Online content provider

Boosting ad revenue over 30 percent with analytics powered by IBM Netezza data warehouse appliance

Published on 30 Jun 2012

Validated on 02 Dec 2013

"The whole goal is to predict behavior, and to see if there’s a good chance the user will click on the ad." - Brad Terrell, Vice President and General Manager, Netezza and Big Data Platforms, IBM

Online content provider

Media & Entertainment

Data Warehouse


A large online content provider developed a new advertising-based revenue model that fits with its philosophy of understanding its customers’ needs intimately and catering to those needs with finely targeted content. In accordance with its new model, the company needs to send targeted ads and content to thousands of customer segments, but standard web analytics platforms struggle to handle the required multi-terabyte data analysis.

Business need:
To move from a subscription-based to an advertising-based revenue model, the company needed industrial strength analytical capabilities to produce targeted advertising for numerous market segments.

Deploying an IBM Netezza® data warehouse appliance, the company has implemented advanced reporting and analytic systems for behavioral targeting, lookalike modeling and content targeting.

The company has experienced more than 30 percent growth in ad revenue; more than tenfold ad response increase; rapid processing of very large data sets; low cost and ease of management.

Case Study

Like so many events in the annals of human endeavor, periods of business growth usher in both the best of times and the most challenging of times, as a large online content provider recently discovered. As it redesigned its websites and migrated its revenue model from subscriptions to advertising, the content provider found it lacked the analytics capacity to support the change. “The company was challenged in its ability to get meaningful information,” says Brad Terrell, vice president and general manager of Netezza and Big Data Platforms at IBM.

In accordance with its new model, the company needs to send targeted ads and content to thousands of customer segments, but standard web analytics platforms struggle to handle the required multi-terabyte data analysis. Traditional web analytics tools deliver little more than canned, page-centric reports without visitor-level views. And these systems fail to adapt to ever-changing optimization tactics. Conventional data warehouses have similar limitations. Off-the-shelf tools are unable to measure unique visitors and page views across the company’s diverse network of web properties. The company needed a manageable, low-cost solution that would meet growing demand. And it found that in the IBM Netezza® data warehouse appliance.

Fast, user friendly and more
In addition to being fast and user friendly, the IBM Netezza data warehouse appliance makes advanced reporting and analysis possible, and can be used to integrate data across systems such as email marketing and customer relationship management (CRM). Plus, the appliance offers scale: the content provider manages 200 terabytes of data on the IBM Netezza data warehouse appliance today, and that number will surely grow as it attracts more traffic and advertising.

The IBM Netezza data warehouse appliance provides the analytic horsepower behind three critical marketing activities:

  • Behavioral targeting
  • Lookalike modeling
  • Content targeting

Behavioral targeting
Behavior, as traditional direct marketers know, is the most powerful indicator of audience intent. The company divides consumers into segments, using a behavioral targeting engine, then serves relevant advertising to these segments across all its sites. “IBM Netezza plays a key role in this,” says Terrell. “When the company needs to match an ad request with the appropriate target audience, they push it to IBM Netezza and run queries for its advertising segments. Then they bring that data downstream to make the segments available.”

Some behavioral targeting is based on content choices. In the case of a sports enthusiast, the company can drill down by sport and team and serve ads based on those choices (for tickets, say, or an airline promotion). But behavior also includes factors like time of day: Does the person read the news in the morning and shop at night? If so, serve him or her with an ad for their favorite store after 6:00 PM.

“The whole goal is to predict behavior, and to see if there’s a good chance the user will click on the ad,” says Terrell. The content provider identifies the ads and websites that people have clicked on from user behavior data provided by off-the-shelf tools, but it needed a more robust data warehouse environment to make that data actionable using large-scale, complex analytics.

“The company has numerous different sites,” says Terrell. “To track the whole network using off-the-shelf tools, they would have to go the sites and download each one of them manually.”

The content provider also clusters channels into logical groups like news and finance. “The company can’t get data from off-the-shelf tools on how a logical group is doing,” says Terrell. “If the person visits both autos and real estate, each area will be recorded uniquely. With the IBM Netezza data warehouse appliance, they can see across the whole network.”

Thanks to the IBM Netezza data warehouse appliance, the company can also store and query granular audience data down to a segment of one. And the appliance helps the company determine taxonomy—that is, it helps classify content areas and groups in an ordered system. The advertising side has a different taxonomy from the publishing side, but topics like sports and apparel match in both. “The IBM Netezza data warehouse appliance enables the company to maximize the relevance of every ad they serve to every visitor across all of their websites,” says Terrell.

Look-alike modeling
The IBM Netezza data warehouse appliance also facilitates the building of look-alike models. In essence, these are predictive models that anonymously identify larger segments of ad prospects that will be likely to respond to particular ad campaigns.

Look-alike models work like this: Audience A clicks through a certain ad. The content provider determines, based on the model, that Audience B shares many characteristics with Audience A, so it targets Audience B for the same ad.

