I have been trying to reconcile two
trends I'm seeing. First, large
companies are acquiring venture-backed startups to accelerate their innovation
efforts. Even as the R&D budgets and
associated efforts of large corporations are increasing,
they have not been keeping up with the accelerating pace of technology and
business model innovation. These
acquisitions fall in two categories.
First, acquisitions as a means of jump-starting corporate innovation
efforts and getting corporations into the “innovation flow.” Good examples of such venture-backed company
acquisitions include Avis’
acquisition of Zipcar, Walmart’s acquisition of Kosmix and of
Small Society, Wellpoint’s acquisition of Resolution Health, and Home Depot’s recent acquisition of Black Locus. These
acquisitions are less about the technology being acquired and more about the
innovations the startup employees will be able to create once they are part of
the acquiring company. Second,
acquisitions as a means of staying in the forefront of innovation. Companies in this category are acquire
frequently in order to enter a new sector or grow a sector they are already
working on. Good
examples include VMWare’s
acquisition of Nicira, and Facebook’s
acquisition of Instagram. Finally, a growing number of corporations from
American Express to P&G, from BMW to GE, and Walmart to Best Buy
establishing operations in innovation centers, such as the Silicon Valley, in
order to tap into the startup and innovation flow.
Second, while the number of
seed-stage companies is increasing dramatically because their founders see
opportunities for a quick exit based on the first observation, the number of
companies that can receive expansion rounds and make viable acquisition
candidates remains small. This is
because
Many of the seed-stage startups that number
in the thousands and are funded primarily by non-institutional investors, i.e., entrepreneurs themselves,
angels, super-angels, friends and families, are not
innovating, don’t have no product roadmap, hypotheses of viable business
models, or even ideas of how to acquire and retain customers.
The
number of management teams that can be backed by institutional VCs for scale,
give the “escape
velocity” and make it a viable candidate for an exit that provides high returns
to a venture investor has remained small. As
shown below, the number of companies that receive additional rounds of funding
by institutional investors has remained largely unchanged in the past 2-3
years.
The
number of institutional VCs who can fund and materially help these early stage
companies is getting smaller. Fewer of
these institutional venture firms are able to raise new pools of capital particularly
capital that can be used for earlier stage investments. The Limited Partners (LPs) that provide the
capital to the venture firms want to take on less risk with the capital they
provide and they want returns faster. The thinking is that investing in later
stage companies shortens the time to liquidity while reducing the risk of the
investment. Because of the overall
venture industry’s returns have been low over the past 10-12 years, the
allocations LPs are making to venture funds have decreased and are now about
25% of their peak in 2000. LPs want to
invest in only a few venture funds that they consider as having the right deal
flow of early stage companies that have higher probability for meaningful
exits. So we are moving from an industry
with a broad investor base to an industry of specialists (SaaS specialists,
biotech specialists, consumer internet specialists, etc.).
Therefore,
because the number of the desirable startup acquisition candidates will remain
small, large corporations will need to find ways to foster innovation from
within. Corporations must also become
better at selecting which companies to acquire.
In this way will be able to identify companies that can provide the
desired innovation in the short term but also have the teams that will stay
with the acquiring company thus providing long-term benefits. The capacity of institutional VCs to invest
in seed-stage startups will not increase.
In fact, it may continue to decrease further. Rather than creating as many seed-stage
startups with weak teams, dubious innovations and no long-term prospects,
entrepreneurs must seek to form strong teams that can innovate and build large
and enduring companies.
Online
advertising is helping corporations monetize the Internet by transforming the
way corporations are interacting with and marketing to their customers and
prospects. The effectiveness of online advertising
is so dramatic that corporations are constantly shifting their budgets to the
digital channel, taking share away from other forms of advertising such as
print and radio. Corporations are attracted by the improved targeting, ability
to easily experiment with formats and messages, rapidly adapt to changing
market conditions, transparency (through measurement and attribution) and
cost-effectiveness offered by the various types of online advertising (search,
display, social) and the channel through which it is delivered (desktop,
mobile, digital radio). At the core of
these attributes is big data management and analytics.
Marketing
is about acquiring, retaining and growing the
value of customers.
Online advertising is proving very effective in achieving these goals.
