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Filtering 'Bad' Traffic: For Best Results, Get Beyond Good and Evil

  |  December 30, 2011   |  Comments

Continued from

There may even be deep-seated reasons we get addicted to the short leash. Economists explain the behavior as "myopic loss aversion," and it can affect investment returns.

Think of it this way. One day, you lost a mitten. When you're five years old, that's bound to happen. But for some reason, the adult brain sees this loss as a significant moral failing and a potential threat to the family's future financial viability. You'd hear about it over and over again, with constant warnings to "never" lose a mitten again (thinking in terms of absolutes), or worse, be fitted with "idiot strings" to ensure the security of your personal hand-warming equipment (shaming). You'd think that after years of training, and in an adult scenario that involves a mandate for profit maximization, it wouldn't be hard to drop the baggage. But it is! Too easily, "should" and "ought" creep into our decision-making in ways that aren't synonymous with "the predicted return on investment."

If you've ever tried to advise Google that it's going about something in the "wrong" way, or asked it to define exactly what a valid or invalid click is, you know that Google and its computers don't think in terms of good and evil. Catchy slogans ("don't be evil") are basically red herrings; they are not, in any shape or form, Google policy.

One way of looking at the Google world of data-driven success is to say that "Google is like a baby's brain" (terms used by one Googler attempting to explain the company's apparent managerial chaos). Systems are built to absorb and learn at a breathtaking pace, just by "taking it all in" and letting the "brain" do what it does best - compute, iterate, and develop more complexity in responses than could be possible through a deliberate effort to "plan." In fact, the "baby's brain" analogy is a compliment to Google, at least in moral terms. A baby is much more judgmental and discerning than a machine-learning system. As inhuman as it may sound, machine learning works at its breathtaking best when it's free of moral baggage.

Take a concrete example. Why prejudge a certain publisher in the display network because it's a "certain type of site"? Just let the machines run and cut off the non-performers at a predetermined point. It could be that you get 200 clicks on a "silly" travel site for the same price as you pay for 30 clicks on the "serious" one, so the two turn out to be equally good buys.

Similarly, you should avoid excluding keyword phrases that "might not be exactly" what is being searched for. What if they aid in research stage awareness, or convert occasionally? Exclude away if the data look pitiful. But please don't leap into a priori negativing-out of phrases including things like "recipes," "cheap," "directions," "software," etc. just because these are slightly off your desired micro-intent. Try keeping them hanging around a little longer to see if they convert occasionally. Or try different ad groups, landing pages, and creative for different types of intent.

In some cases, you'll make some amazing discoveries. We've discovered that searchers interested in high-volume orders actually use a variety of different signifiers, and they're all seeking slightly different things (most of them being some form of bulk order). But at first glance, some of the words ("wholesale," let's say) appear to convert poorly. Until you solve the puzzle, the tight-leash, exclude-whole-hog mentality appears sound, but it doesn't correspond well with the broader potential inherent in the search behavior.

To be sure, you'll still want to use your human judgment to see patterns and to adjust slightly to taste. Just don't overdo it. And try using rounds of lower bidding (signifying something that is worth less to you) rather than exclusions (signifying that the source is literally worthless to you).

This column was originally published on Aug. 12, 2011 on ClickZ.

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ABOUT THE AUTHOR

Andrew Goodman

Goodman is founder and President of Toronto-based Page Zero Media, a full-service marketing agency founded in 2000. Page Zero focuses on paid search campaigns as well as a variety of custom digital marketing programs. Clients include Direct Energy, Canon, MIT, BLR, and a host of others. He is also co-founder of Traffick.com, an award-winning industry commentary site; author of Winning Results with Google AdWords (McGraw-Hill, 2nd ed., 2008); and frequently quoted in the business press. In recent years he has acted as program chair for the SES Toronto conference and all told, has spoken or moderated at countless SES events since 2002. His spare time eccentricities include rollerblading without kneepads and naming his Japanese maples. Also in his spare time, he co-founded HomeStars, a consumer review site with aspirations to become "the TripAdvisor for home improvement." He lives in Toronto with his wife Carolyn.

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