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In this week’s episode, Katie and Chris discuss data analytics requirements gathering. When you’re setting out on any kind of marketing analytics project, how do you go about determining what data you’ll need? This process is called requirements gathering, and it’s not as simple as it sounds. Listen in for tips on how to go about requirements gathering as well as how different development methodologies approach requirements. Plus, learn the situations in data science and machine learning when requirements gathering is premature, and how to handle ever-shifting data.

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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
In today’s in your insights, we have a chicken and egg problem. The chicken and egg problem is this data requirements. Gathering is an essential part of data analytics, the essential part of marketing analytics, you have to know what it is you need, you have to know who has it, where it lives, etc. as a partner, just general good practices around data. On the other hand, when you’re doing advanced machine learning and statistics and stuff, even on your marketing data, there’ll be times when the data that you end up exploring and finding is not in the requirements. So let me give you an example. I was pulling some social media data over the weekend. And I was trying to figure out is there a relationship between some social data and and conversions in Google Analytics, pretty straightforward scenario, one that comes up a lot. And one of the things I wanted to engineer out of this data set extract out of this data set was instead Just having the date itself, which is, you know, somewhat helpful. I wanted to pull out what day of the week was this? Was this a Sunday? Was it a Tuesday, things like that. So that is an example of feature engineering that that day of the week was not in the original data set. And it wasn’t even something I knew that I was looking for until I got into the project. And like, Hmm, I wonder if it’s day of the week matters. As I had to engineer this out of their regular little bit of code to say like, hey, check the date, check what day the week it is put the day of the week in this column in this table. So Katie is somebody who has a lot of experience, dealing with requirements gathering and dealing with planning ahead for what you’re going to need. How do you reconcile this chicken and egg scenario where you, on the one hand, want to know what you have. But on the other hand, you don’t know what you have.

Katie Robbert
You know, the conversation about requirements and sort of project management in general is an interesting one, because there’s this notion and in some methodologies is true is that once you do the documentation, that’s it, you’re done. It’s static, like you can’t change it. And that’s true. In some instances, like waterfall or very rigid industries like manufacturing, where a plus b always has to equal C, and there is no sort of changing it. But when you think about more agile methodology, requirements gathering is something that is meant to be a living document. It’s fluid. And so, you know, you always want to start with requirements gathering, but you may continue to gather requirements along the way. But what shouldn’t change is, what’s the question I’m trying to answer? What is the problem I’m trying to solve? It may evolve a little bit, but ultimately, you should always know what that particular piece of it is and then to put your stake in the ground to say, I think what I need is the following five to 10 things to answer this question. And in your example, Chris, you may find that you may need to do some refining and tweaking or looking in other directions for the answer to the question and that’s okay. As long as you continue to keep your documentation up to date alongside of it. So it it’s, I don’t know, I guess let’s call requirements gathering the egg. And let’s call the data the chicken. In this example, you always want to start with the egg. You always want to start with the requirements gathering. I don’t think it’s a chicken and egg situation. I think it’s a matter of is this a living document or not? So did

Christopher Penn
you just coined the term agile analytics?

Katie Robbert
I might have. I’ve only had two sips of coffee. Excellent. Well,

Christopher Penn
so in your typical Agile process, you have your outcome and then you have your your backlog. You have your Scrum and you have your after action review? Is there such a thing in, in that methodology for agile requirements gathering, we iterate the requirements gathering first, and then you iterate the production of the thing afterwards. Or because obviously, if you have, you know, predefined sets, like you’re saying of something that can’t be changed, like you’re you’re trying to meet, for example, FDA requirements on on something, you can’t mess around with those, you have to do exactly as you’re told. But in marketing analytics, a lot of marketers in a situation where their, their shareholders, their stakeholders are saying, We don’t know what we want, we want you to tell us and we’ll tell you if it’s right or wrong. And so there’s a natural sort of iterative, this to that as well. So in terms of requirements, Kevin, can you Is there such a thing as as agile requirements gathering for marketing analytics?

Katie Robbert
Absolutely. Um, you know, if you think about the software development lifecycle, and where agile methodology, sort of truly lives, you have the two week sprints. And basically, it’s this constant, iterative process where, you know, on day one, you’re starting, you know, Sprint number one. But on day five of sprint, number one, you’re starting the requirements gathering for sprint number two, taking what you’ve learned from sprint number one and think about it like this constant, looping, moving along until you reach the goal, the constant is the end goal. What is it that you’re trying to solve for? So, you know, if your stakeholder comes to you and says, like, I just I’m curious about this thing. I don’t know if I’m right or wrong, there should still at the core of it be that question of what are we trying to prove what are we trying to answer and how you get there is the iterative process. And so that is like you always need to have that in mind. Now if you go and truly research and development and experimental, then it’s fine to just go ahead and like open the box and see what’s inside. But in the sense of there’s, you know, time being spent money being spent resources being used, you probably want to have some semblance of a plan. So yes, you can do your requirements gathering in an agile way you can break it down into smaller milestones. And say I, you know, the bigger question is, I want to know how much more money we’re going to make with social media this year or something along those lines, then you start to break it down into smaller milestones, and say, all right, the very first little nugget of this thing that I’m going to explore is, you know, the value of a like, on my Facebook page, and then you just start there, and then you don’t necessarily worry about the rest of the requirements, because you take what you learn from that little piece of the puzzle and apply it forward. And that’s how you build those additional requirements. Because you may not know like, step 37, what the heck is going to happen? And that’s okay, but at least you have an idea of what step 30 Seven might be