It is proving particularly effective for demand-generation
through increasing awareness and interest.
As a result, and as is shown below, the share of spending for online (or
digital) advertising as a percent of total amount being spent on
advertising
has been increasing and is projected to continue to increase.
Like
with every other form of marketing, for customers and prospects to want to
engage through online advertising they expect to receive through appropriate
channels contextually relevant messages that are specific to them. In a recent study conducted by Forrester,
consumers showed that they notice online ads more than other forms of
advertising even if they don’t click
on them.
Big data is playing an increasingly important role, perhaps
the central role, in the improving effectiveness of online advertising. This is
because over the past couple of years online advertising has been moving to programmatic buying. Programmatic buying refers to the practice of
automating the buying of online ads by using algorithms to drive the best
possible price for each impression. This occurs in real time, on demand and on
an impression-by-impression basis. Real-time
bidding, RTB, refers to the impression-by-impression buying. As it is shown below, programmatic buying saw
a significant influx of activity in 2012, growing over 100% in the US to
$2.2bn, according to IDC, now represents close to 16% of display advertising,
and is expected to grow to over 30% by 2016.
The share of programmatic buying is increasing because of:
Premium ad inventory becoming
available through exchanges developed by public and private companies,
including the Facebook Exchange and Turn’s exchange. We expect additional platforms to enter
the market such as the one being built by Amazon.
The creation of private
exchanges created by brand advertisers.
Improved transparency in
ad placement.
The availability of video
and mobile ad inventory, in exchanges such as Brightroll’s.
Programmatic buying in general and RTB in particular are
generating big data. Interestingly, it is not the volume characteristic of big data that is important in this case. The challenge comes from the velocity and variety of the data that is being used in order to make
decisions. In RTB decisions have to be
made in milliseconds. To make a bid decision,
the RTB system must not only use one of more predictive models that have been
developed using machine learning techniques, but it must also combine and
consider data that is produced, and often changes, at different speeds; some data
changes very fast, other less so.
Specialized Data
Management Platforms, called DMPs, are starting to be used by programmatic
buying systems to address the issues relating to volume, variety and velocity
of the data. They integrate, manage, and
analyze first-,
second-,
and third-party
online and offline data that is used to significantly improve the targeting of online advertising,
increase the ability to measure advertising effectiveness by performing more
detailed attribution. As online
marketing budgets are increasing, and the number of marketing channels is
multiplying (for example, for online marketing alone we use email, search,
display, social, mobile, video), the importance of attribution is
increasing. Marketers are not longer
satisfied with last-click
attribution but they want to understand which marketing channels
contributed to a customer’s decision and by how much. Marketing channel attribution analysis
requires sophisticated big data analytics.
While
online advertising can benefit significantly from the use of big data management
and analytics technologies, digital marketers are facing significant issues applying
these technologies effectively. There
are two reasons for that.
Big data technology is ahead of the use cases, even in online
advertising which has been one of the first sectors that starting using these
technologies.
While we all want to believe that the world is moving from
Mad Men to Math Men, the truth is that it has not moved there yet. Marketers today are asked to make decisions
based on data and information presented to them via a multitude of dashboards and
other increasingly sophisticated analytic solutions that are based on
technologies such as big data, machine learning, real time analytics, etc. But they struggle with the proper use of
these solutions often focusing on the wrong metrics, taking a short view of
performance data, optimizing quickly on metrics such as clicks and “actions”
but often ignoring more predictive metrics such as customer lifetime value
(CLTV). Unfortunately, the ad agencies
the marketers use, which are primarily staffed with creative people rather than
data scientists, are not in much better position to help them.
To
succeed in effectively using online advertising solutions and getting the best
possible ROI particularly from programmatic buying and RTB, marketers must develop the right big data strategies.
These strategies must begin with the development of the appropriate understanding of
the big data that is becoming available and being utilized by these increasingly
sophisticated solutions, i.e., the “new big data,” rather than by just trying to
process the marketing data the organization may have stored and used in the
past, the “old big data,” using modern big data analysis techniques. These strategies must provide the proper
balance between under-utilization and over-reliance on the new big data. Finally,
these strategies must provide the ability to leverage the new big data in a
sustainable way to produce repeatable
outcomes.
