m&a activity of major tech firms

M&A Activity of Major Tech Companies:

Implications for Venture Capitalists

Allen Miller

May 5th 2014

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Table of Contents

Background & Motivation…………………………………………………………………...……3

M&A & VC Landscape in the last 3 Years……………………………………………………….4

M&A Motivations: A Review of the Literature…………………………………………………..7

M&A Motivation Model and Research Methodology…………………………………………...10

Results: Motivations Behind Tech M&A Activity………………………………………………13

Implications for VCs…………………………….……………………………………………….17

Works Cited……………………………………………………………………………………...19

Appendix…………………………………………………………………………………………20

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Background & Motivation

In February of 2014, mobile messaging company WhatsApp was purchased by Facebook

for about $16 billion, $4 billion in cash and $12 billion in stock. This was the largest tech

acquisition since HP bought Compaq for $25 billion in 2001.1 In the process, the company’s exit

generated a tremendous return for Sequoia Capital—the sole venture capital firm that had backed

WhatsApp with approximately $58.3 million over the course of three financing rounds.2 In fact,

Sequoia’s roughly 20% ownership stake resulted in an estimated $3 billion payout or a 50x

return on invested capital.3 Since then there has been a greater focus on the M&A activity of

large tech firms and what this activity implies for venture capitalists (VCs).

The focus of this paper will be to understand the recent (last 3 years) M&A activity of

four of the largest global tech companies: Apple, Facebook, Google and Microsoft. Specifically

the paper will analyze the implications this M&A activity has for early stage VCs focused on

investing in tech companies. In analyzing the acquisition activity of these major tech companies,

this paper will build a two-fold hypothesis around (1) why large tech companies have been

acquiring the specific targets they have acquired in the last three years and (2) with this

knowledge, what types of opportunities should VCs be investing in to best realize a return on

their investments through an M&A exit.

The paper will begin by providing a descriptive overview of the M&A landscape as well

as outlining important trends in the VC industry in the last 3 years. The paper will then review

the existing literature on the motives behind the M&A activity of large tech firms. Building on

1 Jordan Crook, “Facebook’s $19 Billion WhatsApp Acquisition, Contextualized,” http://techcrunch.com/2014/02/1

9/f acebooks-19-billion-whatsapp-acquisition-contextualized/, TechCrunch, (February 19, 2014). 2 “WhatsApp,” TechCrunch, http://www.crunchbase.com/company/whatsapp. 3 Ryan Lawler, “Sequoia’s A Big Winner In Facebook’s WhatsApp Acquisition, With Its Stake Worth About $3

Billion,” http://techcrunch.com/2014/02/19/sequoia-and-jim-goetz-are-big-winners-in-facebooks-whatsapp-

acquisition/, TechCrunch, (February 19, 2014).

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the existing literature, the paper will then propose a model with which to examine M&A activity

of the largest tech firms. In this section, the research methodology and econometric tools used

will be highlighted. The paper will then present the results of this M&A model answering the

motivational questions behind tech M&A activity. Finally, the paper will conclude by

demonstrating the implications of the research conducted here for early stage VCs. In doing so,

the paper will present a framework for VCs to consider when investing in startups.

M&A & VC Landscape in the last 3 Years

Since the financial crisis of 2008, global technology, media and telecom (TMT) M&A

activity has bounced back year after year to a five year high of $510 billion in 2013.4 As shown

in Exhibit 1, within the last 3 years, quarterly M&A volume in the TMT sector has risen by

nearly 100 deals per quarter from 663 in Q1 2011 to 762 in Q4 of 2013. This increasing deal

volume has particularly accelerated in 2013.5 In addition, the average deal size for global TMT

deals has also risen in recent years landing at $443.2 million in 2013.6

Within the healthy rise of TMT M&A activity, the technology subset of deal volume has

been growing particularly quickly since 2011. Exhibit 2, shows transaction volume by subset

within TMT in the last 3 years. While all three sectors have been growing since 2011, the

technology sector has grown particularly quickly. In fact, in Q3 and Q4 of 2013, both quarters

increased to record highs with 543 (Q3) and 539 (Q4) respective transactions. Importantly,

4 Peter High, “2013 Global Tech, Media, And Telecom M&A Up Over 50% From 2012,” http://www.forbes.com/sit

es/peterhigh/2014/01/20/2013-global-tech-media-and-telecom-ma-up-over-50-from-2012/, Forbes,

(January 20th 2014). 5 “Global M&A Report Technology-Media-Telecom Sectors,” BDO, February 2014, 2. 6 High, “2013 Global Tech, Media, And Telecom M&A Up Over 50% From 2012.”

