Someone clicks on an ad and visits your site. Some time later they buy from you. At what point should an ad stop receiving credit for 'driving' this order? In a subsequent post, we'll tackle the notion that "credit/no-credit" demarcations may not be sufficiently subtle, but for now, let's talk about ways to establish just such a line. It's helpful to posit that with infinite cookie windows, each ad would be credited with some fraction of buyers who were driven by the ad and some who simply bought later without the ad having any influence over the purchase. Perhaps the consumer in that later group is now on a different shopping mission, perhaps they've been influenced to visit your store again by other media or word of mouth, but particularly for well-known brands assuming that all orders preceded by an ad click were driven by that ad is hard to justify. The other cohort of buyers were influenced by the ad. However, they may not convert same session because they want to shop around, they want to consult with someone (a spouse?) about a purchase before commiting, etc. Indeed, it's reasonable to assume that as the delay between the ad click and the order gets longer the split between the "considered buyers" influenced by the ad and the "random re-visitors" will change, shifting from heavily dominated by the former, to heavily dominated by the later as the delay gets larger. This framework, if accepted, gives us three different mechanisms for determining the right cookie window. Method #1: The Click-to-Order Curve
- Take non-brand PPC conversion data for the past two or three months. Brand traffic converts differently from non-brand and is generally not incremental, hence we recommend focusing on the competitive ad data.
- Count the number of orders placed within each day subsequent to the ad click that preceded the order. To avoid edge effects and rounding issues, we recommend classifying the click to order delay in seconds, dividing by 86,400 (the number of seconds in one day) and truncating the results rather than rounding, so that any order placed within 24 hours of the ad click is binned as a "0", meaning within the first day. A "1" is then between 1 and 2 days, etc.The data should look something like this (only showing the first 20 rows):
- Make a column showing the running % of total orders, such that the number is always increasing towards 100%.
- Graph the day versus the percent complete as below, make sure all days are represented, filling in days with no orders as needed.
- Using the data from Method 1 Step 2 calculate the Average Order Size for each day delayed. If the data is sparse you might want to take a three- or five-day rolling average to smooth out the spikes.
- Make a graph of Average Order Size by day
- Take 20 orders that closed the same day as the click and see what fraction of orders had items that matched the keyword. Exclude overly general keywords like "apparel" for landsend.com since 100% of orders will match that keyword.
- Repeat this process with orders around the cookie window(s) suggested by Methods 1 and 2 above
- Repeat again with orders as far away from the ad click as you can find as a benchmark on the other end
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