What is marketing intelligence?

Edward:
Cool. Okay, we're back with the newly rebranded podcast, The Marketing Intelligence Show, and I'm joined by Supermetrics' very own CMO, Gabi Stafford. So Gabi, great to have you join us today, and let's dig into our topic, marketing intelligence. And we have terms like business intelligence and sales intelligence, you've heard revenue intelligence more recently, but we don't really hear anyone talk about marketing intelligence. So that's what we are here to do and talk about today. So I guess first things first, what is marketing intelligence?

Gabi Stafford:
So I think the interesting thing about marketing intelligence is that the term has been around for as long as I have been in marketing, which is a very long time at this point. And intelligence has always been about the ability to learn about your market, your customers, and your competitors, and really to understand how what you're doing and the decisions you're making are influencing your business performance. And then, use all of that information to decide your likely next step. So think about it as aggregating all the information you have about your business, about the activities you're doing to influence that, and then using that to decide your next best action.

And this is how marketing intelligence really differs from marketing analytics in a very significant way because marketing analytics traditionally takes a more backward look. It takes a lot of historical data, analyzes what happened in the past, and tells you what did happen. Marketing intelligence is taking that one step further. It's using all that information about what has happened in the past and what you have done to influence that, and it's helping you decide your next best step.

So when you think about intelligence as a whole, the definition I really like and we think about in Supermetrics is the skilled use of reason. And I think for us in the fields that we're in, and as marketers, that we're taking all of the data, everything that we know about what we're doing, and we're using that data to help us with decision-making in terms of what we do next.

Edward:
Yeah, absolutely. And this is something we talk about a lot, as you said, and we've been speaking a lot with our customers, CMOs, and other marketing leaders, and there is this shift happening, and this is also why we've repositioned and changed up the podcast. So some of you who've been listening since the beginning, some of the OG fans, you'll know this was the Marketing Analytics Show, but now we're evolving this to speak more on the marketing intelligence level. So as you said, analytics is more about looking through the rear-view mirror, whereas intelligence is looking through the windshield — where are we going now, and what's happening next?

And so I guess this just want to follow up here if we go back to the why, we're big Simon Sinek fans as well. I know you are. Why is now the time for marketing leaders and marketing teams to really invest in marketing intelligence?

Gabi Stafford:
So I think that use piece is the why because how we're using data to make decisions is changing now, and it's changing in a seismic way. Traditionally it was people making the decisions, and then it was attribution cookies with algorithms making the decisions, but when you're entering a world where machine learning and AI are really taking over a lot of our decision-making, now is the time we need marketing intelligence because whoever is making, whoever or whatever is making those decisions, they need to be using data that they can rely on for that decision-making and for that iterative learning. So now more than ever, and over the course of my very long marketing career, I've seen multiple times where we're going to have a seismic shift, seismic shift to online, seismic shift to mobile, seismic shift to add platforms and real-time bidding, all of those kinds of things. But this, in terms of AI, machine-based learning, and machine-based decision-making, will change marketing in ways we don't know yet. And what you can control is how clean your data is to inform that decision-making.

Edward:
Yeah, absolutely. I think we're on the edge of something big here, that's for sure. And you spoke there about data you can rely on. What I think is at the heart of all marketers' agendas when it comes to making data-driven decisions is that your data is reliable. And so this is a good segue into the next point. Let's look at this more closely. So how important is high-quality clean data in developing your marketing intelligence?

Gabi Stafford:
It's the most important thing because... And you'll excuse my language, but shit in, shit out still stands. If your data is not clean, if you can't rely on it, if it hasn't been stored in the right way, joined in the right way, transformed in the right way, blended in the right way, you're not going to be making the right decisions, you're not going to be taking the next step. And the data that we're using to make decisions is also changing. Traditionally, especially in pre-digital marketing days, marketing intelligence relied on first-party research undertaken by human beings talking to others. Things like competitive intelligence and customer intelligence. And the tools we'd use were polls, focus group fields, studies, and surveys. And that was a slow human-to-human approach to get the intelligence you needed to run your business.

And then I am really old enough to remember when digital first came in, being one of the first digital natives, and with the advent of digital, this first-party approach was augmented by third-party data from advertising platforms. Things started to move much faster, and that speed was driven by the pace that the ad platforms could optimize biddings based on the traffic and the conversion data they were getting from the cookies everyone put on our site. I'm talking about 2010 and 2011 — everything was about, "Are my cookies right? Is my conversion data flowing?" Get to 2014, '15, '16, it's, "Is that now flowing in real-time? Is it telling me what to do next?"