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technology continues to remain the highest relative share of transaction volume in the TMT

space—with approximately 2/3 of over-all deal volume.7

Within the technology sector there are four critical trends that have affected M&A

activity in the last 3 years. The first of these trends is in big data, which has grown rapidly in the

past 3 years. According to IBM, 2.5 quintillion bytes of data are created each day. Gathering and

mining this data is a tremendous problem that many companies are trying to solve. It is therefore

no surprise that this is one of the fastest growing areas of acquisition interest for large tech

companies. In 2013 alone, there were 40 big data companies acquired. Examples include:

BlitzerMobile acquired by Oracle and Infochimps acquired by Computer Sciences Corporation.8

Wearable technology is another increasingly important area for large tech companies.

According to Juniper Research, the wearable device market is expected to grow from $1.8 billion

in 2013 to $19 billion in 2018. Early interest in the area is coming from fitness and apparel

companies like Under Armor, which recently acquired MapMyFitness, and Nike, whose

FuelBand led to an 18% increase in the company’s equipment division profits in 2013. Other

wearable technologies like Google Glass and Sony SmartWatch are likewise receiving much

publicity.9

A third area of interest for many large tech companies is cyber security. As internet

activity continues to grow and many companies move to the cloud, there is a rising demand for

cyber security to protect against hackers and malicious software. In 2013, there were a total of 42

transactions in the security space. Examples include Stonesoft’s acquisition by McAfee and

Sourcefire’s acquisition by Cisco.10

7 “Global M&A Report Technology-Media-Telecom Sectors,” BDO, February 2014, 2. 8 “Global M&A Report Technology-Media-Telecom Sectors,” BDO, February 2014, 4. 9 “Global M&A Report Technology-Media-Telecom Sectors,” BDO, February 2014, 4-5. 10 “Global M&A Report Technology-Media-Telecom Sectors,” BDO, February 2014, 5.

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The fourth and final trend is the industry movement to cloud computing. Over the last 3

years, many large tech companies have transitioned to working, storing and building applications

in the cloud. There were 90 cloud M&A transactions in 2013 alone. Furthermore, within cloud

computing, cloud storage is particularly growing rapidly and is expected to be a $46.8 billion

market by 2018. As a result, companies like Dropbox have soared in popularity and been

primary candidates for acquisition.11

There are a number of important trends in the venture capital industry since 2011 that are

worth highlighting as well. As with M&A activity, venture capital financing has likewise

continued to grow over the course of the last 3 years as there has been a growing sense of

confidence throughout the entire venture ecosystem. As compared to 2011 and 2012, 2013 saw

venture capital deals rise by 3% and 9% respectively. In 2013, venture capital financing hit $29.2

billion across 3,354 deals. Exhibit 3 displays the upward trend in venture deal volume and

investment dollars since Q1 of 2011.12

In the last 3 years, venture capital firms have been moving towards making earlier stage

investments. In particular, there has been a proliferation of seed deals (smaller investments under

approximately $1 million) since 2011. Exhibit 4 shows a chart of quarterly seed deals since Q1

of 2010. As seen in the exhibit, overall seed dollars invested in 2013 increased 22% and 74%

compared to 2012 and 2011 levels. By 2013, Seed deals represented 26% of overall VC deals.

The fundraising climate for venture investors has been improving in line with

improvements to the M&A markets. It has been easier to raise a venture fund, particularly an

early stage fund, now than in any time since before 2008. In 2013, 325 funds were raised—222

of which were early stage funds. Those 325 funds raised a total of $28 billion. Importantly, many

11 “Global M&A Report Technology-Media-Telecom Sectors,” BDO, February 2014, 6. 12 CB Insights, “2013 Venture Capital Financing and Exit Annual Report,” http://www.cbinsights.com/blog/venture-

capital-report-2013, (January 22, 2014).

