Nearly 80% of a marketing analytics team’s time is spent cleaning and organizing the data, leaving 20% left to analyze, glean insights and make strategic recommendations. Maximizing the productivity of your marketing analytics team is always about the data, and the time it takes to analyze large sets to uncover key insights. In this episode, Callahan’s president, Jan-Eric Anderson, and head of data, Zack Pike, talk about the technology they use, how to staff an analytics team with the right roles, and why utilizing front-end data is beneficial.

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Welcome to Callahan’s Uncovering Aha! podcast. We talk about a range of topics for marketing decision-makers, with a special focus on how to uncover insights in data to drive brand strategy and inspire creativity. Featuring Zack Pike and Jan-Eric Anderson.

Jan-Eric:

Hi, I’m Jan-Eric Anderson, president at Callahan

Zack:

And I’m Zack Pike, head of data at Callahan.

Jan-Eric:

Hey Zack, good to see you. So we’ve got a good topic we’re going to talk about today. And it’s a topic around how to maximize productivity of a marketing analytics team or department, whether you’re a CMO and this is in an internal group that you have within your organization, or it’s a resource or a team you’re getting from your agency. But this topic is around how to get the most out of them and how to make them the most productive. And you and I have been having some conversations around this and a lot of it stems from your own experience leading a marketing analytics department, but that one of the biggest issues to productivity and efficiency of that team is actually attacks against the valuable resource and the finite resource, and that is time.

Zack:

Yeah.

Jan-Eric:

And we’ve spent a lot of time talking about time efficiency or lack thereof of operations within marketing analytics. And so today, what I’d love to pick your brain on is where do we see some of the biggest issues around time and efficiency? What are the time sucks on marketing analytics teams and then get your perspective on what could be done about it. So walk me through your thinking on this. What’s one of the biggest detractors of, of time or creating time and efficiency on marketing analytics teams.

Zack:

S,o the biggest detractor is the data itself, the technical side of the data. So we hire marketing analysts thinking that they’re going to be analyzing data.

Jan-Eric:

Makes sense.

Zack:

So marketing analysts, it seems like makes sense that they’ll be analyzing stuff. But before you can ever analyze data, actually try and generate insights from it, figure out what it’s telling you, it has to be structured, cleaned, organized, and nine times out of 10, the data doesn’t start that way. It’s in all these different formats and different dimensions, mean different things and calculations are run differently and there’s all kinds of weird stuff that happens. So what actually happens today, and you’ll see this if you start reading on this topic, it’s generally accepted that any analyst is going to spend 80% of their time fixing data. And what I mean by fixing is cleaning it up, getting it organized, getting it into a technical environment where they can actually use it.

Zack:

There’s lots of things that happen. So if I have a day, if I have eight hours to do an analysis, I’m going to spend 80% of that day just getting my data organized. And then if I get time, I got 20% left over to actually figure out what it’s telling me or build a report on top of it or whatever. And so, I think… And you’ll see articles on this. There’s oftentimes where people who hire analysts are not satisfied with what that analyst is producing. And I think our analyst community has a hard time voicing the fact that I’m not actually analyzing data. I’m a database administrator. I’m cleaning this stuff up and getting an organized.

Jan-Eric:

So what’s the issue? Is the issue that CMOs just… Let’s pick on CMOs for a second. Just don’t get it, don’t understand what the workload is? Or are we tasking analysts with the wrong thing? Is this work that doesn’t need to be done? I don’t know a ton about this, but it seems from what I know, structuring the data and cleaning up data and collecting the right data and getting it ready to be analyzed is a pretty important step.

Zack:

Yeah. At least in my experience, it’s a lack of knowledge around what it actually takes. So a CMO really shouldn’t be a technical data expert. And you’re reading all kinds of different stuff on the internet, seeing all these great things the data can produce for a marketing side. So my inclination would be okay, I need to hire an analyst, because that analyst is going to be able to do this stuff for me. But yeah, I think it’s just a lack of understanding around what it actually takes. And then it’s up to the analyst as well to be honest with their stakeholders in what it takes. And to be vocal about how much time they’re spending doing certain things, the types of tools that would make that more efficient.

Zack:

This is one of the reasons that we have the intelligence platform at Callahan, because it helps bring efficiency to that whole side of the equation. And there’s a big business in marketing around that side of the equation right now from a technology standpoint that people can leverage and are leveraging all over the place.

Jan-Eric:

Is it a different job function than a marketing analyst that’s better equipped to do this, whether you’re leveraging technology or not?

Zack:

Could be. Yeah. Depending on the analyst and depending on the size of the team. So a good example, in a prior life, I worked for a company called freightquote.com and we had a group of analysts and I was on that group who were responsible for finding insights in data, and then implementing strategies in the business out of that, those insights. But our role stopped and started there. We had a whole nother team at that company who were the data cleaners, organizers, structure people, that if I had questions… If I had an analysis I was getting ready to go down, I would define out the data that I wanted. I would go sit down with one of those… We call them business intelligence analysts, oddly enough.

Zack:

But I would sit down with one of those BI developers, tell them what I was trying to do, the data I thought I needed. And then they would go off in the database and go acquire and cleanse that data. They would send it over to me in a nice clean, structured format. So that in that scenario, all I had to do was the analysis side and the strategic thinking around it. That is the perfect setup, but that’s expensive, because now you’re adding people that’s typically a more technically focused, well-paid individual who is doing that type of work. And there’s not a lot of them around. It’s hard to hire that person as well because people like to… The people who know that you need this resource are snatching them up.

Jan-Eric:

So depending on the scale of an enterprise or an organization and what their needs are within this area, it may make sense to staff that, otherwise you might make more sense to have your analysts doing it. There’s technology and approaches that can help them do a much more time efficiently. But the key thing is to recognize that this is a really important step. I guess the analogy I’m thinking of might be a bad one, but is that of a chef. And so I think about a chef, I think about a chef standing in the kitchen and making amazing food that then is brought to my table and I eat and I appreciate the craft of the chef to make the meal.

Jan-Eric:

What I don’t think about is the role that the chef needs to play in identifying which ingredients are right, where to buy those ingredients, and then prepping those ingredients for the meal. And I guess in the chef world, in the culinary world, that’s the sous chef-

Zack:

That’s a perfect example.

Jan-Eric:

-the assistant, the understudy who understands the best way to prep the ingredients so that the meal can be made the best possible way. There’s a special way to cut this onion… I’ll stop there, because I don’t really know what I’m talking about, but I think that’s probably a good parallel analogy, right?

Zack:

It is. Yeah. It’s a perfect analogy. And I should also say that technology can afford us a lot of benefits. So depending on what that analyst needs to do, there are technology solutions you can go out and buy for less than a full-time equivalent employ