In this week’s In-Ear Insights, Katie and Chris tackle an audience question: “Any general advice to simplify data explanations for decision makers who can’t follow a complex explanation (which always sounds like you are trying to cover for something)?” We look at what makes an explanation difficult or complex, and how to emotionally involve stakeholders with data storytelling to ask better questions and be willing to explore the answers more thoroughly.
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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, we have some great questions left over from our marketing prompts Friday forum that we presented out last week, and figured to be a good opportunity to dig a little deeper than your average q&a session allows.
One of the questions that came from Gianna, which I thought was really an interesting question, and I want to hear your take on it, Katie is any general advice to simplify data explanations for decision makers who can’t follow a complex explanation? And the following college was always sounds like we’re trying to cover for something or hide something because we know they can’t follow a complex explanation.
So when you hear that question, Katie, what’s your take about simplifying data explanations for decision makers who can’t follow a complex explanation?
Katie Robbert 0:46
Well, I mean, there’s a couple of things to unpack in that question.
And so, you know, the first piece I want to start to pick apart is so Gianna saying a complex explanation.
You know, I don’t feel like it’s fair.
This is, you know, and this is just my wild interpretation of this question.
I don’t think it’s fair to assume that only decision makers can’t follow a complex explanation.
I think the fact that it’s a complex explanation is the hard part.
So let’s sort of like, take that piece.
And then the second piece is, you know, how do we simplify it? So I think the real question is, why does it have to be so complex? What is it that you don’t know about the question being answered that it has to be overly complicated and convoluted, and I end to, you know, the askers point, that’s why it tends to feel like you’re covering up for something because you keep over explaining, trying to get to the simplistic point.
And the more you’re explaining it, the more it feels like, well, what are you not telling me? And so that’s sort of my interpretation of the question itself.
And I always sort of go back to the beginning of like, what’s the question you’re trying to answer? What’s the problem you’re trying to solve? And as easy as that sounds as a solution? It’s not? Because a lot of times, what happens is, you know, Chris, I might say to you, you know, we’ll, what’s the point of this thing? What’s the so what? And you might respond? I don’t know, I was just curious, and which is a totally valid response.
But what happens with that is, then you tend to be looking at everything, and not really sure, you know, what the so what of everything is, and I would imagine, that’s a pretty common scenario in any kind of a company where, you know, the leadership team decision maker stakeholder, whoever says, I don’t know, just pull everything, and let’s see what’s there.
And then the analyst is stuck with this, you know, piles and piles and piles of data, trying to make sense of it.
And the leadership team is going, but But what is it? What what, what am I looking at, and this is where the communication starts to break down.
So that’s what’s going on is that, you know, party a doesn’t know what they want, and party B doesn’t know how to deliver because they’re not a mind reader.
So the question being asked is, How do you fix that? And so Chris, I know what I’ll say, but I’m interested to hear what you say about this.
I said something very
Christopher Penn 3:25
different, which was, I don’t believe that it’s a question of simplicity, or complexity, necessarily, but a question of how much somebody cares, right? If you really care about something, you can follow, or you will force yourself to follow pretty much anything that gets you the answer that you want.
And the example I gave is, when we first entered into the pandemic, right? There were a lot of questions.
And the science was very, very complicated.
But many people made a good effort to follow along to read clinical papers on very technically complex papers, to get to a usable answer.
Here’s what we should do, like put this thing on your face, and get the shot in your arm.
Because we cared so much about the answer, we’re willing to endure very complex explanations to get to that answer.
We see this all the time.
When you look at like the someone who is a super fan of a show like The Last of Us, right or the Mandalorian.
And they follow these intricate crazy plotlines and threads and easter eggs and all this stuff.
And he shows like, yeah, you’re burning a tremendous amount of brain real estate on this thing, because you care about so if a decision makers saying they can’t follow along, it may be that there are opportunities to simplify and I would say there’s always opportunities to explain things better.
