In this episode of In-Ear Insights, Katie and Chris ask and answer the question, “What is the value of analytics?” For many marketers, analytics is seen as a cost center and an administrative burden. What will it take for it to be seen as a benefit, as something we want to do instead of a necessary evil? Tune in now to find out.
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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 0:02
In this week’s In-Ear Insights, we’re talking about the value of marketing analytics, a lot of the time when we talk to customers to clients prospective customers, we’re talking about things like you know, Google Analytics and attribution models, all the stuff that I personally love to geek out on.
And people are like, Okay, this sounds cool, but I don’t get it.
Or I know I need this.
My boss says I need this.
But it’s an expense.
It’s a cost center.
So Katie, how do we how do we pivot? people’s thinking from analytics is a cost center to analytics is a value centers a profit center, it’s a decision making tool? How do we get them over the hump to realize the value of analytics.
Katie Robbert 1:49
It’s, it’s not a one and done.
It’s something that has to be done consistently.
And so when you are trying to help your team or your boss, or someone see that collecting data, analyzing data, using data to make a decision is a valuable thing.
You can’t just do it once they say no, and then you give up, it has to be a consistent and persistent effort.
And so, you know, starting with one metric, so let’s say your overall business goal is you know, more revenue.
How do you start to use one metric to demonstrate that your efforts are contributing to overall sales, maybe it’s getting people to come to your website.
So you can show that, you know, every month, the number of people visiting our website is growing.
And you start to consistently show that to your team, to your bosses, and say, the more people we get to our website, the more likely we are to convert them into sales.
And so obviously, that’s a very basic way to look at analytics.
But for for companies that are not comfortable using data to make decisions, or it’s brand new to them, you have to start small.
And I think that that’s one of the mistakes that, you know, we tend to make ourselves and other marketers tend to make is we try to do too much, too soon.
Because there’s so many data points that you can use, there are so many advanced techniques, but for a company that is brand new to or that you have a lot of change management that you have to navigate, start small start with one or two metrics to really get people comfortable with using data to have a conversation to make a decision.
What do you think, Chris? What would your approach be?
Christopher Penn 3:38
It sounds like working out, right? It sounds like yeah, you can’t just go to the gym once and you’re fit, right? This is like no, you actually I mean,
Katie Robbert 3:43
if that were true, I would be I’d be all fat.
Christopher Penn 3:48
But in that case, though, the problem is like how, you know, the principles of things like weight loss are pretty simple, right? Eat less exercise more, getting people to actually do that, and endure the discomfort as their body makes changes and adjust to a new reality is really hard, which is why this multi billion dollar industry of you know, kinds of crazy stuff, but also why people don’t do it is because they’re not willing to endure the upfront discomfort until they readjust.
And they don’t see that future state of Oh, I’m going to live to see my grandkids right.
I’m going to I can walk up a flight of stairs without like falling over.
Katie Robbert 4:29
They want that immediate instant gratification.
And I think, you know,
Christopher Penn 4:34
again, I don’t have any data to mutely support this so we’d have to go look for clinical studies, but I think these little guys have an ime for those who are listening on audio I’m holding up my smartphone have made that instant gratification thing worse, where people like I expect to push a button and you know, a car arrives in front of my house, a pizza shows up.
And when it’s a question of, do I go and do 50 pushups or do I lay on my couch, push a button pizza aircrafts in my door, the pizza tends to win an awful lot.
And I think the same thing is true in analytics where, you know, we hear this all the time at conferences, what’s the weather your fast, easy tips for this? Like? Well, you know, what are your fascinating tips for, you know, losing 50 pounds or, or, you know, being able to benchpress 400, there are no fast and easy tips, it’s gotta show up at the gym three times a week.
Katie Robbert 5:23
And as someone who has been working out for a while, the milestones that I’m hitting are small, but I have to keep looking at the information, my own personal data in order to stay motivated and see, nope, every day I’m in it.
So it’s hard to see what’s happening.
But when I step out from it, I can see Oh, I have made, you know, x progress, or I’ve hit you know, these couple of milestones.
