So What? Marketing Analytics and Insights Live
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In this week’s episode of So What? we discuss why should you be using Marketing Mix Modeling? We walk through how to create user stories for your marketing mix modeling, how to use the 5Ps for requirements gathering and the decisions you’ll make with a Marketing Mix Modeling analysis. Catch the replay here:
In this episode you’ll learn:
- How to create user stories for your marketing mix modeling
- How to use the 5Ps for requirements gathering
- Decisions you’ll make with a Marketing Mix Modeling analysis
Have a question or topic you’d like to see us cover? Reach out here: https://www.trustinsights.ai/resources/so-what-the-marketing-analytics-and-insights-show/
Katie Robbert 0:25
Well, hey, how are you everyone? Happy Thursday. Welcome to So What, The Marketing Analytics and Insights Live show. I am Katie joined by Chris and John. Happy Thursday, guys. Hello,
John Wall 0:35
Katie Robbert 0:37
This week, we are talking about marketing mix modeling, and why you should be using them. So we’re going to cover creating user stories for marketing mix modeling, how to use the five P’s for requirements gathering, and most importantly, decisions that you can make with a marketing mix model. And so Chris, you and I have been talking about marketing mix modeling, I have to keep saying it’s the I don’t know this, I’m going to stutter over it at some point. But you and I have been talking about marketing mix modeling all week. And so when we recorded the podcast earlier this week, we talked about what it is. So if you’re curious about that, you can go to trust insights.ai/ti podcast and catch that episode where Chris and I break down, what the heck of marketing mix model is, in yesterday’s newsletter, which you can get at Trust Insights, ready AI slash newsletter, I talk through all of the front preparation you have to do before you can even do an analysis. And so today, what we want to do is cover let’s say you already have the pieces How do you get started with a marketing mix model? What are the user stories that you would want to create, to know what decisions you can make? So Chris, where should we start, this is a big topic,
Christopher Penn 1:52
it is a big topic. And to give you a sense of the level of complexity, we’re a three person company, we were not a huge company. And yet it took me two and a half days to pull together the data for a marketing mix model about a day to write the code necessary to format it to be compatible with each other. And then, you know, literally, just before the show is rolling, finishing up the code and running it so that we actually have a model to show on the air. What the biggest challenges really are round around those five peas because you’ve got to have your purpose, which is the user story, right? What do you what decisions you want to make with this thing? Most of the time, people will use them to make decisions about what should we do less of what should we do more of which we spend more on? What should we spend less on? And we’ll talk about that. Because in the data, it actually does tell you very clearly what what things impact the outcome you care about. Then, obviously, with any kind of model, there’s a people side of things, which is who’s going to do the thing. It may be someone in house, if you have your own data science team that can put it together, it may be a vendor, I will say this vendors in this space all fall in the category of reassuringly expensive, these, these models are require a lot of iteration. So we’re gonna go through the model I put together, and I have absolutely no doubt in my mind that Katie, you could be like, Well, what about this? And why is this in here? And why is that not in here, etc. So there’s a lot of that. And that’s the heaviest lifting in a model is really the process part, which is what goes in? What are we going to use information for what stuff shouldn’t be in here, which is a big set of discussions, and then interpreting the outcome? And then of course, platform is whatever software you want to use. And then the performances will do a did the model even work? And there are some technological answer set. But then there’s also the human answers to that. And then finally, what do you do with it?
Katie Robbert 4:00
So I want to amend a little bit of your process and platform. And this, you know, as I mentioned, I covered this in the newsletter this week. And so, you know, as you were prepping for the live stream, we didn’t talk through our own user story, we’re just putting together a proof of concept of demo for the purpose of this. But, you know, had we gone through a user story, which is as a persona, I want to so that persona being the people involved, want to being the process and platform. And so that being the purpose and performance of your five Ps, you know, the want to, as we’re talking about process and platform, it’s not just how you’re running the analysis, but then factoring in all of the different platforms that you need to extract data from, bring that back into build a process around it to extract it, clean it, normalize it, blend it, all of those things. And so making sure that your users store worry is specific. So, you know, it could be I want to look at the past five years worth of data, which is very different from the past five months, which is very different from the past five days, you know, you’re gonna have a different set of processes for each of those timeframes, depending on what the decision is that you want to make. And so, you know, there’s a lot that goes into it. And so to say, Chris, that, you know, you took about two and a half days is really under estimating the actual amount of time that even for a small company like ours, would want to put into building a model like this. Oh, yeah.