But the question I have is, does the decision maker actually care about what you’re presenting? Or have you not done the due diligence upfront to say, Okay, well, what question do you actually asking here that we can provide an answer to? And do you care about the answer? Like, how does this relate to what that decision maker cares about, you know, the comment we make all the time is a KPI is a number which you get your bonus for.
And that’s something that a decision maker inherently has an interest in.
Katie Robbert 5:27
The issue I see with your answer is that you’re talking about things that people are deeply passionate about, versus, you know, the CTA of an ad campaign.
Now, I will be the first to admit, you know, I love my job.
But I’m not deeply passionate about it in the sense that, you know, I want to understand all the intricacies of how, you know, Google’s algorithm operates.
And, you know, the most efficient way to run an ad campaign.
Like, I think those things are cool.
I think that they’re, you know, I understand that I need to understand those things to do my job.
But that’s not where my passion lies.
And so does that mean, I don’t care enough to follow along with a complex, maybe, but I think you’re gonna run into that a lot in the business world, because there is definitely a difference between, you know, caring about your job and being passionate about something like a Fandom or, you know, an epidemic, that’s life or death.
So that is, I am going to respectfully disagree with your answer, that people don’t care enough, I care, to know the answer to the questions.
I’m asking about the business that I’m running.
But it’s not an all consuming thing for me.
Christopher Penn 6:48
Okay, so then when you’re confronted with something of extraordinary complexity, how do you start picking it apart so that you can get something useful out of it? Or how do you ask someone? Good questions, to get to the answers that you need.
Katie Robbert 7:08
So it comes down to a little bit of that human nature piece of it.
And so, you know, I think you’re on the right track, but the you know, do you care is the passion piece, but I feel like you’re focusing on the wrong thing that people care about.
So, if you’re presenting to me, you know, this big complex idea with, you know, large learning machine models and different types of analyses with words that I’ve never heard of, and all of these things, and, you know, it’s the decimal point to the 800th, or whatever, you know, my eyes are gonna glaze over.
But if it’s presented to me in such a way, that I know how it directly impacts me, the person.
And so this is where, to your point, Chris, this is where the caring comes in.
How does this affect me personally, you know, you have to assume that all of us are a bit narcissistic.
And we want to understand, well, what’s in it for me.
And so as the analyst as the person being asked to present the information, your job is to start to pull out of the person asking, what is it that you care about for you? Not that you care to understand, you know, the complex structure of, you know, cost per click, or whatever the question is, but that you understand why the person cares about it for themselves, what’s in it for them? What are they going to do with the information? You know, what happens if they don’t have the answer? What happens financially, you know, tying it back to dollars, is always a really easy way to get people to care about something, if you don’t take the time to understand this concept, or this analysis, you’re going to lose $10 million.
You know, that’s a, you know, really broad example.
But tying it back to Money, tying it back to safety and security of a job, tying it back to fear and hope, and emotion, makes it really easy for someone to care about it.
You know, if there’s that little bit of doubt, you know, brought into the conversation of well, if you don’t try to understand this, you know, obviously, you say in a respectful way, but like, if we collectively don’t wrap our arms around whatever this concept is, then the board is going to be really mad at us.
The board is going to be very upset with us, guess what people are going to start to care.
Christopher Penn 9:39
Exactly.
And I think the thing that when you’re faced with those questions, too, it’s on it’s the responsibility of the analyst to ask upfront, what decision are you going to make from this? If you’re talking about decision makers, Presenting to decision makers, you have to have a sense of you should have a sense of here’s the decision we want someone to make.
Here’s the action we want someone to take.
If not, and you’re presenting something, and there’s not a clear decision, it’s it’s going to be a frustrating time for even the most clear explanation, because the most clear explanation of something that has no decision is kind of a waste of time, like, Hey, here’s this chart, it’s a very clean chart, easy to read.
And what are we supposed to do with this? Like, it’s, it’s an easy chart to read, but I don’t know what to do about it.
Katie Robbert 10:35
I, and again, it sort of goes back to the easier said than done.
