Connecting Startups and Investors in Mobile Technology: A Network View

Mobile technology is booming. Investments in startups operating in this market are soaring and rose from $160 million in 2004 to a staggering $6,6 billion in 2013. 2014 is set to shatter all records and has already seen investments of over $23 billion during the first 6 months alone. Funding originates from a wide range of investors, varying from private angel investors to large venture capitalist and private equity firms. In most cases, multiple investors team up in consortia to complete the funding rounds. How our startups and investors in mobile technology connected to each other based on these funding rounds? Let’s turn to network analytics to find out.

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Written by André Vermeij, Kenedict Innovation Analytics

The mobile market is diverse and has traditionally consisted of a combination of giant telecom providers, mobile infrastructure specialists, handset manufacturers, et cetera. The introduction of the smartphone has led to a new wave of startups focusing on application development entering the market. Investors are eager to fund the potentially groundbreaking apps developed by these startups and have generously been doing this over the past few years. Funding schemes vary from individual ‘angel investors’ providing initial seed capital to young startups to larger multi-million dollar rounds led by consortia of venture capitalist firms and private equity specialists.

A relatively straightforward approach to analyzing funding data is to provide a wide range of charts and tables detailing funding evolution over the years, listing top investors and funding rounds, and so forth. What if we visualize all startups and investors in mobile technology as a network instead? This has the potential to provide a new perspective on the funding landscape in mobile technology and serve as a strong complement to traditional analysis.

Let’s take a look at all funding activity in mobile technology in the last 2,5 years (from January 2012 until now) to provide an initial view on this. Data originate from CrunchBase (http://www.crunchbase.com), a website dedicated to collecting and disseminating all information on startup activity. Data can be accessed either from their API or downloaded as a monthly Excel report.

The above network is represented as a so-called two-mode network, with square nodes representing investors and circle nodes representing startups. An investor is connected to a startup when it has participated in at least one funding round for that startup. Nodes are sized based on their betweenness centrality, a measure of how often a node appears on the shortest path between any other two nodes. Larger nodes thus occupy key positions in the network and can be seen as hubs. In this way, we’ll be able to see exactly which startups and investors are most central in the network. Node colours represent clusters of startups/investors and are determined using the Louvain community detection algorithm (Blondel et al., 2008). The network is interactive, so you can zoom in, hover any node to see further details and click nodes to see their connections¹. The network contains 886 nodes (613 investors and 273 startups) and 1009 edges between them. Only the largest interconnected cluster is shown; isolated startups/investors are not visible.

The startups with the highest betweenness centrality are:

The investors with the highest betweenness centrality are:

As you can see in the visualization, nodes have a high betweenness centrality when they are at the intersection of different clusters. In the case of investors (square nodes), this implies that an investor with a varied portfolio of mobile technology investments can serve as a key connector based on its participation in multiple sub-communities of the network. A good example is Google Ventures, which invested in a total of 13 startups over the past 2,5 years and thereby secured its number 1 position in terms of its ability to connect other nodes to each other indirectly. Startups (circle nodes) with a high centrality are the result of activity of their investors in funding rounds with other startups during the period analyzed. For example, startup Applauze attracted a total of 10 investors, of whom 6 also invested in various other startups present in the network.

Network analyses such as the above can help young startups to get a clear and objective view on the funding activity in their market and obtain key information on the relationships between investors and clusters of startups. Similarly, investors can use such information to locate potential startups to invest in or find other investors to team up with in subsequent funding rounds. A network-based perspective can thus serve as a strong complement to traditional funding analytics by focusing on actual relationships between actors.