Christopher Penn 5:37
Like you said, this is a demo. This is not, this is not the final, because again, that’s the whole process of stakeholder interviews, and building those user stories, and validating, even validating that the data itself is correct. So I ran into some issues with our Google Analytics data, huge surprise. So even had to adjust for that. But if you think about it, two and a half days of consistent effort for a small company, where I literally know every system inside and out, means that in a bigger company, with same double employees, where we don’t know the systems, you’re talking about, like two and a half months of work, to do all the interviews to ask people the knowledge that’s implicit in our heads, because we’re working with the systems every day,
Katie Robbert 6:22
well, and a dedicated team just to figure out what the heck you want to do with a marketing mix model. And so you know, John, I’ll start with you, if you were to create a user story, if I said to you, we can create a marketing mix model, you know, specifically for you, what would your user story look like?
John Wall 6:40
Yeah, the big thing, you know, from the limited exposure I’ve had to this stuff is always about getting information that you can’t even get from attribution reports, you know, for most of the stuff that I’ve always worked with attribution reports get you there. But when you start doing real large branding campaigns, so if you’re spending a ton unpaid and getting a lot of unattributed traffic, those are the kinds of situations where you roll that out. So it always be, you know, as a CMO, I want to measure the impact of our branding or, you know, a public opinion of the brand. So that I can, same thing, you’d have an attribution model, you know, figure out what programs are working, not working, spend X more, make more. But the the, the big thing is just getting visibility beyond what people just get from an attribution model, which that everybody can kind of do from in house stuff. But to get up to the marketing mix model. Now you’ve got to bring some, you know, heavy data and data crunching to the table.
Christopher Penn 7:35
Exactly. So let’s look at it just just to start what data we even used for this toy model. So this is our studio. For those who are unfamiliar, our studio is a programming interface. I started with our Google Analytics data, right. So we know that Google Analytics is one of our better systems for keeping record what’s happening. Then I went to our friends over at Agorapulse, because we manage most of our social media channels from Agorapulse. And I pulled out all of the data that we have in Agorapulse for the social networks we manage in there. So that’s LinkedIn, Twitter, Instagram, Tik Tok. And I pulled in our personal ones, where we have it as well as the company ones, because either they, John and I have a podcast have been on the air for like 17 years. So that does take some impact. And then we have our YouTube channels, right, we have the Trust Insights YouTube channel, we have my personal YouTube channel, I pulled an email marketing data from our marketing automation system, the Trust Insights, one and the almost timely newsletter, one, I went over to Libsyn and grabbed our podcast data from again from the feed that I have in there and the Trust Insights feed. And then finally, which is where John lives, I pulled in our Hubspot data, too, because the Hubspot data really is the outcomes that we’re after. So, you know, our user story for like a CMO. You know, I want as a CMO, I want to know, what’s working to generate deals, right, just to sales opportunities, so that I can allocate budget and time appropriately. And so that was sort of my back of the envelope. These are all the different data sources that we’re probably going to need. And this stuff that’s still not in here. There is public relations data that’s not in here. I didn’t have time to go and get our brand on, you know unlinked brand mentions from our social from our brand monitoring system. I did not have time to pull in our speaking data. I did not have time to look at outbound emails and stuff that the John does from inside the Hubspot system itself. So there is stuff still missing.
Katie Robbert 9:41
Well, and we again as a small company aren’t taking advantage of every available marketing channel, either. And so this is you know, we do what I would call lightweight marketing for ourselves. And even with that you just cover About a dozen different data sources that we have, you know, good working knowledge of how they operate. But, you know, we don’t run paid ads, you know, we’re not running Google ads and social ads, and, you know, other kinds of ad networks, you know, so if you factor in all of those, you know, we’re not doing SMS messaging. You know, we’re doing lightweight, organic, social, to your point, but we’re not doing, you know, campaigns on, you know, all the available platforms. And then you have, you aren’t able to factor in private social, where we do spend a lot of our time with our community on your Discord servers, those places. And so there’s a lot of data missing, just from this model, just from this small sample that you’re doing as well. So sort of, I wanted to bring that up to acknowledge that, you know, again, as we’re talking through, you know, we’re a smaller company, we still have about a dozen data sources that we’re trying to wrestle with, and it just, it really only scratches the surface of what’s possible.
Christopher Penn 11:03
Exactly. So after you get the data, you then have to deal with the data.
Sounds terrible. They’re all different.
