Are we there yet? Part I
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Are we there yet? Part I

In the venture business, timing is critical. As we think about the next generation of consumer technology platforms, one of the biggest questions we ask is, “when in a platform’s lifecycle should we start making early stage investments in applications?” While Samsung often leads the way in building and deploying new hardware platforms, we at Samsung NEXT invest in software businesses built on top of these emerging platforms. Industry thought leaders continually debate what the next major consumer platforms will look like. And while investors continually struggle to time these markets correctly, surprisingly little research has been done on venture investment timing with respect to consumer adoption of emerging technology platforms.

We set out to identify patterns in when successful venture investments were first made within each of the seminal consumer technology platforms over the past few decades. We analyzed six platforms (identified in Figure 1) and used the lessons learned from them to better forecast when to invest in startups built on the next generation of consumer technology platforms.

Fig 1. Scope of Consumer Technology Platforms Studied

Our research resulted in two key findings:

1. Consumer technology platform adoption is accelerating (a finding that has been well documented by other as well)
2. New consumer tech markets are ready for successful venture investments earlier in their adoption cycles

In Part II of this series, discuss specifically what we think these trends mean for the future.

Consumer Technology Platform Adoption

Technology is being adopted faster today than in previous decades. What factors are driving this phenomenon? Consider how long it took for each of the following consumer platforms to grow from 2% to 70% of total U.S. adoption:

Fig 2. Reduction in Time Required to Scale a Consumer Technology Platform [1]
We attribute this acceleration to three primary factors:

1. Exponential Technological Advancements – These trends are widely discussed in technology literature and media, including Moore’s Law’s impact on processing power over time, as well as similar impacts on the decreasing cost of storage, and increasing speed of networks over time. The following figure illustrates certain order-of-magnitude shifts that have occurred with each successive platform:

Fig 3. Exponential Improvements in Computing Performance and Cost Over Time [2]
2. Network Effects – Also known as Metcalfe’s Law. Describes how the value of a network increases with the square of its nodes; network effects were critical in driving adoption of the early Internet and became even more powerful in driving the adoption of the mobile web via smartphones.

3. Consumer Comfort – As technology has become more deeply entrenched into all aspects of everyday life and younger generations have grown up in a ‘tech native’ environment, studies show that consumers are becoming more trustworthy of technology and the startups that leverage it. [3]

Investment Timing with Respect to Consumer Adoption

In each of the six markets evaluated, we modeled consumer adoption using an S-Curve adoption model—also known as a logistic function—commonly applied to technology markets.

Fig 4. Representative S-Curve Adoption Model [4]
This approach allowed us to identify deeper insights on key inflection points in each platform’s adoption. The following figure shows the adoption of smartphones in the US. Blue dots represent the actual data points that we collected for customer adoption, while the red line represents our “best fit” S-curve approximating customer adoption. In addition, we show the 2nd derivative of adoption in yellow (plotted against the right-hand axis), which represents how quickly adoption is changing in each year; we dubbed this metric “Market Acceleration.” We use the peaks and troughs in this market acceleration curve to identify key inflection points in a technology platform adoption cycle.

Fig 5. Anatomy of the Smartphone Adoption Curve [5]
Expanding our analysis to all six platforms, we can quickly see how consumers have adopted various platforms:

Fig 6. Compressing Adoption Cycles of Consumer Technology Platforms Over Time [1]
The next questions was, “At which point in these adoption cycles were the first successful venture investments made?” We defined successful venture investments as institutional investments (Seed or Series A) in software businesses built on a specific platform that returned greater than a 10X cash-on-cash return. In the following figure we have overlaid the first successful venture investments made in applications on each platform.

Fig 7. Summary of First Successful VC Investments in Consumer Technology Platforms [1]
When we plotted the percent customer adoption of each platform in the year of its earliest successful investment, we found a surprisingly consistent trend: a pattern of successful investments being made earlier and earlier in customer adoption cycles.

Fig 8. Customer Adoption at the Time of the First Successful VC Investment in Each Platform [1]

This plot makes clear that there has been a massive shift over the past 30 years in when consumer technology platforms are capable of supporting successful venture investments.

In the 1980s, the first successful venture investments in software businesses were not made until about ~20% consumer adoption (as a percent of the maximum adoption in those respective markets). For smartphone and 4G platforms, successful investments happened much earlier, with as little as ~7% adoption.

