Posts Tagged ‘Proiftability’

How to test your ad copy for Google AdWords

Monday, July 20th, 2009

In today’s post, I will describe both the ease and importance of testing Ad Copy.

“Ad Copy” as defined here, is the four lines of ad text that the search engines allow in their Sponsored Links section on the right hand side of a search results page.  Here is an example of a typical ad on Google:

roofer ad

  • The first line is the Title of the Ad Copy
  • The second and third lines are the Description of the Ad Copy
  • The fourth line is the Display URL that you want to use for Branding your website.

What exactly is A/B testing?

A/B testing is a commonly used term in statistics where one controls for other factors to measure how Version A of “something” performs compared to Version B.  Within the context of A/B testing of Ad Copy, this holds all other factors constant, and then measures performance of Ad Copy A against performance of Ad Copy B.

As an example, let’s say that you want to indicate in your Ad Copy that you have discounted your product from $50 to $40.  You may now want to know what would work better: “Discounted by $10” or “Discounted by 20%”.  Well, you don’t have to guess.  With A/B testing, you can actually test and know for sure what works better.

What does “performing better” mean?

What does it even mean to say “Ad Copy A is performing better than Ad Copy B”?  There are multiple definitions of “performing better”, all other things being equal:

1)      Which Ad Copy gets clicked more often

2)      Which Ad Copy leads to a higher conversion rate

3)      Which Ad Copy results in more profits

Note that just because an Ad Copy gets clicked more often does not mean that it will lead to more conversions.  For instance, if an Ad Copy overpromises (e.g. “free camera”) which the website itself fails to deliver (no free camera), the Ad Copy will get clicked often, but the disappointed users won’t convert into customers.

Also note that just because an Ad Copy leads to a higher conversion rate, does not mean that it is more profitable.  For instance, consider:

  • Ad Copy A with a 10% conversion rate on an item priced at $20 and
  • Ad Copy B with a conversion rate of 5%, but for an item priced at $200

In this example, Ad Copy B may be more profitable even if it has a lower conversion rate.

What exactly is: “all other things being equal?”

Above, I have used the phrase: “all other things being equal”.  This means holding all other factors constant in the “environment”.  For instance, if we were testing Ad Copy A and Ad Copy B, then one “inferior” way to do this would be to use Ad Copy A today, and Ad Copy B tomorrow.  Then we could measure which one performed better.

The reason this is “inferior” is that external environmental factors could have changed (e.g. new competition) between today and tomorrow.  The right way then to do this kind of A/B testing is by alternating Ad Copy A and Ad Copy B in “real time”, i.e. showing Ad Copy A to a user followed immediately by showing Ad Copy B to the next user.

Implementing A/B testing

A/B testing on the Ad Copy is very easy to set up as all the major search engines including Google provide the infrastructure for this.  Google even has the option where you can ask Google to show the better performing ad more often in an automated manner.  However, if you do use this Google feature, note that Google will use definition 1 (above) of better performing, i.e. the ad that gets clicked more often will be shown more often.

Now that you know how to test and measure the performances of different versions of an Ad Copy, in my next post, I’ll talk about writing good ad copy.

Written by Nadir Hussain, COO, Media Flint, Inc.

Nadir Hussain is an Internet Advertisement and Search Engine Marketing expert. He is both Google and Yahoo certified for their Internet Advertisement programs.  He teaches an Internet based Advertising class both at UC/Berkeley Extension and the Continuing Studies Program at Stanford University.  His education comprises of a Bachelor’s degree in Computer Science & Mathematics from UC/Santa Cruz, a MSEE from Stanford University and an MBA from UC/Berkeley.

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