Former Eloqua CEO, Joe Payne, explains how data will soon deliver the vision of a fully automated marketing machine.

Marketing Automation has been the first revolution in using data to create better and more effective marketing campaigns through scoring, nurturing and revenue attribution. However, a new revolution is fermenting around the idea of Customer Intelligence. With Customer Intelligence, smart algorithms will be able to execute fully automated marketing campaigns by independently learning from users’ responses about interests and needs.

This revolution is similar to automation innovations that are happening in other areas of our economy and are likely to improve our lives. The self-driving car, for example, may free up commuters’ time to work or speak with their friends. Already we have small robots like Roomba that can clean our floors. It’s exciting to see a deep level of automation come to the realm of B2B marketing.

Let’s take a look at the three key technologies that underlay this exciting automation revolution and are changing the marketing landscape:

1. Data-mining

The quantity of data on the Web is enormous and growing by the second. Big-data from the Web includes valuable attributes such as technologies, key hiring, financials, press releases and announcements, as well as people’s bios and social feeds. IBM, for example, is already saying that social data is becoming more important than data stored in organizations’ CRMs. The challenge is that this data, while valuable, is scattered across websites and social networks, is unorganized and constantly changes.

Data-mining techniques can process big-data from the Web and separate the signal (that data points that matter) from the noise (all other data). The ability to separate the signal from the noise is key in identifying and taking advantage of the plethora of data and information about companies and individuals on the Web.

2. Predictive Analytics

Predictive Analytics allows companies to analyze their historical data and apply the results to a set of Web data on prospects and make a set of predictions about them. With a high degree of confidence we can then determine what type of content they are likely to click on, how likely they are to convert, and what their expected lifetime value to the organization could be. These are powerful insights that can help marketers make critical allocation decisions that will drive more revenue to their organization.

3. Recommendation Engines

Netflix, Amazon and many other innovative companies are already using recommendation engines to find and present products and movies that consumers are likely to buy. These recommendation engines are constantly learning from people’s actions and finding hidden links between products based on data from millions of people.

For B2B marketers, recommendation engines can be used to analyze topics that are relevant to their audience and recommend new topics for marketing assets such as eBooks and blog posts.

The Marketing Waze

The combination of data mining, predictive analytics and recommendation engines will create something like Waze for marketers. Waze gets you to your destination as efficiently as possible by automatically taking into account disparate data including traffic count, driving speed, user reports, and distance. All you do is set the objective and the app does the rest.

As a marketer, you will soon be able to do the same. You will choose the objective and the technology will choose the most efficient ways to convert the prospects—all with the power of data. As with Waze, the more data the system ingests, the more accurate and effective the campaigns are going to be.

Improving Performance with Customer Intelligence

The ability to use robust data to drive marketing decisions has produced outstanding results. For example at Birst, a fast-growing business intelligence company, matching content to prospects has improved CTR by 567% over a period of three months. The company used Mintigo to mine data on 80,000 prospects from the Web and used predictive analytics to predict who would respond to their marketing assets.

SmartBear is using Customer Intelligence to match products with prospects. The company has multiple product lines and each one caters to a different audience. By segmenting their marketing database to personas and sending the right eBook for each persona, SmartBear improved CTR by 577% on the first eBook and 176% on the second eBook.

I’m excited about the use of Customer Intelligence technologies—data mining, predictive analytics and recommendation engines—to drive better marketing. By knowing more about their customers and prospects, marketers can better tailor offers that are relevant. This improves the experience for buyers and sellers alike. B2B CMOs that choose to embrace this new technology and data-driven approach will undoubtedly thrive in the years ahead.

About Joe Payne:  Joe Payne is an Executive and Board Member with more than 20 years of leadership experience and a proven track record as CEO of high growth software companies. He currently serves on the Board of Directors of public companies Cornerstone OnDemand (NASDAQ: CSOD) and DealerTrack (NASDAQ: TRAK). He also serves on the boards of private companies TrackMaven and Plex Systems, as well as the advisory board of Mintigo. Joe’s most recent full time executive role was as the Chairman and Chief Executive Officer of Eloqua. He joined Eloqua in 2007 when it was an $11M revenue company. He assembled and led a world-class management team that grew Eloqua into a $125M revenue SaaS business in six years. Joe led Eloqua to a successful IPO in 2012 and a sale to Oracle in 2013. Recognizing Eloqua’s leading market position and its robust customer base, Oracle paid the highest multiple of revenue in its history for a public company. Prior to Eloqua, Joe held executive positions at iDefense, eSecurity, eGrail, MicroStrategy, and InteliData. Joe began his career in brand management where he worked on the Coca-Cola brand and the Mr. Clean brand. Joe received his M.B.A. from the Fuqua School of Business at Duke University where he was a Fuqua Scholar. He is a Magna Cum Laude graduate of Duke University. You can find Joe on Twitter @paynejoe.