An op-ed I wrote for VentureBeat:
Last week, Atlassian made a very smart move by acquiring Trello. While $425 million implies a high multiple (given Trello’s revenue run rate was around $10 million last year), I believe it positions Atlassian to become the next big enterprise software company. I project it will reach a $50 billion market cap in 10 years by taking over software for teams. Here are four reasons why:
Read more …
I’ve received multiple requests to analyze employee churn and new hiring rates for big companies and unicorns with the approach I took earlier for studying engineering and sales retention rates. I figured I’d give it a shot – and combine all of the key metrics in one chart …
How to read this:
Blue bar represents the number of expected new hires that particular company will make in a 30 day (one month) period. Black bars (negative values) indicate how many employees will churn in a one month period. The orange line (the top most numerical labels correspond to the orange line plot) represent the net change in hires per month (new hires less churn). The companies are ranked by churn from left to right in descending order (so highest churn on the left).
As you can see in the chart, the big three companies included in this analysis are Amazon, Apple and Google. The unicorns are Uber, Lyft, Airbnb, Pinterest and Snapchat. “Big 5” combines these unicorns together as if they were one whole company. Also note, this is looking at employees worldwide with any job title.
- Apple is not hiring enough new heads when compared with Amazon and Google. In fact, the Big 5 unicorns combined will hire more net heads than Apple with almost 50% less employee churn.
- Amazon’s churn is the highest – losing a little over 10 people a day. However, this is not bad relatively speaking – Google loses 8-9 people a day, and Apple is a tad over 9 a day (and Amazon has 36% more employees than Google). Given the recent press bashing Amazon’s culture and the periodic press envying Google’s great benefits, their retention rates tell a different a story – that it’s closer to a wash. Big tech companies with great talent churn people at pretty similar high rates regardless it seems (have some more thoughts on this but will save those for another post).
- At these current rates, all of the companies here (collectively) will increase their employee size by 20K (19, 414 to be precise) heads by year end (this is new hires less churn). That’s a measly 5% increase in their current collective employee size – and this is across the Big 3 Tech Companies and Big 5 Unicorns.
- Let’s compare Amazon to the Big 5 Unicorns. The Big 5 will hire 79% as many incremental heads as Amazon in a month, even though their collective employee size is 24% that of Amazon’s. Amazon has been in business for much longer (2-3x days since incorporation), and the Big 5’s churn is 43% of Amazon’s figure – both factors contributing to the closeness in the incremental head rate between the two.
Want more details?
How did I calculate these figures? Take a look at my previous post on engineering retention for more details. Same caveats listed there apply here, and then some (such as how this depends on the participation rates of LinkedIn which may differ considerably internationally compared to the US market which my previous posts exclusively focused on). Feel free to connect or email me if you have any questions or feedback.
Filed under Data Mining, Economics, Entrepreneurship, Google, LinkedIn, Management, Non-Technical-Read, Research, Startups, Statistics, Trends
A company’s sales retention rate is a very important indicator of business health. If you have a good gauge on this, you could better answer questions such as: should I join that company’s sales department, will I be able to progress up the ladder, are reps hitting their numbers, are they providing effective training, should I invest money in this business, etc. But how does one measure this rate especially from an outside vantage point? This is where LinkedIn comes to the rescue. I essentially cross applied the approach I took to measuring engineering retention to sales.
This chart reveals several key technology companies ranked in reverse order of sales churn – so higher on the chart (or longer the bar) the higher the churn (so from worst at the top to best at the bottom).
So how are we defining sales churn here? I calculated the measurement as follows: I took the number of people who have ever churned in a sales role from the company and divide that by the number of days since incorporation for that respective company (call this Churn Per Day), and then I compute the ratio of how many sales people will churn in one year (the run rate i.e. Churn Per Day * 365) over the number of current sales people employed.