Christopher Penn
when it comes to these methodologies, I think an important clarification is that they’re really meant for building a thing, right? I mean, that’s a software development, system development. Even, you know, just a simple Google Data Studio, you’re still building a thing. And there is a point at which you ship the thing to your stakeholders. When we’re talking about things like exploratory data analysis, there really is a thing you’re building it’s more of a process that you’re following other different methodologies for either like a process you’re following to explore something or an operations process where there isn’t a thing that you’re done? You’re doing at the end? It’s it’s more of an ongoing process, like reporting, for example, a, you know, publishing your monthly reports. Yes, there’s a thing but it’s more of an operational tempo, what are the sort of the ways to manage those is there in equivalent to the idea of you know, agile and iterative Those things but not the same sense of like you’re building a product that you’re shipping.

Katie Robbert
Well, in the example of exploratory data analysis, we often refer back to the scientific method, which is tried and true. You know, and it is a little bit more waterfall and agile. I keep using those terms, because I feel like regardless of whether or not you’re building a thing, or you’re exploring a thing, I think it’s important to sort of bring it down to those concepts. Because waterfall means you’re going from one step to the next and you can’t, you know, move on to the next step until you finish the first so you can’t do step two until you finished step one. Whereas agile means you can get partway through step one, and then start step two, and then start Step three, and get sort of partway through each of them and it is more flexible. It’s literally called agile. And I think that it does apply. So even if you aren’t building a thing if you’re just doing something like exploratory data, and now or research, there is still a process. And it starts with some semblance of requirements gathering. So you know, in the example of, you know, you want to understand how, you know, this correlates with this and this and this and there’s really no output. It’s really just more of a curiosity, you still start with, what’s my hypothesis? What is it that I’m trying to prove? Because if you’re just mashing data together, that’s fine, but you’re really not going to get very far. And after a while, you’re not even really going to know what you’re looking at. So you’re starting with some kind of a hypothesis. So, you know, call it a process, call it a methodology. You’re following a plan. And the plan is, I want to know x, I need to have the following pieces of information in order to get to the question. It’s the same across the board.

Christopher Penn
Okay. So when we think about data analytics and the use of that data Obviously, we have the outputs, the outcomes that we’re looking for. What happens when we get feedback, again from these stakeholders who are like, no, this is what I was looking for, but they’re unable to explain advance what it is they’re looking for. It’s it’s very much that, you know, I don’t know what it is, but I’ll tell you what I see it kind of thing. How do you deal with that in a situation where you’re trying to be organized, you’re trying to plan that, you know, you’ve got your marketing analytics mandate, you’re trying to, to show how we’re going to fix marketing and make it more profitable. And you have a CMO or VP is like, I don’t know what I want to surprise me. What do you do in a situation like that, where you have somebody who’s completely unhelpful towards guidance, and you know, generally like yeah, generally we are marketing and perform better. But you have no KPIs. You may not even have business goals. You may be in a say, a marketing unit inside of a much larger organization, where the goals are completely unclear. What do you do then?

Katie Robbert
Well, I don’t mean, that’s where you start is, if the you know, cmo comes to you and says, I don’t know, look at some data and tell me what it says. It is your job to push back and say, What is the goal? You know, and you can, you know, in a polite sense, but it’s your time taken away from doing something else. And I think it’s totally appropriate to position it in that way to say, I’m happy to take a look at this for you, I need your help. Understanding the overall goal is this. You want to understand revenue, you want to understand awareness, you want to understand, you know, productivity, whatever, you know, throw out a bunch of different categories and sort of see what sticks and if they still say, I don’t know just go do it. Then it’s up to you to start to make that decision of like, Okay, let me see what it looks like when I put it in the lane of revenue. Or let me see what it looks like when I put it in the lane. of awareness, you know, you, as a marketer, have enough information to say, I know my company cares about making more money. So let me take a look at it from that angle. And I think it’s appropriate for you to tell whoever’s asking me like, I’m happy to take a look at this, because you have given me very little direction or because you were unsure of what it is that you’re after. This may take me a little bit of time. Are you okay with me being pulled off of, you know, the 20 clients who are actually paying us in order to do this, like, weird little pet project for you probably say it in nicer, more respectful from from that, but that’s the general gist, like it is totally appropriate for you as the person being asked to push back and say, I’m happy to do this. You’ve given me no direction. It’s going to take me a while and then you as the marketer know, okay, I know the basics about what this company cares about. Let me look at it through that lens.