So I think that that’s a really good analogy that, you know, using your data to make decisions is a lot like working out, you can’t just do it once and say, Okay, I’m done, it worked, or it didn’t work.
It’s a slow, consistent process that you need to build up and continue to check in on.
And I think that that’s exactly what companies don’t do.
Because like, get me the ROI in that campaign.
Did it work? No.
Let’s do something else.
There’s not enough information there to make a really good sound informed decision.
You’re looking at one data point one time, and you’re moving on and it just like, that’s not how that works.
And so Chris, how do we get people to be comfortable with the uncomfortable?
Christopher Penn 6:37
Well, I’ll go turn that right back on.
So what convinced you that you needed to start doing strength training? What said, You know, I want to feel sore every other day? Wake up? What made you think that that’s a good idea? I want to do that.
Katie Robbert 6:55
Um, you know, I don’t have a good answer for that.
But what I can say is that I’ve had a lot of fits and starts, where I’ve done the Okay, let me do it one time, okay, nothing happened.
I’m not suddenly on the cover of Sports Illustrated.
Okay, I should just quit right now.
I’ve done that a lot.
I am definitely guilty of the How can I do this faster? How can I do this easier? How can I do this without actually doing the work and it’s never worked out? Well, definitely not in a healthy way, definitely not in a way where I’ve gotten sustainable results.
And so I made the decision that I had to do the hard work in order to make this a habit, make this part of my lifestyle every single day, it’s not, okay, I’m working out, it’s, every morning, I get up, this is part of my everyday routine.
And when you do it consistently, it just becomes part of your everyday and you start to miss it when it’s not there.
And you can feel when you’re not doing it and looking at your data.
And using data to make decisions is very much the same way, if you start to build it into your daily routine, it becomes it’s a habit that when you stop doing it, you’re like, oh, man, I don’t have any data to make a decision with, I don’t know what to do, let me go get some data.
And so, you know, I know there’s like different, you know, studies that say it takes X number of days to form a habit, and everybody’s different.
And when you have multiple employees, multiple people, like an entire team, or CEO, or whoever, also playing into that, like it’s gonna it’s gonna take a while.
So you yourself have to be in the right mindset and make up your mind to say, regardless of the pushback, I’m going to do this every single day.
Christopher Penn 8:48
So what do you think those analytics habits are? Because you’re right, when you step outside, and when you’re when you measure the measurement itself, you definitely see it, you know, in one of my discord groups, we just did the hundred pushup challenge.
And we over the span of eight weeks, we all strove to follow this workout plan, it hurt a lot.
But at the end, you know, being able to crank out 104 push ups in a row is what was really motivating.
But even early on, we could see very rapid changes, even though we didn’t like you look in the mirror and see anything different.
We could see on the data itself.
Oh, last week, I could do 26 this week, I can do 34.
That’s a pretty big change.
What are the parallels? and analytics? Is it just that you stop feeling uncomfortable? Maybe when you show up at a staff meeting, like oh, I don’t have it.
I don’t know what’s going on with the market is gonna hide, you know, as opposed to saying, Oh, actually, I’m ready to talk about our team’s work.
Like, what are the other things that tell you? Yeah, I’m on the right track.
Katie Robbert 9:48
Well, that’s exactly it is having an understanding of what is actually going on.
It’s being able to answer questions, or it’s being able to know where to find the answers to questions and so it’s That comfort level.
And so, you know, if you’ve never opened up Google Analytics before, it might, it could very well be overwhelming.
And you could say, I don’t even know where to start, I don’t even know what I’m looking for.
It’s like going to the gym for the first time you’re like, well, there’s a bunch of equipment.
And I don’t know, I guess I’m just going to sit here on the floor and cry.
You know, it’s opening your, you know, data software, data collection software, it could be very much the same mentality.
And so, you know, pick one thing, pick one machine to start on and start slow.
And start with the lightest weight possible just to get a feel for the movement.
