In this episode, Katie and Chris dig into what data-driven marketing is and is not. What is data-driven marketing? Who is data-driven? Is there a safe balance between opinion-led marketing and data-driven marketing? Tune in to find out!
Data driven means making decisions with data when the data doesn’t tell you what you want to hear. To be truly data driven, you need to be able to assess risk and make decisions accordingly.
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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.
Christopher Penn 0:01
In this episode of In-Ear Insights, let’s talk about what it means to be data driven.
So recently I was at an event and I being the complete and total nerd that I am, I carry around a little indoor air quality meter.
And it tells you all sorts of things like, you know, particulate matter and co2.
And I’ve worked in along because I like to know whether or not I should be wearing a mask in any given space.
If the number is above 800, you know, you should if the numbers below 800, you probably don’t need you.
And I was at inbound, the inbound marketing conference sitting in a session room, and the numbers started at like 900 insides, okay, you know, and then it kept going up and up and up and up as the session went on.
To the point where the air quality was so bad, it was as though you were breathing out of somebody else’s mouth, which is a delight to think about.
And that got me thinking, as I was, because I had on the floor in front of me, and everyone else in my row could see it.
And a few people asked what it was, and I explained it to them.
And what was interesting was that even though everyone could see the data in front of them live in real time, nobody else changed their behavior, nobody else made a different decision to put on a mask, even though they knew the kind of conditions they were in.
So Katie, broadly speaking, when somebody says they are data driven, what do you think they actually mean, given that every marketer says they’re data driven, but then when you see behaviors like this, where clearly the data is in front of them, and they don’t make a decision based on the data.
Katie Robbert 1:34
Um, I would say 99% of the time, when I hear someone say they’re data driven, to me, that means they, they are collecting some kind of data, they may glance at it, but they’re not making a behavior change.
You know, a smartwatch is a really good example of that I look at my data all the time, and I do adjust based on it.
But what I’m not adjusting is the way that I sleep.
Some of it, I have no control over because I’m a terrible sleeper.
But what I do have control over is when I go to bed, my watch isn’t going to change that information.
And so I stopped wearing my watch while I sleep.
Because I already know how poorly I sleep, I don’t need my watch to also tell me.
And because I’m like the Princess and the Pea, it keeps me awake all night, because it’s one thing that I can like, feel and wrestle with.
And so that, to me is an example of, you know, you’re collecting the data.
But are you going to do anything with it? If not, then, you know, give it up.
It’s a waste of time.
And so I think that a lot of companies like to call themselves data driven, because it it sounds right, it sounds like we are using the data to make better decisions.
But in our experience, Chris, we’re giving companies the data, we’re giving our clients the data, and they’re so saying, Yeah, but that’s not what I wanted to do.
So I’m gonna go ahead and get like, you know, the triple scoop of chocolate ice cream, even though my tracker says, hey, it’s time for a banana.
Christopher Penn 3:11
To me, when someone says, when we talk about data driven in the abstract of this, to me is what data driven really means, right? This is, of course, our friend map software on your phone, you put in a destination, and then the system and the software, collect the data and make recommendations like, Hey, you should probably use this route, avoid these places and stuff.
And then you get in the car and you you do your thing.
And you’re literally driving with data, you still set the destination where you want to go.
But then data comes in that says here’s here’s the things to do along the way that will either optimize the journey or hit secondary objectives.
Like there’s a variant of this app called Roadtrippers, which is like, you want to you can tell I want to make interesting points of interest stops, like you know, the biggest ball of twine in Minnesota, you could you could set that as a destination, and we’ll help you get there.
But what, to me defines data driven from a business and marketing perspective.
And whether someone is or is not, is whether you make decisions with data when the data doesn’t tell you what you want to hear.
That to me is the gut defining line.
Like on one side, if you only use data when it says what you want to hear, you’re not really data driven, you’re still opinion driven.
On the other hand, if you use data and you’re like, I don’t really want to make this decision, but it’s hard to argue with objective reality like this, this campaign is just not working.
That to me says you are a data driven.
Katie Robbert 4:42
So I have just learned from this conversation that when I am trying to get to the highway, I am opinion driven.
Because my GPS always wants me to take a certain turn.
But getting onto the main road, I would have to take a left and it’s a pain.
So I always Yep, that section of the directions.
And I go farther ahead where I can just go straight through an intersection.
And my GPS always gets mad at me.
So there are times where I am completely opinion driven, because I think I know better than my GPS.
But I think, you know, you’re absolutely right, Chris, it’s the moments of that uncomfortableness, when it’s not an easy decision that really determines whether or not you’re data driven.
Anybody can make a decision with things that look great with things that are moving in the right direction, okay, so are, you know, we’re hitting our goals, keep doing the thing.
That’s not a hard decision.
