Over the last six months, I’ve been delving deeply into R, linear regressions and machine learning. Part of the rationale has been to remember some of the concepts I learned in grad school studying signal processing.
But a more important driver has been the need to better understand how to qualify, evaluate and hire data scientists because data science is a massive competitive advantage. And many of the companies I work with are hiring data scientists.
Finding the right person ...
Last week, a close friend, who is a product manager/designer, told me he’s starting a company. He asked me where I thought the biggest opportunity lay given his skills and his passions. He’s incredibly capable and driven, but he hasn’t yet found the right place to apply his energy. My friend is in search of a problem to solve. He’s in the right place. After all:
When I walk into a bank today, I might deposit my cash in exchange for some interest rate. The interest rate is my cut of the profits the bank made on my deposit through their trading and lending activities. The more assets under management, the more money a bank can make on its own account.
Like banks, web companies are in the business of maximizing gigabytes of user data under management.
When I use Google products like Gmail, Google Search, Android and Android applications, Google Chrome for browsing, ...
I met the Electric Imp team in April. I had bumped into one of their engineers at a party and he pinged me a few weeks later to say he was working for a startup and the company was raising. The company came in to the office on a Monday at noon.
Hugo, the founder, Electric Imp demoed their product to me. Ten minutes in, I stopped the pitch meeting, pulled 3 partners from their Monday partner meeting, and issued a term sheet that afternoon. The demo was ...
Examining a user’s motivations at the entry point of every major feature in a product and matching the product to this motivation is key to building a great product users love.
BJ Fogg’s Behavior Model Theory is a succinct summary of this idea in a formula:
Motivation + Trigger + Ability = Behavior
This model says that a user will perform a behavior when given the means, the motive, and the opportunity. The user brings motivation, but it’s the responsibility of the product to trigger a ...
It’s tempting to burrow within a garage or basement or apartment to develop a product for several months and emerge from the darkness with a new shiny product. But the launch will likely fall flat.
Products must be launched into ecosystems, in particular, into receptive ecosystems. In my view, there are three types of ecosystems that startups should cultivate. These ecosystems provide distribution leverage - that’s what makes them so powerful and so essential at the start of a company.
Users provide product feedback, build ...
Every social service aims to achieve massive growth and deep engagement. But if forced to choose just one of these attributes, I would pick engagement every time. An active user base implies product/user fit for a social service.
Aside from the core functionality of social services, which is a solved problem (profiles, messaging, feed), the essence of a social startup is culture - the values of the community, the mores, the manners of interaction. The right culture attracts users, encourages participation and drives growth.
The first mobile phones were purchased by corporations and given to employees. Thirty years ago, most people used computers at work but not at home. Most of the innovation flowed from the enterprise into the home. Today, it’s very much the opposite.
The big trends in enterprise trace the opposite movement both at the software layer and the device layer: consumerization of IT means using consumer channels to acquire customers and bring your own device (BYOD) means 66% of employees bring their own devices to work.
Freemium businesses' marketing techniques are immensely powerful. They drive large amounts of users to try a product and convert some small fraction of those to paid, upending the enterprise sales model.
In some sense, freemium businesses are real world Monte Carlo simulations. Because of the large volume of users using the product, freemium businesses can generates gigabytes of interaction data and conversion-to-paid data, which makes these kinds of startups particularly well suited to data science, A/B testing and regression analysis.
Over the past week, I’ve been trying this ...
For Fortune 500 companies, patents can be offensive and defensive weapons generating billions of dollars worth of royalty and licensing revenues.
…if someone at Apple can dream it up, then we should apply for a patent, because even if we never build it, it’s a defensive tool,” Nancy R. Heinen, Apple’s former general counsel
But startups shouldn’t approach patents the same way large companies do. Instead, founders should view patents as downside protection - an asset to be ...