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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.
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 The Patent, as a Sword: But startups shouldn’t approach patents the same way large companies do.
In Silicon Valley, we cherish stories of great struggles, persisting failure, and grind-your-teeth kind of grit that eventually leads to great success. These stories are our collective folklore. Today, I want to highlight one of these stories. Amy Cuddy gave a TED talk on how body language shapes who we are and our career trajectories. Her story is insightful, motivational, and electrifying. First, Amy fascinates with scientific data on how body language impacts our careers.
This week Amazon made public its advertising initiatives. Given the massive trove of invaluable purchasing data Amazon collects, I’m certain Amazon could build a rival to AdSense. But they aren’t. Below is a quote from an interview with Lisa Utzschneider, the head of Amazon Media Group:
Q: Can you give us a sense of how important advertising is to Amazon?
A: I think the way Jeff [Bezos] would answer that is, if we think about Amazon in two worlds, one world is an Amazon with ads and lower prices.
Over the weekend, I analyzed my Twitter performance over the past 4 weeks. I wanted to determine what if any best practices I could tease from the data. Below are my four conclusions:
The best time for me to tweet is 9am Pacific. On average, tweets at 9am generate 2.3 times the number of clicks as those in the 8am hour and 3.3 times those of the 12pm (lunch) hour. Below is a chart of number of clicks per tweet by hour of the day:
The payments ecosystem is complicated. But FastCompany constructed an illuminating diagram walking through a typical payments flow that I’ve copied here. Just look at all those steps!
Simplifying this complexity is the opportunity for many startups like Stripe, Dwolla and others.
The flurry of media activity in entrepreneurship including the spate of new TV shows like X-Factor for tech and Shark Tank, the refocus of the NYTimes and WSJ on technology, and the number of entrepreneurs on mainstream magazine covers gave me the impression that entrepreneurship is on the rise. While this may be true in pockets like New York or California, entrepreneurship in the US is shrinking - at least according to the Bureau of Labor Statistics and a report from the NYTimes: When Job Creation Engines Stop at Just One
Everyone is learning statistics because making sense of data is the difference between success and failure. R, the open source statistics language, is about a third as popular as Ruby and growing fast.
Statistics are essential because data is ubiquitous and volumes are growing exponentially, even in startups. CEOs measure key company metrics. Engineers measure application performance and build machine learning models. Marketers measure campaign performance and reach. PMs measure engagement.
Over the last few months, I’ve been helping a few companies build hiring pipelines to recruit at nearly every experience level and for technical, sales and business development roles. Below are the lessons I’ve learned. Identify your ideal candidate
If you don’t know where you’re going, any road will take you there. Narrow your search focus to find the right candidate. The easiest way to start is by building look-alike candidate lists.