One important outlier is broadband. We characterized the first successful venture investments in broadband as YouTube and Facebook, both of which arguably needed broadband Internet to enable users to upload large volumes of content, specifically photos and videos, in order to succeed. These investments, however, did not occur until broadband adoption approached 40%, much later than in other platforms. We hypothesize that the delay in investment was in part due to the “hangover” from the 2000 dot-com bubble, and an associated lack of appetite to invest in Internet businesses during the early 2000s.

Delving deeper, we can start to overlay additional successful venture investments throughout the adoption cycle. In the following chart we have overlaid a bar graph illustrating the number of VC investments with 10x+ cash-on-cash returns in the Internet era, as well as the total value created by those investments. The bars below represent the companies that received their first institutional investments in a given year – for example, while eBay was founded in 1995, its first institutional capital wasn’t raised until 1997.

Fig 9. Deep Dive Analysis of Consumer Internet Adoption and the First Successful VC Investments [6]
What we found most interesting from conducting this analysis on the adoption of dial-up Internet and on similar platforms was the alignment of successful investments with the peak in market acceleration (the yellow curve).

Final Takeaways

Over the past 30 years, consumer technology adoption has accelerated dramatically, and VCs have invested earlier and earlier in the adoption cycle. Part of this trend in earlier investments can be attributed to increasing competition for deals, driving VCs to take on more risk, but also to the decreasing cost required to start a company, because investors can now write smaller seed checks and get more comfortable making earlier bets.

While these findings may cause investors to think twice about investing in software built on emerging consumer platforms, it doesn’t mean we should wait on the sidelines until the timing is perfect. After all, as early-stage investors, we are in the business of helping great entrepreneurs capitalize on new technological frontiers. If we aren’t mistakenly “too early” on occasion, then we arguably aren’t taking enough risk.

In Part II dive deep into how we expect these trends to continue in the future, and specifically when we expect to see some emerging consumer platforms become ready for successful VC investments.

Reference Notes:
[1] See below for data sources used to model adoption for all platforms.
[2] Data Sources: Computing Power – Our World in Data; Network Speed – Nielsen Norman Group; Storage Cost –
[3] 2016 U.S. Tech Choice Study: Trust in Technology Relates to Age
[4] Image Source: Based on Rogers, E. (1962) Diffusion of innovations
[5] See below for sources used to model smartphone platform adoption.
[6] See below for data sources used to model adoption for all platforms. Investment data from custom analyses in CB Insights and Pitchbook. One of the issues we wrestled with throughout this analysis was finding consistent historical data on venture investments. Ideally would have liked to analyze ALL venture investments ever made in each market to try and pull out more statistically significant findings about when in a cycle VCs are most likely to make successful investments. However, unfortunately we found a large survivorship bias in the data that is available on historic startup financing, particularly from the pre 2000 era. As a result, we decided to do the best we could with the data that we did have available, which was primarily on successful companies.
Modeling Sources:
Smartphone Adoption:
2005 – 2015: Deutsche Bank
Consumer PC Adoption:
1975 – 1980: PC Adoption – eTForecasts
Households 1975 – 1980: US Census
1984 – 2012: US Census
2015: Pew Research
Enterprise PC Adoption:
1975 – 1980: ArsTechnica
1984 – 2001: US Census
2003: US Bureau of Labor Statistics
2014: Reality Mine
Dial-Up Adoption:
1990 – 1994: WorldBank
1995 – 2014: Pew Research
2015 – 2016: Internet Live Stats
Broadband Adoption:
2000 – 2015: Pew Research
2000 – 2015: Pew Research
Gaming Console Adoption:
1977 – 2001: A Brief Social History of Game Play, Dmitri Williams, University of Illinois at Urbana-Champaign
4G Adoption:
2010 – 2012: GSMA and Navigant Economics
2014: 4G Americas
2015: 4G Americas
2016 – 2020: 4G Americas
VR/AR Adoption Forecast, 2015 – 2025, Average of 4 Different Forecasts:
Goldman Sachs
Business Insider
Greenlight VR
US Share Estimate: Digi-Capital
Smart Home Forecast, 2014 – 2020:
Voice Forecast, Extrapolated Based on:
Business Insider
Drones Forecast, Extrapolated Based on:
US Share Estimate: Emberify
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