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How VC-to-PE Buyouts Can Change Market Dynamics for Later-Stage, VC-backed Companies

Every VC-backed company board gets to evaluate company exits from time to time and often these decisions occur when there is an opportunity to raise more capital. If you have been in the industry for a while, you have experienced various forms of the three main types of late-stage capital raises or exits: Late-stage rounds, IPO, and strategic M&A. Late-stage rounds are an interim step for raising capital and forging a path to profitability. M&A is usually appealing to all shareholders only when the company is being “bought and not sold.” Finally, IPO’s are expensive and hard to prepare, and public capital market timing and attractiveness are out of any board’s control. So boards are sometimes unsatisfied with the alternatives available for capital raising and/or transfer of some or all corporate control.

In the private equity world, a fourth option has existed for many years where one PE firm buys out the majority stake of another PE firm that is ready to sell. Increasingly, PE firms are making this “secondary buyout” option available to VC-backed companies and their boards. These “VC-to-PE buyouts” are a helpful liquidity alternative for both employees and investors in later-stage VC-backed companies and are changing the dynamics when boards consider strategic options. Specifically, we have seen software-focused private equity firms like Vista Equity Partners (Ping Identity, Marketo) and Thoma Bravo (Digicert, Qlik) in the market, looking to acquire both private and public software-driven companies.

Capital and Control Matrix

Capital raising strategies and corporate control questions are among the most momentous ones for a company board. These are often areas where the collective experience of the board can provide great value to an entrepreneurial team. At times, the capital raising and control questions can become conflated. Below is a high-level summary of the options available to later-stage, venture-backed companies arranged by percentage of capital raised/transferred and percentage of control/ownerships transferred. A more detailed matrix is included at the end of this piece.

CapControl Matrix

How Did We Get Here?

For the last several years, late-stage capital for private companies has been plentiful, relatively inexpensive and light on control provisions and investor protections. While there have been occasions of sudden and sharp pullbacks, including February 2016 and April 2014, this capital has generally been available for high-growth companies. An increase in the size and variety of sources of capital, from larger venture funds, public institutional investors and hedge funds, have fueled these “company-friendly” trends. These capital sources have allowed companies to remain private later and longer than historically possible, and have given rise to the so-called “unicorns”, or private companies with valuations above $1 billion.

Two main groups of investors look to lead late-stage private rounds. One group consistes of late-stage venture firms like Insight and TCV who prefer to take market execution risk over the product market fit and market timing risks taken by early stage VC’s. The second group is mostly made up of public stock investors like T. Rowe Price, Fidelity and Janus, along with some hedge funds. For rapidly growing, technology companies, these private rounds are often attractive. Companies sell a small portion of their business in exchange for growth capital and the later stage company-building expertise of new investors. These rounds sometimes have a secondary component to provide some liquidity to longstanding employees and early investors. With these private rounds, companies are somewhat sheltered from the quarterly expectations of the public market, which is especially helpful for companies who have customer concentration, lower sales productivity and lumpy sales cycles.

A different path for raising capital and creating liquidity over time is the IPO. A company sells shares (typically 15 to 20% of the company at pricing) to raise capital to fuel growth. The IPO process is expensive and time consuming, generally costing over $3 million to prepare and taking 9 to 12 months. And, being public subjects a less established company to the spotlight and expectations of public company investors. That said, for companies that are taking a long-term perspective and are increasingly mature in their processes and predictability, an IPO has several advantages. It creates a public currency that can be used to buy other companies or leveraged to raise more cash by taking on low-cost debt. An IPO provides a path to liquidity for existing employees and investors, as well as a path for transitioning ownership from early-stage to public company investors. The IPO also has some other advantages; it creates a branding opportunity for the company and a stamp of approval in attracting customers, partners and new employees.