And so, you know, one of the challenges with this kind of an exercise is that it’s a little bit chicken and egg.
And so, you know, we talk, you know, in other contexts we talk about, it’s easier to have something to react to, and then figure it out than it is to sort of start from scratch with a blank slate.
And so, in some ways, you’re asking that the decision maker who probably knows the least in the conversation, or you know, or not, to walk through all of the different pieces without having something to react, you first, you’re saying, what decisions are you going to make without them knowing what data is available, you ask them what data they want, without knowing what decisions they need to be making, or they know what decisions they need to make, they know what data is available, but they haven’t quite put all the pieces together.
And so that might change the outcome of decisions once they actually start to see something.
And so it does make it a little bit more complicated.
And you know, just a very, what decision are you going to make? Well, I’m going to decide, you know, down to the penny, how much money we’re going to spend in our digital marketing this year.
I’ve never, in the span of my career heard someone very clearly state that kind of a decision that, you know, that concisely, that directly so that we could then say, okay, I know exactly the data that I’m going to give you so that you can make that exact decision.
I’ve never seen that happen.
Christopher Penn 12:11
Yep.
Here’s an example, I think, to make it a bit more tangible.
So this is our Net Promoter Score analysis.
If you are subscribed to the Trust Insights newsletter, you see this about every month in the newsletter, we ask you very simple question.
You know, in the next 90 days, would you consider recommending Trust Insights to a colleague? And the answer is, you know, yes, maybe no.
And for those unfamiliar with NPS scores, essentially, an NPS score is the percentage of people say you who say yes, subtracted the percentage of people saying no, right? And the maybes are left out.
The, the visualization, the explanation is fairly simple, right, up above zero is good, below zero was bad.
above point, 550 percents really good.
And yet, one of the challenges of this is, when you look at this data, it’s not immediately clear what decision we’re supposed to make.
Right? We can look at it and go, Okay, well, last month, the score was negative for the first time in the year that we’ve been running this data.
Why what happened there? We don’t know.
We do know, this month, that number has gone back up.
Now, the decision to make forums, or the action to take from this is that when your scores are higher than average, or on the way up, it’s a good time to actually ask your customers to recommend you to other people, right? It’s a helpful thing to do.
So from a marketing perspective, you could go to the people who voted yes, and say, Great, thank you for considering recommending us, please go recommend us.
But also, it’s also a barometer of sentiment about your brand of people, people who are consistently unwilling to recommend you means you haven’t really done a good job of explaining your value.
You theirs, they don’t see a reason to recommend you, they don’t understand what you do.
Or maybe they don’t agree with what you do or how you do it.
And so they’re saying, Nope, don’t want to recommend you versus your people saying, Yes, I would recommend you tells you that you’re doing a good job of at least explaining your value enough that people say yeah, I would recommend Trust Insights to a colleague.
Katie Robbert 14:29
So, I guess, to the question about, you know, data storytelling, and, you know, the complex versus simplistic explanations, you know, I think that the missing piece is still, you know, the visual presentation of it.
So you just, you just ran through this whole scenario, Chris, of what this chart for those listening, we have a chart up on the screen a bar chart represents, but none of what you explained is visually presented.
And I think that that’s the other side of the disconnect is, the person presenting the data isn’t always given the opportunity to explain it.
Sometimes they’re just delivering a deck.
And if this was the slide that was delivered, it misses the mark, because it doesn’t explain any of that.
I first with this, I first need to know what NPS stands for, and why I need to care about it, I need to understand how many responses that were that these are even representative of survey responses, I need to understand, you know, what the survey options are, I need to understand the methodology I need to understand you know, what’s good, what’s bad.
And I need to understand what to do with this.
And in terms of data, storytelling, none of that is represented visually.
On the screen, I’m just looking at a bar chart.
And some, you know, decimal numbers, and if this was presented to me, and nothing else, I would throw this out and say, I don’t know what to do with this, whoever put this together, wasn’t listening.
