The process of indexing an ad is not so different from the process of indexing any other content. The primary difference is that we aren’t looking to return a list of ads (or search results); we want to return a single ad. There are also some secondary differences in that ads will typically have required targeting parameters such as location, age, or gender.
As mentioned before, we’ll only be targeting based on location and content, so this section will discuss how to index ads based on location and content. When you’ve seen how to index and target based on location and content, targeting based on, for example, age, gender, or recent behavior should be similar (at least on the indexing and targeting side of things).
Before we can talk about indexing an ad, we must first determine how to measure the value of an ad in a consistent manner.
Three major types of ads are shown on web pages: cost per view, cost per click, and cost per action (or acquisition). Cost per view ads are also known as CPM or cost per mille, and are paid a fixed rate per 1,000 views of the ad itself. Cost per click, or CPC, ads are paid a fixed rate per click on the ad itself. Cost per action, or CPA, ads are paid a sometimes varying rate based on actions performed on the ad-destination site.
To greatly simplify our calculations as to the value of showing a given ad, we’ll convert all of our types of ads to have values relative to 1,000 views, generating what’s known as an estimated CPM, or eCPM. CPM ads are the easiest because their value per thousand views is already provided, so eCPM = CPM. But for both CPC and CPA ads, we must calculate the eCPMs.
If we have a CPC ad, we start with its cost per click, say $.25. We then multiply that cost by the click-through rate (CTR) on the ad. Click-through rate is the number of clicks that an ad received divided by the number of views the ad received. We then multiply that result by 1,000 to get our estimated CPM for that ad. If our ad gets .2% CTR, or .002, then our calculation looks something like this: .25 x .002 x 1000 = $.50 eCPM.
When we have a CPA ad, the calculation is somewhat similar to the CPC value calculation. We start with the CTR of the ad, say .2%. We multiply that against the probability that the user will perform an action on the advertiser’s destination page, maybe 10% or .1. We then multiply that times the value of the action performed, and again multiply that by 1,000 to get our estimated CPM. If our CPA is $3, our calculation would look like this: .002 x .1 x 3 x 1000 = $.60 eCPM.
Two helper functions for calculating the eCPM of CPC and CPA ads are shown next.
Notice that in our helper functions we used clicks, views, and actions directly instead of the calculated CTR. This lets us keep these values directly in our accounting system, only calculating the eCPM as necessary. Also notice that for our uses, CPC and CPA are similar, the major difference being that for most ads, the number of actions is significantly lower than the number of clicks, but the value per action is typically much larger than the value per click.
Now that we’ve calculated the basic value of an ad, let’s index an ad in preparation for targeting.
When targeting an ad, we’ll have a group of optional and required targeting parameters. In order to properly target an ad, our indexing of the ad must reflect the targeting requirements. Since we have two targeting options—location and content—we’ll say that location is required (either on the city, state, or country level), but any matching terms between the ad and the content of the page will be optional and a bonus.3
We’ll use the same search functions we defined in sections 7.1 and 7.2, with slightly different indexing options. We’ll also asSUM e that you’ve taken my advice from chapter 4 by splitting up your different types of services to different machines (or databases) as necessary, so that your ad-targeting index doesn’t overlap with your other content indexes.
As in section 7.1, we’ll create inverted indexes that use SETs and ZSETs to hold ad IDs. Our SETs will hold the required location targeting, which provides no additional bonus. When we talk about learning from user behavior, we’ll get into how we calculate our per-matched-word bonus, but initially we won’t include any of our terms for targeting bonuses, because we don’t know how much they may contribute to the overall value of the ad. Our ad-indexing function is shown here.
As shown in the listing and described in the annotations, we made three important additions to the listing. The first is that an ad can actually have multiple targeted locations. This is necessary to allow a single ad to be targeted for any one of multiple locations at the same time (like multiple cities, states, or countries).
The second is that we’ll keep a dictionary that holds information about the average number of clicks and actions across the entire system. This lets us come up with a reasonable estimate on the eCPM for CPC and CPA ads before they’ve even been seen in the system.4
Finally, we’ll also keep a SET of all of the terms that we can optionally target in the ad. I include this information as a precursor to learning about user behavior a little later.
It’s now time to search for and discover ads that match an ad request.
3 If ad copy matches page content, then the ad looks like the page and will be more likely to be clicked on than an ad that doesn’t look like the page content.
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