How Prediction Learning Curves Can Improve Digital Ad Effectiveness

/// How Prediction Learning Curves Can Improve Digital Ad Effectiveness

November 29, 2012  |  Blog

In his fascinating new book The Signal and the Noise, New York Times political blogger Nate Silver discusses what he calls the “prediction learning curve,” a model for mapping the relationship between effort and prediction accuracy. Not surprisingly, the relationship strongly resembles the well-known Pareto Effect—i.e. about 20 percent of the initial effort (time, money, effort) yields about 80 percent of the prediction accuracy.

Or, as Silver says: “Getting a few basic things right can go a long way…The first 20 percent often begins with having the right data, the right technology and the right incentives.”

What does this have to do with digital advertising? Optimizing your digital advertising in-flight is essentially a prediction exercise. The goal is to take existing data—while using the right technology and incentives–and make changes to your plans based on a predicted outcome that is better than current results. And, as with the prediction learning curve, the first 20 percent of effort can have a huge impact on digital ad effectiveness.

Right Data, Right Technology, Right Incentives

Data – Real-time online ad effectiveness data is here. Brands can now measure brand recall, likeability, persuasiveness and more on a daily basis across traditional and online video ads, even for small campaigns.

Technology – Today, ad effectiveness technology works by reserving a small amount of ad inventory for measurement purposes. After the ad runs, single-question polls are then served up to the group that was delivered the ad (the exposed or “test” group) and to a statistically comparable group not exposed to the ad (“control” group). This test/control design enables advertisers to understand the single variable impact of individual creative unit performance, site performance and frequency of exposure.

Incentives – Collaborative optimization tools enable agencies and digital publishers to work together to deliver a better result for their advertiser clients. Agencies measure ad performance by digital publisher. Digital publishers know that either ads perform well on their sites, or their sites get dropped from the campaign.

So, How Can the 20 Percent Rule Maximize My Digital Advertising?

Four basic factors can deliver a dramatic improvement in your digital ad effectiveness:

Creative Rotation – Most advertisers do not copy-test their digital advertising. They blindly run multiple creative units without any real understanding of the differences in ad effectiveness across creative units. Identify your bottom-performing 20 percent of creative, and reallocate this media weight to the top-performing 80 percent of creative.

Site Rotation – Similar to creative rotation, but across different websites. Performance varies by site, so quickly assess individual ad performance and identify the bottom-performing 20 percent of sites. Then, rotate out of these sites and into your higher performing sites.

Exposure Frequency – Unlike TV, digital advertisers have the ability to cap their exposure frequency per consumer. The question for advertisers is, where to set the cap? The opportunity is to quickly identify where the frequency of exposure yields little or no incremental ad performance and then cap your digital ad exposure at this frequency.

Collaborative Optimization – Get key players in the advertising eco-system to collaborate toward a common objective of improving their advertising performance. Everyone wins. Advertisers can evaluate digital ad performance across agencies. Agencies have a powerful motivation to improve performance, as advertisers now have performance-specific metrics across agencies. Publishers also have good reason to collaborate with agencies, as they know that agencies will pull advertising from their sites if they underperform. Importantly, there are now technology platforms that enable all three parties to collaborate in real time to optimize ad performance.

Moving Up the Prediction Learning Curve

These four factors represent the 20 percent effort in the digital advertising “prediction learning curve.” If you do these and only these four things, you can improve your digital ad performance. The choice is yours—to execute digital the way many advertisers do today, or to move up the prediction learning curve and deliver improved results for your brand.

Randall Beard will be hosting a webinar on December 6 called “Improving Ad Performance with the 3Rs: Optimizing Reach and Resonance to Heighten Reaction.”

Link: How Prediction Learning Curves Can Improve Digital Ad Effectiveness

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