If your goals start, if your data starts to point to you’re not hitting your goals, now you have to make real decisions.
And you have to do the deep work to figure out are these decisions going to get us back on track, and that’s the part that people skip.
It’s very much like taking care of yourself as a human, you know, eat vegetables, drink water, like, people know that those are the things they should be doing.
You know, don’t smoke, don’t drink all that sort of stuff.
And but like one drink one cigarette, like, it’s fine.
I know I shouldn’t, but like, I, I totally can.
I’m the exception.
I’m the snowflake, it doesn’t affect me.
I’m the one who’s special.
It’s the rules don’t apply to me.
And I feel like that’s the trap that a lot of companies get into as well as like, Yeah, I know, the data says that we’re not going to hit our goals.
But let me just throw more money at it.
And just sort of see what happens.
Because you know, that’s going to be the solution.
Christopher Penn 6:36
I can’t tell you the number of times I’ve heard people come up to the ad cops and say after we talk about, like attribution modeling say, Yeah, but you know, our company is different.
Like, what like, what do you require your customers to like, dance the Macarena before they buy something like, I’m pretty sure customers give you money, and you give them a product or service? It’s not super exactly super different.
Yeah, there are nuances.
But the broad picture of something like attribution modeling pretty much stays the same from company to company.
The only, I think the only exception is anytime you’re dealing with something that is non financial, there’s where there is no measurable outcome prior, like, say, if you were running for office, right, you can’t measure the conversions along the way to the election, you only have the election to tell you whether or not
Katie Robbert 7:28
I have that.
I would disagree with you.
But that’s a conversation for another time.
Christopher Penn 7:32
So given this that how do we help people become more comfortable with data that makes them uncomfortable?
Katie Robbert 7:45
Well, you just said something really important, which is nuance.
And so I think what happens is that people look at the data, and they assume that it’s just black and white, that there’s no room for interpretation, that there’s no room to add in extra layers of information, so that it does apply to them.
You know, so the broad strokes, we were talking about benchmarking last week, for example.
And benchmarking is a really good baseline to get started with.
But if you’re, if I’m comparing my stats to your stats, Chris, I’m never going to be successful, because I’m not you.
And the way that I behave online, in terms of like, how often I post what I post those kinds of things.
It’s just different from how you operate online.
And so me using those me saying, Okay, I can’t use that number, it’s never going to work for me, is the wrong way to think it’s the wrong mindset, because I’ve already set myself up for failure, because I’m using the wrong metrics.
And I’m not factoring in that nuance.
And so that’s what we need to help companies understand is something like an attribution analysis, it’s a jumping off point, it’s not the end all be all you need.
Nobody is ever going to know the company better than they do than the employees.
And so filling in those other data points, that the attribution analysis doesn’t necessarily have factored in, you know, time of day budget adjustments, you know, what color something was, when it went out, or, you know, the how long the piece of content was, when it was published or something like that.
Those are the nuance points that are going to help you make better decisions by understanding those pieces.
But you still need to use the foundational data such as your Google Analytics, your baseline metrics, your attribution analysis in order to start to make better decisions, because otherwise you’re just saying, Well, I published two pieces of content.
One was 50 characters long.
One was 1000 words long, and one did better than the other.
That’s, that’s not really an analysis, that’s just sort of a, okay, did I get lucky that day that I hit the right people that day.
So you need to have those foundational pieces with the nuance layer on top of that.
Christopher Penn 10:13
I think the other thing that took me a long time to understand and it took me many years to accept is that it isn’t either or it’s like you were saying at the beginning of our talk, you start off your journeys, making an opinion based decision, like I don’t really want to make that left turn.
And it’s not you must follow the GPS, or you must not ignore the GPS entirely, right, there’s there is that there is room for you to say that I know these roads better.
And I’m more comfortable making this.
So a part of data driven is the opportunity cost, the opportunity cost of you not making that left turn is like what probably a minute and a half of a little bit of extra of distance, right? But it’s not huge.
So in that case, opinion, or data really doesn’t matter to the outcome.
On the other hand, if you were driving to Cleveland, and you chose ice at instead of i 90, you’re talking about a two hour difference between those two, those two trips, if you choose one, because you’re comfortable with driving on it, or it’s more scenic, whatever, you’re trading off a lot of time, you’re adding two hours to a 10 hour drive.
And so in that case, data driven, probably is the better choice because of a much higher cost.
So how do we help people understand when it comes to your marketing? There are times when Yes, it is okay to be opinion driven.
And there are other times it was like, the cost of that’s going to be pretty high.
If you’re not?
Katie Robbert 11:40
Well, I think that’s where you start to factor in the risk.
And so, you know, the financial risk, the resource risk, the data is only going to tell you so much, quite honestly.
