Here’s why you should develop your own Attribution Model

The good news is that an Attribution model can be a fantastic insight source that can make you generate a huge impact in your company’s bottom line. The bad news is that the impact can be amazingly good or bad, too bad.

As you can imagine our industry works similar than any other industry. Standards are set by the market leaders. The most used solution for Analytics in the world is Google Analytics, ergo, the set standard is, as you can imagine, Googlecentric. So Google was telling us that last click is the standard unless, of course, Adwords is involved in any touch point, in which case the conversion is attributed to Adwords, because…well, the only explanation is because is good for Google. I can not blame Google for that, but I have to say that I’m definitely surprised that analytics professionals where using that attribution model for years. The standard attribution models:

  1. Are not based on a model that tries to represent the reality, with the higher level of certainty, but on a completely discretionary decision (normally from a HIPPO, Highest Paid Person Opinion).
  2. Since they are not trying to represent the reality but to define a criteria to standarize the measurement, all the information that flows from them (Acquisition Cost, repayment period, etc) is wrong, or at least you wont be able to know if it is correct. .

So basically, you are making your marketing decisions based on wrong premises just because someone else, with its own interest, is telling you so. Meaning that you can be nickel and dimmed to death without even noticing it.

People love to simplify things even at a pretty odd extremes. Like using benchmarks to define the average conversion rate of an ecommerce site against another one, as if comparing different systems were that simple. Or like in this case, defining an attribution model based on whatever the heart told the HIPPO that day. “Let’s use a standard attribution model so we can compare each other in a better manner”. But, you shouldn’t care about comparing yourself with others but improving your media investment comparing your previous performance with your current one so you can generate a lift. Why should you care about winning others on a specific metric at a potencial cost of losing money?

The problem of following the industry leader on setting standards is, it’s Google (in this case) chasing the same objective as you?”. Some people believe that Google is a cool tech company, so they don’t see it as a vendor or a competitor but “someone” that improve their life (and I wont judge that). Google is, as a matter of fact, a marketing company that has to demonstrate their stakeholders they are constantly growing. But you can’t avoid the law of diminishing results, at some point you have most of the market and need to look for another one.

So they go from vendor, to competitor to partner all the time. As a matter of fact I’m Google’s vendor, client, partner and competitor all the time.

They are also the biggest media intermediator in the world, and again, good for them. Their main objective is earning money as any other company in the world, and there’s no doubt that they are accomplishing that goal, chapeau!

The point is, all the things they develop are done with the same north in mind, making more money. So the question is, should we relax and leave Google algorithms decide what’s better for us? I don’t think so. Some years ago people got mad when they realized that Google Analytics’ attribution model favored AdWords over other referring channels. Of course Facebook and any other media companies in the world would do the same. Again, this is not a communist complaint about marketing companies, believe me, I’m way far from that. It’s not their fault, come on, you was trusting your budget to your vendor’s “black box” without even asking what was inside that. Don’t call me unfair to believe you were way too naïf.

But the attribution model is just the tip of the iceberg. That was the introduction to put you us all in the same page so we can analyze a very interesting white paper called “Ad Click Prediction: a View from the Trenches”, written by a Google Team on Machine Learning algorithms focused on the “massive-scale learning problem that is central to the multi-billion dollar online advertising industry”.

Again, take a careful read to the white paper tittle and the performance variable they are focused on. Correct, their main goal is ad click, so basically they are developing algorithms that improve the ad clicks. The more clicks, the more money for Google. Makes perfect sense if you are Google, but what if you are not? Well, the advertising value is based on its capacity of converting “people” into clients, today (purchase) or tomorrow (purchase intention). None of them are related to clicks, unless you believe that more clicks will always means more sales, which goes against to one of the main economic principles that rules the entire world of material things, the “Law of Diminishing Returns”.

The problem on using a model that is based on clicks when you are interested on earning money is that you can be in the third stage of this model losing money, but with a high CTR and keep pushing budget pedal to the metal. I got tired of finding companies in that stage…and believe me when I say that at that stage you are too late.

Your media buying algorithm should be based on sales conversions instead, because that’s what you are interested in. The problem is that Google can’t measure your real sales, so your algorithm must be build on your end. We’ve already talked about that we shouldn’t be interested in external but in internal benchmarks, because every system (set of things interacting together with a common objective) is unique, so you want your algorithm to be optimized on predicting in a better manner based on YOUR specific reality.

So my suggestion is reading the paper, which is really interesting, just change clicks per sales conversions. They propose the well known, simple and efficient logistic regression (Yes, supervised machine learning since we are talking about predictive analytics)

with the following process.

Visit here if you want to learn more about the model, very recommended reading for the weekend.

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