So you have different types of summarization needs to do sometimes, like, for example, Google Analytics, I pulled this in as as default channel groupings, because source medium, even more hairy to try and deal with. But even there, removing direct traffic and unassigned traffic, because that’s not something you can make a decision about. Sale, oh, yeah, let’s go get more traffic passes contribution data is probably not going to be something you’re going to enjoy, you know Hubspot data we had all sorts of we actually have a lack of ability to count things natively and Hubspot. So we have to actually do the counting within our our marketing mix modeling code to just count how many deals we had on a given basis. We have, of course, all the heavy stuff that comes with the social different social networks. And then with our email marketing data that comes in. So again, there’s no simple counter email system. So we had to go into the back end database of our email marking system and export a couple of very large tables, just to summarize them for this, which, again, is a fairly heavy lift. And then of course, our podcast data. So that’s kind of, we’re still early in the process part of the five P’s. This part, at a small company, with highly proficient technical resources took days to do.
Katie Robbert 12:32
It reminds me at a very basic level, when I was trying to look at more than one data source, like in an Excel spreadsheet, for example, just even the way that the date stamp comes out, or how it’s broken down, you know, because now we’re talking about feature engineering have, you know, some of these come in, you know, hourly data, and some of them come in daily data, and some of them come in weekly data. But even then how the date stamp is structured, is going to be different. So some of them are going to be, you know, day, month, year, or month, day, year, or a long string of everything, including timeframes and time zones. And those things, and even just cleaning that up to all get it to be the same kind of data, especially when some of the data doesn’t exist at that granular level, is I would imagine that’s a big effort just to clean the data and normalize it.
Christopher Penn 13:25
It is and it gets even hairier when you start having to do manipulation of the data. So one thing that’s not in here, because again, I knew I would not have time to do it as Google Search Console data. There’s branded organic search an unbranded organic, so she would want to be able to split both of those apart. people searching for Trust Insights are more valuable to us and people are searching for, you know, what are the five P’s? Or what is the marketing? What is the marketing mix? Because those are folks who are still on their informational part of their customer journey, not the intent part where they’re like, Yeah, I want to talk to John Wall right now.
Katie Robbert 14:00
He is ready and waiting.
John Wall 14:02
Time Life operators standing by
Christopher Penn 14:04
exactly what Ghostbusters we’re ready to believe.
Katie Robbert 14:08
So I feel like we’re sort of, you know, giving all the bad news, which, you know, to be fair, we need to really sort of paint the picture of all of the work that goes in upfront before doing I mean, yes, a marketing mix model, but really any kind of analysis, you want to make sure that you’re doing your due diligence upfront, because it will save you time on the back when you start to run the model. And one of the processes that you know, as we are building out this marketing mix model software, is that we for ourselves, want to build the processes so that they’re repeatable so that as we’re extracting the data, it’s being extracted regularly and consistently and being cleaned thoroughly in a standardized way so that we can rerun the model over and over again because what happens a lot of times And Chris, we’ve run into this with clients before is that there’s, you know, this big six month effort to gather all of the data, we run the model once. And then the client says, Okay, let’s do it again, we’re like, okay, but you’re gonna spend another six months, and you’re already then a year out of date from the decisions you want to make. And so that’s another factor is how timely do the decisions that you want to make need to be made? That will dictate how you set up your processes.
Christopher Penn 15:30
Exactly. And there’s a minimum amount of data that you want to have to be able to do the model, right. So as at a bare minimum, you want to have about 90 days, in this case, we’re using 16 months of data, we’re going back all the way back to the beginning of 2022 for this particular model, because with less data, you will have the statistical software to say, Wow, I can’t make anything at this, right. So that does happen a lot. So after you get the data from every data source, cleaned it up and get it into a standard format, you then have to glue it all together, which is what this long section code here does. And then you actually get to the, to the modeling part. So here’s where you get into philosophical differences about what kind of marketing mix model you want to build based on the type of regression. So for those who skipped stats, or slept through it, or just failed it like I did, which was embarrassing.
Katie Robbert 16:27
Oh, I also failed it.
Christopher Penn 16:29
John Wall 16:33
Guru here, this was ridiculous.
Katie Robbert 16:35
John, well, I know. We’re gonna Okay, well, John, roll switching now.
Christopher Penn 16:39
Exactly, John. So would you explain to the crowd what regression analysis is?
John Wall 16:44
Regression analysis is when you take a huge pile of data, and you try and prove that there’s some kind of correlation, it within the data, like, there’s a reason why it’s going in a certain direction.
Christopher Penn 16:56
Right, and there’s, what 15 or 16, different types of regression analysis, right, there’s linear, there’s logistic, there’s lasso gradient descent, there is gradient boosting regression, stochastic regression, support vector machines. So there’s so many different choices. And a big part of marketing mix modeling is deciding what kind of regression you should use for the data. And this is, again, is where it helps to have a statistical background, and a machine learning background to know which one to pick is kind of like picking an appliance in your kitchen, like there’s, there’s a bunch of them, they’re all good. But I would not suggest putting a stake in the blender.