Christopher Penn
Right. I think the challenge there at least certainly for folks who are more junior in their careers, they may not be clear about that. They may not be clear about what it is, other than broadly knowing like what the company does, okay, yeah, we sell cars or something like that. But not being able to connect the dots to back to things that they have influence over. I see this the most in social media analytics and social media marketing, where you have social media managers who are publishing content, they’re cranking out videos, they’re putting a post on Instagram, and they don’t know why they’re doing it. And because they have no visibility, and they have no data to work with. They just sort of measured themselves on activity. We you and I saw this all the time in the world of public relations and our previous work, where people would, would report on what they did, but literally have no idea what the impact was, they had no system of measurement of any kind. And in a case like that, how does that tie back to requirements gathering like you got to crank out 15 press releases and make 40 calls this week and send out 52 emails and spam a bunch of people What do you do in a situation where you have that whether there’s there isn’t, there isn’t a, they may not even be a measurable impact?

Katie Robbert
Well, I think that that’s where you sort of like gather your peers gather your team together, because again 99% of the time, companies are going to care about making more money. So, you can always try your best to tie it back to that. So like, get your, you know, group of marketers or you know, even if you are in like a Facebook group of other marketers, or a slack group of other marketers, analytics for marketers is great place to start. Trust insights.ai slash analytics for marketers. It’s our free slack group. And just ask the question, be like, Hey, I’m churning out 40 press releases a month. Can anyone help me figure out how to tie this back to revenue or what have you done to measure this or is there a way to tie this into making more money, I believe Like suggestions like, asking the question is a great first step. And so I think that, you know, if your boss isn’t giving you the information, if you’re not sure with the company, use the lens of money, because money 99% of the time is going to be the right answer unless you’re a nonprofit. And then there’s going to be other things, but 99% of time, tying it back to how do we make more money with this thing is always going to be the right answer. And so start there, and then start asking around, you know, to, you know, your team members to other people in the organization to your, you know, community groups to old professors to whoever might have any sort of, you know, intelligent opinion on the subject, and just start to piece it together and it might not be right and that’s okay, but you’re going to learn a lot in the process.

Christopher Penn
Yep. So to wrap up the story, this the analysis I was doing over the weekend was for an upcoming a talk I’m doing I’m competitive in Alice, and if the stay tuned to the Trust Insights blog over at TrustInsights.ai dot AI slash blog will be publishing that session in a couple of months. But what I found was by engineering some of those extra features, even though they weren’t in the requirements, it did shed light on the things that actually led in this case, the target was branded organic search, because you can’t see a competitor’s revenues by day you can’t see, you know, even their web analytics, you can’t see their conversions by day, but you can see the number of searches by name for for that every day, any given day. And so what we ended up doing was taking all this competitive social media data and then engineering out things like day of the week, hour of the day and things like that, and then saying, does any of this have any impact whatsoever on the number of times that audiences search for this brand by name? And it turns out that actually, there were some some pretty clear indicators that some of the things his competitor was doing in social media actually created Potential lift. And now obviously the next step in that would be as we talked about the hypothesis, if we do the same thing, well, we see a commensurate increase in if we get 15% more Twitter likes, do we see 15% more searches for our brand name? And then do we then see 15% more revenue downline, because for many companies branded searches a big deal?

Katie Robbert
Well, and you know, it’s interesting, because you started with a very specific question, even if you weren’t sure where you were going to find the answer. So you were following? Basically the process that we’ve outlined, it’s okay to not know exactly what it is that you need. But because you had a very specific question in mind, you knew that, you know, data from the National Weather Service wasn’t necessarily going to be helpful. So you weren’t going to go down that road, but maybe Google Trends data, social media data, you know, social listening data, CPC data, those might be Within the right kind of realm, so you had very loose requirements to start with. And now that you’ve gotten a more specific answer, you’re refining it. So you’re demonstrating what it is that we’ve been talking about, that it’s possible to start with the question and only the question and then go from there. Well,

Christopher Penn
good for me.

Katie Robbert
Happy Monday.

Christopher Penn
Happy Monday. Alright, folks, if you have questions of your own about marketing analytics, please let us know. Go to TrustInsights.ai dot AI, and you can find this podcast episode on our website. Just leave a comment on the post and let us know or you can always drop us an email or as Katie mentioned, join our slack group over at Trust insights.ai slash analytics for marketers. We’d love to see you there. ask all the questions that you want the silly questions that you want, and we’ll be happy to answer them. Till then we’ll talk to you soon. Take care


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