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VC firms are doing fewer new deals per year than in the past. Instead firms are re-investing in

their best portfolio companies to maximize their equity stakes and limit dilution.13

A final important macro-trend in the VC industry is the ability for firms to solicit

investments from the public. In 2013, the Securities and Exchange Committee (SEC) gave the

go-ahead for certain provisions in the 2013 JOBS Act—including the ability for firms to publicly

talk about fundraising. Even though, only one VC firm (as of April 2014), ff Venture Capital, has

solicited funds from the general public, it is clear that public solicitation of investments and

crowd funding are here to stay. AngelList has already revolutionized the VC syndicate by

allowing one angel investor to lead a group of accredited investors. Eventually, more investors

(even those who are not accredited) will be allowed to make small investments in private

companies under the crowd funding provision of the JOBS Act.14

M&A Motivations: A Review of the Literature

There is an extensive body of work on the motivational reasons why large tech firms

acquire smaller companies. In his work on M&A activity, Florian Frensch summarized much of

the thought leadership in the field. Exhibit 5 outlines many of these M&A motivational theories

by author.15 In addition to these motivations, Frensch also argues that there may be a number of

high-level macro developments that lead to acquisition. Most notably, Frensch argues that

globalization leads to economy of scale requirements and changes in certain industry dynamics

force consolidation through M&A activity.16

13 “Adapting and Evolving: Global venture capital insights and trends,” Ernst & Young, 2014, 11. 14 Joanna Glasner, “Top Venture Capital Trends in 2013,” http://www.pehub.com/2014/01/top-venture-capital-

trends-in-2013/, Reuters PE Hub, (January 3, 2014). 15 Florian Frensch, The Social Side of Mergers and Acquisitions, (Berlin: DUV, 2007), 27. 16 Frensch, The Social Side of Mergers and Acquisitions, 27.

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Michael Garbade pushed Frensch’s research a bit further by focusing more exclusively on

tech M&A activity. Garbade argues that M&A activity by large tech firms can be categorized

into two distinct buckets: value maximizing and non-value maximizing. Value maximizing

activity entails any activity that results in accrued value to shareholders. Non-value maximizing

activity is driven by other reasons and is not economically sound. Non-value maximizing activity

can include: a firm’s desire to increase market position, management’s desire to increase prestige

and general empire building goals to flex power or status.17

Garbade further argues that there are two types of value-maximizing M&A activity:

operational and financial synergy. Financial synergy centers on increased diversification and

lowered risk. In particular, Garbade argues that large tech firms may acquire a smaller firm in

order to reap the benefits of a reduction in the weighted average cost of capital (WACC). If the

cash flows of the two firms are negatively correlated, and there is a low risk of insolvency that

results from the merger, the acquiring firm can reap the benefits of financial synergy by

purchasing the smaller firm.18

Operational synergy, according to Garbade, consists of revenue-enhancing activities or

cost-reducing activities—essentially affecting both ends of the value stick. On the revenue-

enhancing side, large tech firms may acquire smaller companies in order to increase total sales

by cross-selling products, take advantage of an already installed customer base and exploit

existing distribution channels. On the cost-reducing side of the value stick, large tech firms may

acquire smaller companies in order to take advantage of economies of scale and scope.19

17 Michael Garbade, International Mergers & Acquisitions, Cooperations and Networks in the e-Business Industry,

(Mannheim: GRIN, 2007), 7. 18 Garbade, International Mergers & Acquisitions, Cooperations and Networks in the e-Business Industry, 6. 19 Garbade, International Mergers & Acquisitions, Cooperations and Networks in the e-Business Industry, 5.

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Aswath Damodaran built on Garbade’s initial work by examining the operational synergy

piece of the puzzle more closely. Damodaran argues that there are four specific operational

synergies that a larger firm can benefit from when purchasing a smaller company. The first of

Damodaran’s reasons is economies of scale, which allow the combined firm to become more

cost-efficient. Damodaran argues that this motivation shows up most often in horizontal mergers.

The second operational synergy is greater pricing power due to reduced competition and higher

market share. Damodaran argues that this motivation mostly shows up in mergers of firms in the

same business and often results in the creation of an oligopoly.20

The third operational synergy Damodaran outlines is the motivation to combine a variety

of different functional strengths to shore up on weaknesses in the acquiring firm’s skill set (i.e.

value chain integration). For example, a firm with strong marketing capabilities might acquire a

smaller tech company with a well-developed product line. The fourth and final operational

synergy Damodaran outlines is the pursuit of higher growth in new or existing markets. For

example, a U.S. consumer electronics product firm may acquire an emerging market firm with an

established brand and distribution to increase sales of its products.21

M&A Motivation Model and Research Methodology

The focus of this paper is to test and further expand upon the value-maximizing,

operating synergy motivators introduced by Damodaran. The rationale behind this focus is that

operational synergies are the most relevant and identifiable variables for VCs to focus on as they

think about M&A as an exit option. Operational synergies are also: specific, repetitive (allowing

20 Aswath Damodaran, The Value of Synergy, NYU Stern School of Business, October 2005, 4. 21 Aswath Damodaran, The Value of Synergy, 5.

