There is a history of successful tech companies in Minnesota founded during recessions. These resilient startups didn’t just survive- they proliferated under pressure. Jamf, which recently raised $468M in an IPO, is the largest, latest, and highest-profile example. Jamf was founded in 2002 out of UW-Eau Claire, and is headquartered in Minneapolis. Jamf is now valued at $4.7B.
Looking into startups that were founded during recessions, you wouldn’t expect to find a list of successes. But by digging into public data on Crunchbase and in local publications, a surprising number of successful companies emerged that were started up in during the dot-com bust (2000-01), or the Great Recession (2007-2009).
Here are examples of startups founded during recessions in Minnesota, that found success.
|Company Name||Description||Year Founded, Names of Founders||Exit/Valuation/Funding Raised*|
|ProtoLabs||Automated quoting and manufacturing systems to produce commercial-grade plastic, metal, and liquid silicone rubber parts||1999 (significant growth in 2000-2001),|
|Successful ~$70M IPO in Feb 2012 with a current Market cap of $3.2B|
|Modern Survey||Provider of employee survey services. The company provides employee survey and talent analytics service that enables companies to understand their workforce and drive business performance by creating an aggregated, holistic view of the employee lifecycle— from the candidate experience, new employee onboarding to engagement, and exit interviews.||1999 (significant growth in 2000-2001),|
Don MacPherson and Patrick Riley
|Acquired by Aon Hewitt in Feb 2016. Terms were not disclosed.|
|NativeX (aka W3i and Freeze.com)||PC publishing platform and mobile content and app delivery||2000,|
Ryan and Rob Weber
|We peaked in 2012 at $70 million in revenue and $10 million in EBITDA, with 170+ total employees.|
|Inbox Dollars||Online rewards club that pays members cash for their online and mobile activities. They reward members for their everyday activities such as reading emails, taking surveys, playing games, and signing up for offers.||2000,|
|Acquired by Prodege in May 2019 for an undisclosed amount|
|Ability Network||Connecting thousands of hospitals, skilled nursing facilities, home health agencies, clinics, and tens of thousands of physicians across the country with each other, and with the nation’s largest payer: Medicare.||2000,|
|ABILITY Network was acquired by Inovalon for $1.2B on Mar 7, 2018|
|GovDelivery||As the number one referrer of traffic to hundreds of government websites, GovDelivery enables public sector organizations to connect with more people and to get those people to take action.||2000,|
|GovDelivery was sold to Vista Equity Partners in a $153 million deal in Oct 2016|
|Code42||Code42 provides data loss protection, visibility, and recovery solutions.||2001,|
Brian Bispala, Matthew Dornquast, Mitch Coopet
|Code42 has raised $138M total, through their Series B round in October of 2015|
|CVRX||Medical device company that develops implantable technology for the treatment of high blood pressure||2001,|
|$340.6M total raised in 8 rounds|
Most recently raised $93M in a Series G in 2019
|Jamf||World leader in macOS and iOS management with offices around the world. They deliver, support and service the solution for Apple management needs in education and business.||2002,|
|Raised $468M in 2020 IPO. Current Market cap of $4.7B|
|Compellent||Develops and provides enterprise storage software and hardware solutions that automate the movement and management of data||2002,|
|Acquired by Dell in 2010 for $820M cash|
|DoApp||Mobile ad network and consumer and business app developer for websites, desktops and mobile devices||2007,|
|Acquired by Newscycle Solutions (Now Naviga) in 2016 for undisclosed amount|
|ZipNosis||Hospital and healthcare company that specializes in online diagnosis and triage, telehealth, and virtual care||2008,|
|Zipnosis has raised $25M in funding total through a Series B round|
|Calabrio||Delivers workforce optimization (WFO) and analytics solutions that elevate the customer experience and drive strategic business growth||2008,|
|Calabrio was acquired by Kohlberg Kravis Roberts (KKR) in 2016 for $200M|
|SportsEngine||The leading provider of web software and mobile applications for youth, amateur and professional sports||2008,|
Carson Kipfer, Greg Blasko, Justin Kaufenberg
|Acquired by NBC Sports in 2016 for an undisclosed amount|
|Field Nation||World’s most active Freelancer Management System (FMS) ensuring successful collaborations in today’s rapidly changing world of work. Field Nation enables companies to find, hire and pay contractors anywhere and easily manage their workforce.||2008,|
|Raised a total of $30.2M|
|HomeSpotter (aka Mobile Realty Apps)||Helps brokerages, agents, and MLSs build relationships amongst one another and with clients. Allow agents to collaborate with ease, work on the go, and increase their productivity. Brokerages and MLSs are better equipped to support and retain agents and help grow their businesses.||2009,|
|HomeSpotter has raised $3.9M in funding|
You can see that I’m on this list with my brother, Ryan Weber, with our former company NativeX. Several other founders from this list are people we have recruited as operating partners for Great North Labs, such as Joe Sriver, Carson Kipfer, Mitch Coopet, Brian Bispala, Patrick Riley, and Daren Cotter.