Katie Robbert 17:34
Well, and this goes back to starting with a user story, because knowing the different elements of what it is you are trying to understand. And the decisions that you need to be able to make is going to help you choose the right kind of model that you’re going to run. Because to your point, Chris, you know, I would imagine a linear model and a logistical model and a gradient boosting model, you’re gonna get three different sets of responses. And it really comes down to what is the decision you’re trying to make by running this model, and therefore, that’s the kind of model we should be running.
Christopher Penn 18:13
And we have to take into account the nature of marketing data itself. That’s a big part. So a standard linear regression model, which is just a literal linear regression, doesn’t look at the individual variables and say, Hey, this one doesn’t really matter, right? Like you’ve got, you know, a number of tweets with poop emojis on Tuesdays as as a as a variable here. And just statistically like, Okay, this, this doesn’t make any sense. So, part of the modeling process is looking at the dataset and saying, What does it look like so, are just for this little toy model, we are at 107 different variables, and four and 58 rows of data, so 458 days of data with 107 different variables from all these different sources. So in terms of regression, the that sort of hints at which models you should be looking at so in our case, the two best choices would be ridge regression, or lasso, regression. ridge regression, tries to deal with overfitting on lots of variables. So it’s good for high dimensionality, which is very wide tables. Lasso regression does the same thing, but also allows sort of a tilting in the regression model to to knock out variables that don’t matter. So it does sort of a reducing reduction of irrelevant stuff for you. And that really helps to to make things work better.
Katie Robbert 19:38
So John is our resident statistician. Does that all sit? I’ll check out?
John Wall 19:43
Well, no, I was going to ask that’s actually a great question. Because, you know, back when I was doing, you know, these models, it was a matter of you run one and then you run a second and maybe you run four or five, and then you actually just have to get it out by hand. Go back and look and see which ones match reality, but I mentioned it’s got to be was powerful enough now that you can just run all the models, right and see the output? I mean, what’s, how do you actually discern which one is closer to the mark to what you’re looking for?
Christopher Penn 20:09
There are automated suites like h2o that can do an ensemble. And in fact, for for a bigger project where we didn’t maybe know the data as well, you’d want to use probably an ensemble of models to get to an answer. So we’ve we’ve done this with a couple of our clients in the past, we built a model ensemble, usually it was a combination of lasso regression upfront, to weed out less important variables, and then gradient boosting to amplify the values of the remaining variables to give it, you know, more statistical, if you will. For us for this toy model, I stuck just with lasso regression, because again, it’s a very wide table, it’s not a very long table. And lasso regression is really well suited for that
Katie Robbert 20:53
question on this. Because as you’re describing, you know, and John, you’re mentioning, like, you would just, you know, keep running the different models until you get something that matches reality. I guess I have a couple of questions there. So what if you know, especially as you get into enterprise sized companies, you don’t know what reality is? And that’s one of the reasons why you’re on you’re doing this analysis. But also, you know, if you aren’t sure, or don’t understand the different kinds of models, and you just kind of start picking and choosing, you know, do you run the risk of using the wrong model to answer the question, because I don’t know, the data kind of looks good, it makes me look great. It might not be reality, but I’m a Shining Star. If I use this model over here, you know, is that a risk? Which again, speaks to why people should have a John Wall and MAE team who’s a statistical superstar.
Christopher Penn 21:47
We all stare at
John Wall 21:48
John, for a year? That’s a great question that so it’s, you know, are you using bias? Or is it basically you have to test that after the fact you have to then get a second dataset and actually see if it holds as far as what works?
Katie Robbert 22:02
Yeah, I think that’s a, that’s a really good point, sort of running it against something that is mostly known and then tested using the same kind of model against something that’s less known. Yeah, and
Christopher Penn 22:13
there are statistical methods, there’s there’s technical methods for determining which model performs best. And we’ll talk about that, because we’ll talk to the validation part in a little while. But one of the things that’s challenging here, you’ll notice on line 330, is just this agreeing what the outcome is. Right? So in this case, I said the outcome that we care about is the number of deals in our Hubspot instance, if you’re doing a marketing mix model, this is you’re probably going to be looking at deals or you’re gonna be looking at sales qualified leads, particularly in B2B, because a marketer who does their job should be delivering leads to sales, and those leads should be good quality leads right to a sales qualified lead, where sales said, Yep, we agree this lead is good. That’s really where your responsibility ends. As a marketer, what happens after that is up to sales, right sales has got to get the deal, they’ve got to get agree on a deal size, they’ve got to agree on a closing date, they gotta be able to close the deal. Marketing really has very little impact, particularly for B2B after that point. So even when you’re building a model, you have to agree on Well, what is the outcome that we are responsible for, that we want a model against, because I had the opportunity to use deal amount. But from a marketing perspective, that’s not really something we have much control over?