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for pattern recognition by VCs), have predictive power and can be used to build an investment

thesis.

The paper does not focus on non-value maximizing M&A activity (i.e. management

hubris, empire building, market expansion, etc.) as these motivators are random, personality-

dependent, situation-specific, have no predictive power and do not lend themselves to building

an investment thesis with. Likewise this paper does not focus on financial synergy as these

variables are also very hard to predict, often happen at various points in the business cycle, are

opportunistic and do not lend themselves to investment thesis building. The paper chooses

instead to focus on an area (operating synergy) that VCs can apply directly when making long

term investment decisions.

This paper combines all four of Damodaran’s variables with 3 additional variables that I

wanted to test (both in terms of strength and significance) in building out a complete

motivational model for large tech firm M&A activity. Damodaran’s four motivational variables

(as described above) are: economies of scale, greater pricing power, value chain integration and

growth in new or existing markets. To these variables, I have added 3 additional motivational

variables: enhance core capabilities, tap into large network effects and take advantage of steep

switching costs. These seven motivational M&A variables are outlined in Exhibit 6 below:

Exhibit 6: Miller’s VC-Focused M&A Motivation Typologies

Typology Explanation

Economies of Scale Economies of scale refer to a cost-reducing strategy whereby the

acquirer benefits as a result of decreases in cost per unit due to

total output increasing.

Greater Pricing Power The acquiring firm can have greater pricing power if acquisition

of the target results in reduced competition and higher market

share.

Value Chain Integration Value chain integration refers to Michael Porter’s concept of

either vertical or horizontal integration to take ownership of more

of the activities required to produce a final good or service.

Growth in New or Growth into a new or existing market implies increasing both

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Existing Market market penetration and exposure.

Enhance Core

Capabilities

Enhancing core capabilities translates into shoring up a firms core

business and further enhancing its competitive advantage.

Tap into Large Network

Effects

An acquirer may benefit from a target due to the target’s network

effects—when a network effect is present, the value of a product

or service grows as more people use it.

Take Advantage of

Steep Switching Costs

When consumers are forced to incur costs in switching from one

product to another, the acquirer may benefit from this increased

customer loyalty. *Shaded region represents variables introduced in this study. Remaining region represents Damodaran’s typologies.

After deciding to build the M&A activity model around these seven variables, I then had

to find representative M&A data on large tech firms. I selected Facebook, Google, Apple and

Microsoft as the 4 acquiring firms for several reasons. First, each of these four firms is in the top

ten tech firms globally by market capitalization. This means that they are purchasing venture-

backed tech startups frequently. In addition, because these are global firms, they are purchasing a

variety of companies in many different industries and at various prices. It also means that they

are interested in acquiring both enterprise (B2B) and consumer facing (B2C) companies. In

addition, I purposefully chose 2 long-standing giants in the tech industry (Apple and Microsoft)

as well as 2 relatively “newer” entrants (Facebook and Google). Through this process, I was able

to create a representative sample with which to build a motivational M&A model.

After selecting the four large acquiring firms, I then looked at the publicly available

M&A history from Q1 of 2011 to present of each firm using CapitalIQ.22 I only included

companies where the premium paid was publicly available—this left a list of roughly 25%-50%

of the target companies for each of the four large tech firms. The remaining companies were

acquired for undisclosed amounts. As a result, there were a total of 40 acquired companies that

were examined in this study. For each acquired company I recorded the industry category it fit

under, whether it was a B2B or B2C company and the premium paid by the acquirer.

22 S&P Capital IQ, https://www.capitaliq.com/home.aspx, McGraw Hill Financial.

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In addition, I used a binary variable classification system to record whether an M&A

motivation variable was present in the rationale behind each acquisition. These motivations were

unearthed by reading the acquiring company’s press release as well as industry analyst reports.

For example, in Facebook’s acquisition of Instagram, the M&A motivation variables present

included enhancing core capabilities (photo sharing is a core function of Facebook), tapping into

large network effects (Instagram had 30+ million users23 at the date of sale) and taking advantage

of steep switching costs (Instagram users spend an average of 257 minutes per month24 on the

site). Exhibit 7 provides an overview of the various inputs that went into building the model.

