The Internet ushered in a new era of performance marketing not only for online advertising, but also for traditional offline ad campaigns that are crafted to drive Web site commerce. In the latter instance, the big challenge is in being able to pinpoint exactly which offline ads are responsible for Web site hits, so that media choices and spending can be fully optimized.
It used to be only pure direct-response marketers that grappled with this challenge. But let's face it. If we're running TV commercials that contain a response mechanism in the form of an 800 number or a Web site URL, as most agencies do these days, we're all in direct marketing. Fortunately, the tools and techniques for correlating TV spots to spikes in Web site traffic have improved dramatically in the last 10 years. In this tough economic environment, agencies are looking for any and all ways to improve a client's Web traffic without a significant cash outlay. Just an additional 5% of traffic to a Web site can make a monumental improvement to a client's bottom line. But how do we increase traffic and sales without endangering or altering a client's brand awareness?
Let's say a marketer is running TV commercials that contain a response mechanism on the network news programs of ABC, CBS and NBC. Assume for the sake of this example that each program carries about the same cost per thousand (CPM). How exactly does one determine which network is generating more Web site traffic each time those commercials run? Sounds like black magic, but studying the habits of today's TV viewers and employing advanced analytics tools can yield eye-opening results. In the example above involving commercials on all three network news programs, it is possible to determine that NBC Network News is actually providing 25% more Web traffic compared with CBS and ABC. Armed with this knowledge, a savvy buyer would migrate share to NBC, thus increasing Web site activity without significantly increasing budget.
It is only recently, because of the dramatic increase in laptops and desktop computers either being in the room where the consumer is viewing TV or in an adjacent room, that we have been able to actually see a significant spike on a Web site when a commercial airs. Different systems used by advertisers and agencies have different ways of attributing the spike. In the widely used CoreDirect, for example, one of the attribution models available measures the spike of responses using what is called an "attribution window." The attribution window looks at the proximity of the response to each spot to determine which spot is better at driving Web volume. By studying trends, we can gain confidence that one television program over another is consistently delivering more Web traffic.
The attribution window, which is user-defined and can be stored by creative and length of spot, frames the airings so as to source responses to them. For example, a marketer could choose to set up a window encompassing one minute before a spot airs and 12 minutes after it runs. If a spot runs on ESPN at 5 p.m., any Web site response to it can be sourced from 5 p.m. to 5:12 p.m.
To accommodate cases in which two or more spots air at the same time, an additional response sourcing parameter, called "station weighting," can be activated. With station weighting, users assign a weight value to each station within a media campaign. When spots air in close proximity time-wise (within the same attribution window), the weights are used as a ratio for apportioning responses to each station during the time that the spots' attribution windows overlap. For instance, with a 20- minute attribution window, if a spot airs on USA at 8:02 and on E! at 8:14, responses will source solely to USA until 8:14. At 8:14, responses will source to USA and E! based on station weights, until 8:22. At 8:22, the USA spot is no longer a candidate for responses because attribution time has elapsed. The E! airing will continue to receive responses until 8:34, when that spot's attribution time elapses.
Figuring out the best attribution windows and proper station weighting takes time and experimentation. But there is no better way to correlate offline advertising with the Web site traffic that advertising generates.