In-Ear Insights: What is Marketing Mix Modeling?

In this week’s In-Ear Insights, Katie and Chris talk through marketing mix modeling. What is marketing mix modeling? Why should we care about it? How does it work? You’ll learn the difference between marketing mix modeling (sometimes called media mix modeling) and attribution modeling. Tune in to find out more!


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In-Ear Insights: What is Marketing Mix Modeling?

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Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher Penn 0:00

In this week’s In-Ear Insights, let’s talk about marketing mix modeling, also known as media mix modeling the sort of the the, what is it? Why Does anyone care about it? And what you should be doing to get set up for it.

So Katie, where do you want to start on this very automatic px named tech,

Katie Robbert 0:19

this year thrown a lot of tongue twisters at me this morning.

You know, I want to start with what it is because I feel like, at least for me, and you know, maybe I’m not alone in this, maybe I am, when you said that you wanted to start doing marketing mix modeling, I personally got a little confused, because there’s marketing mix modeling, which is a machine learning technique.

But then there’s also the marketing mix.

So let’s just start by breaking down the differences between the two things.

Christopher Penn 0:49


The marketing mix is pretty straightforward.

That’s just what marketing channels are using.

So what strategies, tactics and channels are you using? And a lot of the time at companies? It’s kind of arbitrary, right? It’s like, oh, let’s do Facebook.

Oh, there’s Tiktok.

Let’s do Tiktok.

You know, that that sort of thing? Like, what are billboards? You know, should we do billboards of bus wraps or direct mails, and that’s the marketing mix.

Marketing Mix modeling is a statistical method to figure out of all these things you’re doing what’s working? And what should you be spending more money on? And what should you be spending less money on?

Katie Robbert 1:24

I’m trying to dust off the cobwebs in my brain, but isn’t the marketing mix the four Ps of Marketing, it’s the product placement price and something else promotion promotion.

You know, I did go to grad school, I promise, but it’s been a while.

But but when I hear marketing mix, that’s what I think of it

Christopher Penn 1:47

is known as that.

They’re the four piece, which I think goes back to, I want to say like the 1940s.

And then there’s been like, a gazillion and a half different iterations of it, like the 70s, and, you know, four piece and three season, you know, who knows? But yes, that’s, that is sort of the classical definition.

Katie Robbert 2:11


So what we’re talking about is not the marketing mix, but the marketing mix, modeling, or media mix modeling.

And so I feel, and again, this might just be me, because I tend to keep my head buried.

I feel like I’m hearing people suddenly talk about marketing mix modeling, more and more, whereas maybe a few years ago, everything was attribution.

Whereas and my understanding is attribution is more.

Here’s what’s working to get conversions, whereas marketing mix modeling factors, and a heck of a lot more data, including revenue data,

Christopher Penn 2:49

is different data.

So the here’s the easiest way to think about this marketing mix modeling is top down.

attribution modeling is bottom up.

So attribution modeling looks at you, Katie, what did you do? What paid you know, What channels do you come in on? What pages Did you visit? What things did you click on, and so on, and so forth.

And when we take you and the 1000s of other people that visit our website, we look at what pages they have in common what channels they have in town, we can build a model, typically with machine learning tool techniques, like Markov chain modeling, that says, hey, every time somebody comes in from Facebook, they convert or every time somebody you know, sees a poop emoji on a Tuesday on Twitter, and clicks through to our webinar page, they convert and statistically, you’re building up from these individual experiences, a an attribution models, and this is what works.

The challenge with that is that you are using individual personally identifying information to do so with the advent of the absolutely necessary new privacy restrictions from GDPR and 2018, to CCPA, and 20.

It was also 2018 to CPRA.

And this year, and seven other states in the USA incorporate things py PL was two years ago in China.

A lot of folks saying, hey, all this stuff that you’re collecting, at the individual personal level, you really shouldn’t be right, you really should not be using this without the user’s informed consent.

And so the data for attribution modeling is slowly whittling away, right.

You know, you we see this in our clients, Google Analytics traffic, we see more and more direct traffic more and more unattributed traffic and we’re liking.

Marketing Mix modeling is top down.

So you’re taking summary level data, like how many visits to the website did we get from Facebook this month? How many visits to on a day to day basis? How many visits from Twitter, how many visits from Tiktok? And so on and so forth? Plus cost data, like, you know, if you’re running Facebook ads, or YouTube ads, whatever, how much do we spend, what kind of impressions and then that goes into a different kind of machine learning model.