Capital Efficiency and Resilience
Resilient startups and founders find ways to adapt, persist, and succeed in spite of the challenges they face. The startups on this list found success coming out of challenging times with limited capital availability.
Across the entire Midwest, both the quantity and value of early-stage deals went down during the past two recessions. You can see in the chart below that the dot-com bust in the late 90s led funding to drop off a cliff, with a long climb back up hindered by the Great Recession in 2008-2009.
We live in a region where startups have to be capital efficient. We simply don’t have the amount of early-stage capital other regions get. This leads to more competition for capital, and to higher capital-efficiency among startups.
“This is good news for investors, as the returns in the Midwest are more favorable for investors compared to investing in VC in other regions.”
While that means the Midwest’s 10% of VC-backed startups receive under 5% of funding, it also means that the Midwest startups that make it to exit return the highest median multiple on investment (5.17x). This is good news for investors, as the returns in the Midwest are more favorable for investors compared to investing in VC in other regions. But, it puts greater demands on early-stage startup operators, who need to operate in a way where they can maximize their chances of success with the capital available.
How do you Scale Resilience?
Great North Labs’s approach to early-stage investing includes cultivating resilience in the regional startup ecosystem, identifying it in opportunities, and developing it into our portfolio startups.
When identifying opportunities and developing resilience in portfolio companies, in addition to our own experience, we include resilient founders with startup success in Minnesota and the Upper Midwest as operating partners. We believe that successful founders and operators make the best early stage investors because they’ve had to scale an emerging technology company before. We also believe that the best way for founders to grow is to learn from the experience of others who have been in their shoes.
By having startup operators who have not only been there before with a startup, but have found a way to thrive coming out of tough times, and have done it all in our region, facing the same regional capital availability issues that persist today, are invaluable when it comes to providing guidance for other early-stage founders in Minnesota and the Upper Midwest.
Using this approach Great North Labs is:
- Building capacity in Minnesota for developing breakout startup opportunities
- Identifying and investing in breakout startups opportunities early on, in Minnesota and the Upper Midwest
- Guiding portfolio companies to success using our operating experience and networks, and the operating experience and networks of our operating partners
Our plan and hope is that after the current recessionary period, we will be able to look back over our portfolio companies and at other Minnesota startups fighting through these times, and add many more to this list of successes.
IoT and Analytics – Organizing the Industrial Internet
Figure 1: The third revolution: IoT and Analytics. [Image credit: General Electric]
The Evolution of IoT – Where we Came From
The first generation of IoT systems (IoT 1.0) was built mostly with data collected from IP-based sensors by monitoring applications. Whether standalone or embedded in phones, low-cost sensors, compact packaging and distributed power enabled new endpoints and systems. These monitoring applications served needs such as asset tracking, fitness monitoring, mood lighting, physical security, and others.
The second generation (IoT 2.0) leveraged the capabilities of infrastructure tools such as edge gateways, publish-subscribe buses, data warehouses, and API-based integration. The edge gateways allowed IP network segments to connect to sensor bus segments using a diverse set of protocols (e.g., RS-422, RS-485, BACnet, CAN, Fieldbus, Hart, LonWorks, Profibus, Seriplex, Zigbee, Z-wave, and others). The gateways extended the reach of these IoT systems across the many incumbent protocols and enabled the integration of the IP segments with legacy systems. The publish-subscribe buses made data-driven software architectures easier to implement and scale. The data warehouses enabled the integration of structured, semi-structured and unstructured data. The integration APIs enabled ingestion of data at scale. Together, these new building blocks enabled larger-scale IoT applications such as home monitoring, smart metering, power grid management, parking systems, next-generation environmental controls in buildings, windmill farms, warehouse management, etc., with varying degrees of commercial success based on the benefit provided vs. the insertion economics of each use case.
With the larger data sets enabled by frameworks such as Hadoop and big data software such as Pivotal, the third generation of IoT systems (IoT 3.0) is integrating analytics for decision-making. These analytic platforms enable the processing and visualization of the IoT data sets. The large data sets and analytic tools identify aberrations with higher levels of confidence (statistical power) and detect ‘signals’ not seen before, they have lower detection thresholds, greater measurement sensitivity, and higher accuracy.
Applications based on these capabilities range from physical security for homes, buildings, and warehouses; to detection of diseases like lung disease, cancer metastases, or cardiac arrhythmias (see the Mayo Clinic and AliveCor’s recent work); and complex chemical analysis as in rare earth element detection. The availability of computing platforms at the ‘edge’ (e.g., gateways) enables distributed/local analysis.
“The Internet of Things is giving rise to a tsunami of data,” said Great North Labs advisor Ben Edwards (founding team member of home automation pioneer SmartThings). “The billions of residential sensors in people’s homes and the personal sensors on their bodies are sources of data of value to each of us, and depending on what we make available to others, to family members for our safety and well-being, to the retailers we buy from, to the health practitioners who take care of us.”
