What if you could use machine learning to predict the next “big thing”?
Trendsetters could use this technology to stay ahead of the pack. Job candidates could use it to identify attractive employers. And investors could use it to make a lot of money.
CircleUp has developed this technology to identify consumer brands with “breakout” potential. Launched in 2012 as an intermediary platform for entrepreneurs to raise “crowd-funded” capital, CircleUp initially used big data to help investors connect to potential investments. Overtime, CircleUp developed a proprietary machine learning platform called “Helio” which powers its Credit fund and its latest initiative launched in 2017: the CircleUp Growth Fund, a $125 million investment fund. CircleUp Growth Fund focuses on early stage consumer companies. Its investments include Halo Top ice cream and HUM beauty.
Machine learning is important to CircleUp’s process improvement for two reasons: (1) it provided the Company with the confidence to evolve from an investment intermediary to a credit fund and equity investment firm and (2) it vastly improves upon existing investing methodologies.
Increasingly, there are a growing number of small consumer brands because of low barriers to entry and changing consumer preferences and a growing number of data points about consumer behavior as a result of improving tracking technologies. In my previous role as an investor in consumer businesses, I spent many hours sifting through databases to identify interesting companies and researching countless data points to assess each company, using everything from financials to social media sentiment and customer reviews.
Machine learning provides CircleUp with two primary competitive advantages: (1) the capacity to monitor and analyze millions of companies, which would otherwise be incredibly resource-intensive and (2) the ability to use many disparate consumer data points to identify companies with “breakout” potential while minimizing human bias. Once the investment is made, the algorithms may also help identify potential operational improvements to improve company performance.
The key issue with Helio’s machine learning is the breadth, depth and quality of the data that informs its algorithms. CircleUp tracks 1.4 million brands and pulls from countless data points that fall into three buckets: public, partnership and practitioner data. Per the Company’s website, the “outcome is a knowledge graph of the entire consumer space that maps reviews, labels, social posts, pricing, location, and more to individual products and brands”.
In the short term, CircleUp is focused on continuous improvement of its data, given numerous errors associated with the sheer scale and diversity of information being processed, which results in faulty and inconsistent data. CircleUp addresses these errors using high level rules and processes. For example, CircleUp applies natural language processing to filter out companies that are not relevant or do not focus on the US or Canada, where they are currently focused.
In the medium term, the Company is focused on improvement of its algorithms and expansion of its data. CircleUp has begun to grow its partnership data sources. For example, it struck a strategic partnership with Nielsen in December 2017, providing Helio with access to Nielsen’s rich retail sales data. The Company is also growing its practitioner data, which includes information from the companies that apply to CircleUp’s crowdfunding function and its conversations with entrepreneurs.
I’d recommend management focus on improving its machine learning algorithm by growing its data sources to stay ahead of its competition in the short term. In particular, I’d focus on partnership and practitioner data which are proprietary and provide a more sustainable competitive advantage.
In the medium term, I’d recommend that the Company test and refine its algorithm as its investments prove out over time. The Company has a critical first mover advantage and should remain focused on using its learnings from existing investments. For example, they could refine the attractiveness of different categories of investments or perhaps build in data points around qualitative features, like characteristics of its management teams or organizational design. Interestingly, the data suggests this data-driven method of investing dramatically improves the diversity of companies getting funded. According to Fast Company, 35% of the companies on the CircleUp platform are women-led, or roughly 17 times more than the industry average. The Company should preserve this unique ability to identify attractive investments based on data, while also exploring its ability to identify potential breakout brands amongst traditionally underfunded companies.
CircleUp’s business model and the disruption to existing private investors raises numerous questions. How sustainable is this competitive advantage? What might this machine learning platform get wrong or where might biases be introduced into the system? Can this machine-based learning be applied to investing in other industries, where customer data may be less prevalent or easy to come by?
 “Backed With $1.5M, Circleup Aims To Be The Angellist For Consumer And Retail Startups”. 2018. Techcrunch. https://techcrunch.com/2012/04/18/circleup/
 “Circleup Announced $125 Million Venture Fund”. 2018. Techcrunch. https://techcrunch.com/2017/10/31/circleup-announced-125-million-venture-fund/.
 Consumer Brands Seeking Innovation Reach Out to Emerging Companies. Garland, Russ. The Private Equity Analyst; New York (Dec 2014).
 “Circleup”. 2018. Circleup. https://circleup.com/helio/.
 “Circleup And Nielsen Collaboration Fuels Growth Of Early-Stage Consumer Products”. 2018. Prnewswire.Com. https://www.prnewswire.com/news-releases/circleup-and-nielsen-collaboration-fuels-growth-of-early-stage-consumer-products-300570909.html.
 “This Investment Platform Funds More Diverse Companies By Focusing On Data, Not Founders”. 2018. Fast Company. https://www.fastcompany.com/40527386/this-investment-platform-funds-more-diverse-companies-by-focusing-on-data-not-founders.