Starfish Ventures

Total investments

51

Average round size

5M

Portfolio companies

36

Rounds per year

2.32

Lead investments

23

Follow on index

0.29

Exits

13

Stages of investment
SeedEarly Stage VentureLate Stage Venture
Areas of investment
BiotechnologyInternetSoftwareAnalyticsInformation TechnologyMobileHealth CareEnterprise SoftwareHardwareTelecommunications

Summary

Starfish Ventures appeared to be the VC, which was created in 2001. The main department of described VC is located in the Melbourne. The venture was found in Australia.

The standard case for the fund is to invest in rounds with 2-3 partakers. Despite the Starfish Ventures, startups are often financed by Startmate, Kleiner Perkins, Khosla Ventures. The meaningful sponsors for the fund in investment in the same round are Technology Venture Partners, 500 Startups, Versant Ventures. In the next rounds fund is usually obtained by Inbio Ventures, DFJ, Wesfarmers.

The typical startup value when the investment from Starfish Ventures is 5-10 millions dollars. Considering the real fund results, this VC is 35 percentage points more often commits exit comparing to other organizations. The top activity for fund was in 2012. This Starfish Ventures works on 5 percentage points less the average amount of lead investments comparing to the other organizations. The fund is constantly included in 2-6 investment rounds annually. Deals in the range of 5 - 10 millions dollars are the general things for fund. The increased amount of exits for fund were in 2010.

The current fund was established by John Dyson, Michael Panaccio. The overall number of key employees were 2.

We can highlight the next thriving fund investment areas, such as Biotechnology, Telecommunications. For fund there is no match between the location of its establishment and the land of its numerous investments - United States. Among the most popular portfolio startups of the fund, we may highlight ICIX, ZoomSystems, Ausra. Besides, a startup requires to be at the age of 4-5 years to receive the investment from the fund. The fund has no specific favorite in a number of founders of portfolio startups. When startup sums 5+ of the founder, the probability for it to get the investment is little.

Show more

Investments analytics

Analytics

Total investments
51
Lead investments
23
Exits
13
Rounds per year
2.32
Follow on index
0.29
Investments by industry
  • Software (25)
  • Information Technology (8)
  • Enterprise Software (8)
  • Internet (7)
  • Analytics (7)
  • Show 65 more
Investments by region
  • United States (25)
  • Australia (25)
  • Philippines (1)
Peak activity year
2012

Discover reliable insights

Leverage validated data, identify key contacts and secure funding opportunities for your business.

Quantitative data

Avg. startup age at the time of investment
15
Avg. valuation at time of investment
4M
Group Appearance index
0.55
Avg. company exit year
9
Avg. multiplicator
0.17

Need more data?

Get access to full data about investors, including their team, contact information, and historic data.

Latest deals

Company name Deal date Industry Deal stage Deal size Location
Aktana 24 Jun 2013 Analytics, Artificial Intelligence, Machine Learning, Life Science Early Stage Venture 5M United States, California, San Francisco
BugHerd 25 Jan 2012 Internet, Software, Information Technology, SaaS, Web Development Seed 550K Victoria, Melbourne, Australia
German Bionic 01 Sep 2022 Artificial Intelligence, Robotics, Open Source, Wearables, Industrial Automation Early Stage Venture 10M Bavaria, Augsburg, Germany
Isopanel Ltd. 01 Aug 2002 Manufacturing, Building Material Seed Volta Region, Ghana, Ghana
Mallfort 01 Oct 2015 E-Commerce, Retail, Mobile Payments, Real Time, Location Based Services, Local Shopping Seed 100K Delhi, Delhi, India

Similar funds

By same location

By same geo focus

By doing lead investments

How we get our data

At Unicorn Nest, we combine cutting-edge technology with human expertise to build one of the most reliable venture capital databases in the market. Our process begins with automated AI-enhanced data collection, leveraging the full potential of Large Language Models (LLMs).

Later, our team of analysts takes it further with manual verification, using proprietary tools for data cleaning and validation to ensure accuracy and reliability. We cross-check and enhance our findings through press and media monitoring, integrating information from trusted news outlets and venture capital aggregators. Finally, we stay ahead of the curve by monitoring social networks like LinkedIn and X.com.