Robert Moskowitz published an article today in iMediaConnection entitled: “Behavioral Marketing: In Focus – 5 Targeting Success Stories.” Two of the stories have some relevance to a topic that I cover on this blog – equating behavioral targeting to event-based targeting and the opening the door to what behavioral targeting can mean when we consider customer re-targeting.
The first story I would like to talk about is Ecommerce – Sequencing Ads. In this piece he describes your basic storyboarding of banner rotations with a twist. The first time someone sees a banner, they see a full price ad. If they click on that banner and visit the site they get cookied and are considered having experienced an ‘event’ (my definition not Robert’s). Subsequently if that individual is encountered on the Web again through an ad campaign, s/he is shown a discounted ad upon being recognized as being someone that has been through the event.
So it is a spin on storyboarding insomuch that storyboarding deals with prospect-targeting and rotating ads in sequence without the event of having visited the advertiser’s web page(s). But this pretty basic event-based targeting example is hardly what I would characterize as behavioral targeting. We know next to nothing about this user other then the fact that they once clicked on an ad and visited the advertiser’s web page. They could be a customer already, they could be a prospect. Who knows.
Like other examples of what people consider “behavioral targeting,” all we have here is someone that has seen a web page before. Had this person registered, logged on or somehow identified themselves through some incentive, and then left, and we later recognized them through an ad campaign, then we could consider it behavioral targeting. We would know something about them and could target them based on some behavior or customer segment model.
The statistical results cited in this article were impressive, re-messaged leads represented 25% of the traffic and 50% of the revenue. But only 7% of the banner impressions served were re-targets. That means that only 7% of the audience composition was the same audience. So all of the effort made in capturing people from the event, cookieing them and then getting setup for the re-target reached that audience 7% of the time. This means that the media buy was not effective in getting to where the audience repeat-visits. A lot of wasted effort.
When we work with people on customer re-targeting, we help them recognize that when there are sites – more so than networks – that consistently perform well month over month, there is a high likelihood that those sites are visited repeatedly by existing customers as well. DirectServe campaigns can potentially find audience compositions of pre-existing customers to be as high as 25% or more when implemented. So when you execute a campaign designed to capture people who have been through an event before, you should be media buying where they will repeat-visit. Networks are a hard place to do that due to shear size and broadness of the networks composition. Whereas sites that consistently perform well will likely be sites that are also visited repeatedly by your customers in addition to prospects to it is a win-win for customer acquisition and re-acquisition.
Customer re-targeting is a lot closer to behavioral targeting than event-based targeting is. But I dare not confuse you by changing the definition for fear of getting lopped in with what is a failure-to-meet-expectation offering. Customer re-targeting can be customer, registrant or applicant re-targeting. It is the re-targeting of any known individual. So if someone clicks on an ad, visits a site and identifies themselves, you can re-target them later based on information you now know about them. How? You build user segment groups for your targeting.
The second Story of Roberts that I think is relevant is Seasonal Retailers – Extra Incentives. This example comes very close to what I am talking about, only it is confined to the 24/7 network. Again it is event-based as it tags users who have been to a flower shop web site, makes the assumptions that they have shopped and then later targets them with ads following the assumption that they were previous customers.
Had this been true customer re-targeting rather than event-based targeting, the flower shop would have already tagged its customers with cookies at the time of the initial transaction. Then when it came time to serve ads, they could recognize its existing customers – segmented based on a model of customer profiles – and message to its audience accordingly. Instead of assuming someone is a customer, you could know they are a customer. Furthermore, if the individual does not carry the cookie tag, meaning they are not a customer (or they deleted the cookie), then you can easily distinguish them and message to them as a prospect!
How does all of this become relevant to the holiday season?
Now is the time that people will be getting ready to shop online in droves. A storm is coming to the e-tailers and they are scrambling to get ready. If e-tailers can create a customer segmentation scheme now – such as a 5 or 10-point schema that describes customer preferences – and cookie customers as they shop between November 1st and December 1st, they could use those cookies for re-targeting online through their online ad campaigns from December 1st through December 24th. Furthermore, all of the cookies that get written during the holiday season that segment customers for each e-tailer could be leveraged by the online ad campaigns for these advertisers throughout the rest of the year.
Sounds simple doesn’t it? It really is. An ad server targets cookies. If the cookies are written to reflect a customer segment target then they can be recognized and targeted. If the advertiser writes the cookies and the ad server can read the cookies, then BOOM you have the integration of online ad serving and online marketing. That is DirectServe.
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