So basically, it’s just a regression model to say here’s our outcome like sales or marketing, qualified leads, whatever.

What combination of these variables leads to the mathematical relationship with this outcome? And then part of that modeling can also go okay, well, what would it look like if we spent 5% more on LinkedIn or spent 5%, less on LinkedIn, and these models will come up with general forecasts.

But because it’s using aggregated roll up data, we’re not using any PII.

And that’s why people are talking about this so much more now is because it’s a way to do an understanding of what’s working without needing the individual information.

Katie Robbert 5:38

It almost kind of sounds like and I could be misunderstanding this, but it almost sounds like you’re talking about on site versus off site.

And so on site being the attribution, okay, here’s everybody on your website, here’s what they’re doing.

So therefore, this is the action they took.

So you can do more of whatever it is, which is, you know, running YouTube videos or Twitter to bring people to your site.

Whereas off site being okay, here’s how many people visited the Facebook page, or here’s how many people visited the Twitter or interacted with it plus, how much did we spend? I mean, is that an oversimplification? Is that completely right or wrong? Because so the top down bottom up makes sense.

But I’m also it sounded to me, like you were also saying sort of on site and off site.

Christopher Penn 6:23

To a degree, yes.

In attribution modeling, typically, most companies are not equipped to do hybrid modeling, where they can look at, you can see where somebody came from, but you can nest, you don’t necessarily have that data in there.

Whereas within a marketing mix model, you absolutely would have the number of Facebook ad clicks, impressions served and stuff, and all Facebook’s BS metrics, like view through conversions, etc.

All that can be a bit.

The other thing that can be in a marketing mix model that does not go well.

And attribution modeling and requires a lot of effort is offline.

So you could have put up a billboard, for example, on AI 405.

And say, okay, you know, we know based on traffic data, that there were 270,000 impressions for these days for that billboard, that can go on the model.

And then you can see, did that have an influence? Did that have an impact? So offline stuff fits easier into a marketing mix model than it does an attribution model.

Katie Robbert 7:23

We worked with one of our clients a few years back, and it sounds like they were trying to build this kind of a model, but without calling it a marketing mix model.

We were doing what was at the time called an attribution project.

But I recall, we were asking them for things like cost data, we were asking them for direct mail data, which is offline, we were asking them for a lot of stuff outside of what we typically need for our standard attribution model.

So we weren’t calling it a marketing mix model, though.

But is that what we were trying to build with them?

Christopher Penn 8:01


One of the key features of a true marketing mix model is the ability to do the recommendations to say like, Hey, this channel is underperforming.

But it’s, you know, it’s still worth investing in this channel is not right, stop doing this, start doing this, spend more money hear those recommendations, which are built into a lot of marketing mix, modeling software, is not something that we had built that time.

And I think that was just a lack of sophistication on our part, we’ve actually have much better technology these days.

Katie Robbert 8:35

So are there.

So I know with like Google Analytics, for instance, you can do very basic out of the box attribution modeling.

Do you think Google Analytics would offer out of the box marketing mix modeling? Are there other pieces of software that do that? It sounds like you would need a lot of different systems talking together into something like a CDP?

Christopher Penn 9:00


So a typical marketing mix modeling project, if you look at like a what a big consulting firm will do, typically is going to charge a couple million bucks, not it’s going to be a six month project, because believe it or not, the actual software part is the easiest part of the project.

Right? It the hardest part is gathering all the data, getting data out of people in a timely fashion.

So going to the brand team saying hey, we need the number of press releases you sent out.

And any metrics you have for that going to the PR agency saying hey, we need the number of influencers you’ve talked to and their metrics and, you know, organizations and multiple agencies in any situation, you’re dealing with a lot of moving parts and some people who are very reticent to share data, because it might highlight the fact that their work is not having the impact that they think it should.

Katie Robbert 9:54

Well, I so I wholeheartedly agree that that is the hardest Part, especially if you want to run the model more than once.

You know, this is something that we’ve seen firsthand, especially as you get into larger organizations and the data gets more and more siloed.

And not collected in a consistent way, just getting the data for one single analysis, you’re right, can take six, if not more months, but then by the time you run it, it’s already six months at a date, because the time you started collecting it.

And so this is something that, you know, we’re always challenging ourselves to do better, which is collect our data in a clean and consistent way.

