In this week’s In-Ear Insights, Katie and Chris tackle an unusual situation. What happens when data doesn’t have answers? What happens when data simply doesn’t provide any useful insights beyond confirmation of what you already knew? We review what your options are, and what might have gone wrong that led to the situation. If you’ve ever been stuck in this situation, listen to this episode for ways to think about solving this problem.
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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 0:02
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In this week’s in ear insights we are talking about what to do when your data doesn’t tell you what to do.
Many times we talked about how you have to be using data to make decisions.
And if you’re not making data decisions with your data, it really is kind of a distraction.
We want to focus on what actions can we take but sometimes, especially with some clients that have very, very steady even, you know, businesses that have no major disruptions, which is astonishing, you know, in the current pandemic times, you have data says, Yeah, there’s nothing to do except stay the course maybe.
So Katie, what do we do when the data doesn’t tell us what to do?
Katie Robbert 1:46
We panic and cry under our desks.
That’s it?
Unknown Speaker 1:50
No, no.
Katie Robbert 1:51
Okay.
All right, what do we do know.
So this is actually a very common issue.
So, you know, if you’re looking at your data every day, like in an automated report, like Data Studio, or if you’re looking at it every few weeks, and you’re not seeing a lot of change, that could be the indication of a good thing that things are working.
But the risk that you run is that if things completely stay static, or they’re just very slowly growing, or just you know, plateauing, then it’s sort of that like bare minimum of like, okay, so we don’t touch anything, things will just keep rolling along.
But couldn’t we be doing better? We ran into this recently, when we were pulling an analysis and a notch in attribution analysis for one of our clients.
And, Chris, your first reaction was, myth doesn’t really tell us a whole lot.
And what we ended up doing in that instance, was we really, you know, for lack of a better term, we stepped outside of the box of the way that we usually look at these analyses.
And we thought, what are some of the questions that we can continue that we can start to ask? So instead of saying, This is what the data says, and this is the action, we took a different tack and said, let’s just start asking questions of, is this the best thing to be doing? Is this the way you want your data to be looking? Are these the results you’re hoping to get? And so instead of just like throwing our hands up and saying, well, this isn’t useful, we really tried to think about it from a different perspective of just questioning everything.
What do you think, Chris? I mean, you’ve been doing data longer than I’ve been doing data.
What do you what do you do when you run into this?
Christopher Penn 3:33
For it depends on whether it’s a strategic or mathematical problem.
In the math side of things, one of the things that happens, like when you run, say, a simple correlation, and you get something that doesn’t really provide any illumination, one of the first questions you have to ask is, is there some piece of data that’s missing? Right? Is there some metric or variable that isn’t accounted for that you then have to say, Okay, if we had this, then we might get some kind of relations? Because we know, some we know, we’re trying to find something we know there’s a there there.
The question is, is, do we have the appropriate type of data to to make that analysis and there actually are statistical tests you can run on data? is specifically time series data to say, Okay, these are the things that could hint at a problem.
There’s techniques like principal component analysis, factor analysis, certain types of regression analysis can hint that Yeah, you might not have all the ingredients that you’re trying to find.
But I think for most marketers, it is the strategic problem more than anything, the one that you alluded to, which is if things just kind of plugging along, what’s missing, why aren’t things you know, the typical growth hacker created, why and things just going up into the right, you know, are there channels you’re not considering? Are there tactics you haven’t used? Are there audiences you haven’t reached? And so it’s a question of what is omitted from the analysis, when we do an attribution analysis, for example, there are things that in digital marketing are omitted, right, you cannot see things you don’t have information for.
If you’re looking at Google Analytics, for example, one of the channels that shows up all the time is called direct, which is a misnomer, it should say unknown, because Google has literally no idea what to do with that data.
It just calls it direct.
And sometimes, depending on your website, and your Google Analytics configuration, that can be 20 3050 60% of your data.
When 60% of your data is unknown, you kind of have a problem.
And so the question then is, what do we do? How do we unpack that box? Because there may be things in there that could drastically change our analysis, maybe email isn’t holding steady, right? Maybe email is actually going up.
But because you didn’t use UTM tags and your emails, for example, the you are missing a whole bunch of traffic, we had that problem with a client where email is showing up for like, 2% of their attribution analysis.
And they were like, Oh, you know, we, we don’t actually put UTM tags in our email.
It’s like, great, but we tried fixing that for a month.
And then the next month, we ran the same report, oh, look, 70% of conversions are from email.
It turns out, the data was missing.
So when we’re talking about is the data, you know, math is the data that tells us what to do.
We’ve got to make sure we’ve got all the data.
Katie Robbert 6:25
Well, you know, and it’s interesting, because you’re focusing a lot on the quantitative side of the data, which is often what we look at.
