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DataDrivesMedia.com is now BradTerrell.com

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I’m moving my DataDrivesMedia.com blog to a new domain, BradTerrell.com, and I didn’t realize that my changes would cause Feedburner to blast a bunch of my old blog posts to my subscribers. Sorry about that.

You don’t need to change anything in order to continue receiving blog updates from me.

I started this blog during my early days at Netezza. It helped me clarify my thinking on topics related to the business I was building, but more than anything, it connected me to a wonderful community of people, many of whom have become close friends. When IBM acquired Netezza, my focus changed and I set this blog aside. Now that I’m moving on to new adventures, I plan to continue blogging here.  Though for more immediate interactions, I hope you’ll also connect with me on Twitter.

Thanks,

-Brad

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Big Data Love for Cloudera from Big Blue

I’m a huge Hadoop fan. If you love innovation and startups, you HAVE to love Hadoop – simply because it’s fueling so much of both. Hadoop adoption has been particularly rapid among Netezza’s digital media customers (which include many startups).  This makes perfect sense given the huge volumes of clickstream data they analyze in order to deliver innovative solutions for problems like ad targeting, ad yield optimization, attribution analysis, and website optimization. Because of this, I think I’ve gotten more questions about Hadoop over the past three years than just about anybody at Netezza.

So I was particularly excited when we kicked off Netezza’s partnership with Cloudera last year. Cloudera’s Distribution for Hadoop (CDH) has demonstrated incredibly rapid market share growth across multiple vertical industries over the past few years, and their team includes many of the world’s top experts on the technology. I’m especially grateful for the work that the Cloudera team has done in helping educate the market about the strengths of Hadoop relative to other technologies typically found in data management architectures. As a result, the most common Hadoop-related question I receive has shifted over the past few years from “Why can’t I replace my RDBMS with Hadoop?” to “How can I most benefit from using Hadoop alongside my RDBMS?”

This week, I’m thrilled that Cloudera has announced the immediate availability of the Cloudera Connector for IBM Netezza appliances, which is the first of its kind for CDH and Cloudera Enterprise and enables high-speed, bilateral data transfer between CDH and Netezza environments. This is a great development for digital media firms (and firms across every other industry) because it increases the value they can create when they leverage the interoperability and integration between these platforms for their own innovation.  It also further validates the important work that Cloudera is doing to enable reliable enterprise-class deployments of Hadoop and speaks to the increasing demand among big businesses for the platform capabilities and services that Cloudera provides.

But most importantly, this week’s announcement underscores the market’s increasing recognition of a new generation of data management architectures that enable big data problems to be solved in ways previously not possible. My friend Ed Albanese does a great job of summarizing this next-generation architecture on the Cloudera blog here.  And my friend and colleague Krishnan Parasuraman describes the complementary use cases we most frequently see among our digital media customers in the following slides:

The Cloudera Connector for IBM Netezza is free and can be dowloaded here – check it out!

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I led a session at the National Center for Database Marketing (NCDM) conference in Miami last month on how to “Target Customers Effectively Through Advanced Analytics” with Arup Ray from Intuit. Full disclosure:  my employer, Netezza, has a business relationship with Intuit and I am therefore limiting what I share in this post to information already in the public domain.

Intuit is No. 1 in every market it serves. Twenty million consumers use its TurboTax software and another 15 million use Quicken. 50 million people use Intuit’s products every year. The 27 year-old company has an impressive history of producing high-quality products.

In recent years, many of Intuit’s products have been transitioning away from shrink-wrapped desktop software distribution toward SaaS web-based delivery.  As a result, Intuit now invests heavily in digital advertising for customer acquisition, buying 20 BILLION display impressions in the fourth quarter of 2010 alone.  Critical to their success is deep in-house expertise in utilizing large-scale, high-performance data analysis to maximize the effectiveness of their digital advertising campaigns.