Katie Robbert 23:32
Well, and this goes back to having one or I would advise multiple user stories, because the last part of the sentence, the so that the that is the outcome. And that’s really going to help you understand when you start to get into developing this model, what is the outcome? So there’s the outcome that you want, overall, and then more specifically, there’s the outcome that you want from the model that’s going to help you understand what’s in the user story. So you know, we keep talking about Chris, how we’re a small business. And so, you know, in B2B, yes, marketing sort of has that cut off point, and then they hand it to sales. But for us, we are all of the things. And so we would want to know, you know, maybe deal amount or, you know, more of the financial side of things, because we are doing the marketing and the sales and the finance and the operations.
Christopher Penn 24:27
Exactly. So what’s happening here, first thing, we do some more cleaning, we remove columns that are empty, because that’s silly to happen. We will move models at a constant rate, if it’s a John Wall has the same number of followers for the entire period. That’s that offers no predictive power. It just kind of screws things up. So we get rid of that. Then we do for cleaning steps. So we have to remove zero values, right because that again, something at zero has no predictive power. We remove highly correlated variables. And this is important because in almost all forms To regression variables that are highly correlated, just screw up your predictive power, right? If the number of clicks on emails and the number of amount of email traffic that arrives at your website through GA, this kind of high gonna be almost perfectly correlated, you don’t need both of those variables, just need to pick one. And the software will pick the one that has more relevance, you also want to get rid of linear combinations. So these are our variables that are essentially almost identical. And you want to then normalize your variables. So that means centering and scaling them all this, you know, back back in the day, you would have had to do by hand, thank goodness, you don’t have to do that anymore. But normalization is an important step, because it makes things apples to apples, that otherwise would not be. So the number of likes I have on my tweets versus the number of likes, maybe John has on his tweets, proportionally, might be the same, but in absolute terms can be very different, because I just have a large Twitter account. So we want to normalize that to make it apples to apples for comparison across all these columns in the table.
Katie Robbert 26:05
I’m just like, just the data cleaning part of it is exhausting. But so necessary, because you know, let’s say you have anomalies in your data, and you didn’t bother to clean it, you’re gonna break the model every single time and not know why. And, you know, you know, depending on how you’re running this, you know, if you’re using an agency or a very expensive piece of software, it can be a huge waste of money. And that’s why we keep going back to using the five fee structure to really make sure that you are gathering the requirements, but specifically focusing on your processes to really understand, am I even ready to run the model? And you know, until you do that cleaning, you’re not, you’re absolutely not ready.
Christopher Penn 26:58
I like broken models better than I like, models that appear to be, in fact are not bad enough, like slightly rotten data is worse than broken data. Because slightly rotten data, you’ll make decisions with like, it looks okay. Like, it feels like you sniff the milk and nuggets smells. Okay, at the first question you poured out in some chunks, like, I guess it wasn’t okay.
Katie Robbert 27:22
I think a better maybe a better example here is, when Chris and I were doing monthly reports earlier this week, we were looking at Google Analytics data, and specifically Google Analytics 4. And we kept coming up with different numbers depending on how we were looking at it. And at the end of the day, it was all roughly the same, but not exactly the same. And so that’s sort of where Chris is talking about the slightly rotten data that could have a big impact on the overall decisions versus data that’s flat out broken.
Christopher Penn 28:01
Right. So now we get to that were the sort of the quality checking of the models, right to look at the outputs. And again, this is something that’s automated. But it’s something that as a human being, you can absolutely go and inspect. What you’re looking for, essentially, in in these two charts here is when does the data substantially change? When does it reach a point where all of these dozens of different candidate models inside this tool, which is the best one, so you’re looking for, in this case here, where the pink line the root mean squared error, where that error gets as low as possible. And then you’re looking at r squared, which is the goodness of fit, when that’s high as possible. So as you know, this model here is the one that’s machine will automatically chooses, hey, the green line is as high as it’s going to get. And the red lines as low as it’s going to get it would never choose, for example, one of the models early on in here was like okay, the red lines as high as it’s gonna get the green lines as low as it’s gonna get, that’d be a model that has very low predictive power. So with that, we finally actually get to the result. Yay. And what is the results say? Well, there’s two different things that come out of the result. And we’re going to switch to full screen here, you get a measure variable importance, right? And which is for for marketing, this model is not super helpful, but when you get what is called the regression coefficients, and this is where this is where the rubber hits the road. John, you want to explain a regression coefficient?
