0 [x+1] NexTargeting Conference: Cross-Channel Attribution and Online Ad Scalability Remain Hot Topics

Continuing my adventures in online ad measurement, I attended [x+1]’s NexTargeting Summit last week. This reinforced and refined my conclusions from last month’s OMMA Metrics conference, which identified the burning industry issues as:

- better understanding of the interactions between online and offline events (both advertising and results), and

- better scalability for successful online advertising programs.

The online / offline connection was covered by MarketShare CEO Jon Vein, who presented studies that showed including the “indirect impact” of online display ads can dramatically improve their reported return on investment. He also said his firm has found that marketing mix models with complete data can explain as much as 98% of the variance in revenue, while optimization based on mix models can typically improve marketing effectiveness by 10% to 15%. Although I don’t recall Vein mentioning it during his actual presentation, he did tell me in a side conversation that his firm purchased JovianData last year in order to expand its ability to work with individual-level data. MarketShare and [x+1] announced an alliance last month to combine MarketShare’s cross-channel analytics with [x+1]’s digital targeting.

Scalability was covered [x+1] itself, which announced extension of its Media+1 audience targeting platform to combine information from direct media buys and ad exchanges. The relationship between that extension and scalability is a bit complicated, but it boils down to this: combined information lets marketers control the number of ads served to individual consumers across both types of media buys, which segment-level purchases do not. This means that marketers can expand their budgets by targeting ads to new individuals (=effective scaling) rather than bombarding the same people with more messages (=ineffective scaling). That this mimics the reach and frequency measures used in traditional mass marketing (i.e., television) is a happy bonus.

I’ve skipped some of the subtleties of the Media+1 product. These include tracking the degree of overlap between the audiences of different direct-buy Web purchases; identifying optimal message frequency by customer segment; using direct-buy Web sites to establish a base of impressions and then supplementing these on a customer-by-customer basis through real time bidding on ad exchanges; and using scorecards to track performance after initial customer acquisition. The bottom line on Media+1’s beta client was reallocating 40% of the online ad budget to achieve a 20% improvement in results.

[x+1] also used the conference to announce an even broader product, called [x+1] Origin, scheduled for release this summer. This will build a customer-level data hub that combines data and sends targeted messages across display ads, Web site, email, and mobile. I asked [x+1] CEO John Nardone whether it’s actually possible to identify the same customer across all those channels, and he said it’s not an issue in many cases, since you can often give the customer a reason to log in or otherwise identify herself.

Of course, the big exception is acquisition, which seems like a pretty big exception indeed. (“Other than that, how did you like the play, Mrs. Lincoln?”) But tracking mechanisms do get better all the time and there’s plenty of value in better treatment within existing customer relationships. So it’s definitely a good start.

0 Fractional Response Attribution is Worse Than Nothing

Summary: Should companies apply fractional revenue attribution when more sophisticated methods are impractical? I think not: it gives inaccurate results that could result in bad decisions. Better to avoid financial measures at all if you can't do them properly.

I spent most of the past week in San Francisco at overlapping conferences for the Direct Marketing Association and Marketo. My Marketo presentation was based on the marketing measurement white paper I recently wrote for them, which argues that measurement should be based on tracking buyers through stages in the purchase process. One corollary to this is not attributing fractions of revenue among different marketing touches. The analogy I’m currently using is baking a cake – it doesn’t make sense to assign partial credit for the final flavor to different ingredients: the recipe as a whole either works or doesn’t. Only testing can determine the impact of making changes.

Given this mindset, I was more than a little surprised to attend a DMA panel discussion where two of the more sophisticated marketing measurement vendors described their systems as providing fractional attribution. Both vendors also offer more advanced methods and both made clear that they used such methods in appropriate situations. But they seemed to feel that when adequate data is not available, fractional attribution is better than nothing.

I certainly understand their attitude. Many of the business-to-business marketers at the Marketo conference have exactly this problem: their data volumes are too small to accurately measure the incremental impact of most marketing programs. The best suggestion I can make is that they run whatever tests their volumes make practical. I’d further suggest that testing may actually be more practical than they realize if they actively and creatively look for opportunities to do it.

But, again, the vendors on my panel knew this. The examples they gave were situations where companies had previously attributed all marketing revenue to the “last touch” before an actual purchase or other conversion event. They used fractional attribution to help people (marketers and those who fund them) see that other contacts also contribute to those final results. The practical goal was to justify funding for early-stage programs that such as search engine optimization and display advertising that precede that “last touch” itself.

I’m all in favor of recognizing that early-stage contacts have value. But I still feel that assigning a fundamentally arbitrary financial value to those contacts is a mistake. The main danger is that people who don’t know any better may use these numbers to allocate marketing funds to the more “productive” uses. Such figures are not accurate enough to support such decisions.

I’d rather use non-monetary measures such as correlations between different kinds of touches and ultimate results. These can highlight the connections between early and later touches without providing financial values that are easily misapplied. Maybe this is just wishful thinking, but perhaps refusing to provide unreliable financial metrics will even highlight the need for tests that can provide truly meaningful ones—thus helping marketers to make the necessarily investments.

So what do you think: is fractional revenue attribution of reasonable compromise or a harmful distraction? Let me know your thoughts.

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

Summary: Many vendors are now proposing to move beyond "last click" attribution to measure the impact of advertising on movement of customers through a sequence of buying stages. This is a definite improvement but not a complete solution.

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.