So we looked at our KPIs, our goals, you know, did we get the traffic did people convert? The other side of it is it’s you can’t know the full story without the qualitative side of it.
Why did that happen? Why did you make that decision? Why are you doing things this way? And so really digging into that behavioral side of the data, asking your customers talking with the rest of your team? Why are we doing it this way? asking your, you know, C suite, why are these the goals, just really digging in.
So if your data is just flat, if your data is just like, rolling along, there’s no peaks, there’s no valleys, you know, you may say, Oh, this is a great thing, you run the risk of if something changes, all of that disappeared.
So you really need to understand why things are the way they are, you know, if your data is flat and plateaued, it’s a great opportunity to run a customer survey or a small focus group, or do some A B testing and say, Is this what we want it to be doing? You know, are we growing fast enough? Are we doing enough? And Chris, you had mentioned, you know, the different digital channels? Like is email doing what we want it to do? Yes, it’s bringing in traffic, but is it bringing in the right traffic? And those are some of the questions that we really decided to dig into when we ran into this recently of like, Yeah, it looks like SEO is doing well.
But are you ranking for the right terms? And so it, we needed to do that additional analysis? And, you know, are you too reliant on any one channel? So there’s really a lot of opportunity, when, even if you think your data isn’t telling you anything useful? It’s actually an opportunity to dig in even deeper and see why, what’s going on?
Christopher Penn 8:25
Exactly.
I was doing some homeschool science with my kids this weekend, what things we were looking at was calculating momentum.
momentum in physics is a simple form.
That’s mass times velocity.
And when we think about momentum in the bigger picture context of, you know, is our marketing growing as our results growing, are they staying flat? Are they declining? That in, in a sense, is momentum? Are we growing? Are we staying flat? Well, you look at your data and the data is kind of flat, that means your momentum is pretty stable.
Well, momentum is a formula mass times velocity.
In the context of marketing, that means things like your audience, your conversions, your traffic and stuff, and then the speed at which you’re able to get people to do stuff.
So even if your conversions or your audience is growing, if you’re slowing down, in your ability to convert people, you will have consistent momentum.
So it may be a question of which of those two things is the problem that you’re not growing? And once you can diagnose that, you can say, Okay, what do I have control over? That could increase either mass or velocity to make momentum increase? For a lot of marketers? velocity is easier to fix than mass, right? You may not have a ton of money to throw at ads.
You may not have a ton of people on your mailing list to mail appealing, but you absolutely can do things like looking at the most valuable pages on your website and say, How can we tune these pages up? How can we accelerate the process to conversion? How can we remove obstacles From, but what’s getting in people’s way to convert? How could we have a website that doesn’t suck.
And that’s how those the easier things for marketers to tinker with to increase velocity.
And if you increase velocity while your mass remains the same, your momentum still goes up.
Now the best case scenario is you can get both to go up the same time, and then you have acceleration, then you’re talking about force, which is a totally different, totally different, slightly different formula.
But from this perspective, if the data is flat, there are these two fundamental reasons why that data could be flat.
And you need to diagnose that first.
Katie Robbert 10:40
One could argue that, you know, given everything that’s going on this year, the goal was to keep things status quo and keep things as flat as possible.
So there’s definitely a good argument for having your data look that way.
However, we are now nine months past the start of the pandemic, and you know, 2020 is coming to a close.
So then I would also say, Yep, great, you kept everything status quo for the year.
Now it’s time to turn those knobs and increase that mass and increase that velocity, and figure out because now everyone, you know, not to undermine it.
But everyone has kind of settled into this new normal routine.
And so there’s really no excuse at this point, to really sort of say, Okay, I’m just going to throw my hands up and keep everything status quo.
Obviously, you know, that doesn’t apply to every industry.
But when we’re talking about marketing, especially digital marketing, the internet is more important now than ever, because it’s how people are staying connected.
It’s where they spend their time.
You know, online, shopping, e commerce, all of those things are peaked at this point, especially now with the holiday season.
So to say, Oh, no, we just want to make sure that everything stays flat is actually a really poor excuse.
So this is the time to start doing that testing to do that.
Additional customer research, you know, to figure out, are we doing the right things? Are we doing things that the right times? And you know, Chris, to your point, you know, tuning up the website? What pages are leading to conversions, what channels? and in what order are assisting to conversions? Are we really meeting our goals? Or are we just complacent?
Christopher Penn 12:20
Exactly.
And when we think about velocity, it’s emotion, right? It’s, it’s the distance you go over a period of time.
To your point, if you can have speed and movement in a direction, until you hit a wall, if, for example, like your Guitar Center, which just declared bankruptcy this morning, they were unable to change directions.
So there’s they weren’t going in a certain direction, they had the speed and they hit a wall called the pandemic.
The way around that is, agility be able to change directions very, very quickly, depending on what’s happening in the data.
