The Boulder BI Brain Trust


April 2013 Archives

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

  1. 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. 
  2. 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.  Figure 1
  3. 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.

Big Data Strategies for Online Advertising

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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:

  1. 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.
  2. The creation of private exchanges created by brand advertisers.
  3. Improved transparency in ad placement.
  4. 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. 

  1. 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.
  2. 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.




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