A Company's (First) Tipping Point

I like to think of the first sale as being the first major tipping point for a startup. It is the hardest sale to make, since little is known about the product's efficacy or the company's stability in the marketplace. This is a classic lemon problem, where information asymmetry between the seller and the market result in a breakdown of trust and propensity to buy at fair price. Overcoming this inertia is extremely difficult but there are very clever ways to overcome this by creating strong positive signals in the marketplace. The following (true) story, shared by Dr. Macmillan at Wharton, is a great example of this:

A salesman came to pitch an industrial plant manager on an innovative new water filter. The filter was twice as expensive as the next best alternative but lasted 5 times longer with the same efficacy. The investment would have justified itself but he did not make the purchase, because he had to somehow justify why his expenses had risen in the quarter to the company before any ROI from the purchase was evident. Having gotten this response, the salesman went to one of Proctor and Gamble's factories and gave the filter away for free. In doing so, he created an invoice and had it signed on delivery by the company. He then went back to the manager and showed a signed delivery slip (not showing that it was actually given for free). The fact that a major multinational had implemented the technology was all the manager needed to justify the expense to his bosses so he purchased the new filter product.

This is just one great example of how one can leverage the trusted/positive image of a client and create a strong signal in the marketplace that inspires confidence in the company and product. The key here was that the manager thought that P&G had puts its trust in the product by purchasing it, signaling to him that it was indeed a good investment (whether this is withholding or clever positioning may be debatable, but the signaling effect is clear). Over the years, I have seen both good and bad signaling strategies.

Good ways to signal the market:

• Get a customer with a strong reputation in the marketplace. If you work with a company that is known to have very high standards in their business relationships, you will benefit from this. This could also be the case with an adviser or investor who has a strong reputation.
  
• Set up free trial periods. This will signal your confidence in your product by shifting the risk to you, while still not making the client's purchase totally 'free'.
• Make use of great PR. There seems to be a positive signaling effect in peoples' minds from reading about a product in a reputable source (ie: "as seen in the NY Times").
• Collect data. Lots and lots of data. Try to get the study conducted/verified by an independent source. Show where the product works best and where it is least effective (this will show you are transparent and not only showing the positive data).
  

Bad ways to signal the market:

• Sign up early customers for free. While this can be great for collecting data, it does not say much about whether the counter party actually sees the value of your business. Anybody will try something if it's free. When you pitch the next company, it really does not send any valuable signal.
  
• Working to get 'the wrong' early customers who do not help you 'signal' your product's value to your target customer base. For example, if your target is a Tier 1 company do not waste too much time going after Tier 3 companies. This may in fact create a negative signal (ie: Tier 3 companies use this product).
  

These are just a few examples that come to mind from my last startup. Obviously there are cases where these rules will not apply but in general I think this is a decent framework.

Under the Googview Hood

I recently made an update to Googview, my first web project. The new functionality automatically updates the Googview background to current Bing search backgrounds from around the globe. This exposed me to a few new web technologies that I thought I would share here.

Search Functionality:

• Made using Google Custom Search Engine. This tools has a wizard that makes it very simple to create custom Google search tools.

Bing Images:

• Images are taken from Bing's global sites and put into an RSS feed by Long Zheng at istartedsomething.com
• This feed goes into Yahoo Pipes and is modified to make the media asset URL the 'title' of each item.
• A simple PHP script on the Googview page runs on load, takes in the Yahoo Pipes feed using MagpieRSS, parses the media asset url and writes it down to the page as HTML.
  

The Power of Instant Gratification

Today I was thinking about what it was in the fundamental design of web apps like Twitter, Facebook and Zynga that made them so successful in acquiring and keeping users (causing enormous changes in online behavior).

I was reminded of an analysis that I did for a large social gaming company as part of their recruiting process. They asked me to play one of their Maffia games and determine what features were driving 'virality'. At first, I had absolutely no idea how the game had become so popular. The GUI was limited to having a user click a handful of buttons that would instantly give them points and 'virtual goods'. There was no story-line, no mental challenge, no advanced visualization; just an instant reward for performing a simple action (for example, to rob a store, one would just click 'Commit Robery', and instantly see an increase in wealth points without any chance of failure). It was the equivalent of flipping a series of light switches on and off and getting some psychological reward for it. "Strange" I thought...

After playing for an hour however, I was rather surprised. I had progressed quite far in the game and started to enjoy it. I'd built a mini 'empire' and had no idea how I'd gotten there. Each click had given me some slight enjoyment and they had magically added up to an hour of gameplay. Furthermore the game kept kicking me off every time I made progress so I never got to the point where I was bored of it.

