In-Ear Insights Strategic Value of Analytics

In-Ear Insights: Strategic Value of Analytics

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the crucial role of analytics in digital marketing and how it’s often seen as the bearer of bad news. They explore the common pitfalls companies face when they only sporadically review data, leading to missed opportunities for timely adjustments. Katie emphasizes the importance of integrating a thoughtful and focused measurement plan from the onset of marketing campaigns. The conversation also delves into the challenge of making effective decisions when data is not utilized properly, highlighting the importance of iterative and agile program development for better outcomes. This episode offers valuable insights into transforming analytics from a dreaded task to a strategic asset.

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Machine-Generated Transcript

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

Christopher Penn 0:00

In this week’s In-Ear Insights, let’s talk about analytics, the sort of forgotten redheaded stepchild of digital marketing, particularly in the era of generative AI, where everyone is always talking about it all the time.

One of the things that and this was something that Katie, you and I talked about at the end of last year, and we’re sort of setting the course for the company as a whole, as we’ve been doing.

analytics, I’ve been working in the analytics field since really 2005.

And it has always been the Department of bad news, right? It is, it is very rarely ever something people look forward to.

And a lot of stakeholders are in the mindset of, I only want to hear good news, I don’t want to hear things that are going to get me promoted, that are gonna get me a bonus, etc.

And a lot of the time analytics tells you what’s happening.

It doesn’t tell you why.

But it tells you what’s happening and a good chunk of the time, it’s not good news.

So it’s not the the crazy news that you would that people want.

How should companies how should marketing executives and absolutely, you know, executives in general, people like us, the the corner office, folks get to a mindset where yes, analytics will tell you, sometimes things are not great.

And see the value in bad news as well as good news.

Katie Robbert 1:18

I think it has a lot to do with the frequency of the information.

So if you are looking at the data every single day, if you’re someone who has the luxury of having the time to look at the data every single day, you can see those little changes and sort of start to tweak things, and build out your programs in an agile way.

The challenge is that a lot of marketers, a lot of executives only look at the data, once a month, once a quarter, maybe even once a year, when it’s too late to do anything about it.

So I don’t actually think that it’s we get all the blame, the analysts get all the blame, the data gets all the blame.

But that’s not the problem.

The problem is, they’re looking at a situation they can’t change.

And that’s on them, not on the data, the data has been there all along.

The data has been there to provide guidance and the ability to fix things.

A lot of companies put the data last and that’s we know, that’s the wrong way to approach it, you need to put the data first.

And I’m what I mean by that is when you are building out your campaigns, building out your strategy, choosing your tactics, there needs to be a really thoughtful, and a really focused measurement plan that ties back to every single thing you’re doing, and building it in such a way that you can actually make changes.

And this is where companies are like, well, I don’t want to look at the data, it’s just telling me what’s wrong, because you built the programs wrong, because you didn’t build the programs in an agile way that allows you to, you know, make changes, or you didn’t build them iteratively, you built them in such a large, overarching, clunky way that once you start even if it’s going wrong, you can’t stop the train from leaving the station like it’s gone, you just have to roll with it.

And then what you’re left with is a program that didn’t succeed.

And data that tells you that data that reinforces all the mistakes that you made.

You’re like, oh, well, I don’t want to look at the data, the data is wrong.

Well, guess what? You were wrong in the first place Bozo, because you built it the wrong way.

So it’s, it’s gonna take a lot to switch that mindset, because the data is not the problem.

The planning is the problem.

The data is just the end result of poor planning.

Christopher Penn 3:47

So a lot of companies right now are in their 2024 annual kickoffs right now, as we speak, the lot of companies having this their sales kickoffs for the year, here’s what’s going to happen, you know.

And those plans were conceived of largely in q4 of last year, given that for a lot of companies that the train is about to leave the station, if it hasn’t already, if you have not already committed to your 2024 goals.

And now you’re having your sales kickoff to say here’s the goals we’re going to achieve or you’re all fired, right? What role does analytics play in this? Because you know, the train is leaving the station right now, if

Katie Robbert 4:21

you were to talk to those companies, they would say, well, these are data driven plans, analytics played a lot of, you know, roll into conceiving these plans.