John Wall 29:42
As far as you know, from what I remember, of our is over point five then that’s considered statistically relevant and anything below is basically not
Christopher Penn 29:52
no regression. So for r squared, that be true for regression coefficients or regression coefficient is the change in the independent variable and it’s act on the dependent variable. So Katie’s like, what
John Wall 30:04
was that measure of impact?
Christopher Penn 30:06
Right? So here’s what this means. It a regression coefficient, essentially saying for every one of the outcome, this many of the thing is required. So for organic search for if we want to, if for every one bit of traffic essentially in organic search will result in point one three of the outcome, right? That’s sort of what a regression coefficient means. The longer bar is going to the right mean those those variables have more impact. So this is now the actual marketing mix well, so things that matter. Katy, getting mentions brand mentions on a Twitter account seems to matter with this model. That’s me, that’s a matter, whoop, organic search traffic matters, right? The impressions, the number of impressions that the Trust Insights posts, get on LinkedIn matters. engagements, likes, specifically on John’s tweets, matter. And again, brand awareness mentions on my Twitter account matter. The more we crank out of these things, the closer we will get towards convert the outcome we care about, which is the Hubspot deals number. On the other side, when the bars go far to the left, that means those things matter, very little eight, they kind of have an almost a negative impact, right? So you see total followers there on both from my Twitter account and the company’s Instagram account, that would effectively say to a CMO. This would require a lot of messaging to explain this. Don’t bother chasing followers, right? If you say, oh, we need more followers? No. In fact, depending on based on this data, it looks like the quality of the followers on my Twitter account are so irrelevant to the outcome. That means spending time chasing followers would be would reduce the number of deals we get.
Katie Robbert 32:13
Well, and you know, this is sort of the same thing we say about our newsletter numbers is I’m okay, having a smaller number. If it’s the right number of people, if we’re reaching the right audience, than it should be smaller, because we can’t be everything to everyone. And it’s interesting how this model very much mimics the way that well, not interesting, but it’s refreshing that the model mimics the way that we operate our content, where we’re not trying to be everything to everyone, we’re trying to reach the right people with the right information.
Christopher Penn 32:48
Exactly. All the things that this model says the number of impressions on Tiktok really don’t matter
Katie Robbert 32:55
which the number of hits hearts are not great.
Christopher Penn 32:58
Now, here is the gotcha. Here’s the gotcha. If you’re sitting here thinking, Okay, we’ve got a marketing strategy. We know what to do, by run this section of code here. Let’s see, where am I? Oh, the wrong section.
Katie Robbert 33:21
Well, and to be fair, that wasn’t a marketing strategy. That was just the initial data, there’s still a lot of human intervention needed to put that together. And to make a plan from that. And I think that that’s the other piece of it. When you go back to the people in the five P’s, someone has to interpret this data and turn it into something actionable. I would be surprised of any system that just hands you on a silver platter, here’s exactly what you need to do.
Christopher Penn 33:55
But this goes back to what John was saying earlier, the R squared on this model is point 153. Which means the model is invalid. Oh, good. So everything that you just saw, does not in fact, matter because the model has no statistical significance. The R squared number effectively says how well the the regression line describes the data describes the trend in this case, it it describes it very poorly. Generally speaking, you’d look at a minimum of point two five what John said it was exactly as you really looking for point five or above, right at that point, you’re saying this, this model describes reality. So going back to what you were saying earlier, Katie, you know, does How do you know this describes reality? Well, you have a statistical measure that says this is reality. And in for this particular model, this is not reality does not meet that that test.
Katie Robbert 34:50
But this doesn’t tell you why. And that’s that becomes one of the challenges is it doesn’t tell you why it’s not systemically valid. Is it because of a lack of Data is that the wrong data? You know, whatever the thing is. And so without having that person or agency who can actually build and run the model for you, you’re like, oh, I don’t know, it gave me data, I can make decisions from this, can I.
