Select Page

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.

Subscribe To This Show!

If you're not already subscribed to In-Ear Insights, get set up now!

Advertisement: Data Science 101 for Marketers

Do you want to understand data science better as a marketer? Would you like to learn whether it’s the right choice for your career? Do you need to know how to manage data science employees and vendors? Take the Data Science 101 workshop from Trust Insights.

In this 90-minute on-demand workshop, learn what data science is, why it matters to marketers, and how to embark on your marketing data science journey. You’ll learn:

  • How to build a KPI map
  • How to analyze and explore Google Analytics data
  • How to construct a valid hypothesis
  • Basics of centrality, distribution, regression, and clustering
  • Essential soft skills
  • How to hire data science professionals or agencies

The course comes with the video, audio recording, PDF of the slides, automated transcript, example KPI map, and sample workbook with data.

Get your copy by clicking here or visiting TrustInsights.ai/datascience101

Sponsor This Show!

Are you struggling to reach the right audiences? Trust Insights offers sponsorships in our newsletters, podcasts, and media properties to help your brand be seen and heard by the right people. Our media properties reach almost 100,000 people every week, from the In Ear Insights podcast to the Almost Timely and In the Headlights newsletters. Reach out to us today to learn more.

Listen to the audio here:

Download the MP3 audio here.

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: In this week’s In-Ear Insights, we’re talking about all things analytics, such as data analytics, business analytics, marketing analytics, and one of the challenges that we face in the marketing world—and you probably face too—is that 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 exactly what it sounds like: ‘Cup of soup’ marketing. It is the collection, processing, and analysis of data to discover insights. Data analytics can literally apply to any form of data. What makes it different from data science is that there’s no scientific testing. There’s no hypothesis, there’s no AB. It is just the analysis and the processing of that data.

Business analytics 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 healthcare drug trial tests, the business analytics will be looking at the healthcare market to figure out if they have good product marketing to fit. And finally, marketing analytics is a subset of business analytics that looks 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 what they should be focusing on? Obviously, some of it is gonna be role-based, but as a CEO, how do you think about all these different forms of analytics?

Katie Robbert: That’s an interesting question, and we were chatting about this earlier this morning because we even sort of had to take a step back and say, “Are we clear on what the difference between all these things are?” Really what it boils down to is, it’s all data. And the skillset for analyzing data should be roughly the same across disciplines, up and down the hierarchy. You need to know the basics around cleaning the data, the six C’s that we promote, which you can find on our website.

With people who are trying to understand what data they should be looking at, it always comes down to what’s the question you’re trying to answer. 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. If you are a marketer, then you primarily care about how the work you’re doing rolls up into the goals of the business.

This is where a KPI map exercise is typically very helpful if you’re not sure which data you should be looking at, because you may need to look at business analytics, and marketing analytics, and IT analytics, and sales analytics to get to the answer to the question that we’re being asked. So, I would say first and foremost, what’s the question you’re trying to answer? What is it that you’re working toward and then start working on a KPI map which is broken down into three sections: Your goals, your KPIs, and your metrics.

CP: Is part of the reason for the conflation of all these that there probably isn’t enough work to do? I’ll give you an example. If I take it out of analytics and we just go with the job titles: data analyst, business analyst, marketing analyst, I know from our experience that someone who’s a marketing analyst doesn’t spend a whole lot of time on analysis and analytics, at least at all but the largest companies. Many times they’re doing the execution there as well so you may be a marketing analyst, but you’re setting up that week’s emails, you’re doing social media, you’re doing all these things that are not analytics, nor analysis, nor 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 are muddy, and the expectations we have on people who have these job titles are equally muddy? Because in a lot of cases, managers may not know what an analyst is supposed to do.

KR: The short answer is yes. The long answer is, in my experience, job descriptions very rarely encompass what that person actually does. 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 talked about this where 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, that you’re very much of the hierarchy that if you’re in this title, you do this, if you’re in this title, you do that. There are 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 may or may not have anything to do with what it is that they do. That’s sort of the people piece of it.

The process piece of it is that a lot of organizations—ourselves at times included—tend to be a bit of a hot mess, and they’re scrambling and pretty much just reacting to everything versus 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.

So I think that a lot of it is people playing catch up. There are inefficient processes at play where someone says, “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 a new data set and there’s no real stepping back to say, “Is this the best way to do this?” It’s more “My hair’s on fire, I just need to do the thing.” Then when you have the platforms or the technology, you may or may not even have the right data coming in. But again, 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, without giving the opportunity to experiment with new tools that may have come out or new ways to collect that data that you may have to build yourself. 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 the services industry, then you’re at the mercy of whatever the client needs. So you may have to drop everything and start executing new ad plans or emails or whatever whims have come up. There’s a lot of out-play that we could spend hours unpacking.