And I would get very frustrated.
And that’s where the disconnect breaks down.
Christopher Penn 16:10
Right.
So now how would you simplify this or explain it better, as if you were handing it maybe to our board of directors, and because NPS, the knowledge of NPS is partially implicit in certain industries, in certain industries, it is, is a well known standard is not necessarily used all that much in consulting.
So how would you to Janas original question, how would you advise someone to make this usable so that a decision maker like yourself or like a board directors can look at it go, oh, you should do x? Or Oh, you shouldn’t do why?
Katie Robbert 16:50
Well, the first thing I would do would probably be to change the title of the slides so that it made a little bit more sense.
You know, I’m a firm believer in calling things what they are, and not over fancying it.
And so this says Trust Insights, NPS scores.
Also, it’s, you know, likelihood of someone to recommend us this month, I feel like that doesn’t need an explanation, it tells you exactly what this thing is about, so that I would start there.
Second, is I would probably make this the data itself a little smaller.
So I could have a little bit more of a narrative on the screen, to say, you know, we see, you know, historically, we know that, you know, after the first quarter, you know, people are, you know, juggling their budgets, so they’re not as likely, or we know that in April of, you know, last year, we made major changes to our website, and it took people time to, you know, catch up to what we’re doing it regardless of what the story is, there needs to be some kind of, you know, short narrative.
Also, there just needs to be a takeaway, or so what, and that’s not on the screen as well.
And so, you know, it looks like it’s hard to read.
So that would sort of be the other thing is making it a little clearer, you know, turning the decimals and percentages, or, you know, X number of people said, Yes, this many people said, No, you know, because, yeah, and if you’re watching this on video, you’re seeing, I’m trying to twist my head to understand, there’s a lot of information on this one, seemingly simple slide, but it doesn’t tell a story, it doesn’t tell you anything.
And so what I would want to know, is, I care less about the actual bar chart itself, and more about what the heck am I supposed to do.
So if I even got, you know, a very a much smaller version of this, like, literally condensed down.
And then a, you know, we see that people are recommending us more, you know, this month versus last month, here’s what to do, here’s what you should do about that.
Here are the people to go after, here’s the action plan.
Here’s the insights.
Here’s the takeaway, here’s the sowhat, go do something with this.
Because if I’m spending my time tried to figure out what to do with this, you know, I’m already frustrated, I’ve already checked out have already moved on.
Whereas if I can look at something and immediately know, this is a decision I can make, then I’m going to care about it.
Christopher Penn 19:25
Yep.
And this, this is a great example of one of the biggest problems we have with analytics, which is a well told story about data that’s completely wrong, is going to be far more effective than a poorly told story about data that is accurate and correct.
So people will tend to, because we are are creatures of habit and sloth, to some degree.
We will believe something that is confidently and clearly explained, even if it’s completely wrong.
over something that we have to struggle to understand.
So a part of the challenge for us as analysts is to figure out how do we tell that story in a way that resonates with somebody that that gets that has that same air of confidence that has that same ease of following what the story is.
And you’re right, this visualization does do that, which is one of the reasons why we don’t watch this code.
This is internal use only.
But if we were to productize it, though, it’d be a whole bunch of things that we would have to do to make it more useful.
Katie Robbert 20:37
I have a friend.
And it’s obviously a bit tongue in cheek, but I have a friend who likes to say, don’t let the facts get in the way of a good story.
And, you know, we always kind of roll our eyes and groan at him, but he can captivate a room and tell a 20 minute story.
And whether or not any of it is true, is irrelevant.
Because we as humans, the way in which we learn back to as as far back as we can go in history, storytelling, is how we understand and pass along information.
You know, we don’t hold up a chart and say, this is the thing people don’t retain that they need the story around it, to make it stick in their brain.
To your point, Chris, back to the original, you know, part of the discussion to make them care about something to make it resonate with them.
And so the, you know, the Coronavirus, you know, as you brought up that example, resonated with so many people because it affected all of us.