Christopher Penn 35:16
And this is where so generally speaking, this is a generalization with marketing mix modeling, if you get a number that is specifically invalid, it means that you’re missing something, there is missing data that would help describe reality better. So there’s some aspect of our marketing, we’ve talked about brand mentions and missing public speaking is missing. All those things are not in here. So what is in here, you can make a model, but it does not. It’s missing those key ingredients. It’s like, you bake a cake, you taste like, Okay, it looks like a cake, but it has no flavor. Like, oh, because there was no salt, no sugar. In the recipe, those key things are missing. And yet it looks like a cake. You can put frosting on it, but it’s not going to it’s not cake. That’s what’s happened here. So we know now looking at this, based on that error message or that error metric. This isn’t reality.
Katie Robbert 36:08
So if you saw this, and then you saw the R squared number, would you say no, we can’t make decisions based on this data? Because it is not statistically valid.
Christopher Penn 36:21
That’s right, I would say this is this does not describe reality. Right? So making decisions based on this would be a poor choice. Here’s the problem. There are a lot of companies that offer a lot of software for marketing mix modeling. None of them tell you the statistical stuff happening on the back end. So when it presents the answer, you don’t know what if that actually is the answer or not. Right? Because unless you say, show me the RMSE and the R squared for this model, and I can then judge myself, how good is this? Right? How accurate is it? But a lot of software packages, a lot of SaaS software packages will say, here’s the answer. And that’s it. And then you’re like, okay, and then you know, one says, What is it right?
Katie Robbert 37:08
Well, and the other piece of data, the huge piece of data that’s missing is any kind of costs, from our side our time like timesheets, expenses, those kinds of things. And with a marketing mix model, that’s really, if you’re if you’re looking for, where should I spend more time? Where should I spend less time? Where should I tune up and down budgets, you want to have that data included, and so you’re going to have to look in your time tracking systems, you’re going to have to look in your HR systems, you’re going to have to look in your ad systems, and other soft dollar costs too. And so that is not included here. Now, as we said, sort of in, you know, here’s what we’re covering, let’s just say for the sake of argument that that analysis that you showed, was statistically valid, what kinds of decisions? I mean, I can sort of rattle them off. But I want to ask both of you, what kinds of decisions would you start to make, assuming that this out data was the correct data?
John Wall 38:12
Yeah, that you just marched down from the top right, or you can go from the bottom, but the bigger was, you know, look at the first three there that seemed to be exponentially better than everything else that’s out there and just increase doing those. But like you said, you also layer price on top of that, because you find, you might find out that, hey, the second one, you know, only takes four bucks to triple and the other one, you know, we’re not gonna get another Superbowl ad. And then class can also help you prioritize? And actually that’s the right answer. If you can prioritize this by cost, that’s the way to go. Because you look at the bottom four, two, if pulling the plug on any of those is going to make a huge difference in your budget that could make, you know, a world the difference in the stuff that actually works for you.
Christopher Penn 38:52
Exactly. So John nailed it. There’s there’s three actions you take, you do more of what’s at the top, assuming that the costs workout, do less of what’s at the bottom. And what’s in the middle. That’s kind of a if you Milgram, and by the way, this chart is truncated, this, this table is 107 items long. There’s a whole bunch of metrics in the middle, that don’t seem to move the needle. Either way, they’re not reducing performance, but they’re not goosing fours. So those are the ones you look at and go. Are we doing that wrong? Right? Are we doing something wrong there that, that maybe the channel is valid, and we’re just we’re just not good at that. Right? You know, maybe we’re just not good at Tiktok. We just, I can’t dance. And so that’s those are the three big things you get from a marketing mix model, what we do more of what we do less of, and then what has potential, but maybe we’re not approaching it the right way.
Katie Robbert 39:49
No, and that makes sense. And so, you know, hypothetically, if we were focusing a lot on getting that new followers to our social media accounts, that would be the first thing To get cut up, all right, you know, we have a team of people who are, you know, trying to get followers and spending time on these, you know, influencer programs trying to figure out who to follow and who’s going to follow back and, you know, creating a lot of social posts of like, if you follow me, I’ll follow you, whatever the thing is, you know, that’d be the first thing to think, okay, great. Knock it off, because that’s not helping us at all. So let’s reallocate those resources into something more useful, that is bringing us closer to that higher deal count, which as we go back to the user story, you know, John, that you were sort of getting and Chris, that you played out through the analysis, that’s really what it comes down to is, what are we doing, that’s going to bring us to that higher deal, count that bigger revenue number that more, you know, qualified leads in the pipe that someone like John can go chase and close the contracts on. That’s really what this for, at least for us is all about. And so making sure we’re not losing sight of that overall user story of as a CMO, I want to understand, you know, my digital marketing so that I know what to do more of us have to get bigger deal counts. Yep.
