2011 Jul 01

Marketing Attribution Beyond the Last Click
David M. Raab
Information Management
July / August 2011

As online advertising consumes a larger share of marketing budgets, order measuring its impact correctly becomes more important. Information management professionals will inevitably become involved because they control so much of the data needed to construct sound answers. So, whether you work in IT or marketing, it’s worth taking some time to understand the issues.

– fractional attribution. If attribution experts agree on anything, it’s their disdain for the most common approach to online marketing measurement. This method, called “last click attribution”, assigns all credit for each sale to the last message received. The obvious problem is that this ignores the impact of all previous messages. One common alternative, assigning all credit to the first message a customer received, has the same flaw. Most discussion among online attribution experts is aimed at finding logical ways to split the credit among all messages.

This often involves assigning weights to messages based on their type (a display ad is assumed to have less impact than an email), when they were received relative to a purchase (later messages get higher weight than earlier messages), or simply by assigning an equal weight to each message. Weights can also be based on more elaborate statistical methods, but this requires structured tests that few marketers actually conduct. Regardless of how the revenue is allocated, the portion attributed to each message can be compared with the cost of the message to calculate a Return on Investment.

– incremental attribution. While fractional attribution methods often feel arbitrary, most marketers can find a rule they feel gives reasonable results. But the technique has a more fundamental flaw: it assumes that all revenue is the result of marketing messages, and that the sum of revenues created by each message equals total revenue. Neither assumption is correct. Most businesses have a base volume of revenue they would earn even if they did no advertising, at least in the short term. And sales that are driven by advertising are often the result of several messages working together; taking away one message might have no impact or a very high impact depending on the circumstances.

Marketers who recognize these issues are increasingly turning from fractional attribution to incremental attribution, which attempts to calculate the change in revenues resulting from a particular message. This can be measured directly through structured tests, although these are often hard to execute. A more common approach is based on stages in the sales process. A typical set include includes the initial contact; learning more about the product; and making the purchase. Messages are classified by the stage they support and buyers are tracked as they move through the stages. Marketers use this structure to compare the effectiveness of different messages in moving buyers from one stage to the next. This lets them estimate the incremental impact on cost and revenue of sending on different messages, allowing a meaningful ROI calculation.

– cross channel attribution. Fractional and incremental attribution are both designed to measure messages across multiple marketing channels. But most implementations consider only the several digital channels, including paid search, display ads, email, mobile and social media. This is better than measuring one channel but still ignores offline activity. That’s a big problem because online marketing often influences offline sales, just as offline activities influence online sales. Combining online and offline measurement is more challenging than merging online channels because many offline channels don’t lend themselves to tracking individuals. You probably don’t know whether a particular person saw last night’s TV ad or what they bought in the grocery store this morning. And even when you can track an offline individual, perhaps through a loyalty program or credit card, you often can’t link their offline data to online identities such as cookies and email addresses.

These tracking problems are not insurmountable. Many vendors offer databases that merge online and offline identities with varying degrees of success. Surveys and research panels can provide detailed information on a sample of individuals, even though their actual identities are unknown. But most offline attribution programs still feed aggregate data for marketing spend and sales into marketing mix models, without attempting to track specific individuals. Although less precise, these can still show major correlations between online and offline activities. This is often enough to substantially change the value attributed to different marketing programs.

– long term impact. Attribution discussions are generally framed in terms of assigning credit for a single sale. But one message can actually affect multiple purchases and may also impact behaviors such as payments, service requests, and recommendations. In other words, serious marketing measurement must include multiple time periods and multiple transaction types as well as multiple channels. This usually requires tracking individuals over time, or at least tracking groups that can somehow be separated – for example, by making different offers in different cities and seeing how future behaviors in those cities diverge. This sort of analysis is much easier in industries where companies have direct customer relationships, such as bank accounts or service contracts. Even in those situations, it can be difficult to isolate the impact of a single marketing treatment. Many analyses therefore look at short-term results and use simulation models to project their long-term consequences.

Final thought: The common thread linking all attribution issues is the importance of data capture. Although online activities generate enormous volumes of data, much of it can only be used for attribution if tied to identifiers that persist over time and are recognized across channels. This doesn’t happen by accident: systems must be designed to capture identifying data in ways that are both useful to marketers and respectful of their customers’ privacy. Industry vendors are constantly offering new ways to achieve this, but the true strengths and weaknesses are rarely apparent – and may not even be understood by the vendors themselves. It will increasingly be the job of information management professionals to assess these methods and understand how they fit into the broader picture of marketing measurement.

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David M. Raab is a consultant specializing in marketing technology and analytics and author of the B2B Marketing Automation Vendor Selection Tool (www.raabguide.com). He can be reached at draab@raabassociates.com.

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