre-investment due diligence process for a startup is the capitalization table. Also known as the cap table, it shows how the startup’s equity is distributed among its owners. These owners include the founders of the company, team members with equity grants, investors, and in rarer cases advisors. The cap table is an important source of information because it shows how the founders allocated equity among themselves and to other parties in the past. It reflects the thoughts of the founders regarding the contributions of each owner to the business. The main contribution of founders and team members is in the form of time and effort. This is also called sweat equity. Investors contribute their capital, network, and strategic guidance, while advisors act in a similar capacity to investors without providing capital.

In the post Don’t give away more than 30% of your startup in a single funding round, I provided guidance on the allocation of equity to investors. As the title of the post suggests, I recommend that startups not concede more than 30% of their equity to investors in a single round. This will ensure that the founders have sufficient equity and hence financial incentives to continue working to grow their startup even after the 2 to 3 rounds that are often necessary to scale a VC-funded business. This post will address a different but equally important equity allocation decision: that among founders.
I’ve discovered that many startups allocate equity rather arbitrarily among founders. The most common allocation is an equal split according to the number of founders. So if a startup has two founders, each gets 50%. If it has 3, each gets 33%. This approach is particularly common when the founders have been friends for quite some time. While this is an easy way to get started without stepping on each other’s toes, it often leads to problems down the road when there is a clear divergence between the relative contributions of each founder to the business. This is why I recommend that equity allocations among founders be based on fairness, not equality. In particular, a fair allocation is one where each founder is compensated according to their contribution to the startup. Although, as stated earlier, this mainly takes the form of sweat equity, it can also include capital, especially at the seed stage, as well as a relevant network or other inputs. The specifics will depend on the responsibilities which must be carried out in order for the startup to succeed. However, by applying the principle of fairness to equity allocations among founders, a startup can avoid many of the problems which would otherwise emerge in the event that there is much greater value at stake in the future.
As a founder, a good test to see if you’ve allocated equity fairly among your team is to try to justify the allocation. I request this from all of the founders with whom I enter later stage discussions, and the results range from incredibly premeditated to downright amusing. At one end of the spectrum are those founders who have clearly thought about each founder’s contribution to the startup, including such intangible but important inputs as a founder’s ability to motivate the team. At the other end are founders who have clearly placed the question on the back burner with the hope that it will somehow miraculously take care of itself. Unfortunately, in some of these cases the issue has been left simmering for so long that it becomes an obstacle to investing. If the founders agree that the current allocation isn’t fair but cannot agree on what a fair allocation should look like, it makes little sense for an investor to back a dysfunctional team.
My recommendation to founders reading this post is simple. Set aside half an hour this week to meet among yourselves and voice your thoughts about the fairness of your equity allocations. In most cases, there shouldn’t be a major problem, so thirty minutes is all you will need. If you find yourself taking much longer than this, you may have identified an important obstacle to your future success. Address it now. Your team and investors will thank you for it.

The benefits of the Series A crunch

An entrepreneur recently asked me what I thought about the upcoming shortfall in Series A funding. This topic has been widely covered in articles such as Fortune’s Series A crunch: By the numbers. After reading such articles, it’s easy for entrepreneurs to feel pessimistic. This is especially true for first time entrepreneurs who haven’t experienced the ups and downs of a tech cycle in the past. The pessimism is also greater for entrepreneurs who recently launched their venture, with or without seed money, and are thinking about raising a Series A round within the next year or two. The short-term cost of a shortfall in Series A funding is clear. In particular, many seed stage companies will not survive the crunch. However, this short-term cost is greatly outweighed by the long-term benefits produced by the crunch.

The first positive impact of the crunch will be to separate the wheat from the chaff. Those startups who survive the crunch, whether by fundraising or by cutting their burn rate, will prove their resourcefulness during tough times. What is a startup if not a team of people who can do a lot with little? By learning to do even more with even less during the crunch, these startups will find themselves on very solid footing once the market starts to pick up again in the future.