Christopher Penn 41:12
The other thing that’s really important in here is if you look at those data sources, right, those data sources are all over the map, we talked about that at the beginning of the show. One of the things that and then John John hit the nail on the head with this, that’s powerful about these models is that they can accommodate a lot more than digital marketing, attribution models count, right? If you’ve got a billboard on nine 995, and you have traffic, you know, the Eastbound traffic, or northbound traffic on that, that can go in here. Right. So here’s the number of impressions for that billboard. And then you have your billboard cost per day that can go in here, too. If you have radio spots on terrestrial radio, and you know, when that’s when that’s being played, you can incorporate that and build what’s called an ad stock model for those impressions on radio, where impressions are run by through a time decay. So like you have, after a certain number of impressions, they lose their impact if you have a TV ad. So everything and anything that you can get data for can go into a marketing mix model. And as we found out with our model, there’s some things that are based on the statistical invalidity of this, we’re missing stuff, we are missing stuff that needs to be in here just to even make the model valid. That’s why these projects take so long, right? So for Trust Insights, yes, we slap this together very haphazardly, to create an a statistically invalid model in two days, like, no one’s gonna say, Oh, well, I would like that. To do this, well, would require us to now iterate, it’s okay, well, what other data do we need? What data are there other additional screening factors of stuff we need to take out of here. And this little, even just for us, again, is that three person company, this could take a good month, maybe two, of seeing what’s the big blind spot in this model that makes it statistically invalid. And that’s why a marketing mix modeling takes so long, and be why it costs so much.
Katie Robbert 43:09
Well, and making sure that you are not skipping over any part of the requirements gathering. And you know, we’ve talked about this in other episodes and piece of content. It’s the part that is skipped over because for a lot of people, it’s the boring part, they just want to get to the doing. But hopefully, we’ve demonstrated why requirements gathering is so essential, especially as you’re dealing with multiple data sources and the way in which those data sources are brought into one central database, it’s going to be a mess. But with that, if you want help, you can contact our Redis resident statistician John Wall, and he can tell you exactly how Trust Insights can help you put those pieces together. So John, any step facts that you want to leave us with before we close out this episode? Yeah, I’m going
John Wall 44:01
to be doing a webinar on pre internet statistics we’ll be talking about using the TI 35 If you’re cutting edge so we’re gonna get right to the heart of it. That’s perfect.
Christopher Penn 44:13
How to do your marketing mix model after an EMP on paper.
Katie Robbert 44:19
Get a big eraser,
John Wall 44:22
graph paper for everyone. All right.
Katie Robbert 44:24
Love it. I would I would show up for that.
John Wall 44:31
Goodbye dummy are the show.
Christopher Penn 44:37
All right. Any final thoughts, Katie.
Katie Robbert 44:43
I mean, I think we’ve covered it. So if you want to hear more about what exactly a marketing mix model is, you can catch that episode of our podcast at trust insights.ai/ti podcast. And if you want to get more detailed information around using the five d EAS to gather those requirements for a marketing mix model, you can subscribe to our newsletter that came out this past Wednesday at trust insights.ai/newsletter. And if you just have any general questions about marketing mix modeling, or want to share what you’re working on, you can join our free slack group at trust insights.ai/analytics for marketers, where Chris and I and John are in there every day, you know, creating chaos and then trying to rein it back in.
Christopher Penn 45:29
Exactly. I’ll leave you with the with these thoughts. Modeling is an iterative process, it’s it is almost never a one and done something that even just to get a single model you can make decisions with takes a lot of time. So if it’s something you’re considering doing, if it’s something you’ve been asked to do, go into it with those expectations that yeah, it’s gonna take some effort to do it’s gonna take some mathematics to do. And if you don’t have those resources, and you don’t have the ability to acquire them, then you have to let your stakeholders know here’s why. This is probably not the best choice for us. Maybe we start off with something smaller like a standard digital attribution model. Until we get the revenue we need to pay for the big model, but expect it to take a while, expect it to take a few tries. And don’t get discouraged if you run into all sorts of data quality landmines, processed landmines and things you may even run into depending on on your company. You may even run into some people landmines where some people don’t want their data included because you’re not put that paint them in the best light, which is always a delight. So thanks for tuning in. We will see you all next time. Thanks for watching today. Be sure to subscribe to our show wherever you’re watching it. For more resources. And to learn more. Check out the Trust Insights podcast at trust insights.ai/t AI podcast and a weekly email newsletter at trust insights.ai/newsletter Got questions about what you saw in today’s episode. Join our free analytics for markers slack group at trust insights.ai/analytics for marketers See you next time.
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