And to your point, if you’re not watching the data, all the time, if here’s here’s a good test of your agility.
Da, do you have a dashboard of KPIs? That’s number one.
Number two, is it the start page in your browser, right? If it’s not the start page in your browser, when you open a new tab, whatever that means that you’re probably not keeping as close an eye on those KPIs as you could be, that doesn’t mean you have to, like live in it all the time.
But it should be one of those things that you see every single day like, Oh, that’s what’s going on.
You know, for example, I look at the COVID statistics every single day just to see what’s going on.
Because I don’t want to be taken by surprise.
I look at basic things like Google Analytics traffic, just make sure it’s not zero, because that would be a really nasty surprise.
Every time I send an email newsletter, I look at the data to see Okay, did my newsletter perform, as well as has previously could have done a little bit better? Or did it like go down like by half like, Oh, that’s a big unpleasant surprise.
And so we have to be very cognizant of keeping an eye on our speed and knowing when to change directions.
And to point we’re nine months into this craziness.
And there are still new changes every single day, you know, a bunch of restaurants or a bunch of locations have just gone to no more indoor dining because they’ve just issues with contagion.
So we have to be really, really agile.
And that comes from looking at the data.
So if you have not got a dashboard, put together of your KPIs, and yet, making it something that’s in your face every single day.
You have some room to to add some paying attention to your data.
Katie Robbert 14:35
I would argue so you know, you just mentioned I believe you said the phrase like things are changing every day.
I would argue that, yes, things are changing every day.
But the outcomes themselves from a marketing perspective, are predictable in the sense of like, if you run you know, three or four scenarios like okay, let’s say you do the marketing for a restaurant chain, and the restaurant chain, you know, no longer offers indoor dining? Well, this is not new, like, this is a scenario that you’ve been through before.
So you can already think through how do I, you know, market for now, you know, only outdoor under heater or you know, whatever the scenario is, or just take out or this that the other.
And so, you know, with all of this time that we’ve had to see all of the different changes in the industries, you know, obviously, we can’t plan for every single scenario.
But you know, the basics, you know, you know, people are stuck at home, people are going to start hoarding weird random items, people are going to be looking for outlets from, you know, the insanity of being of looking at the same four walls with the same four people all the time.
And so these are scenarios that you can plan for.
And so it’s a great time to test out some of those things within your own marketing so that your data doesn’t stay flat.
Because we have a good sense of what, you know, the situation is around us now.
There’s obviously there’s those black swans, those unpredictable things, the natural disasters, the, you know, I would argue even the behaviors by people who were in charge, at this point, are pretty predictable, in the sense that you can plan for things not working, or things not getting done or whatever, you know, without turning it into a political thing, whatever the situation might be.
There is some predictability to it because it just it’s the same situation over and over and over again, with just a slightly different shade.
Christopher Penn 16:41
Yeah, I agree.
And given the familiarity of some of these situations, now, we’ve talked about some past episodes.
This is an opportunity just to look at what you did say in March or April go, Well, what could we have done better? Right? We panicked, we scrambled? We we got through it? Maybe.
But what didn’t we do? Like, ah, if only we had done that, I wish we could have done that.
And again, this is where it pays to go back and look at your data from that period.
And say, okay, you know, we had a 40% decline in traffic or whatever, what did we do to pull out of that? And what could we have done faster? What, what’s on the list of Oh, if only I had known at that time? Well, now, to your point, we do know.
So it’s an opportunity to bring that stuff back that playbook back and say, Okay, what was on our wish list that now we know pretty much what’s going to happen?
Katie Robbert 17:33
That’s a really good point.
Um, you know, I, I don’t know that enough companies, or enough marketers are going back to that specific point in time to say, what decisions did we make? And what could we have done differently? I know that a lot are looking at what goals did we set for the year? And did we reach them? Did we not? But you really have to factor in that specific point in time? And how much should we panic? How much? How unprepared? Where are we? What decisions did we make? Why did we make those decisions versus different things? Because clearly, we’re not out of the thick of it yet.
And therefore, it’s an opportunity to continue to do better and get out of that plateau that you might be in with your data.
Christopher Penn 18:17
Yep.
So to summarize, if your data doesn’t help you make decisions, it’s probably because something is missing, either mathematically or strategically.
to figure that out, use the basic formula for momentum, mass times velocity, what stuff do you have budget resources, people, etc? And how quickly can you get your audience to move to change directions to to move with you, if you are stuck, it means one of those two things is probably not working and figure out which one’s not working.
And start making changes as quickly as you can to get your momentum back on track and start getting your data moving in the right direction.
If you got questions about this, or anything else that we’ve talked about, on today’s show, hop on over to our free slack group analytics from Marcus Trust insights.ai slash analytics for marketers where you can chat with up to 1400, other digital marketers every day about all your analytics and data questions.
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