That’s again, that’s not the problem.

The problem is the plans are so black and white.

They roll out the plans on January 1.

And then maybe on April 1, say how are we doing? But you’ve had three whole months of activity where you haven’t checked in and three whole months of activity where you haven’t decided, You know what the train is going to the wrong station.

Let’s reroute the train.

And that’s where we all get into trouble is that we don’t factor in.

We’re going to have to make change As along the way, we basically set the plan and think it’s like a Ron Popeil, set it and forget it.

And that’s not how any of this works.

There’s a commercial right now, that plays a lot during the football games.

And it starts with, you know, a whole bunch of executives sitting around a board meeting, it’s very stereotypical.

And this woman who’s running the meeting goes, the first rule of marketing is that you never do the same thing twice.

And I was watching this, and I was like, That is the stupidest thing I’ve ever heard.

Like, absolutely stupid.

And it just, it strikes me that it just no matter where you are, in a company, what company it’s the planning where everything goes wrong.

Christopher Penn 5:45

Okay, so take, take Trust Insights, as an example, as our fearless leader.

How do you use the data that we have access to? I mean, I know you’ve talked in the past about saying, you know, some systems, for example, the data is in there.

But you can’t really get at it in the sense that way, for example, doing individual level attribution analysis, and Hubspot is pretty much impossible.

How are you using that data as a guide for other executives to say like, here’s how you should be using the stuff that you already have.

Katie Robbert 6:16

It starts with those user stories.

And that’s something that when you know, it’s funny, when I start asking you questions, you’re like, hold on, slow down, what are your user stories? Because I, I’m like anybody else? I’m human.

And I’m like, what about this? What about this? What about this? And you’re like, slow down? What’s the user story? And it’s a really good reminder that this is the tool that’s going to help us figure out what do we need to know and the frequency.

So a user story is a three part sentence that tells you as a persona, I want to so that the persona being the audience, the want to being the intention, and the action and the so that being the outcome.

And this is the tool that I would recommend for other executives, for other marketers for pretty much anyone in general trying to understand their data.

So for me, it’s as the CEO, I want to understand the profile of our most engaged newsletter subscribers, so that we can find more of those similar type people.

That was what I was working on last week.

And that makes it very focused and very directional.

And so then when I go and look at the data, I’m less overwhelmed, because I already have this user story in mind.

So that when I am analyzing the data, I can go Wait, what was the question I was trying to answer with this information? Because we all do it.

I mean, you do it I do it everybody does it, we start to look at we’re like, what about this? And what about this, and it could do this.

And, oh, that’s interesting.

I didn’t know that insight was in there.

And it just it spirals and it continues to spiral and we lose focus very quickly.

And, you know, to your point, there’s a lot of systems that are harder to get data out of, and for us, you know, that was a bit of a misstep when we were setting up the company because we didn’t do those user stories.

At the beginning, when we were choosing platforms, we did what every other company does.

And we chose platforms, and have now tried to retrofit in our processes into those things.

We chose platforms we could afford, versus thinking through what are we going to need to use these platforms for long term with, we felt like we knew enough.

And it turns out, we probably if we had done a little bit more planning, we may not have chosen those platforms, regardless of cost.

Because we would have said, but they don’t suit our needs, they suit our needs today, because of cost.

They don’t suit our needs five, 610 years from now.

Christopher Penn 8:45

Right, which for folks who have platforms that aren’t, aren’t where they want them to be there’s the challenge for folks is to, to choose platforms, but with an eye towards here’s eventually where we will go so that you don’t build weird customizations that you then can’t port to a different platform, for example, or you don’t collect the data that is not going to be useful outside of that platform having having more universality.

A lot of folks right now are staring into the gaping abyss that is Google Analytics 4 and saying, Well, we’re on this thing, and it sucks.

And we can’t get data out.

But we can’t really make decisions with it and what I think is happening and I would love for folks in the comments to let us know if if this is the case at your organization, or if you want to let us know free slack of go to TrustInsights.ai slash analytics for marketers, with the difficulty that Google Analytics 4 poses to a lot of people.