The second outcome of the crunch will be the return of seed stage valuations to normal levels. As I covered in the post Why high convertible debt valuation caps harm both investors and entrepreneurs, seed stage valuations are currently unjustifiably high. This is in large part due to new investors who are bidding up valuations simply for the kick of being part of a sexy startup. Once these investors experience the pain of losing money and startups are no longer sexy, they will exit the market and valuations will return to normal levels.

This leads to the third positive impact of the crunch. In an environment of normal valuations, only those entrepreneurs who are truly passionate about building an enduring business will choose the startup path. Those chasing a quick buck or simply following the herd will leave to pursue safer jobs. Similarly, investors who support entrepreneurs only for the possibility of a future payout, without enjoying the act of working with talented and motivated risk-takers, will also exit the startup ecosystem. Only the most committed entrepreneurs and investors will remain. It is when these two groups of people work together away from the external clutter and noise that magic happens.

Big data is too big, be more specific

This post is inspired by a tweet I made on November 12, which I’m pasting here: “Altho also guilty of this, I’m tired of hearing the buzz words gamification & big data. I promise to talk specifics – please do the same 🙂 ” I’ve stuck to my promise, and am now going to address why I believe we need to be more specific when talking about big data. It’s been over a month and a half since I made the comment so my thoughts on this topic must have grown subconsciously for quite some time. If something similar has been happening with the part of the comment addressing gamification, you’re also likely to see a post on that topic soon.

The term “big data” is simply a way to highlight the tremendous amount of data which is being generated in today’s world. Mobile phones, sensors, and cameras are just some of the devices which create this data. The proliferation of these devices, coupled with the rapid rise in their computing power, is producing reams of complex data sets. The objective of big data startups is to analyze these data sets so as to extract insights from otherwise meaningless pools of information. This is where things get tricky. What is insightful depends largely on what you’re trying to do. A bank manager has very different needs than a soccer coach or the manager of a data processing center (each of these is the target customer of a big data startup that I’ve recently evaluated). In order to meet these needs, big data startups need to understand what factors influence the performance of the industry which they’re serving, and how these factors interact with each other to produce insightful information. If you’re familiar with regression models, you can think of this as identifying the right set of explanatory variables to track and determining their relative impact on the response variable which you’re trying to optimize.

The core issue is that it takes a lot of industry experience to really understand the drivers of a particular business. Take the example of the bank manager who, among other goals, is looking to optimize the amount of cash on hand at a particular branch. Hold too much cash and you forego valuable interest. Hold too little and you may not be able to meet customer withdrawal requests. It takes years of experience to know which factors you should track to determine how much cash you are likely to need on any particular day.

If you’re an industry outsider, you won’t know this information yourself. Speaking to the bank manager is one way to get the information. However, this approach has two drawbacks. First, you’re likely to lose or not understand valuable information which the bank manager is conveying due to your recent introduction to the industry. Second, the bank manager may be overlooking an important variable which, if tracked, would greatly improve the quality of the assessment. However, since you’re not familiar with the industry, you’re unlikely to recognize this. The solution to both these problems is to be an industry insider. To build a big data startup which improves the quality of the critical decisions which your target customer makes, you need to have years of experience walking in his shoes.

The best big data startups are not those filled exclusively with data scientists who have the technical ability to manipulate spreadsheets containing millions of rows and columns. Although this technical ability is valuable, it needs to be complemented with a deep understanding of the industry which you’re trying to serve. What matters, what doesn’t, and how can we analyze data to improve what matters? The best big data startups are those where the founders and many team members have years of experience in the industry which they’re serving. They have their own answers to each of these questions, and are able to engage in constructive dialogues with their customers to arrive at even better answers. It’s for this reason that saying that you’re a big data startup isn’t enough. You need to be specific about why your team is uniquely positioned to understand your target customer better than anyone else, and provide examples of insightful analyses which support this claim.