When will current Series A startups raise their Series B? 

Someone recently asked me this question, so I built a prediction model to help generate an answer. Keep reading as I discuss my process and give actual Series B predictions. 

Where to begin? 

I began by pulling together metrics that made sense for a company trying to raise a Series B. My thinking first went to a company’s finances and whether they seem attractive to venture investors.   

  • – The company should achieve $10M in Annual Recurring Revenue (ARR).  
  • – The startup should grow 50% over the last two years with a 70% gross margin and 90% retention. 
  • – The company should have a diversified customer base and a strong LTV/CAC Ratio (Lifetime Value / Customer Acquisition Cost).  

The issue here is that we are talking about private companies, and their financial information is not readily available. So, I started speaking about this more and more with my team and other fund managers. These discussions presented more data points that we look at:  

  • – The amount of cash raised in the last financing round and total?  
  • – What is the current burn rate?  
  • – How many founder shares have been diluted?  

In reality, it is difficult to predict when a company would or could raise their Series B financing round, even if all of these variables are known. This problem motivated me to create a prediction model using data science and machine learning techniques.   

Building the dataset 

To build a predictive model using machine learning, you need to start with a dataset. I began scraping and extracting web content of over 700+ companies that raised a Series A and/or a Series B round in the last six years. Once this was generated, I cleaned the data by removing redundant variables, and also created new variables like the following:  

  • – Year Founded  
  • – Length of time between Year Founded and Series A  
  • – Difference between Series A and Series B pre-money valuations  
  • – Length of time between Series A and Series B  

(Actually, there were a lot more variables 😊 – appropriately showcased in Figure 1.) 

I used these inputs in a random forest regression analysis. The table below is a sample representation of how I trained the model. There are five companies below in Table 1 that have all raised a Series A and Series B rounds within the last six years. This makes it easy compare differences in employee count, pre-money valuation, and time between the Series A and Series B rounds. 

Company Name Employees during the round Raised to Date (USD Million) Year Founded Pre-money Valuation (USD Million) Series Deal Date Round Size (USD Millions) Post Valuation (USD Millions) 
15Five 37 12 2011 43.9 Series A 29-Nov-18 8.2 52.1 
15Five 160 42.7 2011 102 Series B 17-Jun-19 30.7 132.7 
Abstract 17 8.25 2015 19 Series A 19-Jun-17 25 
Abstract 32 24.25 2015 60 Series B 24-May-18 16 76 
ActionIQ 40 14.7 2014 45 Series A 3-Apr-17 13 58 
ActionIQ 40 44.7 2014 104 Series B 23-Oct-17 30 134 
Algolia 21 21.21 2012 50 Series A 6-May-15 18.39 68.39 
Algolia 115 74.2 2012 255 Series B 8-Jun-17 53 308 
Allbirds 15 9.95 2014 25 Series A 7-Sep-16 7.25 32.25 
Allbirds 80 27.45 2014 350.2 Series B 5-Sep-17 17.5 367.7 
Table 1 – Historical Table of companies who have raised Series A and Series B 

In looking at the random forest regression’s feature importance weighted scores, the following filters were given the highest weight for predicting an upcoming Series B Round. 

  • – Difference in numbers of employees from Series A to Series B 
  • – Difference in mark up in Pre Money-Valuation from A to B  

The new addition of capital is directly linked to an incredible markup in the employee count. A startup would receive a ton of inbound interest after publishing that they have raised a new round. See the histogram below for a complete list of weighted scores. 

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Table 2 – Random Forest Feature Importance Weightage 

Additional insights from the data: 

The three main data points I found interesting are: 

  1. – Within 3 Years of being founded, 83% of startups raise their Series A, and 66% of these startups go on to raise a Series B.  
  2. – Series A startups take 10-18 months before raising their Series B financing round. 
  3. – A little less than half of Series A startups (44% to be precise) have a pre-money valuation of $20M-$30m before raising their Series B Financing round. 
Table 3 – Histogram of Difference between Year Founded & Series A (in years) 
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Table 4 – Histogram of Difference between Series A & B (in months) 

Table 5 – Histogram of Pre Money-Valuation 

So, who is likely to raise? 

This is the real reason why you kept reading. After running the prediction model – below is the group of Series A companies that could end up raising their series B Round in the next four quarters starting in Q2 2021. 

It would be fair to assume that the model does not take into consideration financing rounds that are pre-empted or companies coming out of stealth raising large Series A rounds. Startups closed out 2020 in a much stronger position than the one they started the year in, with global venture funding up 4 percent year over year to $300 billion. Moreover, deal volume has grown significantly through the decade. For Instance, 2011 featured 10,000 rounds from seed to late-stage mega-rounds, and now, the deal volume is close to 30,000 rounds (seed to late-stage) since 2017. 

Figure 1 – Global Venture Dollar Trajectory 

However, after looking at the results and the histograms above, the model could do well. 

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 *As I was writing this blog post, a Techcrunch article came out announcing that Privacy rebranded to Lithic and raised a $43M Series B round. According to my prediction, I was off by a quarter.  

Good luck investing! 

  1. I hope you enjoyed reading. I would love to continue the conversation if you want to play around with the model or if you are one of the companies mentioned in the prediction analysis, and I got it wrong.  

To learn more about me and what is going on at Arc Ventures follow us both on Twitter: @MMaulik11 and @Arc_Ventures. Shoutout to Rachel Payne and Eric Kohlmann for helping me structure the blog post.

________________________ 

About the Author: Maulik Mehta is an Analyst at Arc Ventures. His role involves deal sourcing, conducting due diligence and creating value-add for Arc’s portfolio companies through customer and venture firm introductions. If you are a founder or a startup in deep tech, enterprise, automation, digital health, or fintech, please feel free to reach out at mmehta@arc-vc.com.    

About Arc Ventures: Arc Ventures is a New York based venture capital firm invested in over 50 funds and startups across the US, Israel and Europe. The firm prefer to invest predominantly at the early stage (pre-seed to series B) and reserves a smaller share for late stage/ pre-IPO companies across Enterprise SaaS, FinTech + Prop-Tech, and FrontierTech. 

About the Author Maulik Mehta

Maulik Mehta is a Venture Analyst at Arc Ventures. His role at Arc involves sourcing new deals, conducting deep dives on prospective investments and assisting portfolio companies with follow on funding. Before his work at Arc, Maulik was an Associate at Mumbai Angels Ventures, India’s most renowned angel investing network. He also gained operational experience at Azuro, a property management startup that later got acquired by India’s largest real estate brokerage company. Maulik holds a Masters in Finance from Fordham University, where he was the Founding President of the Venture Capital and Private Equity Club. On weekends, you can spot Maulik at Smithfield Hall NYC, showcasing his support for Roger Federer and his boyhood soccer club – Manchester United.

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