The proliferation of machine learning algorithms with new programming environments such as Python and dataflow libraries such as TensorFlow has opened up a wide range of new applications. These include anomaly-based security alerts, health and fitness monitoring, genomic analysis and biomarker detection for disease prediction, drones, and self-driving cars.
The addition of machine learning libraries to established platforms such as Matlab, R, SAS, and SPSS, is enabling insertion of machine learning into legacy applications.
The availability of these tools in public and private clouds has made their accessibility and deployment even easier.
Together, with supervised and unsupervised learning, the machine learning software is processing data sets with high data dimensionality, like those from mining, voice processing, drone navigation, and self-driving cars.
The integration platforms and IP-based communication are also enabling the integration of the IoT world with the enterprise world, making applications possible across hybrid computing and control environments such as airports, buildings, cargo ships, factories, hospitals, refineries and oil rigs. While this creates security issues for the enterprise as well as control systems, solutions such as micro-segmentation of hybrid systems are beginning to emerge.
Tomorrow – The New Startups
With products from companies such as Nvidia, Intel, Qualcomm, Broadcom, and now Google, real-time computing power is becoming available at the edge. With easier integration and low cost, it is becoming embeddable at sensing endpoints for applications such as drones, self-driving cars and trucks, personal walking/talking robots, personal assistants, point-of-care diagnosis, no-POS retail, smart logistics, and smart city applications from parking lots to secure airports and intelligent highways.
Beyond analytics and monitoring, this fourth generation of IoT systems will be able to use analytics and machine learning for controls.
What is the outlook for the adoption of these applications? The answer is: it depends. And it is best found through analogies.
How confident do today’s chess masters or masters of the game of Go today feel betting against the machine? IBM’s Deep Blue computer beat chess champion Garry Kasparov in 1997. And as Great North Labs advisor Mitch Coopet (CEO of AI-focused Aftercode) points out, “Since 2016, Google’s Alpha Go platform has won against several Go masters using improved deep learning techniques.”
Or, when will the day come when your x-ray machine will have better diagnostic accuracy than your radiologist? Ahem, that day is already here.
Or, when will Alexa be able to detect tonal infection to assess mood? Based on indications from Amazon and makers of social robots and AI assistants, sentiment analysis will progressively improve the way machines will interact with humans.
Or, when will we be comfortable with self-driven cars? Completely autonomous navigation in 5-7 years may be unlikely, but it is equally likely that in 20 years, self-navigation will become a required safety feature for new cars.
Given the range of answers above, it is not a matter of if, but when, that real-time control using machine learning will be common. These systems will be able to handle use cases as diverse as (i) detecting rare earth minerals to help navigate the earthmoving equipment towards richer ore in a mining operation, (ii) making real-time sweeps at airports to pinpoint explosives across large masses of people, luggage, and infrastructure, (iii) ensuring that the robots deployed in automotive assembly stay within the extremely tight tolerances of frame construction, and (iv) predicting the failure of a component in a high value CT scanner or remote ATM to dispatch the skilled repairman in a timely way to avoid downtime (a business that Great North Labs has invested in).
The Innovation Ecosystem of the Industrial Internet
“Business Insider projects that there will be 55 billion IoT devices operating in the world by 2025, impacting a broad set of industries including automotive, consumer products, electronics, medical devices, and industrial equipment,” notes Great North Labs advisor Robert Bodor (Vice-President and GM, Americas, at Protolabs).
At Great North Labs, with an ambitious vision, we aim to help build the innovation ecosystem of the Industrial Internet visualized by IoT 3.0. This is because we believe the ingredients to build it are uniquely within reach for us.
The three pillars of any tech-enabled disruption are entrepreneurs/developers, adopters/enablers, and capital.
- Entrepreneurs/developers. The Upper Midwest created the industrial enterprise. Companies such as 3M, Caterpillar, Emerson, Ford, GM, Honeywell, Johnson Controls, Rockwell, Toro, and many others, have been in the industrial enterprise as their core business for several decades. Their alumni understand the problems and opportunities of the industrial enterprise unlike any others in any other region of the world. The hungry entrepreneurs studying machine learning, paired with vertical experts who have worked on these problems, comprise the ideal startup teams to build the IoT 3.0 applications. The Upper Midwest uniquely provides this talent.
- Adopters/enablers. While the industrial enterprise companies themselves may have limited appetites for leading innovation, they understand that market inflection is around the corner, and they are prepared to have their customers lead the way to achieving market alignment. Partnerships with these companies through co-investments, pilots, and sales affiliation to reach their customers and insert the innovations with minimal risk is the most effective path to adoption.
- Capital. Channels for entrepreneurial capital include venture funds, incubators and accelerators, and corporate investment funds. Of these, we believe that the first two provide the most efficient path for innovators, and that they create the on-ramp for in-house corporate teams to acquire well-formed companies that have demonstrated a strong product-market fit and, through later-stage funding, have even scaled their businesses. The Silicon Valley startups of yesterday that comprise some of the biggest market caps today have done exactly that. We believe that over an extended period, the Industrial Internet can deliver similar outcomes in the Upper Midwest.