After organizing and collecting the data outlined above on Apple, Facebook, Google and

Microsoft, I was equipped with enough information to provide both descriptive statistics as well

as econometric analysis. For the descriptive statistics portion of this study, I simply used the data

collected and performed a number of calculations. For the econometric portion of this analysis I

ran two linear regressions. First, I regressed all 7 of the M&A motivation variables on premiums

paid by the acquiring firms. I noticed that several of the variables were not statistically

significant, so I ran a second regression where the statistically insignificant M&A motivation

variables were removed. The results of this analysis follow.

Results: Motivations Behind Tech M&A Activity

Of the 40 acquired firms included in this study, Google (16) acquired the most followed

by Microsoft (9), Facebook (9) and Apple (6). Exhibit 8 shows the breakdown of deal volume by

industry while Exhibit 9 shows the break down by consumer facing (B2C) vs. enterprise facing

(B2B). By industry, Consumer Media and Entertainment companies were the highest share of

23 Bruce Upbin, “Facebook Buys Instagram For $1 Billion. Smart Arbitrage.” http://www.forbes.com/sites/bruceupb

in/2012/04/09/facebook-buys-instagram-for-1-billion-wheres-the-revenue/, Forbes, (April 9, 2012). 24 Craig Smith, “April 2014 by the Numbers: 70 Interesting Instagram Statistics,” http://expandedramblings.com/ind

ex.php/important-instagram-stats/#.U12x71ca2qU, DMR, (March 6, 2014).

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startups being acquired at 33% followed closely by Cloud Platforms and Data Organization

companies with 28% share. B2C companies represented 60% of the acquisition deal volume as

opposed to 40% for B2B companies.

The total amount paid for these 40 companies was $56.9 billion. The average premium

paid was $1.4 billion, but this was skewed by a few outliers (standard deviation of $3.5 billion).

The median, a far better measure of central tendency in this case, was $108.3 million. The

minimum transaction price paid since 2011 was Facebook’s acquisition of Little Eye Software

for $14.5 million in Q1 of 2014, while the maximum paid was Facebook’s acquisition of

WhatsApp for $16.4 billion (also in Q1 of 2014).

Exhibit10 shows the breakdown of premiums paid by 5 different price ranges. As seen in

the exhibit, 61% of the startups acquired by the four large tech firms were acquired for less than

$150 million and only 10% were acquired for more than $5 billion. The four companies acquired

for more than $5 billion were: WhatsApp (Facebook), Motorola (Google), Skype (Microsoft) and

Nokia’s phone business (Microsoft). These four were joined by Instagram (Facebook), Oculus

VR (Facebook), Yammer (Microsoft), Waze (Google) and Nest Labs (Google) in the billion

dollar club. Notably, Apple has not spent more than $400 million in a single transaction since Q1

of 2011 (it did so on its acquisition of Anobit Technologies.)

By industry, as shown in Exhibit 11, the largest average premiums paid were for

companies in the Consumer Media and Entertainment and Hardware and Devices categories.

These high averages were a result of a number of high profile acquisitions in these categories

(for example: Facebook’s acquisition of WhatsApp and Google’s acquisition of Motorola.) A

final observation worth noting, and as seen in Exhibit 12, is that the average premium paid for

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B2C companies was nearly 10x the average amount paid for B2B companies: $2.2 billion vs.

$210.5 million.

The econometric tools employed (i.e. linear regression) yielded notable findings. The first

regression looked at the effects of all seven of the M&A motivation variables outlined above on

the premium paid by the acquiring firm. The regression results, including the equation, variables

and measures of statistical significance are displayed below in Exhibit 13 (the full regression

output is included in Exhibit 14):

Exhibit 13: Regression Results of M&A Motivation Variables

Premium Paid = -$5,500 + $2,950(β1) + $324(β2) + $3,780(β3) + $109(β4) + $2,168(β5) + $4,193(β6) + $1301(β7)

Regression Variable M&A Motivation Variable Coefficients P-value

β0 Intercept -5,499.905383 0.00107

β1 Economies of Scale 2,950.104683 0.0179

β2 Greater Pricing Power 324.1651438 0.71664

β3 Value Chain Integration 3,779.654943 0.0037

β4 Enhance Core Capabilities 108.9788665 0.90296

β5 Growth in New and Existing Markets 2,167.603976 0.03084

β6 Large Network Effects 4,193.220288 0.0006

β7 Steep Switching Costs 1,301.206602 0.13808

*Shaded Rows represent statistically significant variables.