Instant Gratification (aka the Bon Bon Theory)
My theory on what has made web apps is that they deliver instant gratification in limited doses. In the game described above, I felt instant utility through building my collection of virtual goods. The game design allowed me to instantly enjoy small doses of pleasure, while forcing me to come back later for more. In the case of twitter and Facebook, we are gratified by the emotional value of seeing a constant stream of information from those we value most. Once again, this utility is enjoyed over time as updates/tweets trickle in small doses throughout the day.

I call this the Bon Bon Theory because it reminds me of how quickly one goes through little wrapped sweets when they are lying around the house. They are just the right size to give you instant satisfaction but never sizable enough to satisfy a craving (and the solution is always simple-- you just go back).

This is an extremely powerful concept and should be considered key when looking at future web services as we become more and more connected (since being 'always on' makes it easier to administer each 'dose' of utility).

ESAF Part I: Testing the Business Model

I will be writing a small series to provide further insight into the Early Stage Analysis Framework (ESAF). I recently posted to the blog. This will be somewhat out of order, as I will try to follow up based on discussions that I have with others in the blogosphere.

Today I wanted to address the question of how to test the business model. In ESAF, I suggest that there are 3 key steps to understanding whether a business model is sound:

1. Create a unit economic model of the business to understand the key revenue and cost drivers
2. Conduct break-even analysis on the unit level to see if the company can generate margins on each unit. If this isn't the case, expand the model to take into account multiple units and see what scale is required for the company to reach break-even.
   
3. Conduct Crystal Ball analysis to understand expected profit values based on a Monte Carlo simulation and de-risk the business model
   

I devised this approach after working for quite some time with both entrepreneurs and investors in early stage companies. I found that very often they spent a lot of time breaking down complex 3-5 year models and questioning key assumptions that the model makes. This is often a highly time consuming (and subjective!) process and often means very little since both sides stand firm behind their own assumptions. The challenge for me was to devise a way of testing business viability in an objective way without taking any position on what assumptions were 'reasonable'. This was key in getting buy-in from investors and entrepreneurs on the validity of my analysis. I outline my approach below.

First, creating a unit economic model is crucial because it strips the business down to its core revenue/cost drivers and eliminates the unnecessary complexity that most models have. This model is created by identifying an economic unit for the business (whether it is a cookie, a box of cookies, a delivery truck of cookies or the daily supply an average supermarket purchases) and allocating all revenues and costs (both variable and allocated overhead) to that unit.

Once we have this unit economic model, we simply ask the objective question of whether the business model is viable given the existing market size. If the unit has a positive margin in the model, immediately we know that the business model is validated. If the unit runs a loss, the model can be enhanced to account for multiple units to see if the company can break even as it scales up (and economies of scale lower overhead cost down across all economic 'units'). Once break-even volume is determined, this can then be compared to market size to judge whether or the business is viable. For example, if you found that it would take 9 billion customers to sign up for an annual subscription to your web service, you have objectively proven that the business is not viable since there are some 6 billion people in the entire world. When we conduct our break-even analysis , all we test is business viability. This does not take into account whether the business can actually achieve the market share necessary to break even (we examine this question later). It is an objective analysis that can be understood by everyone around the table.

If the business seems viable from a unit economic model perspective, the next thing to test is whether it can actually meet the ROI targets necessary to make the business worthwhile to the entrepreneur and investor. To do this, a Monte Carlo Simulation software such as Crystal Ball can be used. This software allows you to create a basic financial model and designate 'ranges' for inputs (ie: sales figures, resource costs, etc) instead of coming up with distinct best/worst/expected case scenarios. For example, if you think that salaries might be in the $40k-60k range with an average of $48k, it will allow you to input these ranges into the system. Once this is complete the software will iteratively simulate thousands of possible scenarios across all model inputs and generate two key outputs. The first output is a chart outlining results from the simulation with expected outcomes. Lets take this chart as an example:
This chart shows that across several thousand simulations performed on the input model , the business can indeed suffer a loss, although the expected value is a net income of $6K a year with an optimistic scenario of ~$12K. This data can be extremely useful when trying to compute ROI targets and value a company.

The second key output is a tornado chart that illustrates the key sensitivities of the business model. While running random simulations, the software isolates the most sensitive drivers of profitability and presents them in the form below:
This tornado chart shows the most sensitive variable in the model is Website Development costs (which will in the best case be ~$12K and in the worst case ~$18K.) Using this insight, entrepreneurs and investors can understand the key risks in the business and take steps to mitigate the key drivers of risk.

My experience has shown me that 'objective' analysis is crucial in the initial stages of discussing business models with early stage companies, as it ensures buy-in from both entrepreneurs and investors. Otherwise, there is simply too much room for subjectivity when looking at assumptions one by one and debating whether they are reasonable.

Early Stage Analysis Framework

This is a little project I developed over a weekend. It is the culmination of 4 years of experience in working with bootstrapped and VC/PE backed early stage companies. I welcome any and all feedback!

Early Stage Analysis Framework v2