In this week’s In-Ear Insights, Katie and Chris discuss the differences between data analytics, business analytics, and marketing analytics. What do all these terms mean? What’s the difference? How do you know when you’re doing one versus another? They also tackle differences in job descriptions and titles as ways of thinking about the various forms of analytics – and why analytics is so underinvested in many organizations. Listen in for advice on how to clarify your role in analytics and much more.
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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.
In this week’s in your insights, we are talking all things analytics, such as data analytics, business analytics, marketing, analytics, and what has it all been one of the challenges that we face in the marketing world.
And you probably face too is there’s a lot of these terms floating around there.
And they all kind of sound like the same thing.
So what is it? Why does it matter? And how do we think about it? So, to set the table, data analytics is the exactly what it sounds like cup of soup marketing.
It is the processing, collection, processing and analysis of data to discover insights and data analytics can literally apply to any form of data from marketing it.
You name it, it’s data, you should be analyzing it.
What makes it different than data science is that there’s no scientific testing.
There’s no hypothesis there’s no AB it is just the anomaly.
In the processing of that data, business analytics, this is a subset of that where we’re using data to make practical concrete decisions about the business itself solving business problems, typically management and strategy problems, but anything within the business specifically.
So whereas data analytics might be looking, for example, at the results of a healthcare drug trial tests, the business analytics will be looking at the healthcare market to figure out okay, what we do we have good product market fit.
And finally, marketing analytics is a subset of business analytics, looking at the data and the challenges in marketing.
So Katie, when you hear all these terms floating around, and you see how much confusion people are in, how do you help them get some sense of like, what should I be? What should I be focusing on? I was someone’s gonna be role based, but what as a CEO, how do you think about all these different forms of analytics?
You know, it’s an interesting question, and we were chatting about this earlier this morning because we even sort of had to take I step back and say, Do We Are we clear on what the difference between between all the things are really what it boils down to is, it’s all data.
And the skill set for analyzing data should be roughly the same across disciplines.
And up and down the hierarchy.
You need to know sort of the basics around cleaning the data, the six C’s that we promote, which you can find on our website.
And so, you know, with people who are trying to understand what data should I be looking at, it always comes down to what’s the question you’re trying to answer.
And so if you are a CEO, then you need to understand a couple of different things you need to understand internally, the health of your company and externally, what other people are doing and sort of what those bars of measure are that you need to be reaching or exceeding.
And then if you are a marketer, then you primarily care about how the world You’re doing rolls up into the goals of the business.
And this is where a KPI map exercise is typically very helpful if you’re not sure you know which data you should be looking at, because there may be a blend of, you need to look at business analytics, and marketing analytics, and it analytics and sales analytics to get to the answer of the question that we’re being asked.
So, I would say first and foremost, what’s the question you’re trying to answer? What do you what is it that you’re working toward and then start working on a KPI map and a KPI map is broken down into three sections, your goals, your KPIs, and your metrics
is part of the reason for the conflation of all these because there probably isn’t enough work to do.
Like Give me an example, if I take it out of analytics.
And just we just go with the job titles, data analysts, business analyst, marketing analyst I know from our experience, you know, someone who’s a marketing analyst doesn’t spend a whole lot of time on malice and analytics, at least at all, but the largest companies, many times they’re actually doing the execution as well, bad.
So you may be a marketing analyst, but you’re setting up that week emails, you’re doing that with social media, you’re doing all these things that are not analytics that are not the analysis and the the drawing of insights from data.
And I have to wonder is part of the reason why these terms are so muddy, because the job roles themselves embody, and the expectations we have of people who have these job titles are equally money because in a lot of cases, managers may not know what an analyst is supposed to do.
So the short answer is yes.
The long answer is, you know, in my experience, job descriptions very rarely encompass what that person actually does.
I think the title, you know, I don’t want to say that titles are irrelevant, but in some ways, in a lot of companies, a title is irrelevant.
Now, you and I have you know, talked about this where You know, I am somewhat of the mindset that I don’t care what your title is, I don’t care where you stand in the company, if work needs to be done, you do it.
Whereas I know, when I first met you, you’re very much of the hierarchy of if you’re in this title, you do this, if you’re in this title, you do this.
And so there’s definitely different schools of thought around how that works.
And all of those factors do lead to what you’re describing, which is a lot of ambiguity around what someone should be doing.
So the word analyst in someone’s title, you know, may or may not have anything to do with what it is that they do.
So that’s sort of the people piece of it, the process piece of it is that what we’ve seen is that a lot of organizations ourselves at time included, tend to be a bit of a hot mess, and they’re scrambling and pretty much just reacting to everything versus that, you know, making sure that things are automated making sure that reports are just running by themselves, so that you can spend your time doing the insights and actions.
And so I think that a lot of it is people are playing catch up, or, you know, there’s inefficient processes at play where someone’s like, well, I usually do it this way.