There are several characteristics of the regression worth pointing out. First, the intercept

(β0) is negative, which limits the full application of the regression equation. This is likely due to

a small sample size and data that is not normally distributed. Because of this negative intercept,

we cannot make a direct dollar connection between each M&A motivation variable and the

premium paid by the acquiring firm. That being said, we can make some relative observations

based on the size of each beta coefficient.

Additionally we can make some important observations regarding statistical significance.

As seen in the exhibit above the four M&A motivation variables that were statistically significant

include: economies of scale, value chain integration, growth in new and existing markets and

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large network effects. The remaining three variables are not statistically significant according to

our model. Re-running the regression on the four statistically significant variables we get the

results displayed below in Exhibit 15 (full regression output can be found in Exhibit 16):

Exhibit 15: Regression Results of Statistically Significant M&A Motivation Variables

Premium Paid = -$4,547 + $2,649(β1) + $3,649(β2) + $2,105(β3) + $4,228(β4)

Regression Variable M&A Motivation Variable Coefficients P-value

β0 Intercept -4,546.658515 0.00062

β1 Economies of Scale 2,648.970749 0.027376

β2 Value Chain Integration 3,648.626285 0.004524

β3 Growth in New and Existing Markets 2,104.69961 0.017684

β4 Large Network Effects 4,228.180754 0.000483

The regression equation above shows us the relative values large tech firms place on various

M&A motivation variables. The results can be broadly bucketed into B2C variables and B2B

variables—although there is certainly some overlap.

On the B2C front, unsurprisingly, tech firms like Apple, Facebook, Google and Microsoft

place the largest premiums on startups with large network effects. Acquiring companies like

Instagram, WhatsApp and Skype allows these firms to essentially acquire a massive customer

base with a large customer life-time value. Because of the large network effects, these customers

are unlikely to switch to substitutes. Big tech firms can then monetize these acquired customers

over a long period of time as well as cross-sell products and services on their existing platforms.

According to our regression output, these big tech firms also place an important (though

not nearly as large) premium on B2C companies that allow them to grow in new and existing

markets. B2C companies like Snaptu (a mobile platform for feature phones in developing nations

acquired for $70 million) and Oculus VR (a virtual reality and gaming device company acquired

for $2.3 billion) allow big companies like Facebook to enter new markets—whether geographic,

customer-segment specific or newly emerging industries.

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When it comes to B2B acquisitions, the M&A model provides evidence that large tech

companies place a heavy premium on value chain integration and economies of scale—both

means to maintain a competitive advantage. Apple’s acquisition of semiconductor company

Anobit Technologies for $400 million is a great example of value chain integration. Apple has

slowly been moving away from hard drives to flash memory beginning with the iPod and most

recently its MacBook Air. Flash memory allows Apple’s products to be thinner and run on less

power. Acquiring Anobit allowed the firm to acquire the hardware component needed to

complete value chain integration and transition fully from hard drives to flash memory chips.

Though not as important as value chain integration from a relative perspective, large tech

companies also consider economies of scale when acquiring B2B companies. Within that realm,

companies that provide a service or toolkit that enable a bigger tech company to take advantage

of scale economies are also often worth acquiring. Microsoft’s acquisition of Pando, a file-

sharing technology that works peer-to-peer like bit-torrent, is a great example of this. Pando’s

technology can be applied to Microsoft products like Xbox and Windows Phone App Store, to

reduce costs in these divisions and enable Microsoft to take advantage of its economies of scale.

Implications for VCs

The implications of the findings in this paper are critical to venture investors and can be

used to make high potential investments in early stage startups with the end goal of an M&A

exit. The first thing venture investors must realize is that we are currently in a very “frothy”

market environment—both on the M&A front and on the venture investing front. Acquisitions

and venture deals are at post-financial crisis highs in terms of volume and size. On the venture

side, seed investing has particularly accelerated in the last few years. While optimism is

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abundant in the venture community, it is critical to understand that business cycles ebb and

flow—the current environment will not last indefinitely.

But while the current environment continues to last, now is an ideal time for VCs to start

a venture firm or raise a new fund. Not only is optimism high among the limited partners (those

who invest in VCs) pouring money into the venture community, the SEC is also making it easier

for firms to solicit funds from the general public through the JOBS Act. There is a particularly

high level of outside interest in early stage investing on both the B2C and B2B ends of the

spectrum.

