Using a Purchase Funnel to Measure Marketing Effectiveness: Better than Last-Click Attribution But Far From Perfect

Marketers have long struggled to measure the impact of individual promotions. Even online marketing, where every click can be captured, and often tracked back to a specific person, doesn’t automatically solve the problem. Merely tracking clicks doesn’t answer the deeper question of the causal relationships among different marketing contacts.

Current shorthand for the issue is “last click attribution” – as in, “why last click attribution isn’t enough”. Of course, vendors only start pointing out a problem when they’re ready to sell you a solution. So it won’t come as a surprise that a new consensus seems to be emerging on how to measure the value of multiple marketing contacts.

The solution boils down to this: classify different contacts as related to the different stages in the buying process and then measure their effectiveness at moving customers from one stage to the next. This is no different from the “sales funnel” that sales managers have long measured, nor from the AIDA model (awareness, interest, desire, action) that structures traditional brand marketing. All that’s new, if anything, is the claim to assign a precise value to individual messages.

Examples of vendors taking this approach include:

- Marketo recently announced new "Revenue Cycle Analytics" marketing measurement features with its customary hoopla. The conceptual foundation of Marketo’s approach is that it tracks the movement of customers through the buying stages. Although this itself isn’t particularly novel, Marketo has added some significant technology in the form of a reporting database that can reconstruct the status of a given customer at various points in the time. Although this is pretty standard among business intelligence systems, few if any of Marketo's competitors offer anything similar.

- Clear Saleing bills itself as an “advertising analytics platform”. Its secret sauce is defining a set of advertising goals (introducer, influencer, or closer) and then specifying which goal each promotion supports. Marketers can then calculate their spending against the different goals and estimate the impact of changes in the allocation. Credit within each goal can be distributed equally among promotions or allocated according to user-defined weights. While such allocation is a major advance for most marketers, it’s still far from perfect because the weights are not based on directly measuring each ad's actual impact.

- Leadforce1 offers a range of typical B2B marketing automation features, but its main distinction is to infer each buyer's position in a four-stage funnel (discovery, evaluation, use, and affinity) based on Web behaviors. The specific approach is to link keywords within Web content to the stages and then track which content each person views. The details are worth their own blog post, but the key point, again, is that the contents are assigned to sales stages and the system tracks each buyer’s progress through those stages. Although the primary focus of LeadForce1 is managing relationships with individuals, the vendor also describes using the data to assess campaign ROI.

Compared with last click attribution, use of sales stages is a major improvement. But it’s far from the ultimate solution. So far as I know, none of the current products does any statistical analysis, such as a regression model, to estimate the true impact of messages at either the individual or campaign level. They either rely on user-specified weights or simply treat all messages within each stage as a group. This lack of detail makes campaign optimization impossible: at best, it allows stage optimization.

Even more fundamentally, stage analysis assumes that each message applies to a single marketing stage. This is surely untrue. As brand marketers constantly remind us, a well-designed message can increase lifetime purchases among all recipients, whether or not they are current customers. It’s equally true that some messages affect certain stages more than others. But to ignore the impact on all stages except one is an oversimplification that can easily lead to false conclusions and poor marketing decisions.

Stage-based attribution has its merits. It gives marketers a rough sense of how spending is balanced across the purchase stages and lets them measure movement and attrition from one stage to the next. Combined with careful testing, it could give insight into the impact of individual marketing programs. But marketers should recognize its limits and keep pressing for solutions that measure the full impact of each program on all their customers.