But this person likes it this way, or someone asked it to be this way, or they introduced new data set, and there’s no real stepping back and say, Is this the best way to do this? It’s more my hair’s on fire, I just need to do the thing.
And then when you have the platforms or the technology, you may or may not have even the right data coming in.
But again, sort of if you’re constantly in this fire drill state, then you never know if you’re even collecting the right data from the right systems.
And there may be new systems out there.
We see questions all the time around social listening tools, like what’s the best social listening tool and at a large organization, at an enterprise size, they’ve likely just purchased something and said this is what you have go with it.
Really not giving the opportunity to explore it.
experiment with new tools that may have come out, or new ways to collect that data that you may have to build yourself and health.
So there’s a lot of reasons why I think that the title marketing analyst just really doesn’t represent what it is someone does.
And you’re absolutely right.
If you’re in a services industry, then you’re at the mercy of whatever the client needs.
And so you may have to drop everything and start, you know, executing new ad plans or emails or you know, whatever whims have come up.
So there’s a lot out play that, you know, we could spend hours on packing,
you have a does that vagueness of role
lead to ineffective results? Like if I think if someone says it, we ran into this recently with a customer where they had someone who’s who was an analyst, and then they rebranded that person’s title as as Product Marketing Manager, I’m like, those are two very different things.
You know, someone who is a marketing analyst is not a Product marketer by definition they they are responsible for the analysis of marketing data the the there’s they should be and the drawing of insights about that and ultimately help marketing do its job better a product marketer does totally different things they do the four P’s you know Do we have the right product market fit replaced rapidly we priced properly just have the right features and things those are those are not even the same skill sets so that a company feels like okay we can take this person is rename their their thing entirely and expect them to to not keep up then fulfill the expectations baked into that that role no matter what title you apply to.
It seems like a a disaster waiting to happen.
like it’d be like saying, you know what, Katie, in addition to CEO, I would also like to be head chef.
That would be a disaster.
If anyone has ever tasted my cooking, they would know what a disaster that would be.
But you know, it’s interesting, and I feel like that This is a topic that we should explore perhaps on another podcast, and I would like to actually ask the community, what their thoughts are.
But job titles are one of those funny things that are I feel like they’re completely subjective based on who you’re talking to.
So, you know, take that, take that job title Product Marketing Manager, I guarantee if you asked five product marketing managers across five different organizations, you will get five different explanations of what a proper product marketing manager was.
I at one point very briefly in my career, before I left, one of my jobs was given the title Product Marketing Manager, but they had disassembled and let go of the entire marketing teams.
And so there was nothing to market or there was no support to for marketing, and they had one salesperson, so I could tell you what a product marketing manager did at that organization and it was basically an IT project manager and So it’s a lot, I think a lot of it comes down to making people feel like they have how I want to say this basically like, some people want to have a certain title because it makes them feel better about the job that they’re doing.
And so there’s a little bit of that ego at play.
And then there’s externally What does it look like to people if we have certain roles? And so that makes the company perhaps more attractive if they have those job titles? And then you have the internal of what does that person actually do? Well, we don’t care what their title is, we just need them to do the work.
And so I think that’s the ambiguity.
So, you know, it’s a complicated question.
So, if you take a job title out of it, and just think about your typical marketer, or someone who is in within the marketing discipline, who is doing, you know, sort of the two sides so if they are executing and they are analyzing, I think you could safely say that 99% of their time is spent executing and 1% of their time is spent analyzing.
And that 1% is just not enough because you don’t know if the things that you are executing are effective or not.
So does that make sense and in smaller organizations where you don’t have, you may not need a full time marketing analytics professional to look at that role more as we go back up that ladder, either business analytics, or even just data analytics, hey, you are this organization’s data analyst in marketing gets, you know, 10 or 15% of your time, which is, you know, 10 times more than that 1% that you’re currently doing.
But finance gets 10% of your time, HR gets 10% of your time.
And that way that person stays focused on the analysis of data, data analytics, and provides benefit throughout the company.
If If one department doesn’t have enough, have enough work for that person to stay busy doing what they should on paper be good at.
Again, I would say it’s a complicated Question because, you know, I think that siloing, someone down to only one single responsibility definitely has pros and cons.
So if someone is only ever analyzing data that comes in, then they’re going to get really good at analyzing all different kinds of data.
However, if they never have the experience on the front end of doing the thing to see the process start to finish, then you may be putting that person at a disadvantage to not really know the context or insights or nuance.
And so I think that, yes, having a person or a team that crosses a lot of different teams, in terms of someone who’s analyzing data makes a lot of sense.
And a data analyst as a data analyst, it shouldn’t matter what kind of data it is that they’re looking at, they should be able to analyze it.
That said, if they don’t have the context for what it is that they’re analyzing, then they’re likely just looking at numbers and saying, Well, two and two equal Forests are here you go.
And it’s that isn’t super useful.
And so how does that you can you could argue,
yeah, well if you’ve worked in CMOS, like organizations will have a project management office or PMO.