But it’s something that, you know, if you’re looking to bring us on or look for yellow software that’s going to do this, you’re not going to run it tomorrow, you’re not going to set it up today and run it tomorrow, you can, but you’re not going to get any results from it.

And I think that that’s the piece that is going to be the hardest hurdle for a lot of companies for a lot of people to get past is that the data has to exist.

But the data has to exist in a way that the machine can read it and can do something with it.

And that is meaningful so that you can take action with it.

And those that’s a lot of variables.

Christopher Penn 11:17

Especially when you start taking into account opportunity costs and soft dollars, then you run into a huge Bramble right.

You know, Katie, you spent seven hours on social media last week, which can go into the model, right? What is that? What how much? What’s your billable rate, right? Or what’s your salary, and things that okay, if it’s, you know, $800 an hour, it’s like, okay, well, so that was $5,600 of soft dollar cost that went into the social media program.

As part of figuring out what to invest in, you have to convert time for, you know, employees, particularly salaried employees into dollars, so that the model knows, the model takes into the two things, it takes in metrics, right.

So things that are numbers and takes in costs.

There’s no space in there for hours.

So you have to convert your anything that is an investment of time or resources into $1.

Cost first, that’s where things get really hairy.

Katie Robbert 12:14

I’m thinking about these larger consulting firms that do this kind of work.

And I could imagine, you know, also having been a project manager, you have people who are full time assigned to literally chasing around and harassing the company team members to get this information.

And that is all they are doing.

They are not running the model.

They are not doing the analysis.

They are not doing the recommendations.

They are literally Hey, Chris, I need that data.

Okay, Chris, but don’t forget, I need that data.

Chris, what can I do to get that data? Okay, Chris, I put together a spreadsheet, can you fill it out? Okay, Chris, I filled out the spreadsheet, can you make sure it’s correct.

Okay, Chris, I’m going to just assume that this data is correct.

So unless you say it’s not, and it’s just like, that is mind numbing.

But it’s what you need.

Christopher Penn 13:00

This is why a consulting firm is going to charge a few million dollars to do this, and drop an airdrop a team of 50 people into an organization, right, because 45 of those 50 people are going to be chasing other people down getting meetings and stuff like that.

That’s why marketing mix models traditionally have been so expensive for companies to do because it to your point, if you don’t want a model that six months out of date, you’ve got to have that that that SWAT team go in.

And within a month or two months at most gather all the information so that the modeling team can put it together and assemble the recommendations.

Katie Robbert 13:37

So let’s talk about the recommendations for a minute.

So with an attribution model, we know that the recommendations are typically, you know, here are the channels that are working best for you.

So this is where you can allocate your resources and budget.

Are the recommendations from a marketing mix model worth it? What kind of recommendations does someone get?

Christopher Penn 13:58

If they’re very similar, they’re very, very similar.

Where you can you’ll see like, here’s the channels, here’s their impact on the end result here is the ROI which is a big part.

So the cost versus the outcome.

And again, that gets really hairy to gather because you you have to spend a lot of time with finance saying okay, well how much revenue did this bring in? How much revenue this bring in and so on and so forth.

And then you can say, Okay, this channel has a 2% ROI, this job has a 12% ROI.

If you are talking billions of dollars in ad spend that that delta can be huge.

And so the very often the recommendations fall in the line of you should spend more money here, spend less money here, you know, fire this team, hire some people over here, and you know, do these things to make your marketing work better.

Katie Robbert 14:50

So let’s say I’m, let’s say I’m CEO, let’s say I am out of touch with what the heck is going on in my business.

When I say up, people are talking about marketing mix modeling, that’s the answer to all of our problems.

If I come to you, and I say, Chris, I want a marketing mix model, and I want us to tell me exactly what to do what exactly how much to spend on each channel on each day, go, Is that a reasonable request?

Christopher Penn 15:20

Everything except the day part, I can tell you exactly exactly what to spend exactly where to spend it.

So that but it’s going to be in the timeframe, whatever the model is, typically a model, a model is gonna be run, sort of at a quarterly level resolution, okay, because that’s that, that’s how long it takes to gather the information, you can run them as frequently as you have data, you know, for a company like Trust Insights, where there’s three of us.

And we all have pretty good idea of what each other are doing, we could probably assemble the data in under a week.

And then you know, and there’s a limited number of channels, we could build the model with with any of the software packages that are out there or write our own, and then come up with a list of recommendations and say, Okay, we need to do more of this and less of this.