And I think that that’s the other piece of the puzzle is that, to your point, Chris, the data is not the be all end all, you still can choose not to do what the data is telling you to do, I can still choose to get on the highway somewhere else other than where my GPS is telling me to, you can choose to send out even more email, even though your attribution model says email isn’t working for you, that is totally your choice.
However, you then have to live with the consequences or the risks of making those choices.
So to your example of i 80, versus i 90, can I live with driving an extra two hours, maybe not, maybe some days, I can maybe some days, I can’t, maybe I’m driving out there on a road trip.
And it doesn’t matter how long it takes me versus I’m trying to get to an event.
And if I take the one, that’s an extra two hours, I’m going to be late for my own thing.
And so that’s the way that companies and decision makers need to be thinking about using the data to be data driven is what can I live with? If I choose to follow the data? Exactly? You know, can I live with, you know, a more conservative outcome, maybe it’s lower risk, because this is the direction the data is pointing me into.
Versus I’m just gonna go completely off script, I’m gonna take a bigger risk, and maybe it’s going to pay off, but maybe it’s not can I live with it not paying off, it’s a lot like having stocks, you sort of by your age group you have you’re conservative, you’re moderate, you’re aggressive.
And it’s always a trade off, it’s always a risk.
If when you’re younger, you can theoretically afford to be more aggressive with your investing, because losing money when you’re younger, is not as devastating as it is when you’re older, and you don’t have as much time to build that money back.
And so when thinking about being data driven, that’s really where my brain goes with it is can you live with the amount of risk, if you can, great, do your own thing doesn’t matter if you lose millions of dollars, because you can live with it.
If you can’t live with that risk, follow what the data is telling you to do.
Christopher Penn 14:01
So how does somebody assess that risk? How does somebody assess the risk in a in a fashion where to a stakeholder ago? Okay, I see the this here is the data driven approach.
I see this approach here is purely opinion approach, I see a blended approach in the middle.
How do you make that case to a stakeholder to say like, this is the direction we should probably go.
Katie Robbert 14:22
So herein lies the gotcha.
You’re still data driven, because you’re still using data to make those decisions.
And so it’s all about perception.
It’s all about the way you’re looking at being data driven.
So if you’re going to go off script and do whatever the hell it is you feel like doing, but someone’s still gonna hold you accountable for it.
You still have to use data to prove why your campaign or your idea or your opinion, is the right one.
So if we go back to that very small example of me deciding a different place to take a left hand turn I have No, even internally to myself said, it’s only going to take me an extra minute and a half, if I go up the street a little bit farther to the intersection to go through versus taking a left hand turn here.
And so I’ve used a data point, data point being time to justify why it’s okay that I take this particular, you know, route instead of what the GPS is telling me because I’m not going to be late for a doctor’s appointment if I go this other way.
And so in that example of, you know, YOLO, I’m just going to do whatever I want, I’m just going to spend a million dollars.
And then your boss is like, so Chris told me why it is you think it’s okay for you to spend a million dollars? What do we get out of that? You still have to use data to justify what it is you’re doing.
So you are still being data driven? So Haha, suckers, you’re gonna be data driven, even if you even if you don’t think you are.
Christopher Penn 15:52
I might suggest also
Katie Robbert 15:56
not calling people soccer’s okay.
Christopher Penn 15:57
I mean, you can, that’s totally fine.
No, in terms of, of tactically bringing that to life, if you think about the the five P process that we use purpose, people process, platform and performance, choosing an approach data driven, blended, you know, opinion driven, their purpose should be the same, right, which is, whatever you’re trying to achieve.
And then the eat in each of those categories.
You can say, here’s the people we need, here’s the processes we need, here’s the platforms and technology, and the resources.
And then here’s the expected outcome, the performance.
And so if you present that to stakeholders that way, like saying, we want to we want we know we need more leads, we’re going to want approaches, we’re just going to wing it, and spend a whole bunch of money on Facebook.
So you probably don’t need a ton of people for that, right, you probably have some defined processes, and you have your platform of choice, and you have your expected performance.
To your point, Katie, you are still being data driven.
In this instance, in other cases, you would say, let’s figure this out.
So you have your your purposes the same, the people now might include some of those data science skills to run a marketing mix model or a attribution model, your process would be very different, because you’d be creating these models, to then say, Okay, here’s maybe the spread of investment we should make.
And then you choose your platform from that.
And then you say, here’s our expected performance based on that.
I think, if you presented a case of, you know, the different approaches towards achieving a goal with that framework, it’ll be easier to get a stakeholder to say I want A, B or C, like pick one from the from the menu.
Katie Robbert 17:32
Well, and that’s, you know, that’s exactly the way that you should be approaching it, the five P’s basically act like a mini business case, for each of the different options.
