Notes from the MuleSoft S-1 IPO Filing

MuleSoft, a fast-growing data and application integration software provider, just released their S-1 IPO filing. As more companies move to the cloud, the demand for connecting these applications, and the legacy installed applications, has grown as well.

Here are a few notes on the MuleSoft S-1 IPO filing:

  • Key metrics as of December 31, 2016 (pg. 1)
    • > 1,000 customers
    • 117% dollar-based net retention
    • 70% revenue growth
    • $188 million in revenue
    • -1.4% operating cash flow margin
  • Customers use the Anypoint Platform to connect their applications, data, and devices into an “application network” in which these IT assets are pluggable using application programming interfaces, or APIs, instead of glued together with custom integration code. (pg. 1)
  • Estimate the current market opportunity to be $29 billion. (pg. 3)
  • 30 customers with over $1.0 million in annual contract value of subscription and support contracts. (pg. 3)
  • Revenue (pg. 3)
    • 2014 – $57.6 million
    • 2015 – $110.3 million
    • 2016 – $187.7 million
  • Net losses (pg. 3)
    • 2014 – $47.8 million
    • 2015 – $65.4 million,
    • 2016 – $49.6 million
  • Professional services revenue (pg. 8)
    • 2014 – $9.1 million
    • 2015 – $22.2 million
    • 2016 – $34.9 million
  • Accumulated deficit of $236.2 million as of December 31, 2016 (pg. 12)
  • In 2014, 2015, and 2016, total sales and marketing expense represented 102%, 84%, and 65% of revenue (pg. 15)
  • Outsource the cloud infrastructure to Amazon Web Services, or AWS, which hosts the platform (pg. 16)
  • Platform is deployed in a wide variety of technology environments, both on-premises and in the cloud (pg. 16)
  • 38% of the revenue from customers located outside the United States in 2016 (pg. 27)
  • 156 employees located in Argentina at the end of 2016 (pg. 29)
  • Ross Mason created Mule in 2006 to address the frustrations of manually connecting disparate systems and applications. Mule took its name from Ross’s desire to take the “donkey work” out of legacy approaches to technology integration. (pg. 57)
  • Annual contract value of $169,000 in 2016 (pg. 58)
  • Subscription pricing is based primarily on the amount of computing capacity on which the customers run the software (pg. 58)
  • Founder owns 5.9% (pg. 134)
  • VCs own 67.8% (pg. 134)

MuleSoft is a hybrid cloud and on-premise software provider with a pricing model that bills everything like SaaS. Data and application integration is a massive market and MuleSoft is well positioned to grow for many years and have a strong IPO. Like AppDynamics, look for large strategics to take an interest in MuleSoft as well.

Video of the Week: Artificial Intelligence – What Everyone Needs to Know

For our video of the week, watch Jerry Kaplan present Artificial Intelligence: What Everyone Needs to Know. Enjoy!

From YouTube:
Over the coming decades, artificial intelligence will profoundly impact the way we live, work, wage war, play, seek a mate, educate our young and care for our elderly. It is likely to greatly increase our aggregate wealth, but it will also upend our labor markets, reshuffle our social order, and strain our private and public institutions. Eventually it may alter how we see our place in the universe, as machines pursue goals independent of their creators and outperform us in domains previously believed to be the sole dominion of humans. Jerry Kaplan is widely known as an artificial intelligence expert, serial entrepreneur, technical innovator, educator, bestselling author and futurist. He co-founded four Silicon Valley startups, two of which became publicly traded companies, and teaches at Stanford University.

Join Kaplan for an illuminating conversation about the future of artificial intelligence and how much humans should entrust to machines.

Video of the Week: Artificial Intelligence is the New Electricity

For our video of the week watch Andrew Ng: Artificial Intelligence is the New Electricity. Enjoy!

From YouTube:
On Wednesday, January 25, 2017, Baidu chief scientist, Coursera co-founder, and Stanford adjunct professor Andrew Ng spoke at the Stanford MSx Future Forum. The Future Forum is a discussion series that explores the trends that are changing the future. During his talk, Professor Ng discussed how artificial intelligence (AI) is transforming industry after industry.

