This week, I visited a startup whose office had a very unusual feature: a usability lab. There I was in a soundproofed room with a table in the center flanked by two chairs, one for a user and one product manager or UX researcher. On the table, a constellation of web cams record a user’s facial expressions and interactions with a mobile phone or laptop while a microphone captures the user’s voice.
More and more companies realize their proprietary data contains insights that drive tremendous competitive advantage. Enabling an organization to make data driven decisions is a long term process. Below is the current big data adoption process and where we are within it:
While the ecosystem is quite young and there will be innovation at every step in the process above, the next wave of innovation in the data science market will occur in the last stage of the process above: the democratization of data analysis.
I once read a book by Antonio Damasio, a neuroscientist researching decision-making from USC, about a man who had lost the capacity to feel emotions after he was struck by lightning. Much to my surprise, his man was totally incapable of making decisions.
His cognitive ability, the capacity to process and analyze data, remained fully intact. He could articulate the pros and cons of every alternative to each decision. His logical ability was flawless. But, he never could decide on an option.
One of my favorite courses in engineering grad school was Marketing which was taught by a brilliant quirky professor. On the first day of class our professor wrote on the board this equation:
Innovation = Invention + Marketing
Addressing a group of engineers who prided themselves on their technical skills, this professor of marketing tried to instill in us that invention alone isn’t enough to create innovation.
The invention has to be coupled with a way of understanding the customer, speaking to that customer, educating the customer and ultimately convincing the customer to adopt the invention. Only when the customer base has adopted the invention at scale have we truly innovated.
We’re already seeing the impact to hardware vendors of peak smart phone. Over the past six months, Apple, Samsung and HTC share prices have fallen 20 to 25%. These share prices have tumbled because smart phone penetration is hurtling towards 70% in the US. True saturation has occurred in certain segments: 87% of 30 to 49 year olds with income greater than $75k own a smart phone.
Does the app economy suffer from the same challenge of decreasing growth as hardware?
It is easy to write off content marketing as a waste of time. Effectiveness is difficult to measure; it is time consuming and the payback period on the investment is uncertain.
But unlike most forms of paid marketing, content marketing has a cumulative and compounding return. Each of the posts of a blog continues to attract traffic from SEO and social channels long after it has been published.
Below is a chart of the 9 most popular posts in the last 12 month on this blog. It is scaled to eliminate the peaks to better show how page views on these posts trend with time. There is a pattern of a huge spike on the first day of publication, followed by a tail of steady and consistent visits.
I started playing around with Google’s programming language called Go yesterday. There’s a tour of the language found here. Clearly, Go has been designed by a group of incredibly smart people and makes a series of terrific design decisions that enable a tremendous amount of flexibility and performance while reducing the amount of code an engineer has to write.
But after spending about 90 minutes running through the tutorial, I decided to throw in the towel.
I’m asked with some frequency which startup sectors are booming. Mobile messaging and big data are knee-jerk reactions at this point. But these days I often respond “financial services.”
In the last two years, financial services startups have been innovating impressively quickly and challenging some of the fundamental ways in which capital and credit are distributed. I count seven major categories of innovation to date:
Math Based Currencies/Payment Networks - Bitcoin might be the most well-known and best publicized math-based currency, but there are a handful of others in the market, like OpenCoin and LiteCoin. Math-based currencies are novel ways of enabling instant settlements, low cost transactions and foreign exchanges. The promise of these startups is to create new payment networks and more efficient ways of transferring value.
At about 11pm on a Tuesday night in 2006, I began coding a skeleton CRM web app for our internal use at Google called Toothpaste that tracked the key details of our larger inside sales customers.
It was my first real Rails project after spending quite some coding Java. After I ran “rails server”, I watched the terminal as the program spit out a few lines from the built in app server, Webbrick, and refreshed my browser to see the Ruby on Rails Welcome page. Within a few hours, the first version was functionally complete and I went to bed excited by the prospect of demo'ing what I had built.
When I first started in venture capital five years ago, I wanted to create a programmatic way to analyze companies well. My goal was to be able to step into a meeting with an entrepreneur with some kind of form that I would fill out throughout the meeting, so that by the end of the meeting I might have an understanding how the startup fits into its ecosystem.
It took quite a while to devise this framework and to revise it until it became useful, practical and insightful. I spoke with friends who were consultants and who analyze companies for a living. I read many books on the topics of competition and strategy. Last, I spoke with other investors.