In a world of Software-as-a-Service (SaaS) and subscription-based businesses, companies are stronger candidates for being public. They have greater visibility into key metrics like revenue, product usage, customer renewals/upsells, customer acquisition cost and lifetime value than was ever possible in a world of on-premises and licensed software. And, they can typically drive toward free cash flow and positive operating margins as they get to scale and continue to invest in growth. For these reasons, we expect more of the mature SaaS and subscription-based technology companies to go public if the IPO market stabilizes in the later portion of 2016. But, the demands of being public, the volatility in capital markets, and the leverage of gatekeepers in the IPO process have led many boards to choose other paths.

M&A and the new VC-to-PE Buyout Trend

Selling a company in an M&A process is another path. But, according to Pitchbook, full-year 2016 transaction value is projected to be well below the $83.9 billion dollar of M&A reported in 2014. This trend is especially surprising given that five of the largest technology companies (Apple, Microsoft, Alphabet, Oracle, and Cisco) had $504 billion dollars of total cash in May 2016 and, financial sponsors have over $1 trillion of “dry powder” available to invest. Notwithstanding the recent acquisition of LinkedIn by Microsoft, NetSuite by Oracle, and EMC by Dell, larger technology companies have been relatively cautious to make medium to large acquisitions.

What we have seen emerge over the past few years, is a group of discerning private equity firms including Vista Equity, Thoma Bravo and Warburg Pincus who have sensed opportunity and increasingly led the way on initiating M&A discussions and acquiring VC-backed, software companies. For the same reasons that mature SaaS companies are less risky to take public, they are better able to fit within a leveraged buyout model. Boards of later-stage private companies are evaluating these VC-to-PE buyouts as an alternative to strategic buyer acquisitions or raising capital in IPO’s or private rounds.

Several broader factors, that may not persist, are contributing to these current VC-to-PE buyouts. Valuations for SaaS companies growing 20%+ on an “enterprise value to next twelve-months sales” (EV/NTM) basis have recovered, according to Goldman Sachs; but are still modestly below the 5.5x EV/NTM 12-year average. The cost of debt remains low, and more innovative debt structures are available. Finally, in some cases like Marketo (a public company example where more data is available), the EV/NTM multiples paid by private equity buyers outbid potential strategic buyers. The Marketo deal was done at a 64% premium to prior stock price with a 5.8X EV/NTM multiple. The company grew roughly 40% in revenue and billings in 2015, and generated $2.3 million of cash. In Madrona’s portfolio, the purchase of PayScale a few years ago was a leading indicator of this trend. With SaaS companies growing and maturing, other VC-to-PE buyout deals are expected.

But why would a VC-to-PE buyout be attractive to the management and board of a later stage private company? Management has the opportunity to continue fulfilling their dream as an independent and private company. New PE owners offer a high degree of autonomy and flexibility to executives who are meeting or exceeding agreed-to milestones (admittedly with greater EBITDA emphasis over growth), as well as additional capital to take advantage of strategic opportunities. These deals create immediate liquidity for long-standing shareholders. Typically, executives remain in their leadership roles and receive new equity in the new entity. The opportunity cost of a VC-to-PE buyout is the promise of holding out to build even greater value through an IPO or strategic exit. However, this new form of buyout could be considered the “best of both worlds” for a management team that is committed to continuing to build business and equity value.

A force as significant as VC-to-PE buyouts will likely have ramifications across the capital and control matrix. A few we anticipate are:

  1.  Strategic buyers will be motivated to move more quickly and aggressively on acquisitions – and be pressured on pricing and terms as they look to acquire later stage private companies.
  2. We may see a loosening of the burden of the IPO process. Investment bankers, public equity investors and other gatekeepers will become more flexible on IPO pricing and processes in taking companies public.
  3. Later-stage rounds, for 20%+ growth companies that can demonstrate compelling unit economics and a path to cash-flow breakeven, will enjoy a rebound in company-friendly pricing and terms and will continue to offer partial liquidity for employees/investors.
  4. PE buyout firms will increasingly emphasize the “best of both worlds” value proposition to CEOs and their executive teams as a reason to choose the VC-to-PE buyout route.