Machine Learning and Marketing Automation

Continuing with yesterday’s post Machine Learning and the Startup World, a friend asked what machine learning applications might look like for marketing automation platforms (e.g. Pardot). Good question. Considering marketing automation platforms collect so much information about leads through email opens, form submissions, web page visits, ebook downloads, webinar signups, etc., there’s a tremendous amount of training data for machine learning to find insights.

Marketing automation is essentially human-defined rules based on what they think is best (e.g. send email A, wait two days, send email B, etc.) Machine learning “learns” over time and can figure out patterns humans can’t because there’s too many dimensions to analyze. Here are a few ideas on how to use machine learning in marketing automation:

  • Email Send Time – Analyze when leads opened emails in the past and automatically schedule future emails to be sent at the same time (time of day, day of week, etc.)
  • Email Message to Trigger – Analyze what emails (or other types of content) were most closely associated with pushing the lead through the current phase of the lifecycle (e.g. engaging with a sales rep) and trigger the email automatically (as opposed to human-defined static rules)
  • Lead to Opportunity Probability – Analyze every piece of historical data for leads that turned into opportunities (the training data or learning set) and come up with a probability for all other leads that haven’t turned into an opportunity based on how their behavior matches the training data
  • Lead to Customer Probability – Like the lead to opportunity probability, do the same thing for leads that became customers (not just those that had an opportunity in the pipeline associated with them) and come up with a probability that any given lead will become a customer

Machine learning has applications in all fields, especially marketing automation. Look for existing vendors to add this type of functionality as well as new vendors to emerge that take advantage of this new technology.

What else? What are some more ways machine learning can be applied to marketing automation?

Predictive Sales and Marketing Technologies

One of the sales and marketing technologies I’m most excited about (along with account-based sales and marketing) is the whole predictive area. At a simple level, predictive takes existing contacts and opportunities and scores them against a dynamic model based on other contacts and opportunities that became customers. Put another way: find great-fit companies that look like our existing customers so we can target them.

Account-based sales and marketing platforms (see SalesLoft and Terminus) solve the major problem of running programs in a scalable manner against hundreds (or thousands) of target accounts. Traditional marketing, and sales as the opportunity progresses through the funnel, casts a net, sees what is caught, and then works the qualified leads. Now, as a more modern approach, account-based sales and marketing goes spear fishing and proactively seeks out best-fit accounts based on a number of dimensions. Only, there’s often not an easy way to find and refine best-fit accounts — enter predictive technologies.

Here are a few thoughts on predictive sales and marketing technologies:

  • As more companies implement account-based sales and marketing platforms, predictive systems become more important to find and analyze best-fit accounts.
  • Predictive sales and marketing systems are clearly in the path of revenue.
  • Machine learning, a subset of artificial intelligence, is now more accessible with the advent of systems like AWS Machine Learning, making predictive systems more powerful.
  • Finding lookalike companies requires technology. Combing through billions of records by hand simply isn’t possible.

Look for the category of predictive sales and marketing systems to grow fast as the technology crosses the chasm and becomes more well known.

What else? What are some more thoughts on predictive sales and marketing technologies?

The Intersection of Sports and Technology

Earlier today I had the chance to participate on a panel at the new home of the Atlanta Braves: SunTrust Park. After seeing the stadium progress (very cool!), we talked for 30 minutes about the intersection of sports and technology, especially the innovation opportunities that lie ahead.

Here are a few of the ideas discussed:

  • Teams want to use technology to enhance the fan experience, especially the downtime when the game isn’t happening (e.g. between innings, pitcher changeovers, etc.) and the community element
  • Bandwidth is no longer a concern at modern stadiums as every fan can stream video real-time, opening up new opportunities (drones? more cameras on wires?)
  • Startups play an important role in the sports technology world even though most will fail (it only takes one to change the world)

I’m excited about the future of sports and technology.

What else? What are some more thoughts on the intersection of sports and technology?