Posts

Atul Gawande, the American surgeon known for his book “Better”, wrote an article in this week’s New Yorker called “Slow Ideas: Some innovations spread fast. How do you speed the ones that don’t?”

He describes the challenges faced by healthcare institutions all over the world: despite the advances in research, the most difficult part of improving care isn’t availing doctors and nurses to these breakthroughs, but changing their behavior.

Some doctors simply won’t wash their hands no matter how many times they are told it reduces infection rates. While the stakes in technology adoption are less dire, changing user behavior is just as challenging for information workers as doctors.

Atul Gawande, the American surgeon known for his book “Better”, wrote an article in this week’s New Yorker called “Slow Ideas: Some innovations spread fast. How do you speed the ones that don’t?” He describes the challenges faced by healthcare institutions all over the world: despite the advances in research, the most difficult part of improving care isn’t availing doctors and nurses to these breakthroughs, but changing their behavior. Some doctors simply won’t wash their hands no matter how many times they are told it reduces infection rates.

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.

Earlier this week, I met an experienced head of sales. During our meeting, the candidate shared a simple way of thinking about a startup’s sales process that resonated with me.

A startup’s sales evolution contains three phases: beta, reference customer, ROI calculator.

Beta: A founder of the startup develops relationships with a handful of customers who will work in tandem with the company to design, tune and improve the product. Typically, beta customers use the software for free or at heavily discounted rates. These customers benefit by disproportionately influencing product development.

With more than 80% of venture capital investments occurring in enterprise and with the public markets disproportionately rewarding SaaS companies with huge enterprise value-to-revenue multiples (median is 7.6), it’s no surprise that interest Software-as-a-Service is booming. After meeting quite a few SaaS companies, I’ve compiled a list of my ideal characteristics for a SaaS business below.

Characteristic 1: Product Is Core to the Operation of the Business The product is essential to the operation of a customer’s business. For example, Zuora enables subscription billing; Expensify manages employee expenses; ZenDesk builds customer support systems. Customers can’t function without it.

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:

1. Companies generate proprietary data whose volumes can’t be handled by existing tools. The main adopters of these technologies are financial services, healthcare, genomics and web companies.
2. Companies build or buying the tools and expertise to store and process that data. Major vendors include Cloudera, MapR, HortonWorks, Splunk, GoodData, Vertica, Greenplum and many others.
3. These new tools demand new skill sets within the organization: data storage expertise, data processing acumen, analytical ability, modeling skills, visualization expertise. As the infrastructure to support data analysis matures, data scientists become the bottleneck within the organization as they are beseiged by data questions.
4. Tools emerge to enable analysis at edges of the organization so sales, marketing, product and everyone else can perform the analysis without having to submit a request to the data science team.

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?