Boards are responsible for helping guide management teams to maximize long-term shareholder value in the context of balancing risk and reward. The increasing opportunity for later-stage companies to evaluate VC-to-PE buyouts as a change of control option improves the menu of possibilities boards can consider. Thinking about it in matrix form can help clarify the real trade-offs between options leading to better capital raising and control decisions.

CapControl Matrix Detail

This post first appeared on Venturebeat in shortened format.

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Takeaways from the 5th Annual Data Science Summit

The 2016 Data Science Summit just wrapped up in San Francisco and it was bigger and better than ever.  With over 1,300 attendees over two days, the conference combines business and academic leaders in a broad mix of machine learning areas – bringing together the latest in research with the state of the art in the industry. Many of the speakers are both leaders at key technology companies and involved with the top research institutions in the U.S.

Carlos Guestrin, with both Turi (previously Dato) and University of Washington, framed the world of intelligent applications including the opportunities for automating machine learning processes, creating online, closed-loop systems and increasing trust in machine learning applications.

Pedro Domingos, author of The Master Algorithm and also a UW professor, outlined the five schools of machine learning, their underlying philosophical approaches and the types of problems they best address.

Jeff Dean from Google highlighted their powerful new service TensorFlow along with its rapid adoption and independent forks in the open source community.  Jeff emphasized that TensorFlow has potential beyond the deep learning area as an end-to-end system for Machine Learning applications.

While Jeff highlighted several Google ML use cases, Robin Glinton from Salesforce.com and Jure Leskovec from Pinterest (and Stanford University) impressed the audience with detailed examples of how to build and continually improve intelligent applications.

Stepping back, there are several observations from this conference that generally confirm and expanded upon learnings from Madrona’s recent AI/ML Summit in Seattle.

  1. Deep Learning is both real and overhyped. Deep learning is very well suited for image recognition problems and is growing in areas like speech recognition and translation. However, deep learning is only one branch of machine learning and is not the best approach for many intelligent application needs.
  1. Greater agility is required for intelligent applications in production. Agility comes in many forms, including automating development processes like data munging and feature engineering. It also applies to model training and ongoing model iterations for deployed intelligent apps. Automated, end-to-end pipelines that continually update production applications are rapidly becoming a requirement. These applications, like the ones consumers experience with Netflix and Spotify recommendations are increasingly referred to as “on line” applications due to their agility in both making real time recommendations and bringing data back to update models.
  1. “Closed” loops and “humans-in-the-loop” co-exist. Many intelligent applications become business solutions by involving humans to verify, enhance or act on machine outputs. These “humans-in-the-loop” cases are expected to persist for many years. However, intelligent applications increasingly require automated, closed-loop systems to meet narrow business requirements for performance and results. For example, product recommendations, fraud predictions and search results are expected to be more accurate and relevant than ever and delivered in milliseconds!
  1. The strategic value of differentiated data grows by the day. Intelligent applications are dependent on data, metadata and the models this data trains. Companies are increasingly strategic about the data they collect, the additional data they seek and the technologies they use to more rapidly train and deploy data models. Google’s internal use cases leveraging data like RankBrain are expanding. And, their decision to “open source” data models for image and speech recognition built on TensorFlow is a leading example of engaging the outside world to enhance a model’s training data.

Overall, I found the conference extremely energizing.   There was substantial depth and a diversity of backgrounds, ideas and experiences amongst the participants. And, the conference furthered the momentum in moving from academic data science to deployed intelligent applications.

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Our Investment in Shyft – Transforming the Lives of Hourly Shift Workers

It is exciting and super fun for me to announce our seed investment in Shyft and to welcome the team to our Madrona family.

Shyft serves the huge market of shift workers worldwide – from retail to food service and beyond.  They make it very easy for these hourly workers to find someone to cover a shift. It is a real time communication system that has been broadly embraced by users.  Workers at 1/3 of Starbucks’ U.S. locations are already on the platform – all driven bottoms up rather than top down.

