Re: Sample Size for Online Pay-Per-Click Advertising
From: Mack (macckone_at_a_nospamjunk123_ol.com)
Date: 12/27/04
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Date: Mon, 27 Dec 2004 05:12:54 GMT
On 26 Dec 2004 13:58:42 -0800, "espeed" <james@unifiedmind.com> wrote:
>I am trying to determine how long we should test an online
>pay-per-click advertising campaign before we can have a good idea of
>how it will perform if we let it run continuously.
>
>I need to find the sample size, but I am not sure if it can be
>considered a random sample since we will not be selecting the subjects
>at random, they will be selecting us by choosing to click on the ad.
>Further, since market conditions change over time, a sample that
>represents the population now may not represent the population a few
>weeks later so the test may be useless for projections. So, am I
>testing a sample or am I only testing the entire population for a
>shorter period of time?
>
>I am not a statistics expert so please let me know if my analysis is
>correct...
>
>An Overture ad for a given keyword costs $10.65 per click for the top
>position. I hypothesize that we will maximize profits by paying for it
>and letting it run continuously.
$10.65 seems a little steep. At a 2% CTR that is something like .21
per impression. Of course you should really be concerned with the
conversion rate from click through to sale. I am assuming this is
really high dollar merchandise because if the CTR to sale rate is
10% you are spending $106.50 per sale. From below it looks
like you are forecasting closer to a 5% conversion from
click through to sale. That pushes the cost up to $213/sale.
>From a business perspective the lower end should be used for
calculating profit potential. You forecasted a bottom end of 3%.
That is $355/sale.
>
>We can test this theory if we can commit to paying for enough clicks to
>equal the sample size of the total possible clicks for a given
>click-through rate (CTR), for a given time period. Overture provides
>estimates on how many impressions we will have for the month so once we
>determine the CTR for our ad, we can estimate the total number of
>clicks we would have if we let the ad run for an entire month.
The company providing the banner space should also be able to give
you an average CTR. Obviously a marketing consultant is more helpful
than a statistician here.
>
>The total number of clicks for the month is the population size (notice
>that the population size is not the number of people whom see the ad,
>but it's the number of people whom click on the ad because we are
>testing the performance of the Web page).
If you are testing web page performance then the correct statistic
is maximum views/min. The average monthly clicks given below is
about 1 view every 17 minutes. If you are concerned with total
monthly bandwidth that will be a small fraction of the total cost for
the CTR.
>
>For example, let's assume a 2% click-through rate and 612,740 ad
>impressions for the month so at a 2% CTR, that would result in 12,254.8
>clicks for the month. If you set the population size to 12,254.8 with a
>confidence level of 95% and a confidence interval of 2, the sample size
>would be 2,008 clicks at a total cost of $21,385.20.
>
>If the conversion rate for the sample is 4.93%, 2,008 clicks will
>result in 98.9944 conversions. Because we are using a confidence
>interval of 2, we can be 95% sure that if market conditions do not
>change and we let the ads run for the entire month, for the entire
>relevant population, we would have a conversion rate between 2.93%
>(4.93%-2) and 6.93% (4.93%+2).
>
>Please poke holes in my analysis and let me know if I making any false
>assumptions, any leaps of logic, etc? Or, please let me know how to
>change the test to make it more valid.
I would start with a lower cost CTR for testing. The click to sale
conversion rate is what you are looking for. A lower cost spot may
give you a better idea of what to expect before you shell out a lot of
cash and find out that your click to sale conversion is 1/2% instead
of 5%. You may find that there is a "sweet spot" that is not the top
rate that gets a better click to sale conversion.
Leslie 'Mack' McBride
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