Mutli-Channel Attribution and Understanding Interaction

I'm no cosmologist, but this post is going to rely on a concept well known to astrophysicists, who often have something in common with today's marketers (as much as they might be loathe to admit it). So what is it that links marketing analytics to one of the coolest and most 'pure' sciences known to man?

I'll give you a hint: it has to do with such awesome topics as black holes, distant planets, and dark matter

The answer? It has to do with measuring the impacts of things that we can't actually see directly, but still make their presence felt. This is common practice for scientists who study the universe, and yet not nearly common enough among marketers and people who evaluate media spend and results. Like physicists, marketing analysis has progressed in stages, but we have the advantage of coming into a much more mature field, and thus avoiding the mistakes of earlier times.

Marketing analytics over the years and the assumptions created :

• Overall Business Results (i.e. revenue) : if good, marketing is working!
• Reach/Audience Measures (i.e. GRPs/TRPs) : more eyeballs = better marketing!
• Last-click Attribution (i.e. click conversions) : put more money into paid search!
• Path-based Attribution (i.e. weighted conversions) : I can track a linear path to purchase!
• Model-based Attribution (i.e. beta coefficients) : marketing is a complex web of influences!

So what does this last one mean, and how does it relate to space? When trying to find objects in the distant regions of the cosmos, scientists often rely on indirect means of locating and measuring their targets, because they can't be observed normally. For instance, we can't see planets orbiting distant stars even with our best telescopes. However, based on things like the bend in light emitted from a star, and the composition of gases detected, we can 'know' that there is a planet in orbit of a certain size and density, that is affecting the measurements that we would expect to get from that star in the absence of such a hypothetical planet. Similarly we don't see black holes, but we can detect a certain radiation signature that is created when gases under the immense gravitational force of the black hole give off x-rays.

This is basically what a good media mix/attribution model is attempting to do, and it's why regression models can work so well. You are trying to isolate the effect of a particular marketing channel or effort, not in a vacuum, but in the overall context of the consumer environment. I first remember seeing white papers about this mainly about measuring brand lift due to exposure to TV or display ads, but those were usually simple linear regression problems, connecting a single predictor variable to a response, or done as a chi-square style hypothesis test. But outside of a controlled experiment, this method simply won't give you an accurate picture of your marketing ecosystem that takes into account the whole customer journey.

As a marketer, you've surely been asked at some point "what's the ROI of x channel?" or "How many sales did x advertisement drive?" And perhaps, once upon a time, you would have been content to pull a quick conversion number out of your web analytics platform and call it a day. However, any company that does things this way isn't only going to get a completely incorrect (and therefore useless) answer, but they aren't really even asking the right question.

Modern marketing models tell us that channels can't be evaluated in isolation, even if you can make a substantially accurate attempt to isolate a specific channel's contribution to overall marketing outcomes in a particular holistic context.

Why does that last part matter? Because even if you can build a great model out of clean data that is highly predictive, all of the 'contribution' measuring that you are doing is dependent on the other variables.

So for example, if you determine that PPC is responsible for 15% of all conversions, Facebook is 9%, and email is 6%, and then back into an ROI value based on the cost of each channel and the value of the conversions, you still have to be very careful with what you do with that information. The nature of many common methods for predictive modeling is such that if your boss says, "Well, based on your model PPC has the best ROI and Facebook has the worst, so take the Facebook budget and put it into PPC" you have no reason to think that your results will improve, or change the way you assume.

Why not? Because hidden interactivity between channels is built into the models, so some of the value that PPC is providing in your initial model (as well as any error term), is based on the levels of Facebook activity that were measured during your sample period.

It's a subtle distinction, but an important one. If you truly want to have an accurate understanding of the real world that your marketing takes place in, be ready to do a few things:

1. Ask slightly different questions; look at overall marketing ROI with the current channel mix, and how each channel contributes, taking into account interaction
2. Use that information to make incremental changes to your budget allocations and marketing strategies, while continuously updating your models to make sure they still predict out-of-sample data accurately
3. If you are testing something across channels or running a new campaign, try adding it as a binary categorical variable to your model, or a split in your decision tree

Just remember, ROI is a top-level metric, and shouldn't necessarily be applied at the channel level the way that people are used to. Say this to your boss "The marketing ROI, given our current/recent marketing mix, is xxxxxxx, with relative attribution between the channels being yyyyyyy. Knowing that, I would recommend increasing/decreasing investment in channel (variable) A for a few weeks, which according to the model would increase conversions by Z, and then see if that prediction is accurate." Re-run the model, check assumptions, rinse, repeat.