Intuit uses DoubleClick to monitor all of their online acquisition channels and Omniture to track visitor movement across their web properties. But those tools don’t enable Intuit to track and analyze individual web behaviors. Nor do they facilitate behavioral targeting, time series analysis, or predictive modeling and scoring. Therefore, Intuit unifies nearly 40 terabytes of DoubleClick and Omniture data in their “customer experience data warehouse”. Intuit’s data warehouse is a strategic asset for their business. It supports a broad variety of custom solutions focused on customer acquisition and retention, including attribution analysis, visitor-level behavior analysis, channel analysis, website optimization (and A/B testing), and more. Intuit discussed these solutions and highlighted four key digital advertising insights at NCDM:

Insight #1:  Attribution analysis creates value for Intuit by enabling better decisions that maximize channel effectiveness and minimize channel interference.

I’ve written about how attribution analysis solutions create value in the past (see “Attribution Analysis and Campaign Efficiency – Getting More Bang for your Buck“), and Intuit’s experience reinforces many of those ideas.  Intuit’s key goals for attribution analysis are as follows:

Maximize channel effectiveness:

Intuit utilizes display advertising, paid search, organic search, affiliates, and email for their digital campaigns. Optimizing and coordinating spend across all of these channels is critical to Intuit’s success. One specific example cited by Arup:

We were wasting a lot of money by continuing to advertise to customers that had already converted, and we were annoying those customers by showing them irrelevant ads. But now we are able to filter converted customers out of our campaigns so that we no longer waste those impressions, and even better, we often now use those impressions to cross-sell with more relevant offers.

Minimize channel interference:

This topic led to one of the session’s more interesting exchanges, when Arup revealed that – contrary to conventional wisdom – the “last click” in Intuit’s campaigns are typically NOT coming from search advertising:

Our data shows that if users became aware of our products through a display banner ad, typically they would follow a display banner ad to come back, use our product, and convert. So the data is saying that it is the same channel through which they became aware, that they convert. This was a new learning for us – the fact that on our campaigns, click-thru behavior is very consistent across the purchase funnel and we see little cross-channel overlap. We don’t know exactly why this is the case, but we spend a lot more on display than we do on search, and it could be that this is part of what drives this channel-centric behavior.

Insight #2:  Visitor-level web behavioral analysis for ad targeting creates value for Intuit – increasing conversions and subscription revenue.

Intuit analyzes every click from every user of its products, creates behavioral models from that data, and matches current users against those models in order to take action in real-time – targeting each individual user with relevant messages and experiences – both within Intuit’s products and across the Internet.  Note that achieving this person-centric view of website visitor behaviors is essential for efficient marketing – an important concept I wrote about in the “Seven Reasons Your Website Analysis Belongs in a Data Warehouse“ – but it’s also critical within Intuit’s desktop software.

One specific example we discussed involves Intuit QuickBooks, the popular desktop accounting software product (there is an online version, too).  Intuit collects in-product discovery (IPD) data from QuickBooks users with custom instrumentation built into its software (data is only collected from users that explicitly opt-in).  Analysis of this IPD data enables in-product behavioral targeting and turns QuickBooks into an important sales channel for Intuit Payroll and Payment Solutions.  For example, if Intuit sees that a small business has hired employees, it will offer Intuit Payroll—immediately.  The final outcome of this in-product behavioral targeting:  a “non-trivial” lift in revenue.

Insight #3:  Funnel-aware messaging creates value for Intuit – both onsite and across their customer acquisition campaigns.

The QuickBooks scenario cited above is a good example of this, and Intuit take this even further, incorporating some of the ideas I discussed previously in “Using Dynamic Message Optimization To Increase Campaign Response Rates“.  Arup also stated:

We have created a predictive model that detects within a few clicks whether a user’s navigational behavior is likely to lead to a conversion, and if not, we present a “click-to-talk” window offering assistance or possibly even make a one-time offer.

I’ve written about this idea a lot so I won’t go into detail on this point, other than to say that Intuit’s experience illustrates the significant value that can be captured with an analytical approach to delivering the right messages to the right people at the right times based on their positions in a conversion funnel.

Insight #4:  It’s nearly impossible to execute on the first three insights without a data analysis platform that enables extremely rapid execution of complex analytical queries against extremely large data sets.

