Maximizing campaign ROI and profit from advertising is among every marketer's top priorities. Because of this, yield optimization - increasing the spread between the cost of advertising and the revenue derived from it - is a critical focus for digital marketers. Fundamental technology breakthroughs in big data platforms now enable new approaches to advertising yield optimization that promise to dramatically enhance profitability for digital marketers who take advantage of them.
Different buyers often value the same advertising inventory differently. Underpriced inventory leaves profit on the table, and overpriced inventory left unsold does the same. Yield optimization techniques focus on finding the perfect price in order to maximize profit by ensuring that each ad unit is sold to the right buyer at the right time for the right price.
From an analytical perspective, yield optimization typically requires the construction of a price/volume curve that plots the expected impression volume available for a range of ad prices, or bids, in a targeted market. Once constructed, this curve helps answer questions like:
Here is an example of how one might use a price/volume curve to find the optimal bid for an advertisement in a specified market:
Importantly, the analytical complexity involved in constructing price/volume curves properly is amplified by several factors that bring anything other than modern big data platforms to their knees.
Attribution analysis is a necessary building block for yield optimization, as is near-real-time analysis. In addition, yield optimization solutions must be highly automated in order to apply complex analytical calculations to large data sets that are being updated in near real time. In fact, one leading online advertising network that reaches 86 percent of all U.S. Internet users estimates that its ad servers generate up to 10 terabytes of data per day, and they leverage up to 280 terabytes of data in constructing thousands of price/volume curves across multiple campaigns each day for yield optimization. This firm found that porting their existing yield optimization algorithms to a modern big data platform that enabled much quicker analysis of much larger data sets - reducing analytical execution time from over a day to just five minutes - led to dramatic increases in campaign performance (case study available here).
This is why the aforementioned big data technology breakthroughs are so important to marketers. Yield optimization is among a class of big data problems for which increased analytic performance translates directly into increased business value. In other words, when it comes to yield optimization, the marketer who analyzes the most data fastest wins.
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Brad Terrell is responsible for maximizing the value that Netezza technology delivers to the many innovative digital media firms that power their large scale data analysis initiatives with Netezza's analytics appliances.
Before joining Netezza (IBM acquired Netezza November 2010), Brad led digital media initiatives at Endeca as director of business development and helped fuel the firm's rapid growth from $10 million to more than $100 million in annual revenue. Prior to Endeca, Brad helped design and launch products including the Spyglass Mosaic web browser, IBM PowerPC microprocessor, H&R Block online investing website, and FlightSafety International's FAA Level C-certified flight simulators.
Brad's entrepreneurial experience includes co-founding and serving as president of ElectricWish.com, ranked among the top five e-commerce sites on the web by ZDNet in 1999, and M-Nova, a pioneer in product development outsourcing to Eastern Europe. Brad holds an MBA from MIT Sloan and a computer science degree from Rice University.
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