Big Data Is Enabling Marketers to Do More With Less

  |  March 12, 2013   |  Comments

What is the real ROI associated with big data for marketers, and what marketing metrics does it impact and why?

Marketers are always looking for an edge in their ability to increase targeting precision and improve the relevance of their messages. There is a lot of buzz in the industry about the promise of "big data" technologies and how they can help provide that edge. Big data platforms provide the ability to incorporate and analyze marketing data across multiple audience touch points - be it email responses, site activity, search or display click-throughs, social engagement, location preferences, or behavior across a diverse set of channels. Using larger volumes of cross-channel data assembled in real time dramatically improves the output of analytical models. For example, predictions of propensities would be much better and that has a direct impact on conversion and marketing ROI.

Previously, marketers found this type of cross-channel analysis against large and diverse data sets to be either impossible or too cumbersome to achieve at scale. However, like with any new technology, big data brings with it its own challenges - the costs associated with deployment, lack of available skills in the marketplace, governance, etc. So that begs the question as to what is the real ROI associated with big data for marketers? And what marketing metrics does it impact and why?

We now have practical examples from the first wave of marketers who have successfully leveraged the benefits offered by big data. One thing is becoming clear from their experiences. They are now able to do more with less. For example:

  1. Epsilon is able to analyze very large volumes of campaign data for its clients. Its big data platform enables its clients to test out new ideas and learn immediately what works and what doesn't. By using big data-enabled technologies it is able to execute seven times more campaigns per week, and that translates to 20 percent higher revenues.

    The ability to analyze larger volumes of data in a shorter amount of time directly impacts targeting precision and that lowers marketing costs. Epsilon's clients can now execute a higher number of campaigns to a pinpointed audience instead of broadcasting a generic message to a large prospect base. This results in lower marketing costs, since campaign dollars are not being spent marketing to disinterested audiences.
  2. MediaMath's advantage in the highly competitive digital media trading technology space is its ability to gain a transparent view of every impression and factors affecting the performance of billions of ad impressions it processes daily. The company uses this information to optimize bids and conduct cross-channel attribution analysis that enables advertisers to gather and de-dupe all user data across display, email, and search from a multitude of sources. Its use of sophisticated analytics on large volumes of data coupled with speed of processing enabled one of its clients to achieve campaign goals while reducing CPA by more than 50 percent. MediaMath has realized that adoption of big data technologies requires half the manpower to deliver 10 times the output with triple the number of advertisers and more media channels.
  3. Merkle underwent a transformation of its analytic infrastructure to consolidate and analyze the hundreds of millions of new marketing transactions it receives every day. By adopting big data technologies it was able to achieve a 50 percent decrease in end-to-end run time for marketing campaign execution and up to a 25 percent decrease in managing its clients' environment. Across the board Merkle realized a 70 percent reduction in processing time for complex marketing campaigns. The benefits were not just limited to the enhanced performance gains provided by the big data platform. One client saw a 25 to 90 percent revenue lift through the use of new analytic models. The company was able to achieve all this by containing the cost of infrastructure. For certain types of data processing it was able to decrease operational costs by 66 percent.

These early results are astounding. They clearly spell out the tangible benefits associated with big data adoption for marketers. They are now able to incorporate more data in their analytics and increase speed of execution with fewer resources than before.

Delivering relevant and personalized messages using digital channels involves a sophisticated ad targeting system. In the below video clip, we explore some of the common misconceptions associated with data-driven ad targeting. We also explore the underlying challenges involved in integrating diverse data sources and running complex analytics in real time and how all of that relates to big data.

Big Data Cloud image on home page via Shutterstock.


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Krishnan Parasuraman

Krishnan Parasuraman is responsible for driving revenue growth across IBM's digital media clients through the use of big data technologies. In his role, he works very closely with some of the largest marketing service providers and digital ad networks in an advisory capacity, driving big data solution architectures and best practices for management of Internet scale analytics. Krishnan drives the technology strategy for IBM's big data product portfolio and is an authority on the use of technologies, such as Hadoop and massively parallel data warehousing technologies, towards solving analytical problems in the online digital advertising, customer intelligence, and real-time marketing space. He speaks regularly at industry events, writes for trade publications, and is the author of the book, Harness the Power of Big Data.

Prior to his current role, Krishnan has worked in research, product development, consulting, and technology marketing roles across multiple disciplines within information management. He has enabled marketing and customer analytics solutions for large media and consumer-focused organizations like Apple, AOL, Yahoo, Microsoft. and Motorola. He holds an M.S. degree in computer science from the University of Georgia.

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