Rapid analysis of granular, record-level detailed data is required in order to make all of the above strategies work.  The volume of data involved and the complex nature of the analytical queries that are required create fundamental technical challenges that can hinder even organizations full of great engineers.  As Arup said, “Before Netezza we simply did not have the ability to track individual users at the level of a cookie.”  Good engineers can always find workarounds for their big data challenges, but solving these types of problems in a way that simplifies development and delivers high performance is particularly hard – and particularly valuable.

Arup highlighted the keys to Intuit’s success in his last slide – note his emphasis on reducing data latency (both for data loads and query processing) – a tactic that is proven to increase targeting precision and campaign lift:

Here is the complete slide deck for your reference:

If these insights pique your interest, you might also appreciate this webinar recording that my colleague, Krishnan Parasuraman, led with Intuit’s Vineet Singh and Predictive Analytics World‘s Eric Siegel just a few months ago discussing Intuit’s data analysis platform in greater detail:  ”The New Age of Analytical Marketing“.

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New Research: Real-Time Data Fuels Targeted Marketing

Winterberry Group has just published new research that promises to provide marketers with a road map for navigating the changing landscape of marketing data that is increasingly critical to their success. This research is the product of significant contributions from nearly 200 thought leaders representing virtually all corners of the commercial marketing data industry (full disclosure:  my employer, Netezza, sponsored this research along with our customer and partner, Acxiom).

Two industry trends stand out

These findings in the research are particularly well-aligned with trends that I’ve noticed in the market:

  • A new industry structure is emerging—one marked by key competencies in analytics and technological integration. A shakeup is underway, and the winners will be those firms that invest in these competencies.
  • Marketing services firms are increasingly focused on reducing data latency in the pipelines they create to integrate and analyze vast arrays of data from disparate sources.  Their focus is on “executing campaigns in milliseconds”—not days, weeks, or months—using the most recently available data.  Doing so enables them to increase campaign targeting precision, buy media more efficiently, and achieve competitive differentiation.

As the chart below from the study shows, table stakes issues for marketers related to leveraging more data, controlling their data, and making their data more useful also rank as high priorities:

Why do marketers struggle with data?

The study finds that:

  • “making efficient use of that vast array of data…remains a significant issue”
  • “the most fundamental execution challenge…is rooted in technology and infrastructure”

Is IT holding marketing back?

That’s my interpretation.  After all, the study makes it clear that marketers want to maximize the value of their marketing data infrastructure.  What other reason could there be for these “execution challenges”?  :-)

Interestingly, nearly every marketing service provider, ad network, demand side platform, data provider, ratings firm, and publisher I speak with has already solved the top 6 challenges in the chart above.  In fact, of the challenges listed in the survey, most of them are focused on problem number 7—reducing data latency (because, as the study says, “real-time data—rather than the digital channels themselves—is the real fuel of effective marketing”).  But given the nature of my work, it seems likely that my sample is biased in favor of the more technically sophisticated firms in the market.

It’s time for marketers to step up

Given this latest research—the call to action for marketers is clear:  Marketers must step up and lead their teams through the technology infrastructure challenges involved in leveraging real-time data sources to increase campaign performance. Solving this problem is now part of every marketer’s job description—because marketing and data analysis are now inexorably intertwined.  After all:

  • Marketers are responsible for the outcomes of their campaigns, and top-performing campaigns are now more than ever enabled by cutting edge technology, so it only makes sense for marketers to exercise more control over the infrastructure necessary to ensure their success.
  • There are now plenty of examples of marketers succeeding in this endeavor—because technology has evolved and solving these problems has become easier than ever.

To learn more, download the free eBook here: “The Changing Mission of Marketing Data”. Note that this eBook links to the in-depth Winterberry Group report it summarizes, too.

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I spoke last week at the Merkle Executive Summit about the top four opportunities for marketers to create value through high performance large-scale data analysis.  My talk highlighted the reasons that increasing analytic performance in solutions to the following four data-related challenges creates competitive advantage for marketers:

  1. The data aggregation problem
  2. The segmentation problem
  3. The matching problem
  4. The bidding problem

While I’ve written about these ideas before, this short video contains the key excerpts from my presentation on these topics:

Click here to view on YouTube.

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