Even a 30 person company, I mean, the model in some ways almost scales with headcount, right.

So it’s going to take you much longer for 30 person company that a three person company, for a company like IBM with 300,000 employees, you’re gonna, I mean, you would need a strike team, like 500 people to go to the four corners of the earth, to gather everything that IBM is doing.

And so you’ll see for companies like that, they will typically do scaled down marketing mix models, they will only look at a certain subset of channels, they’ll say, Okay, we know, these channels are the ones that we want to focus on anyway.

So let’s build a smaller model that doesn’t take into account everything.

And we know it doesn’t take into account everything.

Katie Robbert 16:49

So theoretically, and I’m not suggesting that we do this, because it sounds like a lot of mindless mind numbing work.

But if we were to have the right systems in place, like time tracking systems, where we categorize all of our time, you know, in 30 minute increments, or whatever it is, plus, we have all of our different digital channels, data feeding into one system plus the time tracking system, thankfully, we’re not really using anything offline.

So that’s not a factor.

But if we had all of that, on a regular basis, feeding into one database, could we run like, it would just take the time to build the marketing mix model, and then we theoretically just hit the run button at a time we want to see the updated information.

Christopher Penn 17:38


And because it’s essentially just a big regression machine, you can include as little or as much data as you want.

So in our case, if we don’t have timesheet data, it doesn’t go in the model, right.

But then we, you know, you have sort of the disclaimer, hey, we can’t make a recommendation about how much spend a time, the time to spend on something because you didn’t include that information.

That’s, that’s true of any model, but especially something like a marketing mix model, or any regression level model.

It’s only as good as the data you put in it.

So if you want to look at just three channels, sure, you can get a mix of the three different channels, right? You just did Google search, and LinkedIn and Twitter, you could get a model that would give you a mathematically valid answer for which of those three channels is working best? Is that what’s actually working best for your business? That’s a good question.

Because there may be other things at work that are not in there.

And if you don’t put them in there, the model can’t take advantage of it.

That’s where stuff like process management and governance matters a whole lot.

Katie Robbert 18:40

Well, I feel like the same is true of an attribution model.

It’s only as good as the data that you put in.

So if you’re looking at your Google Analytics data, to run an attribution model, that’s fine.

But to your point, if more and more if it is coming up as unknown and direct, that really doesn’t tell you anything, you know, and so if you don’t have good UTM governance, if you don’t have the system configured correctly, you may be saying to yourself, but it’s just one system worth of data, it should be fine.

There’s still a lot that goes into it.

Christopher Penn 19:09


And these systems still cannot take into account sort of second order distortions.

I’ll give you an example.

Last week, over on the our website, we released our our new talk about large language models, right.

And we got literally 1000 leads from it was a very large number of registrations.

If you were to put all that data into a marketing mix model, it would say, hey, LinkedIn, and the almost timely newsletter, were the primary drivers, these channels working really well.

What it will not say is that we shipped my butt over to London to do this talk.


And that speaking is the genesis of the content that led to the newsletter that led to LinkedIn posts that led to the the MQ ELLs.

So again, this is a Question What data goes into the model? Is that one speaking engagement that I did in London? Was it worth it? Well, if you trace the chain of events, you would see like, yeah, I got 1000 leads out of this thing, right.

But if you were to look at just the event itself, like, hey, there’s, you know, calm speaking and it was a one on that day, and then the zeros for the rest of the week.

The marketing mix model is not going to know what to do with that, computationally, it’s just, it’s gonna say, Well, this is kind of irrelevant.

But we can see the the second and third order effects of it.

So that’s a consideration when you’re doing marketing mix modeling is there will still be things that you have to understand where that data came from, to be able to properly assign it.

Katie Robbert 20:44

So in the event that you do have, the information of where that data came from a marketing mix model could be more effective than just a straight attribution model.

Because the attribution model in that instance is going to say, well, I know all of these, you know, prospects came from Chris Penn’s newsletter.

So that’s just that’s all it’s going to tell us.

But I don’t need a model to tell me that.

Christopher Penn 21:09


And this is a this is this, the ongoing battle with all things AI, right, it is, the machine will tell you something, but you have to understand the context, you have to have that subject matter expertise.

In this case, you have to have the subject matter expertise of Trust Insights as a company and the marketing team and what it’s doing, I mean, arcade game, just the three of us.