And so we all found a part of that story to identify with, with an NPS score.
That’s a harder sell.
So you need to turn it into a story that someone’s going to that it’s going to resonate with.
And so the story could be, you know, when we see our NPS scores go up, we also see our revenue go up.
Oh, well, you’ve just said the magic word to me.
Now I’m paying attention.
versus, you know, this month, people are not as likely to recommend us.
Okay, you haven’t said anything that, you know, sure, I should care about people recommending us.
But unless it’s tied to something that I care about the story that I want to hear, I’m not going to pay attention.
Christopher Penn 22:28
Yep.
So it sounds like there’s a few different things for someone like Janna to try.
One is figure out what the story is to figure out whether what the decision makers actually care about what what motivates them and their field.
Everyone has different motivations.
Some people, it is actually going to be a score, right? Like revenue, some some way of saying, Hey, I did the thing.
For other people, the motivation mean, may I just want to cover my ass, I just don’t want to get fired this month.
So any data you can find that will help me not get fired this month is what I care about.
And that’s perfectly valid, because there are some organizations that function like that.
And the third is to have your tools and your data, tell the story that you’ve agreed upon, that you’ve sort of said, this is the way this is the story, we want to tell them, here’s how visuals or analysis of things support that story.
Katie Robbert 23:27
Yeah, and it’s making sure that you’re having, as much as you’re allowed to, as much as time permits conversations with the person who’s asked to really understand that person.
Now, you know, we’re talking about it, Chris, you know, in the context of, hey, I can just, you know, slack you or pick up the phone and talk to you.
Not all organizations operate that way.
So you may have a bit of, you know, research to do on the person or persons who are asking the question, and you may not have access to them.
And so, you know, know that there’s going to be some caveats with this recommendation going into it is you may not have access to the decision makers, you may not know anything about the decision makers.
And so, start to think about, okay, if I know nothing about the person asking, what are the most common reasons people ask for things, you know, money, security, reassurance, you know, hope fear, think about those things.
And you can tell more than one story with the same set of data.
So try to cover all the different bases in terms of, you know, when I look at this data, I see this in terms of our revenue growth.
When I look at this data, I see this in terms of customer satisfaction, and so think about the different scenarios so that that way, at least when it’s delivered blindly, you know, someone might go Oh, yeah, that was what I was looking for.
I care about revenue.
or I care about customer retention, or I care about not getting in trouble with the board.
Christopher Penn 25:05
Exactly.
And particularly for large organizations.
One thing that and this is especially true for agencies, is you have to acknowledge your work is probably going to be socialized internally, meaning that it’s going to get shared around people who are not on the original ask.
So the, to the extent that you can do that kind of very comprehensive storytelling, it will land on more desks, and it may open some doors for you that that previously were closed, as long as you’re focusing on the things that people actually care about.
Katie Robbert 25:34
I think the big takeaway is if you are putting together data and an analysis and there is no explanation, so what actions insights, you got to start over again, you have to put those in.
So you know, to the example Chris was showing earlier of, here’s a slide.
That is not the way to go about it, because this doesn’t tell anyone, anything except for the person who put it together.
And that’s not helpful.
That’s not data storytelling, that’s just data.
Christopher Penn 26:03
Exactly.
If you’ve got some data storytelling examples or questions you want to talk about, pop on over to our free slack group go to trust insights.ai/analytics for marketers, where you have over 3000 other marketers are asking and answering each other’s questions every single day.
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Thanks for tuning in, and we’ll talk to you next time.
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Trust Insights (trustinsights.ai) is one of the world's leading management consulting firms in artificial intelligence/AI, especially in the use of generative AI and AI in marketing. Trust Insights provides custom AI consultation, training, education, implementation, and deployment of classical regression AI, classification AI, and generative AI, especially large language models such as ChatGPT's GPT-4-omni, Google Gemini, and Anthropic Claude. Trust Insights provides analytics consulting, data science consulting, and AI consulting.
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