Attribution Will Be Critical for AI-Based Marketing Success

I gave my presentation on Self-Driving Marketing Campaigns at the MarTech conference last week. Most of the content followed the arguments I made here a couple of weeks ago, about the challenges of coordinating multiple specialist AI systems. But prepping for the conference led me to refine my thoughts, so there are a couple of points I think are worth revisiting.

The first is the distinction between replacing human specialists with AI specialists, and replacing human managers with AI managers. Visually, the first progression looks like this as AI gradually takes over specialized tasks in the marketing department:

The insight here is that while each machine presumably does its job much better than the human it replaces,* the output of the team as a whole can’t fundamentally change because of the bottleneck created by the human manager overseeing the process. That is, work is still organized into campaigns that deal with customer segments because the human manager needs to think in those terms. It’s true that the segments will keep getting smaller, the content within each segment more personalized, and more tests will yield faster learning. But the human manager can only make a relatively small number of decisions about what the robots should do, and that puts severe limits on how complicated the marketing process can become.

The really big change happens when that human manager herself is replaced by a robot:

Now, the manager can also deal with more-or-less infinite complexity. This means we no longer need campaigns and segments and can truly orchestrate treatments for each customer as an individual. In theory, the robot manager could order her robot assistants to create custom messages and offers in each situation, based on the current context and past behaviors of the individual human involved. In essence, each customer has a personal robot following her around, figuring out what’s best for her alone, and then calling on the other robots to make it happen. Whether that's a paradise or nightmare is beyond the scope of this discussion.

In my post a few weeks ago, I was very skeptical that manager robots would be able to coordinate the specialist systems any time soon.  That now strikes me as less of a barrier.  Among other reasons, I’ve seen vendors including Jivox and RevJet introduce systems that integrate large portions of the content creation and delivery workflows, potentially or actually coordinating the efforts of multiple AI agents within the process. I also had an interesting chat with the folks at Albert.ai, who have addressed some of the knottier problems about coordinating the entire campaign process. These vendors are still working with campaigns, not individual-level journey orchestration. But they are definitely showing progress.

As I've become less concerned about the challenges of robot communication, I've grown more concerned about robots making the right decisions.  In other words, the manager robot needs a way to choose what the specialist robots will work on so they are doing the most productive tasks. The choices must be based on estimating the value of different options.  Creating such estimates is the job of revenue attribution.  So it turns out that accurate attribution is a critical requirement for AI-based orchestration.

That’s an important insight.  All marketers acknowledge that attribution is important but most have focused their attention on other tasks in recent years.  Even vendors that do attribution often limit themselves to assigning user-selected fractions of value to different channels or touches, replacing the obviously-incorrect first- and last-touch models with less-obviously-but-still-incorrect models such as “U-shaped”, “W-shaped”,  and “time decay”.  All these approaches are based on assumptions, not actual data.  This means they don’t adjust the weights assigned to different marketing messages based on experience. That means the AI can’t use them to improve its choices over time.

There are a handful of attribution vendors who do use data-driven approaches, usually referred to as “algorithmic”. These include VisualIQ (just bought by Nielsen), MarketShare Partners (owned by Neustar since 2015) Convertro (bought in 2014 by AOL, now Verizon), Adometry (bought in 2014 by Google and now part of Google Analytics), Conversion Logic, C3 Metrics, and (a relatively new entrant) Wizaly. Each has its own techniques but the general approach is to compare results for buyers who take similar paths, and attribute differences in results to the differences between their paths. For example: one group of customers might have interacted in three channels and another interacted in the same three channels plus a fourth. Any difference in results would be attributed to the fourth channel.

Truth be told, I don’t love this approach.  The different paths could themselves the result of differences between customers, which means exposure to a particular path isn’t necessarily the reason for different results. (For example, if good buyers naturally visit your Web site while poor prospects do not, then the Web site isn’t really “causing” people to buy more.  This means driving more people to the Web site won’t improve results because the new visitors are poor prospects.) 

Moreover, this type of attribution applies primarily to near-term events such as purchases or some other easily measured conversion.  Guiding lifetime journey orchestration requires something more subtle.  This will almost surely be based on a simulation model or state-based framework describing influences on buyer behavior over time. 

But whatever the weaknesses of current algorithmic attribution methods, they are at least based on actual behaviors and can be improved over time.  And even if they're not dead-on accurate, they should be directionally  correct. That’s good enough to give the AI manager something to work with as it tells the specialist AIs what to do next.  Indeed, an AI manager that's orchestrating contacts for each individual will have many opportunities to conduct rigorous attribution experiments, potentially improving attribution accuracy by a huge factor.

And that's exactly the point.  AI managers will rely on attribution to measure the success of their efforts and thus to drive future decisions.  This changes attribution from an esoteric specialty to a core enabling technology for AI-driven marketing.  Given the current state of attribution, there's an urgent need for marketers to pay more attention and for vendors to improve their techniques. So if you haven’t given attribution much thought recently, it’s a good time to start.

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* or augments, if you want to be optimistic.