But you can see in a larger corporation, right? If you started seeing these these data points, you might not know which team was responsible for a particular distortion.

So you’d be able to say like, yes, whatever this was here that was caused this spike that makes Tiktok worth doing.

You got then got to do some homework and say, Okay, well, was that something that an employee, say? Or McDonald’s? Was that something that the the corporate marketing team put up that Tiktok video? Was it an employee? Was it a customer? What happened there? So you got to do some digging to say, Okay, we know Tiktok is working as a channel, we know that the media mix model says this is worthwhile.

Now we got to figure out why.

And is it something that’s repeatable? Did we just get lucky and Kelly Clarkson showed our video on her show? If you got lucky, it’s gonna be real hard to repeat that.

Katie Robbert 22:25

So it sounds like if your company considering doing marketing, mix modeling, put it on your roadmap for at least a few months from now, because there’s a lot of upfront work that needs to be done, such as data collection, process development, data governance, data cleaning.

Oh, yeah, yeah.

But good news, is that something we can help with all that upfront stuff.

And I feel like that’s the part that people are going to skip, sometimes knowingly, sometimes unknowingly, not realizing all of that upfront work that has to get done before they can have their shiny new analysis.

Christopher Penn 23:08

It’s like going in the fridge, right? You assume that you’ve got all the ingredients for steak dinner, you open the fridge like, Hmm, bought steak and weeks like, Okay, well, now you need to go out and buy some steak, right? It sounds silly.

But you could see at a beginning a large organization, the CMO says, oh, yeah, we all have that data, you know, and the VP of DEV of marketing Ops is like, we don’t have any of that.

And you get these disconnects.

So, again, one of the key things to remember is a marketing mix model is a flexible model, it can take in as little or as much data as there is with the understanding that the accuracy improves with more data, the more data you include, so you want to try to include as much as you can feasibly get your hands on.

And if you include more data and say the second time around, then the first time around, the models are no longer apples to apples.

So you can’t say this is what’s changed from quarter to quarter, because the data has changed.

All that said, though, you still can do this with a relatively small subset of data.

So just because you hear it’s a $2,000,000.06 month project does not mean it will cost that for your company, specifically, if you have the information and you’re willing to accept the trade offs of like, here’s what we do have, that we don’t have, if you got that you can run a marketing mix model product for, you know, 50 $100,000 Maybe a, you know, two or three months wouldn’t be the $6 million project that you know, a big company is going to have.

That’s, you know, within reason.

Katie Robbert 24:34

I feel like it’s definitely you don’t know, you don’t really know the scope of it until you bring someone like Trust Insights into assess what you have what you don’t have, because you may say, Oh, sure, I’ve set aside you know, X dollars to run this.

But we may come in and say cool, but you haven’t been collecting data for the past, you know, two years.

So are you putting it together with hopes and dreams? or we may find, Wow, your datasets are pristine.

You’ve been collecting them, we can run this much, much faster.

And so, you know, I think the the takeaway is Don’t skip the upfront requirements part of, you know, what are the things that matter to you the most? What questions are you trying to answer by running a marketing mix model, you know, don’t just dump on the marketing mix model bandwagon because everyone else is talking about it, because data privacy is making attribution reporting harder.

There’s a lot of upfront work that goes into it.

But again, that’s something that Chris and I would be happy to help with.

I would love to get my hands on that kind of a project and like, get in there and figure out what’s going on.

Christopher Penn 25:41


Yeah, I think we if you want, we can come over and look in your fridge and see.

Katie Robbert 25:51

Unsurprisingly, my fridge is pretty well organized.

I know exactly what types

Christopher Penn 25:56

of questions.

If you have done some marketing mix modeling or media mix modeling of your own and you want to share your stories about it, pop on over to our free slack group go to trust for marketers, where you have over 3000 other marketers are asking and answering each other’s questions every single day.

We have tons of exclusive content happening there all the time.

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Thank you so much for tuning in and we’ll talk to you next time.

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Trust Insights ( is one of the world's leading management consulting firms in artificial intelligence/AI, especially in the use of generative AI and AI in marketing. Trust Insights provides custom AI consultation, training, education, implementation, and deployment of classical regression AI, classification AI, and generative AI, especially large language models such as ChatGPT's GPT-4-omni, Google Gemini, and Anthropic Claude. Trust Insights provides analytics consulting, data science consulting, and AI consulting.

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