CP: Does that vagueness of role lead to ineffective results? We ran into this recently with a customer where they had someone who was an analyst, and then they rebranded that person’s title as Product Marketing Manager and I thought, those are two very different things. Someone who is a marketing analyst is not a product marketer. By definition, they are responsible for the analysis of marketing data, and the drawing of insights about that to ultimately help marketing do its job better. A product marketer does totally different things. They do the four P’s: Do we have the right product-market fit, are they replaced properly, are they priced properly, do they have the right features. Those are not even the same skill sets. So that a company feels it’s okay to take this person and rename their title entirely and expect them to fulfill the expectations baked into that role no matter what title you apply it to, seems like a disaster waiting to happen. It’d be like saying, “You know what, Katie, in addition to CEO, I would also like to be head chef.”

KR: That would be a disaster. If anyone has ever tasted my cooking, they would know what a disaster that would be. But I feel like 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. Job titles are one of those funny things that I feel are completely subjective based on who you’re talking to. 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 is.

I, at one point very briefly in my career, was given the title Product Marketing Manager, but they had disassembled and let go of the entire marketing team. So there was nothing to market. There was no support 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. I think a lot of it comes down to making people feel like they have a certain title because it makes them feel better about the job that they’re doing. There’s a little bit of that ego at play.

Then there’s external. What does it look like to people if we have certain roles? That makes the company perhaps more attractive if they have those job titles. Then you have the internal. What does that person actually do? Well, we don’t care what their title is, we just need them to do the work. I think that’s the ambiguity. So, you know, it’s a complicated question. If you take a job title out of it, and just think about your typical marketer or someone who isn’t within the marketing discipline, who is doing the two sides—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 they are executing are effective or not.

CP: So does it make sense that in smaller organizations where you may not need a full-time marketing analytics professional, you look at that role more as either business analytics or even just data analytics. Like, “Hey, you are this organization’s data analyst and marketing gets 10 or 15% of your time, which is 10 times more than that 1% that you’re currently doing. But finance gets 10% of your time and HR gets 10% of your time.” That way that person stays focused on the analysis of data and provides benefits throughout the company if one department doesn’t have enough work for that person to stay busy doing what they should, on paper, be good at.

KR: Again, I would say it’s a complicated question because I think that siloing someone down to only one single responsibility definitely has pros and cons. 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 to see the process from start to finish, then you may be putting that person at a disadvantage by not really knowing the context or insights or nuance. 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.

A data analyst is 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 equals four so here you go.” That isn’t super useful.

CP: Well you’ve worked in PMOs. Organizations will have a project management office or PMO, and they’re fairly agnostic. How did they balance projects? It may be IT, it may be marketing, it may be sales. How did they work?

KR: Boy, that really is a whole different episode. (Laughter). At a high level, while the projects 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. 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 project managers 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.

There are software systems like Microsoft Project Online, and other things that can help you sort of doing 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?” So I would like to say it’s the same thing if you had a data analyst that was shared across all the different organizations. 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.

CP: Interesting. I feel like that model of PMO is something you could partially either replicate or blend a data analyst into; 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 the model that you’re building or the algorithm that you’re using. If you come to me with a business problem, and say, “Hey, 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 we could do 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—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—.

I may not know anything about sales, I may not be a salesperson, but if I understand data and have that subject matter expert to say, “Hey, fact check me on this. Are you calling these contacts the required number of times that your processes set out?” then we can build that curve to say here’s where your effectiveness as a salesperson drops off when you’ve called the person for the 208th time. I don’t need to be a salesperson. 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 essential skill?

KR: Absolutely. I wouldn’t necessarily put them under the PMO, but there are different techniques that you can employ, such as steering committees, where you have representatives from each major discipline that’s working on that particular service or product come together on a regular basis and have those exact conversations. Because where I’ve found 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 the people within your team talking about what they have going on. You don’t bring in the data analyst, the salesperson, the marketing person. 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 all the challenges. Then you start to get into this other skill set of leading effective meetings, which again, is a whole other episode that we should probably tackle.

There’s a lot of different ways to approach it, and I think what we’re circling around is that while 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 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 give-and-take both ways.

Data analytics as a skill set is that larger umbrella. Then you have business analytics, which is how you’re running your business and what your competitors are doing, and what the market has. Then you have your marketing analytics, which is another subset where it covers 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.

CP: I would say if you are listening to this and you’re thinking about data science or data analytics or marketing analytics as 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, 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 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.

If you have questions on this stuff, join our free Slack group. Go to TrustInsights.ai/analyticsformarketers where we talk about data analytics, business analytics, marketing analytics, all things analytics. The Slack group is totally free of cost and we have almost 900 members now.

So come and get your questions answered. Provide your insights as well. And of course, subscribe to the Trust Insights newsletter over at TrustInsights.ai/newsletter and we’ll talk to you next time. Take care.


Need help with your marketing data and analytics?

You might also enjoy:

Get unique data, analysis, and perspectives on analytics, insights, machine learning, marketing, and AI in the weekly Trust Insights newsletter, Data in the Headlights. Subscribe now for free; new issues every Wednesday!

Click here to subscribe now »

Want to learn more about data, analytics, and insights? Subscribe to In-Ear Insights, the Trust Insights podcast, with new 10-minute or less episodes every week.

Pin It on Pinterest

Share This