And so, Chris, if you were coming to me saying, we can either spend $50,000, or we can spend a million dollars, my first question to you would be like, Well, what do I get for each one.
And so by using that structured, basically, you know, analysis of, you know, going through, well, here’s the people, we’d need to do the million dollar one or the $50,000.
One, here’s some potential outcomes, it’s easier to make those decisions, because you have at least outlined what’s expected.
So even if you feel like you’re going off script, you still need to have some kind of expected outcome, in order for people who feel comfortable with you going against what the data is telling you.
And so the five Ps is a really great way to approach that, because you can sort of go through all the different factors.
You know, it’s, it’s interesting, because I feel like the term data driven, it’s a couple of things.
One is, it’s almost like a dirty word.
Because people are like, Oh, I can’t be data driven data takes too much time.
The other side of that is, let me just, you know, use the term data driven just to like, you know, as a band aid for things, I don’t really know what it means.
And so, ultimately, at the end of the day, we’re all data driven.
To some extent, we’re all using data.
And I think that it’s that term data that needs to be redefined.
Because you know, as you’re going through the five P’s, for example, that is not your, you know, individual binary number data points.
That’s your qualitative data.
So you are being data driven.
So you’re saying I need Christmas time, I need Jon’s time, I need Katie’s time.
Each of us are actually also data points.
It’s just not the traditional spreadsheet of data.
Christopher Penn 19:32
And the other thing that I think and this comes from from my machine learning background, there’s a type of data environment called sparsity, sparse data, which essentially means you don’t have very much to work with.
And in those situations, being fully data driven, you know, taking opinion out of the equation, in some cases is not possible, right? Because there isn’t enough data to make a decision.
It’s like, if you have one conversion in your Google Analytics overnight Any days, like the built in attribution modeling just can’t work, it will simply say, I don’t have enough information to make a decision.
And in those cases, particularly for people who are more quantitatively minded, yeah, you gotta just kind of wing it.
In those instances, you got to use your best guest informed by experience, by your understanding of human behavior and psychology informed by macro trends to say, this is probably the direction we should go.
I can’t be certain, but we don’t have enough information to make a good decision.
Katie Robbert 20:31
Well, and in that case, to a different data point is asking your audience, what are we doing wrong? What’s not working? What aren’t we giving you and those are all usable data points to inform your decisions.
You know, I think that that sort of like, as you’re trying to demystify, you know, being data driven, you don’t have to use machine learning process or artificial intelligence data.
In order to be data driven, asking your customers what they want, is being data driven, talking to your audience, looking at what people are saying about you on social media, that being data driven, as long as you’re using that information, to inform your next steps.
Christopher Penn 21:11
the other thing I think, should be a point that marketers should keep in mind is, the further along in time you are, the more data you have available to work with.
So if you’re at the beginning of a project or a campaign or something, there may not be information to work with, right, you may not have had time to run a survey, you may not have any data in your new Google Analytics installation.
And in those times, you do have to use as much macro and background information as you have because you, you will not have the quantitative data to even make those decisions.
Conversely, if you are at a company that’s now 130 years old, and you’ve got great data, the systems have been in place and working well.
You probably should be at least that hybrid blend of opinion and data, right? If not be leaning more towards the data driven side, because you at that point should know your market, you should know your audience, you should have been doing those surveys, and had that customer advisory board, all that stuff in a mature environment should be in place.
And if it’s not, that’s a good blueprint for the things you need to remediate to, to get that ship righted.
Katie Robbert 22:20
So it sounds like, you know, people, marketers, companies, businesses don’t need to be afraid of the term data driven, it’s not going to, you know, squash your creativity, you can still have your big ideas and execute them and sort of like, I know best.
But using the five P’s, using that foundational data, using the information you already have, is going to just help you make better decisions.
It doesn’t have to bog you down, you don’t have to be stuck in spreadsheet, hell of like 1000s and 1000s of pages of numbers in order to make a decision that’s doing it wrong, that is absolutely the wrong way to do it.
So if that’s what you think data driven, is, take our advice and know that that’s not being data driven, that’s just being silly.
Like you’re just like, that’s just like sitting on top of pile of numbers go in I don’t know, like that’s not data driven.
Data driven is using the information around you to inform your decisions, whether it be a chart, you know, a date or a number, a numerical data point, customer feedback, you know, feedback from other people in your agency.
Those are all usable data points that allow you to become data driven.
Christopher Penn 23:39
And as we say, often data without a decision is just a distraction.
So try to make sure that you are you are making decisions along the way.
And speaking of decisions, if you’ve got some decisions and some data driven stuff you want to share, pop on over to our free slack group go to trust insights.ai/analytics for marketers, where you have over 2700 other marketers are asking and answering each other’s questions all day long.
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Thanks for tuning in, and we’ll talk to you soon
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