VCs should likewise realize that they can be successful investing in either B2B or B2C

companies, though they ought to adopt different strategies for each group. With B2C companies,

venture investors should realize that there is a greater likelihood of a billion dollar exit (as

opposed to B2B companies). That being said, while there are more billion dollar B2C exits, the

average venture-backed exit is just slightly north of $100 million. In order to achieve successful

B2C M&A exits, VCs should focus on companies that are building products with large network

effects or that have witnessed significant growth in new and existing markets. In particular, they

should focus on Consumer Media and Entertainment and/or Cloud Platforms and Data

Organization companies.

Venture investors investing in B2B companies should understand that the exit prices of

this group are on average lower (as compared to B2C companies), but that these prices are also

more consistent. VCs focused on enterprise companies should focus on finding gaps in large tech

firms whereby the acquisition of a startup would provide the acquiring firm the ability to take

advantage of economies of scale or integrate along the value chain in an advantageous way.

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Regardless of whether focused on investing in consumer facing or enterprise facing

startups, VCs targeting an M&A exit should invest based on four trends that major tech firm are

reacting to. First, big data is everywhere and can be mined, analyzed and applied across every

industry. Second, wearable technology is an important platform providing an even greater

extension of mobility than the phone. Third, the world is moving from the desktop to the cloud,

creating a need for tools to work, store and build in this cloud computing environment. Fourth, as

we move to the cloud, a new layer of security will be needed to protect against cyber-attack.

To conclude, it is an exciting time to be an early stage venture investor. Despite the

challenges and risks involved in the business, there is certainly much potential to achieve a high

level of success. Focusing on the M&A activity of the largest tech firms is a great way to begin

building toward that success.

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Works Cited

“Adapting and Evolving: Global venture capital insights and trends.” Ernst & Young.

2014. 11.

CB Insights. “2013 Venture Capital Financing and Exit Annual Report.”

http://www.cbinsights.c om/blog/venture-capital-report-2013. (January 22, 2014).

Crook, Jordan. “Facebook’s $19 Billion WhatsApp Acquisition, Contextualized.”

http://te chcrunch.com/2014/02/19/facebooks-19-billion-whatsapp-acquisition-contextualized/.

TechCrunch. (February 19, 2014).

Aswath Damodaran. The Value of Synergy. NYU Stern School of Business, October

2005, 4-5.

Frensch, Florian. The Social Side of Mergers and Acquisitions. (Berlin: DUV, 2007). 25-

27.

Garbade, Michael. International Mergers & Acquisitions, Cooperations and Networks in

the e-Business Industry. (Mannheim: GRIN, 2007). 5-7.

Glasner, Joanna. “Top Venture Capital Trends in 2013.” http://www.pehub.com/2014/01/

top-venture-capital-trends-in-2013/. Reuters PE Hub. (January 3, 2014).

“Global M&A Report Technology-Media-Telecom Sectors,” BDO, February 2014, 2-6.

High, Peter. “2013 Global Tech, Media, And Telecom M&A Up Over 50% From 2012.”

http://www.forbes.com/sites/peterhigh/2014/01/20/2013-global-tech-media-and-telecom-ma-up-

over-50-from-2012/. Forbes, (January 20th 2014).

Lawler, Ryan. “Sequoia’s A Big Winner In Facebook’s WhatsApp Acquisition, With Its

Stake Worth About $3 Billion.” http://techcrunch.com/2014/02/19/sequoia-and-jim-goetz-are-

big-winners-in-facebooks-whatsapp-acquisition/. TechCrunch. (February 19, 2014).

“WhatsApp.” TechCrunch. http://www.crunchbase.com/company/whatsapp.

20

Appendix

Exhibit 1

Global TMT Transaction Volume (2011-2013)

Source: BDO Paper (Global TMT M&A Report)

Exhibit 2

Number of Transactions by Sector within TMT (2011-2013)

Source: BDO Paper (Global TMT M&A Report)

21

Exhibit 3

Quarterly VC Deals (2011-2013)

Source: CB Insights

Exhibit 4

Quarterly VC Seed Deals (2010-2013)

Source: CB Insights

22

Exhibit 5

Summary of M&A Rationale by Author

Author Reason for M&A Transaction

Buhner (1990):

