Posts

Vik Singh wrote a great post in VentureBeat last week titled “Why Salesforce Needed to Buy RelateIQ” in which he talks about a new era in SaaS, the Predictive Era, the era of intelligent software. We’ve just seen one of the first acquisitions in the category with RelateIQ*, but I believe we will see many, many more for a few reasons.

First and most importantly, prediction provides competitive differentiation in an increasingly competitive market. It’s no longer sufficient in most horizontal SaaS categories to provide a cloud-based alternative with similar features to traditional software incumbents. RelateIQ showed this to some extent in CRM. The company uses natural language processing to reduce data entry. But this trend is as true for email marketing tools as logs analysis, for calendars as lead prioritization tools.

An entrepreneur asked me the question, what is the maximum viable churn for a startup? Within that question, a few others are embedded. How should a founder think about trading off efforts to grow revenue and mitigate churn? What is the impact of account growth on net churn? Startups must walk a tight-rope to balance growth, churn and cash. Below is the framework I use for working through maximum viable churn.
​

Aside from a startup’s internal considerations about the right time to raise money, founders should weigh the seasonality of the fund raising market when planning their raise. There’s a rule of thumb batted around the valley that the worst times to raise capital are in the dog-days of summer and after Thanksgiving. As it turns out, this aphorism is only a half-truth.

Below is a chart of the dollars VCs have invested by month of year. I’m using Crunchbase data since 2005 for tech companies in the US. There are a few notable trends in the data.

Last week, I spent some time at HeavyBit, the community for developer focused companies in SoMa, chatting with a few companies reaching scale. Across a handful of meetings, a recurring theme surfaced for these B2D (business-to-developer companies).

How should their sales and marketing apparatuses be built? Do the field sales models of infrastructure companies or the inside sales models of software companies apply when the initial user is a developer?

When I worked as an engineer, I loved crafting code and feeling the satisfaction of having built something each day. But there was one thing about coding I never grew to love, despite its importance: forecasting my coding time.

Every two weeks, I trudged into a planning meeting that exposed my incompetent forecasting. During these meetings, each person in turn would review their commitments for the last two weeks and provide an update. Inevitably, I was wildly off. Chalk it up to inexperience, exuberance or ineptitude, but I never developed the knack.

While the phrase data scientist may be growing exponentially in its usage, and the number of data scientists job requisitions following a similar trend, the definition of the term is hard to pin down precisely.

I wasn’t sure I could define it well until I watched a talk by Hilary Mason, former chief scientist at Bitly, called Dirty Secrets of Data Science at a NYC meetup. During the presentation, she highlighted a chart created by the Data Community DC team that demystifies term data scientist.

As recently as six months ago, it was easy to disregard the Internet of Things (IoT) as just a theoretical market that Cisco measured in the trillions, but whose potential never seemed to materialize. That’s all changing.

The past year ushered in a new era for the Internet of Things for three reasons. First, venture capitalists invested nearly $1B of capital in the IoT in 2013, more than 3% of all VC investments by dollars. Second, the sector witnessed its first IPO, Control4. And third, Nest’s sale to Google last week set the high water mark for IoT acquisitions, measured at more than $3B, firmly establishing the category as a strategic imperative for the world’s largest technology companies.

Each year the National Venture Capital Association and Thomson Reuters release data characterizing the state of the startup market. I’ve analyzed the 2013 data and there are three important trends I observed. All in all, the startup exit market is quite healthy.

1. Startup exit values are increasing more than 7% per year, on average.
2. The number of exits is flat-to-down during the ten year period I studied.
3. The public markets have opened to startups again, doubling their share of exits.

On the heels of last week’s post about the Health of the Public Technology Market, Felix Salmon asked the thought-provoking question above. Despite the 68x growth in the value of technology market caps since 1980, are newer average technology companies worth less?

Surprisingly, yes.

The average public tech company value has falled by more than 2/3rds from $4.3B in the early 80s to $1.4B today, as measured in 2014 dollars.

Churn is one of the most important metrics for businesses. Churn dictates customer lifetime, lifetime value (CLV), customer acquisition spend and customer success spending. In short, churn is pivotal number to evaluate a startup’s business, both for founders/management teams and investors.

Unfortunately, accurately measuring churn rates/lifetime value is more complex than I initially thought. I was researching the topic after Ryan Shank asked me how best to calculate an average customer’s lifetime value. The more I dug into the math, the deeper the rabbit hole went.