You Ask, I Answer: Cookieless Marketing Attribution?

Mateo asks, “Question: Outlook on a “cookieless” ecosystem and traditional ad channels, like print, or OOH?”

You Ask, I Answer: Cookieless Marketing Attribution?
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Christopher Penn 0:13

In today’s episode, Matteo asks question, what’s your outlook on a cookieless ecosystem? And traditional ad channels like printer out of home? Well, here’s where, and this is my opinion.

Just so we’re clear on that, here’s where I think things are going.

There are a couple of different ways to do to answer the question like what’s working in our marketing, right? There’s very tactical campaign level stuff like did this campaign generate the appropriate results.

And that’s probably not going to change a whole lot.

There is attribution analysis, where you take individualized data, and you sequence it to create path paths of conversion, right, these are the channels in in in the relative order of the occur.

And then there’s what’s called Marketing Mix modeling, where you take your aggregate data, and you take, the activities you’ve done and the outcomes you get, and you build essentially a really, really large complicated regression model, to say these are the things that seem to have the highest correlation to the outcome we care about.

My feeling is that with cookieless, which is a cookieless is a narrow to narrow scope, what’s really happening is the privacy restrictions are increasing on marketing, frankly, as they should be, because a lot of marketers, not you and me, but a lot of marketers are really abused customer information and done really bad things with it.

Look at all the stuff that happened with Cambridge Analytica and using using, you know, racial data to do voter disenfranchisement, you know, that’s abusing marketing data.

And so the world, particularly places like the EU with GDPR, California with CPRA, Virginia has got some stuff, the People’s Republic of China has the py PL law, that was a doozy.

If you violate that law, not only do you get fined, but if your executives show up in China, they get arrested.

And yeah, you don’t want to be going to Chinese prison.

But there’s all these increasingly strict privacy laws because marketers can’t be trusted with people’s data.

It’s the the ugly truth of it.

So when we talk about cookieless, what we’re really talking about as marketers losing trust of people, marketers losing the right to actually not losing the privilege of working with people’s data and people saying, I don’t want to give you my data anymore, I don’t trust that you’re not going to do bad things with it.

So the thing that I would pay attention to is can you do privacy friendly analysis on your data? Can you do stuff like marketing mix modeling, so that you don’t need personally identifiable information to understand what’s working in your marketing, that I think is going to be one of the most powerful things that you can do, because in that style of regression model, you can incorporate things like out of home, like print, television, radio, non digital things, things that break the Clickstream, word of mouth, all that if you have the activity data for it, you can use very sophisticated software to extract out and say like the yes, this had an impact, or no, this didn’t have an impact.

If you ran a blimp over a stadium, right with no trailing a banner, or you were trolling your competitors by flying a plane over the Jersey Shore with a sign behind it.

All of those things, you have activity data, right? And you probably have some sense of the number of people you reached with those tactics.

And so with a good model, you could say yes, this tactic had this much uplift, right? Here’s all our businesses usual data.

Here’s the uplift, we saw, discounting all the things that we’ve already done, that were business as usual, and be able to create that kind of privacy friendly, blinded attribution model that tells you, hey, we spent X we got x.

Let’s do that again.

Or let’s not do that again.

Now, there are a lot of software packages that can do this.

I’m personally experimenting with like three or four of them right now that are open source that are free and open source and some of them are really not very reliable.

But to set expectations, you need about two years worth of data.

Christopher Penn 4:52

Your data has to be very thorough, very, very thorough.

And you have to have the computational power and the The skills to use the software because in a lot of cases, it’s not plug and play, right? There’s so many different ways to categorize data.

That’s not just to turn the system on and get an answer it is like a 12, to 16 week consulting project to build a marketing mix model.

And then once it’s built, yeah, you can then rerun it and rerun it, you also have as a rolling two year window.

But the sooner you get started with something like that, and the more comfortable you get your stakeholders with something like a marketing mix model, the easier it’s going to be, when the inevitable hammer drops even more, and you lose even more data about the customer that you’re not allowed to get.

Right.

We have got start pivoting as an industry now to these privacy friendly models.

Because if we don’t, what’s going to happen is we’re going to hit one deadline after another new legislation takes place or chrome drops cookie support or whatever, and will be taken by surprise will be taken by surprise.

And we will not be able to produce good answers for our stakeholders.

And that will obviously endanger our companies, our revenues, our results and our careers.

So get good at privacy friendly measurement systems.

As soon as you can.

That’s my outlook on it.

It is a trend.

It’s not going to go back.

You know, it’s not going to go back to the way things were people are not going to say sure, here’s more data that you can use to do all sorts of awful things to me, the genies out of the bottle and it’s not going back in.

So our remit is to adapt our marketing to fit the where the audience is going as best as we possibly can.

Anyway, good question.

Thanks for asking.

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