The days when companies could trace a customer's purchase decision to just a few entry points are over. It has become the norm for consumers to interact with companies through numerous digital and offline channels. While the need to evaluate a company's interactions with customers is not new, the continuing growth of channels has marketers scrambling to identify the approaches that work and those that have fallen flat.
This technique, called cross-channel attribution measurement—assigning credit to online and offline marketing communications to evaluate their effectiveness in converting consumers into paying customers—is "white hot," according to Forrester Research analyst Tina Moffett.
"The conversations that we're having with clients indicate that marketing leaders understand the value that [cross-channel attribution measurement] brings from a measuring perspective," says Moffett, "and understanding the actual value that channels, campaigns, etc., bring lets marketers better allocate their budgets on a tactical basis."
There are several ways companies tend to approach an attribution measurement strategy: last-click attribution, a rules-based model, or an algorithm-based model.
Under the first model, the last piece of marketing content seen by a consumer before making a purchase receives full credit for generating the sale. The second involves assigning custom values to each marketing channel. The third relies on statistical data to inform marketers of the value of each interaction.
The simplest approach, last-click attribution, is the most common, according to a 2012 report from Google Analytics and market research firm Econsultancy. More than half (54 percent) of the 600 marketers who responded to a survey used a last-click model; 20 percent used a methodology developed in house.
However, the last-click attribution models often yield misleading results, especially for multichannel brands, Moffett says. Last-click attribution models are "not an acceptable way to measure the success of a campaign or a channel, given all the other marketing exposures and activities that a person may be experiencing," Moffett argues.
Rules- and algorithm-based models are more reliable, but they include challenges as well. Implementing an effective rules-based model requires anticipating the various scenarios that will call for certain rules. In addition, getting all the involved parties to come to a consensus on what criteria to use can be difficult.
Basing an attribution strategy on algorithms could provide accurate results, but it typically requires having a thorough understanding of statistical formulas. As George Michie, cofounder and CEO of digital marketing firm RKG, writes in a blog post, "Asking program managers to accept the results of an algorithm that affects the perception of their performance without being able to meaningfully explain how it works is asking quite a lot."
Even after arriving at an attribution strategy, marketers will need to continuously test and adjust it. Attribution measurement, Moffett says, is "a journey, and…depends on the condition of your data, your technology's flexibility, and your organization's willingness to support its development."