Have you switched to a different system of measurement? Or have your stakeholders just given up on measurement? And that’s something that I would like to know because I suspect it’s more of the latter than the former people like Well, fine, we’re just not gonna measure anything.

You have very prominent folks even within the marketing space saying like yeah, attribution is a waste of time.

There’s you can’t attribute so Don’t bother,

Katie Robbert 10:01

which I hate when people sort of have that attitude about it.

It’s, it’s not that you can’t do attribution, it’s that you can’t do it as easily as you want could with Universal Analytics.

Now you have to think more differently about what that attribution looks like.

And I feel like with Universal Analytics making lightweight attribution so easy.

Now, you have to do more work to really think through, do I need it? How would I use it? Because I don’t know a lot of people at least like in the ones that we’ve encountered.

This is not a blanket statement, who’ve done attribution, and then used it to make a decision.

It’s more of a Oh, that’s cool.

Or, hey, look how good I look, because my channels winning, but then it kind of stops there.

And so I would sort of call BS on these people who are saying, you can’t do it, why bother? I would challenge them and say, Why do you want to do it? What are you going to do with the information? We’ve been doing it for ourselves, and something you and I have been talking about is looking at it a little bit differently, because I desperately want to make decisions with the data.

But I don’t feel like the data that we have at the moment is the right data to make decisions with and so I’m rethinking how we would look at that information.

And so maybe it’s not attribution.

But maybe it’s the same data, I still need to think through what my user stories are.

And it always goes back to what’s the question I’m trying to answer.

If the question you’re trying to answer is, does my team look better than Chris’s team down the hall? Then? Yeah, maybe attribution is the answer.

But you’re not really making a decision, you’re just sort of like, rubbing his face that you got a little bit more budget to work with last year.

Christopher Penn 11:50

Yeah, and the other thing that I think people forget about is, if the systems you have are not delivering end to end answers, there’s still a very good chance that the systems can deliver at least something to make a decision on.

The classic example is looking at your sales and marketing funnel, right? If you can’t do attribution all the way down to the close deal, but maybe you can do attribution down to the marketing qualified lead, that is better than nothing.

That is better than guessing.

And at that point, you could say, Okay, well, you know, maybe we can’t even get to that, but we can at least see what’s getting traffic to your website, because if your traffic is zero, you have a problem, right? It is, it is on the more towards the van, the end of things like if you have zero followers on social media, clearly that channel is not going to work for you.

And I think having that willingness to say, Okay, well, let’s do the best with what we’ve got, is a mindset that people have sort of forsaken to say like, either we have the information or we don’t do it at all.

And that’s a very dichotomous thinking, as opposed to saying, Well, it’s, there’s, there’s a spectrum of total information awareness, and complete blindness.

And it’s not one or the other.

It’s like, you can have grades of that along the way.

And I think people forget that.

Katie Robbert 13:04

Well, and this goes back to your question of why is analytics, the Department of bad news, because it’s not easy.

And so it’s, it’s not easy to execute, it’s easy to blame, because it’s not easy to execute.

And so, you know, we’ve run into our fair share of executives and leaders who refuse to look at the data at all, because their guts always right, they just know, they have so much experience, they have all the experience in the world that nobody else can possibly have.

So therefore, just listen to what they say and don’t use data to second guess, any of their decisions.

And a lot of people operate this way.

And it’s really frustrating.

You know, to be fair, I have been guilty of this not to that same, you know, extreme, but definitely the, you just got to trust me, I know what I’m talking about.

Like, I don’t have time to look at the data.

And admittedly, that’s a mistake on my part.

Because then, you know, maybe I’m wrong.

And this is where companies look at the analytics team and the data as the bad news because nobody wants to be wrong.

In a business setting.

We have created a culture where being wrong is a fatal error.

Not that being wrong is a learning opportunity.

But it is a fatal error.

It’s a fireable offense, and just everything you can do to avoid being shown that you’ve been wrong.

And that’s why the data is the bad news because a lot of times we make decisions that are the wrong ones.

But we wait too long to do anything about it.