I first had the opportunity to meet Brett Patrontasch (CEO) and Daniel Chen (CTO) as a part of the most recent TechStars Seattle class. Right from my first interaction, Brett and Daniel were very impressive.  Their first company approached this market from a different angle and they learned a lot.  They were brave enough to shut it down and take what they learned to build something better.  Brett has a strong conviction about this market and is eager to leverage the expertise of his advisors.

Madrona is investing in this seed round because we like the team, see the massive market opportunity and are impressed by their early growth.  Shyft’s viral adoption and growth is impressive.   For example, Starbucks has hourly workers in their stores on Shyft actively trading shifts each day.  One person from McDonald’s tried the app and a day later 70 of her co-workers were on the platform. And though Shyft is primarily focused on workers, store managers are already starting to use the product as having a full staff is crucial for them.

I fundamentally believe that Shyft can build a massive platform and community for hourly shift workers.  The app is a game-changing way for hourly workers and managers to coordinate with one another and manage their busy schedules.  The mobile first approach speaks directly to the majority of this hourly smartphone-wielding audience.

This is the 5th company we have backed from the Seattle TechStars program.  TechStars and all the people who run and support it are an incredibly important part of the Seattle ecosystem that brings together great founders, angel and VC investors and experienced operators.  We are glad it is here and love participating.

I am looking forward to working with Brett, Daniel and the Shyft team as they build their team and business.

 

 

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Machine Learning and AI. Why Now?

Trying to go to the moon, but today we’re at the top of a tree

You can hardly talk to a technology executive or developer today without talking about artificial intelligence, machine learning, or bots. Madrona recently hosted a conference on ML and AI bringing together some of the biggest technology companies and innovative startups in the Intelligent Application ecosystem.

One of the key themes for the event emerged from a survey of the attendees. Everybody who responded to the survey said that ML is either important or very important to their company and industry.  However, more than half of the respondents said their organizations did not have adequate expertise in ML to be able to do what they need to do.

Here are the other top 5 takeaways from the conversations at the summit.

Every application is going to be an intelligent application

If your company isn’t using machine learning to detect anomalies, recommend products, or predict churn today, you will start doing it soon. Because of the rapid generation of new data, availability of massive amounts of compute power, and ease of use of new ML platforms (whether it is from large technology companies like Amazon, Google, Microsoft or from startups like Dato), we expect to see more and more applications that generate real-time predictions and continuously get better over time.  Of the 100 early-stage start-ups we have met in the last six months, 90%+ of them are already planning to use ML to deliver a better experience for their customers.

Intelligent apps are built on innovations in micro-intelligence and middle-ware services

Companies today fall into two categories broadly – ones that are building some form of ML/AI technology or ones that are using ML/AI technologies in their applications and services. There is a tremendous amount of innovation that is currently happening in the building block services (aka middle-ware services) that include both data preparation services and learning services or models-as-a-service providers.  With the advent of microservices and the ability to seamlessly interface with them through REST APIs, there is an increasing trend for the learning services and ML algorithms to be used and re-used as opposed to having to be re-written from scratch over and over again.  For example, Algorithmia runs a marketplace for algorithms that any intelligent application can use as needed.  Combining these algorithms and models with a specific slice of data (use-case specific within a particular vertical) is what we call micro-intelligence that can be seamlessly incorporated into applications.

Trust and transparency are absolutely critical in a world of ML and AI

Several high profile experiments with ML and AI came into the spotlight in the last year.  Examples include Microsoft Tay, Google DeepMind AlphaGo, Facebook M, and the increasing number of chat bots of all kinds.  The rise of natural user interfaces (voice, chat, and vision) provide very interesting options and opportunities for us as human beings to interact with virtual assistants (Apple Siri, Amazon Alexa, Microsoft Cortana and Viv).