So you don’t injure yourself, the same is true of your data, pick one or two data points and start consistently collecting those to see, do they go up? Do they go down? Is there seasonality, you know, am I able to collect this consistently? Are there gaps in the data? What am I not understanding and get really comfortable without one or two data points and be able to explain everything around it, and you’ll start to see, well, I can explain this.
But if I had this other data point over here, then I could really tell a story.
And I could really answered questions.
And so it’s slowly building on that progress that you’re making.
Instead of trying to do it all at once, like, I can’t walk outside and run a marathon, it’s just not going to happen.
But I can walk outside and run a quarter mile.
And tomorrow, I can run a little bit further and then a little bit further and then a little bit further.
And that’s the kind of approach you need to be able to take.
And that’s actually how change management works, too.
You can’t just overhaul everything all at once, like Chris, you and I have both been in organizations that have been, you know, bought out and turned over.
And when the new company comes in and says we’re gonna make some changes, when they start to just sort of like, turn everything over how well does that work?
Christopher Penn 11:58
Oh, it’s fantastic.
No, but I think by that point that it’s, you know, you pick something you start with it, maybe that’s probably it is some of what’s missing, when it comes to the communicating of the value of analytics to people is communicating to them, like, here’s what you’re going to get in the first week, and the first month, right, and there’s small gains is something like you’ll be able to open up your analytics application and find an answer, you know, and six months down the road, it’ll look like this, you know, a year from now, you’ll be running a marathon kind of thing.
But I don’t know that anybody and you know, our hands up, I don’t know that anybody in our industry communicate that? Well, the end state well of here’s the end state that you’re going for, when you start working out, you have an end state in mind, you’re maybe going to look a certain way, you’re going to wait a certain amount, or you’re going to do a certain thing that you couldn’t do before.
And you’re like, Okay, that’s a goal I want to go after.
I don’t know that we communicate that and analytics very well.
Katie Robbert 13:02
We don’t and and you know, shame on us.
Because that is one of the things that Trust Insights that we You and I are supposed to do very well.
And I think that’s that continual learning and growing and iterating, you know, for us, you know, let’s say were the personal trainers.
You know, for us, it’s second nature, like, Well, duh, if you just do this, if you just do 10 push ups a day, you’ll be great.
But we need to step back and explain to the person that we are training, here’s why I want you to do 10 push ups a day, every day, for the next six weeks, here’s what will ultimately happen, you know, you will stop having lower back pain, or you will be able to pick up your child or whatever the outcome is.
So it starts with the conversation of what do you the person being trained want to be able to do? What is your goal? Okay, here’s how we the personal trainers, plan to get you there.
Here’s why we’re asking you to do certain things every single day.
Because building on those, you know, skill sets will help you get to x goal.
And that’s exactly right, Chris, we don’t do that.
Well, but starting today, that’s exactly how we’re going to be communicating it to those customers and potential customers and our audience members listening who have questions about, you know, I want to be able to measure everything, and I want to use all the data in my 2021 planning.
Let’s start with where you are today, we have to assess where you are today to see how long it’s going to be able to take you to run a marathon.
Christopher Penn 14:36
And I love the personal trainer analogy because I think there’s other components of that too, that get missed when we talk about analytics.
You know, one of the, you know, with the hundred pushup challenge thing.
I and one other person in our Discord server, we’re kind of the the leaders, the informal leaders of that nobody appointed us that we just took it upon ourselves to do it.
But a big part of that was the accountability part.
You know, US setting an all channel message every other day.
Hey, it’s Push up time.
Here’s today’s workout, you know, and at the end of the day, you’ll roll call, hey, who didn’t check in with their numbers and stuff? Yeah, we always got people like Bill frowny angry faces.
But people did it.
And the idea of being your personal trainers for someone’s analytics workout, a big part of that probably also is the accountability like, Hey, did you check your web analytics this week? How are you making your progress towards your goals? Have you hit your revenue numbers? Is there something wrong? And I also love the analogy of that as the personal trainer? Because you’re right, when you so when you show up at a gym, they ask you like, what are your goals, like, some people will say, I need to lose 50 pounds.