If we were building our programs in a more iterative and agile way, being wrong would be an opportunity to get it right.

Christopher Penn 14:58

And we just don’t train station that As far as so you can still change tracks, right? You can, like

Katie Robbert 15:03

half the train is still half the train is out and half the train is still in.

And you can just be like, Whoa, just hold right there.

Let me just reroute you a little bit.

And then you can keep going.

And like, thank goodness, whoo, that I found out that I was wrong.

Before I got too far.

And now I can make it right versus? Well, I mean, I just I have to be right.

So don’t tell me you’re wrong.

Christopher Penn 15:26

So that brings up an interesting challenge.

Then if we think about the five P’s purpose, people process platform performance, right, we obviously have analytics platforms, we have analytics processes that are right now badly configured, because there it’s, you know, do the due to the analysis, and then the trains left the station, we know that performance is an issue, because analytics is still the Department of bad news.

We know the purpose of analytics, right, which is to help us stop making bad decisions sooner.

But the block is the people, the specifically the block is the culture that you just outlined, which is the no mistakes or else mindset.

And that has a lot to do with all sorts of cultural things that we don’t have time to get into on the show today.

How does a company fix this? And if you are, if you are the CMO, or maybe you are the analyst, what do you do? When the P or the problem and and you do not have the role power to create change, like you as the CEO, you can say, this is the way it’s gonna be I’m the CEO, here’s how we’re doing things.

And yet I still get questioned, and what do you do when you don’t have that power? And you’re in a situation where you’re in charge of the Department of bad news? Yep.

And no one wants bad news?

Katie Robbert 16:39

I think that’s a really great question.

And you’re absolutely right, sometimes you have zero authority to change the people.

So that’s where I focus on the process instead.

And so, I look at, well, how often are we looking at this data? How often are we taking in this data? Is there a way that we can make this data more iterative and actionable, and, you know, highlight more of the good along with the bad so that it feels a little bit more balanced? And so if you’re in a situation where you know, the people are the problem, but you can’t change it at all, my my, you know, hands down advice is to then look at the process and see what of the process of ingesting the data, analyzing the data, pulling insights from the data, what of that can change, to reach the people who are the problem.

So Chris, let’s just say for example, you know, I’m the analyst, and you’re the, you know, untouchable executive who just doesn’t want to be bothered with bad news, okay, great.

What I’m going to start to do is almost Pavlovian, and I’m going to start to feed you good news, on a regular basis, so that you get used to seeing the data.

And you’re like, Okay, I know that at 10 o’clock every day, I expect to get a data point.

And I know that I’m going to get it from Katie, and I know that it’s going to be good news.

And then you know, so I get you into this pattern to the point where you’re like, Katie, where’s that data? You know, I know we said 10 o’clock, but can I get it at like 945, I really look forward to getting this data.

And then you’re like, Okay, sure.

In addition to that, I’m going to start showing you some of the stuff that I think we need to fix.

And it’s all about how you position it, it’s not, here’s what’s going wrong, you still have to put it in those positive tones of this is an opportunity for us, you know, or here’s something that we need to, you know, alter, but here are three positive solutions that are going to continue to make you look great.

And so it’s really just a matter of changing the process and working with the people that are the problem.

Much easier said than done.

You know, I’m just sort of glossing over it in this very short, you know, couple of minutes that we have on the podcast to talk about it.

It’s a lot of work.

But if the people are the problem, focus on the process,

Christopher Penn 18:57

and that process, maybe update your LinkedIn profile? No, I think that’s I think that’s a good place to to wrap up there is to say like, yeah, there are options.

There are ways to to navigate this.

And if you’re in a situation where you need some help navigating this, let us know TrustInsights.ai slash contact if you’ve got some stories that you want to share, about how you have pivoted the analytics teams from the Department of bad news to the Department of let’s focus on this as a strategic imperative pop on by our free slack group, go to TrustInsights.ai AI slash analytics for marketers, where you have over 3000 other marketers are asking and answering each other’s questions every single day, and we’re busy watch or listen to the show if there’s a challenge.

rather have it on instead, go to Trust Insights podcast where you can find us on most major channels.

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