Putting Big Data to Work for Marketing – Finally We Can Connect All the Dots

Big Data technology is a perfect fit to answer questions like “which programs work?” and provide account views for Account-Based Marketing. We pulled our Marketing data together into a Data Lake in just 60 days. Finally, we can connect all the dots.

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Every time chiefmartec.com blogger Scott Brinker updates the Marketing Technology Landscape graphic, in which he lists all the vendors of marketing applications, I need to zoom in to decipher all the logos (1,876 vendors in 2015!). I can’t wait to see what 2016 will bring!


Clearly, there is plenty of technology out there and many businesses feel they are “just one app away from greatness.” Most of these apps are only good for one thing: they take a siloed and partial view and they don’t integrate naturally with anything else. The result is like looking through 15 straws to make sense of the objects in the pool below. Although we, at Informatica, didn’t quite feel we were “one more marketing app away,” we did know that we couldn’t win the war with a siloed view of data.

Marketers can’t see the forest for the trees

The analytics available in many of the available marketing apps have gotten quite good, yet most marketers still struggle with answers to questions like:

1.      Which channels are driving the most net-new names that eventually convert into customers and revenue?

2.      Who are all the members of a buying team we need to influence to have the deal go our way?

3.      What is the value of the different marketing touches that eventually lead to not just an opportunity or pipeline, but revenue, both booked and banked?

It is easy to understand impressions, click-through rates, conversion rates, and cost-per-lead for paid media. But, particularly in B2B, when you run your trusted Salesforce pipeline report and filter for paid media as the lead source, often the resulting number looks disappointingly small.

Rarely will paid media be the last touch of a lead that converts straight into an opportunity. But how do you get a view across all the marketing systems and connect the dots from bid management and tracking (like Kenshoo or Marin), to the Web (like Google or Adobe analytics), to marketing automation and database (Eloqua or Marketo), to CRM (Salesforce, etc.)? Now add in the influence of targeting and personalization on the Web and in email, offline events, etc., and the struggle to get to an integrated view becomes enormous.

Most marketing organizations today (52%) cite integration of technologies as their most challenging obstacle to marketing success. Fifty-one percent of companies are now using 21 or more technologies, up 42 percent from just three years ago.

Winning the integration battle in two months

Can you really integrate data from different marketing systems to power analytics using currently available tools?

Yes. And we did it in just 60 days.

Essentially, we created a framework that delivers a single-lens view into all marketing activity across all channels. We wanted a holistic view that would enable marketing attribution and cohort analysis as well as the ability to mine the huge pile of marketing data (from all touch points) for opportunities by account and individual. And we felt confident that we had the tools to do it. Now, we can easily see which customers are showing real interest and not lose them amid the noise of others who are just kicking the tires. And while we integrated data from a long list of sources in just 60 days, none of that would have been possible without having created (and implemented) strong marketing operations practices.

As a foundation, we always:

  • Apply consistent taxonomy across all marketing applications
  • Apply campaign codes religiously
  • Utilize tag management and a sophisticated data layer to marry up data across Marketo and Web, in real-time, for targeting

All of this is just as important as the integration. After all, there’s no point integrating all the marketing data if the quality is not there (we’ll discuss this topic in a future blog).

How it works

Here’s a quick overview of the nuts and bolts of how we integrated marketing analytics. In upcoming blogs, we’ll drill down into more detail and share the how-to’s.

Basically, these are the steps we took over 60 days:

  • Brought together data from various marketing apps—Marketo, Salesforce, Adobe Analytics, Lattice predictive lead scores, Demandbase demographics for user’s IP addresses, RioSeo social sharing data, and LinkedIn—into one centralized marketing data lake (Hadoop) using Informatica Big Data Management
  • Used Tableau to build a single view of all the integrated data
  • Created a data pipeline to deliver fast results to queries
  • Developed use cases in an agile approach as we created the marketing data lake
  • Designed a means of continually monitoring data quality and integrity

It really is getting easier

Across the board, integration is getting easier. A big reason for this: marketing technology vendors are providing better and simpler APIs, and vendors like Informatica complement them with cloud integration platforms to integrate both data and applications. As Scott Brinker recently said, “Integration is getting easier. Marketing, however, is not.”

This is why we put big data to work for us—and only by connecting all the dots, end-to-end, can we understand what works and what doesn’t so we can invest in more of the former and skip the latter.

For details on exactly how we built an integrated marketing system that delivers answers we use every day, please see these posts:

Naked Marketing, Post 1 – A Big Data Marketing Operations Odyssey

Naked Marketing, Post 2 – Who’s Who Behind Our Big Data Marketing

Naked Marketing, Post 3 – 5 Foundations for Big Data Marketing

Naked Marketing, Post 4 – The Business Case for Big Data Marketing

Naked Marketing, Post 5 – The Big Data Marketing Technology Stack

Naked Marketing, Post 6 – Big Data Marketing Checklists for Marketo and Adobe Analytics

Naked Marketing, Post 7 – The Data for Big Data Marketing

Naked Marketing, Post 8 – The 60-Day Sprint to Our Big Data Marketing Data Lake

Naked Marketing, Post 9 – The Sales Leaders’ View of the Marketing Data Lake


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