And they’re fairly agnostic.
How did they balance that like hey over this this project has come in.
It may be it, it may be marketing, it may be sales.
How did how do they work?
Boy, that really is a whole different episode.
But so at a high level, the PMO while the project’s themselves may be siloed.
The team as a whole is very collaborative and meets frequently because there’s a lot of resource sharing that happens within a PMO.
So you may have four different project managers and a pool of developers and marketers and IT staff and every project manager has to work with the other product project manager To share those resources, so there’s a lot of collaboration that happens at the PMO level.
And then it sort of starts to dribble down into those silos of each project.
Because by nature, project managers have to be collaborative.
And so there’s software systems like Microsoft Project Online, and other things that can help you sort of do that resource sharing.
But a lot of it is just talking to each other saying, Hey, what do you have going on this week? Is Chris fully slammed at 40 hours? Or can you give me two hours of his time to help me with this project over here? Right.
So it’s, I would like to save it’s the same thing like if you had a data analyst that was shared across all the different organizations, and there’s a lot of planning tools, obviously, to go into it.
But I think without that context of someone actually being able to do the work or be a part of the planning, they’re going to miss that context and not be as effective as a data analyst as they could be.
Interesting because I feel like that model of PMO is something you could partially either replicate or blend a data analyst into, into a PMO sort of a hired gun within to bring those skills, but then still have that collaborative environment where the subject matter experts, the people with the context could provide input on, you know, the model that you’re building or the algorithm that using a fight.
If you come to me with a business problem, and say, Hey, you know, we need to optimize sales.
We need to optimize the prospects we have in our sales pipeline.
What are the data analytics methods that you have available? We could say, well, we could do you know, regression analysis, we could do logistic regression.
If you think that you’ve tapped out this pool of eligible candidates, which by the way, is a really, really important difference.
sidebar, if you have a limited population, like you’re trying to market to a certain group of individuals, you have to use logistic regression but that’s another show.
Knowing I may not know anything about sales, I may not be a sales person.
But if I understand data and have that subject matter expert to say like, hey, fact check me on this.
Have you really actually, you know, do you? Are you? are you touching? Are you calling you to these contacts, the required number of times that your processes set out? If so then we can build that curve to say like, here’s where your effectiveness as a salesperson drops off when you’ve called the person for the 208.
And you said, I’m going to get coffee, this person, you know, yet again at yet another conference and buy an expensive dinner that that may or may not generate results, that model will be able to say, Yep, you probably stop after the 17th time instead, I don’t need to be a salesperson.
No, I just need to know the parameters and have that understanding from the sales professional.
So could you approach a data analytics office like a PMO, or put it into the PMO and give more departments in the company access to that, as we all know, essential, essential skill?
I wouldn’t necessarily put them under the PMO.
But there are different techniques that you can employ, such as, you know, steering committees where you have representatives from each major discipline that’s working on that particular service or product or whatever it is come together on a regular basis and have those exact conversations because we’re I’ve found, you know, companies to be the most effective is when you really do have all of the different teams working together.
So you have the notion of a team meeting, but that typically means just your team, the people within your team, talking about what they have going on.
But then you don’t bring in the data analyst, the salesperson, the marketing person, you know, whoever you’re, you’re still limiting.
So you can extend that to get the product team meeting or the steering committee of department heads who can sort of talk through, here’s all the challenges and so then you start to get into this other skill set of leading effective meetings, which again, is a whole other An episode that we should probably tackle.
But there’s a lot of different ways to approach it.
And really, I think what we’re coming down to what we’re circling around is, having someone who is really good at analyzing data is great.
It still needs to be collaborative, you can’t just stick them in a box and say, here’s the data, analyze it, because that person needs to be engaging with other teams, they need to understand what are the problems you’re trying to solve? What are you seeing, how can I be helpful, not just here’s my data, analyze it, it needs to be a given take both ways.
And so data analytics as a skill set is that larger umbrella, and then you have, you know, business analytics, which is how you’re running your business and what your competitors are doing, and what the market has.
And then you have your marketing analytics, which is another subset where it’s, here’s what we’re doing to help people understand who we are in what we do.
All three of those layers have the same skill set in terms of analyzing the data.
and I would say if you are listening to this and you’re thinking about data science or data analytics or marketing analytics as a professional development and training goal for 2020 and beyond, look at the big picture of data analytics.
If you’re going to take a course on something, go take a data analytics course, there’s a number of good ones, I recommend the one from IBM called cognitive class AI, it has a huge number of totally free of cost courses, develop those broad data analytic skills.
And then you can apply those skills within business analytics within marketing, analytics, sales, analytics, so on and so forth.
So from a professional development and training perspective, data analytics is going to give you the best bang for the buck as opposed to taking a course specifically on marketing analytics, where Yes, you’ll you learn some of the same techniques, but you won’t see the diversity and the variety of data that you will in a data analytics course.
So that’s your takeaway for professional development.
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