For ex. if you look at the top row, which is Zenefits, the value is 0.40 – which means that 40% of the current sales team size will churn in a one year period. In order to maintain that sales team size and corresponding revenue, the company will need to hire 40% of their team – and sooner than in a year as that churn likely spreads throughout the year as well as given new sales hire ramping periods (if you’re churning a ramped rep and say it takes one quarter to ramp a new sales rep, then you need to hire a new head at least one quarter beforehand to avoid a revenue dip).
A few more notes:
The color saturation indicates Churn Per Day – the darker the color, the higher the Churn Per Day.
Caveats listed in the previous post on engineering retention apply to this analysis too.
Filed under Data Mining, Economics, Enterprise, Entrepreneurship, Job Stuff, LinkedIn, Non-Technical-Read, Startups, Statistics, Trends, Venture Capital
An op-ed I wrote for TechCrunch:
There are several enterprise players that want a share of Salesforce’s business, but just aren’t making headway by knuckling up against the company’s dominant, entrenched SaaS CRM offerings. Rather than competing head on, a smarter approach for these businesses is to “front door” Salesforce, instead.
By acquiring Marketo, a competitor could get into Salesforce’s accounts, then, over time, work themselves down the funnel and leverage better integrations with Marketo in order to eventually displace Salesforce. Marketo’s strategic foothold in the enterprise and its current market value relative to potential acquirers like IBM, Microsoft, Oracle, SAP and even Salesforce make this a great time to buy the leading marketing automation vendor.
Many industry watchers overlook the mission-critical role Marketo plays in its customers’ go-to-market operations. The majority of Marketo’s 4,000 customers also use Salesforce, but the marketing automation system has access to more data about the funnel than its CRM counterpart. Marketo can sync bi-directionally with Salesforce, capturing all the data stored there, while also holding top-of-the-funnel lead behavior data that doesn’t get stored in CRM. Hence, it has access to an invaluable superset of data about a company’s potential and existing customers.
Read More …
A guest piece I wrote for TechCrunch on how predictive-first (like “mobile-first”) applications will change the software game:
Over the past several decades, enterprise technology has consistently followed a trail that’s been blazed by top consumer tech brands. This has certainly been true of delivery models – first there were software CDs, then the cloud, and now all kinds of mobile apps. In tandem with this shift, the way we build applications has changed and we’re increasingly learning the benefits of taking a mobile-first approach to software development.
Case in point: Facebook, which of course began as a desktop app, struggled to keep up with emerging mobile-first experiences like Instagram and WhatsApp, and ended up acquiring them for billions of dollars to play catch up.
The Predictive-First Revolution
Recent events like the acquisition of RelateIQ by Salesforce demonstrate that we’re at the beginning of another shift toward a new age of predictive-first applications. The value of data science and predictive analytics has been proven again and again in the consumer landscape by products like Siri, Waze and Pandora.
Big consumer brands are going even deeper, investing in artificial intelligence (AI) models such as “deep learning.” Earlier this year, Google spent $400 million to snap up AI company DeepMind, and just a few weeks ago, Twitter bought another sophisticated machine-learning startup called MadBits. Even Microsoft is jumping on the bandwagon, with claims that its “Project Adam” network is faster than the leading AI system, Google Brain, and that its Cortana virtual personal assistant is smarter than Apple’s Siri.
The battle for the best data science is clearly underway. Expect even more data-intelligent applications to emerge beyond the ones you use every day like Google web search. In fact, this shift is long overdue for enterprise software. (Read More)
A guest article I wrote for VentureBeat today:
Yesterday, Salesforce.com acquired RelateIQ for $390 million. With Dreamforce right around the corner, this was a significant — and smart — move on the part of Salesforce.com to show the industry that it is finally serious about data intelligence, which it completely lacked in its customer-relationship management (CRM) offerings to date … (read more)
Just posted a guest article on The Next Web on some of the key startup learnings my team and I have picked up while building up our company Infer. Although our company is emerging and in the enterprise space, I think you’ll find many of these insights to be broadly applicable.