295 Market power, especially for horizontal mergers

Information advantage, which allows the recognition of

undervalued companies

Synergies, which allow an increase of performance

Inefficient management of the acquisition target

Financial benefits through tax advantages or lower cost of

capital through risk diversification

Management interest such as prestige, power and

recognition

Hubris of the top management

Free cash flows

Bamberger

(1994) General Acquisition Motives

o Growth

o Capacity increase

o Risk Diversification

Specific Acquisition Motives

o Time advantage compared to internal investment

projects

o Acquisition of specific, non-tradable resources

o The acquisition target is undervalued and an

opportunity

Miscellaneous acquisition motives

o Motives of individuals, such as top manager hubris

Hayward &

Hambrick

(1997)

Synergies

Poor past performance of the management team of the

target

Hubris of the CEO of the acquiring firm

Koegeler (1991) Risk diversification

Stagnation of markets

Usage of cash resources to further diversify rather than

consolidate

Top-management desire for further growth

Realization of synergies Source: The Social Side of Mergers and Acquisitions

23

Exhibit 6

M&A Model: Data Collected

Source: Created by Author

Company Type

B2B

B2C

Industry Category

Consumer Media & Entertainment

Ecommerce & Financial Services

Cloud Platforms & Data Organization

Hardware & Devices

Advertising & Marketing

M&A Motivational Variable

Enhance Core Capabilities

Greater Pricing Power

Steep Switching Costs

Large Network Effects

Growth in New and Existing Markets

Economies of Scale

Value Chain Integration

24

Exhibit 7


Exhibit 8


15%8%

33%28%

18%

Acquisitions by Industry (2011-2013)

Advertising & Marketing

Ecommerce & FinancialServices

Consumer Media &Entertainment

Cloud Platforms & DataOrganization

Hardware & Devices

40%

60%

Acquisitions: B2B vs. B2C (2011-2013)

B2B

B2C

25

Exhibit 9


0

2

4

6

8

10

12

14

$0-$50 $50.01-$150 $150.01-$500 $500.01-$5,000 Greater than$5,000

Number of Companies in each "Premiums Paid" Range (in $mm)

26

Exhibit 10


Exhibit 11


$129.19 $89.00 $197.17

$2,169.42

$3,650.42

$0.00

$500.00

$1,000.00

$1,500.00

$2,000.00

$2,500.00

$3,000.00

$3,500.00

$4,000.00

Advertising &Marketing

Ecommerce &Financial Services

Cloud Platforms& Data

Organization

Consumer Media& Entertainment

Hardware &Devices

Average Premiums Paid by Industry (in $mm)

$210.53

$2,233.25

$0.00

$500.00

$1,000.00

$1,500.00

$2,000.00

$2,500.00

B2B B2C

Average Premiums Paid: B2B vs. B2C (in $mm)

27

Exhibit 12

Regression Using all M&A Motivation Variables

Regression Statistics

Multiple R 0.773564202

R Square 0.598401574

Adjusted R Square 0.510551918

Standard Error 2480.386638

Observations 40

ANOVA

df SS MS F

Regression 7 293352281.2 41907469 6.811655314

Residual 32 196874172 6152318

Total 39 490226453.2

Coefficients

Standard

Error t Stat P-value

Intercept -5499.90538 1528.498364 -3.59824 0.001066473

*Economies of Scale 2950.104683 1181.852584 2.49617 0.017898047

Greater Pricing Power 324.1651438 885.2725605 0.366176 0.716643989

*Value Chain Integration 3779.654943 1207.076666 3.131247 0.00370477

Enhance Core Capabilities 108.9788665 886.8077375 0.122889 0.902963623

*Growth in New and Existing Markets 2167.603976 959.6169174 2.258822 0.030842904

*Large Network Effects 4193.220288 1100.721788 3.809519 0.000596206

Steep Switching Costs 1301.206602 855.4825992 1.521021 0.138075561 *Indicates statistical significance.


28

Exhibit 13

Regression Using all Statistically Significant M&A Motivation Variables

Regression Statistics

Multiple R 0.749243073

R Square 0.561365183

Adjusted R Square 0.511235489

Standard Error 2478.653959

Observations 40

ANOVA

df SS MS F

Regression 4 275196062.4 68799016 11.19825685

Residual 35 215030390.7 6143725

Total 39 490226453.2

Coefficients

Standard

Error t Stat P-value

Intercept -4546.65851 1209.018873 -3.76062 0.000620209

Economies of Scale 2648.970749 1150.514901 2.302422 0.027376032

Value Chain Integration 3648.626285 1202.422783 3.034396 0.004524044

Growth in New and Existing Markets 2104.69961 845.3624606 2.489701 0.017684409

Large Network Effects 4228.180754 1098.739855 3.848209 0.000483176
