Paid search is an industry that’s grounded in data and statistics, but one that requires practitioners who can exercise a healthy dose of common sense and intuition in building and managing their programs. Trouble can arise, though, when our intuition runs counter to the stats and we don’t have the systems or safeguards in place to prevent a statistically unwise decision.
Should you pause or bid down that keyword?
Consider a keyword that has received 100 clicks but hasn’t produced any orders. Should the paid search manager pause or delete this keyword for not converting? It may seem like that should be plenty of volume to produce a single conversion, but the answer obviously depends on how well we expect the keyword to convert in the first place, and also on how aggressive we want to be in giving our keywords a chance to succeed.
If we assume that each click on a paid search ad is independent from the others, we can model the probability of a given number of conversions (successes) across a set number of clicks (trials) using the binomial distribution. This is pretty easy to do in Excel, and Wolfram Alpha is handy for running some quick calculations.
In the case above, if our expected conversion rate is 1 percent, and that is indeed the “true” conversion rate of the keyword, we would expect it to produce zero conversions about 37 percent of the time over 100 clicks. If our true conversion rate is 2 percent, we should still expect that keyword to produce no conversions about 13 percent of the time over 100 clicks.
It isn’t until we get to a true conversion rate of just over 4.5 percent that the probability of seeing zero orders from 100 clicks drops to less than 1 percent. These figures may not be mind-blowingly shocking, but they’re also not the types of numbers that the vast majority of us have floating in our heads.
When considering whether to pause or delete a keyword that has no conversions after a certain amount of traffic, our common sense can inform that judgement, but our intuition is likely stronger on the qualitative aspects of that decision (“There’s no obvious difference between this keyword and a dozen others that are converting as expected.”) than the quantitative aspects.
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