There are also some more troubling examples of how we interact with artificial intelligences. For example, at the end of one online course at Georgia Tech, students were surprised to learn that one of the teaching assistants (named Jill Watson after the IBM Watson technology) they were interacting with throughout the semester was a chat bot and not a human being.  As much as this shows the power of technology and innovation, it also brings to mind many questions around the rules of engagement in terms of trust and transparency in a world of bots, ML and AI.

Understanding the ‘why’ behind the ‘what’ is often another critical component of working with artificial intelligence.  A doctor or a patient will not be happy with a diagnosis that tells them they have a 75% likelihood of cancer, and they should use Drug X to treat it. They need to understand which pieces of information came together to create that prediction or answer.  We absolutely believe that going forward we should have full transparency with regards to ML and think through the ethical implications of the technology advances that will be an integral part of our lives and our society moving forward.

We need human beings in the loop

There have been a number of conversations on whether we should be afraid of AI based machines taking over the world.  As much as advances in ML and AI are going to help with automation where it makes sense, it is also true that we will absolutely need to have human beings in the loop to create the right end-to-end customer experiences.  At one point, Redfin experimented with sending ML-generated recommendations to its users. These machine-generated recommendations had a slightly higher engagement rates than a users’ own search and alert filters. However, the real improvement came when Redfin asked its agents to review recommendations before they were sent out. After agents reviewed the recommendations, Redfin was able to use the agents’ modifications as additional training data, and the click-through rate on recommended houses rose significantly. Splunk re-emphasized this point by describing how IT Professionals play a key role in deploying and using Splunk to help them do their jobs better and more efficiently.  Without these humans in the loop, customers won’t get the most value out of Splunk.  Another company Spare5 is a good example of how humans are sometimes required to train ML models by correcting and classifying the data going into the model.  Another common adage in ML is garbage-in, garbage-out.  The quality and integrity of data is critical to build high quality models.

ML is a critical ingredient for intelligent applications.  But you may not need ML on day one.

Machine learning is an integral part and critical ingredient in building intelligent applications, but the most important goals in building intelligent apps are to build applications or services that resonate with your customers, provide an easy way for your customer to use your service, and continuously get better over time.  To use ML and AI effectively, you often need to have a large data set. The advice from people who have done this successfully before is to start with the application and experience that you want to deliver, and in the process think about how ML can enhance your application and what data set you need to collect to build the best experience for your customers.

In summary, we have come a long way in the journey towards every app being an intelligent app, but we are still in the early stages of the journey.  As Oren Etzioni, CEO of the Allen Institute for AI said in one fireside chat, we have made tremendous progress in AI and ML, but declaring success in ML today is like “Climbing to the top of a tree and declaring we are going to the moon.”

Previously published by TechCrunch.

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Dan Weld – The Real Threat of Artificial Intelligence

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‘Intelligent Apps’: Seattle Area At Forefront Of Next Big Thing

The Seattle Times 5/11/16 – Chances are the entity managing your favorite smartphone app or Internet service isn’t a person.

Algorithms are setting the price of your airline ticket and hailing your Uber driver. They’re placing the vast majority of stock-market trades.

And we’re only at the beginning of a transition that is going to make the algorithms behind the software people interact with better able to understand and react to humans, technologists at a gathering of Seattle’s burgeoning artificial-intelligence industry said Wednesday.

“Every application that is going to get built, starting today and into the future, is going to be an intelligent app,” said S. “Soma” Somasegar, a venture partner with Madrona Venture Group and a former Microsoft executive.

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Julie Sandler on Why Seattle is the Cloud Capital of the World–and a Great Place for Startups

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I’m always eager to learn more about the cloud — and also about the Seattle area, which has been my home for the past year and a half. So I followed up with Sandler to ask what (besides the presence of the two big cloud players) makes Seattle the cloud capital, and what that means to entrepreneurs.

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These Startups Are Selling Vinyl Records, Graphic Novels, and Indian Food Via Text Message

Bloomberg 4/14/16 – Back in the day, vinyl geeks schlepped to a record store or flea market and spent hours going through bins of dusty albums. ReplyYes is a whole lot easier.

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