The Must-Have Big Data Tools

  |  June 29, 2012   |  Comments

A look at how predictive analytics, data-driven automation, and adaptive learning tools work to optimize marketing programs.

In the column, "Claiming your Unfair Advantage, I describe four types of "Big Data" tools. Before I elaborate on these tools, I want to share a lesson from my friend Mark Huffman of P&G. When Mark joined the world's largest advertiser in 1984, it was all about data analysis. "In God we trust," one P&G marketer told him, "all others bring data!" Today, Mark is executive production manager at Procter & Gamble and is responsible for all integrated marketing. At SES New York in March he was on the integrated marketing keynote panel and discussed the most important tool at P&G in the war with big data, and that is the "big idea."

P&G relies on its huge marketing research library and access to large volumes of consumer behavior to deeply understand consumer insights. But he warned marketers that the big money is not in just having the data - it is in discovering and executing on big ideas that come from those insights.

He shared the story of one of P&G's most successful integrated campaigns.

"Febreze's Breathe Happy campaign launched exactly one year ago. Since then it has won top creative and ad effectiveness honors, most recently the Black Lion at last week's Cannes Advertising Festival. It is obviously a big idea that drove consumer preference in the category. Where did this idea come from, and did big data have anything to do with it?

Yes, big data played key roles in creative briefing, and also in optimizing its amplification. P&G uses analytics to assess the landscape (competitive activity, brand performance and perception, etc., media mix modeling, etc.). It also relies on measures to decide how to optimize media to amplify the idea. But the idea in this case was literally created on the back of a cocktail napkin. A creative team somehow internalized all our big data and created the big idea. It would not have happened without the creative spark.

The brand briefed the ad agency with this data-informed challenge: Wake up consumers from their world of broken promises and help them see that Febreze is a breath of fresh air in the category!

The ad agency came back with this creative idea: Febreze surprises people with real proof of its transforming freshness and scent so anyone can Breathe Happy."

Does this mean you can't leverage big data without having the big ideas? I don't think so.

There are two types of gains to be had from big data. Insights into consumer behavior and trends and incremental improvements by adaptive learning systems that automate the actions you might otherwise take if you had an army of people.

Analytics and predictive analytics tools provide you with ways to uncover consumer behaviors. Data-driven automation and adaptive learning optimization tools leverage big ideas baked into their algorithms to make incremental changes or use personalization to take advantage of marketing inefficiencies and close the gap to help you take advantage of opportunities to maximize market share, revenues, or profits without the overhead of an army of people doing these things.

Let's look at a couple of these adaptive learning and optimization tools (there are a handful I know of) to understand how they work.

BloomReach is an SEM dream tool that recently came out of stealth mode with over 80 customers. It brings a natural language learning model engine, deep site interpretation, and continuous user observation to adapt your site by connecting relevant pages and content to each other and creating optimized and continually adapting landing pages for SEO and SEM.

Last October, Jeremiah Owyang commented on digital price signage at Kohls. He wondered if the company would "experiment with changing prices by time of day, social influence, or social referrals or location." This is a much more efficient way to preserve margins compared with giving away the same site/storewide discounts at all times. Just as Orbitz discovered that it could preserve greater margins by charging Mac users more for the same hotel rooms than non-Mac users. Others, have been doing yield optimization effectively for years and you will be able to personalize prices on your website soon as well.

Consider Runa, which is coming out of stealth mode after three years of working on its data insights models with over 200 small- to mid-size retailers. It works with retailers like and eBay and uses over 90 variables in real time to optimize the offer given to your visitors during their session to maximize the opportunity that they will convert. They typically can optimize at around ~3 percent margin cost to see lifts of over 10 percent in revenues. (Disclosure: I am an advisor to Runa.)

While many believe the future of advertising looks like this scene from "Minority Report" and in-store experiences are personalized like this scene from the same movie, we can learn one key thing from these scenes. If the ads, or data-driven/digital experiences don't leverage the big idea to break through the clutter and become relevant, these ideas will just continue to be noise and ignored by your potential customer.

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Bryan Eisenberg

Bryan Eisenberg is co-founder and chief marketing officer (CMO) of IdealSpot. He is co-author of the Wall Street Journal, Amazon, BusinessWeek, and New York Times best-selling books Call to Action, Waiting For Your Cat to Bark?, and Always Be Testing, and Buyer Legends. Bryan is a keynote speaker and has keynoted conferences globally such as Gultaggen,, Direct Marketing Association, MarketingSherpa, Econsultancy, Webcom, the Canadian Marketing Association, and others for the past 10 years. Bryan was named a winner of the Marketing Edge's Rising Stars Awards, recognized by eConsultancy members as one of the top 10 User Experience Gurus, selected as one of the inaugural iMedia Top 25 Marketers, and has been recognized as most influential in PPC, Social Selling, OmniChannel Retail. Bryan serves as an advisory board member of several venture capital backed companies such as Sightly, UserTesting, Monetate, ChatID, Nomi, and BazaarVoice. He works with his co-author and brother Jeffrey Eisenberg. You can find them at

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