Untangling the Gordian Knot of Campaign Tracking, Part 2

Last time , I started looking at the knotty problem of campaign tracking. Since the birth of offline marketing, we’ve been using simplistic views of how campaigns work and which channels produce results and which channels don’t.

The classic approach is “the last click gets the sale.” In other words, the channel or campaign that generated the last visit before the conversion event gets credit for that conversion. When the Internet was viewed purely as a direct-response medium and campaign plans were simple, this approach might have been sufficient. But in today’s multichannel, marketing-mix-optimization world, this approach is naive.

One of the main problems is the technology landscape that most advertisers face; a number of different systems are involved. Each marketing channel generally has its own system for deploying and managing marketing activities. For example, advertisers use bid management systems for PPC (define) search campaigns, an ad-serving systems for display ads, an e-mail system for managing e-mail, and so on. Marketers may be using these tools in-house or an agency may be using them on their behalf.

Each marketing system will have its own data capture and reporting capabilities built in. This is important so the channel activity can be optimized against the performance of the campaign in terms of clicks to the site and conversions. But it also means that while campaigns can be optimized within a channel (e.g., PPC search), it’s difficult to optimize campaigns across channels because the data resides in different places.

A key issue, then, is to get all the campaign response data in one place. You can use a single campaign management tool across all channels or collect all your campaign response data in one place, like your Web analytics system or an outsourced data warehouse. Once you have all the data in one place, you can begin to look at optimizing campaigns across the different channels you use.

Campaign management tools from companies like DoubleClick and Atlas can increasingly be used for multiple channels. Although originally developed as ad-serving technologies, they have expanded their capabilities by adding bid-management capabilities. It’s also possible to track activities on other channels through either redirects or the universal tags that are beginning to appear, such as DoubleClick’s Floodlight tag. One advantage of the ad-serving technologies is they can measure post-impression effects of display advertising as opposed to just click-throughs.

Post-impression effects, also known as “view throughs” are where someone is served an ad impression on a site but the person doesn’t click through. The ad impression is recorded and if that person subsequently arrives on the advertiser’s site within a certain period and converts, that conversion can be attribution to the post-impression effect of that advertising.

The ability to understand post-impression effects is very important for some advertisers, particularly for branding campaigns. However, there are a number of issues to consider about measuring these post-impression effects. First, advertisers must ensure the post-impression data is also discounted against other marketing channels. For example, post-impression effects of display advertising need to be discounted against search activity. If display advertising drives increased search activity, there’s a risk of double counting the sales effect if the two channels are measured and analyzed independently. This comes back to the issue of having the campaign data in a single repository.

The second issue is after the ad has been served, what time window do you allow for the visitor to come to the site? The point of measuring post-impression effects is that ads don’t always generate a direct response and that (similar to TV ads) exposure to display ads builds awareness and consideration that indirectly leads to conversion. Although it varies, typically advertisers or their agencies use a window of 30 days after the person was served the ad.

Advertisers must consider the difference between association and correlation when evaluating post-impression effects, particularly for large brands running large campaigns. In determining the effects of marketing activity, we’re looking for correlation as well as cause and effect. Just because someone was on a page where my ad was displayed and then came to my site up to 30 days later, I can’t be confident there’s real correlation. It may just be a coincidence. However, if she saw my ad five times and came to the site a couple of days later, I can be more confident that I’m seeing a real effect.

Next time, I’ll look at the differences in measuring campaigns using campaign management tools and Web analytics tools. They won’t necessarily tell you the same thing.

Till then…

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