Look-alike models are built by leveraging user behavior data and analytics. The company layers geographic data, another potent variable, onto behavioral data. For example, if a visitor is from the Washington, DC area, without even knowing the person, there’s a good chance the visitor is going to like the Redskins.

Geographic data comes from the ad servers, but the IBM Netezza data warehouse appliance does the data crunching to analyze trends within specific zip codes. The company also uses the appliance to marry that information with third party data from Epsilon (on local shopping patterns, for example) to create more effective models leveraging offline data.

But the ability to identify these trends and produce accurate look-alike models requires speed. Consumers change, and predictive models have ever-shorter shelf lives in digital media. Audiences have to be defined, and quickly moved through the conversion funnel.

“The IBM Netezza data warehouse appliance provides the near real-time performance that helps the company quickly analyze big data sets and produce meaningful results,” says Terrell.

Content targeting
The content provider puts as much effort into content targeting as it does ad targeting. It uses different algorithms for each, but they both lead to the same goal: Relevant content results in higher traffic and ad inventory.

Content targeting is based on the principle of “passive personalization.” A historical user cache is created and content is provided to the person based on site visitation history. But this can be tricky. The user cache might show that the user has visited the news channel in the past month, but what does that really mean? An auto enthusiast may jump into the news when there’s a hot story, for example.

“Just because they visit the news channel a couple of times doesn’t mean they’re changing the pattern,” says Terrell.

Data from an off-the-shelf tool is 48 hours behind, so the company employs its user cache on IBM Netezza to analyze whether people are changing their user group or if the behavioral change is just a temporary one.

Breaking news can open the door for additional content. It isn’t enough to read a news report on Michael Jackson’s death—true fans also wanted to know about his home. The company was able to predict how all the channels can benefit from this one topic.

“The more the company understands customers and the way they use the site, the better they can provide relevant content,” says Terrell.

The return on investment (ROI)
The content provider uses analytics to understand shoppers during peak seasons, compare trends from year to year and forecast hot products. The IBM Netezza data warehouse appliance makes it simple to run such complex, large scale analyses. “The company can run queries on the IBM Netezza data warehouse appliance regardless of the volume or the data set,” says Terrell. “And they can connect any analytical tool to IBM Netezza. That’s one of the big strengths. People with less technical training can use it—they don’t have to learn the Netezza architecture.”

Then there’s speed. Staff members recently had to work with a new application. They would have had trouble even loading the data in the previous environment. “In less than a week, the company was able to load a few terabytes of data, process it, slice and dice it and produce a meaningful report,” says Terrell.

And the total cost of ownership for IBM Netezza was minimal, compared with other database vendors.

The content provider’s new direction and tools produced quick results. Unique page views increased by 35 percent after deploying the IBM Netezza data warehouse appliance. The company also achieved stronger advertising performance, reporting a 41 percent revenue increase one year and a 35 percent hike in first quarter of the next. In addition, its behavioral targeting solution increased response for advertisers from 300 percent to 2,000 percent.

“The IBM Netezza data warehouse appliance improved the company’s campaign performance and helped the organization grow,” says Terrell.

About IBM Netezza data warehouse appliances
IBM Netezza data warehouse appliances revolutionized data warehousing and advanced analytics by integrating database, server and storage into a single, easy-to-manage appliance that requires minimal set-up and ongoing administration while producing faster and more consistent analytic performance. The IBM Netezza data warehouse appliance family simplifies business analytics dramatically by consolidating all analytic activity in the appliance, right where the data resides, for industry-leading performance. Visit: to see how our family of data warehouse appliances eliminates complexity at every step and helps you drive true business value for your organization. For the latest data warehouse and advanced analytics blogs, videos and more, please visit:

About IBM Data Warehousing and Analytics Solutions
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IBM products and services that were used in this case study.

IBM Netezza Performance Server, IBM Netezza 1000

Legal Information

© Copyright IBM Corporation 2012 IBM Corporation Software Group Route 100 Somers, NY 10589 Produced in the United States of America June 2012 IBM, the IBM logo and are trademarks of International Business Machines Corp., registered in many jurisdictions worldwide. Other product and service names might be trademarks of IBM or other companies. A current list of IBM trademarks is available on the web at “Copyright and trademark information” at Netezza is a registered trademark of IBM International Group B.V., an IBM Company. This document is current as of the initial date of publication and may be changed by IBM at any time. Not all offerings are available in every country in which IBM operates. THE INFORMATION IN THIS DOCUMENT IS PROVIDED “AS IS” WITHOUT ANY WARRANTY, EXPRESS OR IMPLIED, INCLUDING WITHOUT ANY WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND ANY WARRANTY OR CONDITION OF NON-INFRINGEMENT. IBM products are warranted according to the terms and conditions of the agreements under which they are provided.

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