Other people say I want to benchpress 400 pounds, and the path to get to those goals is gonna be very different for each person, one person may be doing a lot more cardio, the person’s like, okay, go pick up heavy things and put them down repeatedly for the rest of you.
To do that, you know, if you want to look a certain way, there’s some workouts that will be actually counterproductive to that, that look, because you need a ton of different muscles to pull down.
So when we talk about analytics, you know, and those end states people are after, it’s almost like designing workouts for them.
Katie Robbert 16:15
It absolutely is, and you know, the accountability piece.
So you would ask me earlier, what is different this time around, I have a trainer this time around, because I, I needed that accountability for someone to say, did you do the thing today, and now I am an adult, I’m responsible.
But there are some things that I need someone looking over my shoulder, making sure I’m doing the thing, because they’re not fun, I don’t enjoy working out.
But it’s something that I need to do to stay healthy.
And your data collection, your data analysis is something that you need to do to continually monitor the health of your company, it’s very similar.
And, you know, if if we continue with this analogy, you know, working out doing cardio lifting weights is only part of the equation, there’s nutrition and mental health and all of those things that factor in.
And so when you’re doing data collection, there’s the people the process and the platforms.
And so you have to make sure that everybody is onboard.
And in the right mindset, or, you know, there’s roles and responsibilities defined and that you have the right things happening in order to collect the data and the resource.
And so it’s a full, comprehensive 360 plan for your data health, just like your physical health is a full comprehensive 360.
And having someone who has experience in it, like a trainer is a really good place to start, because they can guide you, you may not need a trainer forever.
But starting with someone who knows what they’re doing is probably a good idea so that you don’t, you know, get on the benchpress machine and you know, stack up the weights to 250 yourself in crush yourself Exactly.
You know, people who can show you proper form to avoid injury, you know, the same thing to in data, people who can show you the right data to collect so that you’re not overwhelming yourself.
And you can use it to make decisions,
Christopher Penn 18:16
the right algorithms and the right models.
Yeah, no, that makes total sense.
Especially because, like with the thinking back to the pushup challenge, there was no equipment, right? It’s just like you is you and the floor.
As long as you have a flat surface, you can do it.
And what happens in marketing analytics an awful lot is we focus so much on the tools and the technology and stuff, which is like focusing on the machines, the weight room, when really the challenge for almost everyone was the motivation, okay, I’m going to motivate myself to do the push ups, right? I don’t want to I feel like I don’t feel good.
I didn’t have my coffee today, whatever the thing is, now, we eventually got to the point where like, yep, like you were saying it became a habit.
In analytics, the same thing holds true you don’t need your multimillion dollar machine learning capabilities out of the gate, what you need is the people and the motivation to to do the thing, right to get out of bed and do the push ups.
The same thing is true in analytics, get out of bed and and start looking at a piece of data and start trying to use it.
Katie Robbert 19:15
That’s exactly it, you know, and so if you can commit to five minutes a day, okay, within five minutes, I could probably do a handful of push ups.
And then done, I can move on with my life.
In five minutes, you can probably pull, you know, your website traffic data for the day and throw it into a spreadsheet.
And so those non equipment workouts and those non equipment, data collection techniques, they exist.
Like if you have a spreadsheet if you have a piece of paper, you can hand write it down like is that ideal? No, but it’s a great place to start as you work up to your comfort level of having more advanced equipment and knowing how to use those pieces.
So it’s, you know, the hardest part is getting started.
And showing up every single day.
That’s half the battle.
Christopher Penn 20:05
So, to wrap up, proving the value of analytics is like proving the value of fitness.
It’s a long haul.
It’s something that you don’t stop doing and it’s certainly not something that is instant all at once.
If you want to chat about having the equivalent of personal trainers drop us a line at TrustInsights.ai dot AI slash contact we promise we will not make you do push ups as your
Katie Robbert 20:27
workout no promises are less you
Christopher Penn 20:29
ask us to do that.
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