And having real-time data at that point was a huge step forward for marketing because, especially in a big-budget online B2C business where you're spending millions or millions a day, marketing very often became more about the data and analytical skills than about the first-party or creative skills than we had traditionally seen, big seismic shift in marketing at that point. And suppose you look at what's happening now with the advent of AI. In that case, the term marketing intelligence is taking an even deeper layer of meaning for us because if you look at the speed of marketing that happened between the 90s and the noughties and the teens, it's an exponential growth in how fast marketing teams are having to operate for success.

If you take that feedback loop in machine learning, it's immediate, and it's exponential. And that means that the importance of reliable real-time data flow from which the learner bots can build continuous learning feedback for your business can't be understated. We don't really know how it will work or what will happen, but we absolutely do know and can say with certainty that you need clean data to facilitate this, and this is happening now. It's not happening years in the future. This is where we're at. The speed at which this will be able to happen for all kinds of marketing activities will astound us. And within six, 12, and 18 months, there will be no world where any data, siloed data systems can drive performance. The concept of leaving your Google data in Google, your Facebook data in Facebook, and optimizing in individual platforms isn't going to work anymore.

Edward:
Yeah, absolutely. It's really looking like getting your data flowing across the org and moving with you. And at Supermetrics, we speak a lot about turning marketing data into opportunity. So let's dig into this process or life cycle of what happens to the data. So how do you take that raw data and then turn it into growth for your business?

Gabi Stafford:
So I think data is a huge opportunity, but to turn it into an opportunity, you need to make it easy for your teams to use it. And whether your team again is a human being or a machine, it needs to be easy to use. You need to create an environment [inaudible 00:11:44] instant access to the tactical data you need to run your business ad hoc to get the data they need to make those day-to-day decisions that every marketing team needs. And that involves multiple touchpoints into a single source of truth for your data, which will be a data warehouse or a data lake. So that data can be used in different teams in different ways.

Some teams will run the same data set to run complex data modeling. Some teams will use the data set for simple queries. Some teams will be using the data set through BI tools or visualizations, and most marketers still spend a lot of their time in spreadsheets, but the key thing is that it's a single source of truth data set that you store in one place that can be used across your organization. So how we're thinking about this in Supermetrics and how we're running our marketing data platform is that we're thinking about it in terms of having a marketing intelligence methodology where you connect, you store, you transform, you understand, and then you use what all of those previous steps to activate your next step.

So taking it again, you connect your data from all the different ad platforms, your conversion data, and your traffic data. You could take the weather data, the retail calendar data, whatever it's, connect it all in one place and store it securely with a single source of truth in some place you own or a vendor your trust owns. Transform the data from the different sources, and blend it so that you have one set of data that's easy to use. Use modeling, AI learning, and analytics professionals to help you understand that data and then use that data to predict what is your best next step.

And for us in marketing, how can I be sure that every dollar I spend, every next dollar, gets me as much money as possible? Because very often, the next dollar will get you slightly less than the last dollar, and the data can help you do that. And the more real-time that data flow is, the better your decision-making will be.

Edward:
Yeah, absolutely. And I like that framework. So it's connect, store, transform, understand, activate. And here, as you said earlier, that if you get shit in, you get shit out. So it's so important to make sure that what is coming in and what is stored is then orchestrated and aligned so that you then activate that data back into your activities. So make sure you have the good stuff coming in, and then the good stuff will come out. And as you said there, it's really about the confidence to make decisions in terms of where to invest that money, which for you as a CMO and any other marketing leader out there is really the million-dollar question, almost literally to many.

So let's look at this a bit more then. A big part of marketing intelligence is using the data you have to then have the confidence to make what you think is the right decision. How do you use data when making that decision on where do I spend your budget? Where does the next dollar of our marketing budget go?

Gabi Stafford:
As with any decision-making, marketing decision-making is really based on what you're trying to achieve. What is your ultimate goal? And at different times in your career or different times in a single business, that could be different things. What data you'll focus on or how you'll look at that data will depend on the business outcome you're trying to achieve. Are you looking to grow the business but only care a little about profitability? Do you want stability? Would you like to not take risks and just know that your forecast will come in as is? Are you risk-averse, or are you focused on profitability? Is it the bottom line that you care about?

The marketing decisions, particularly your spending decisions, will be very different based on what you're looking to do. If you're chasing growth, you need the data that will tell you where best to invest to grow the top line of your business. In that case, you might not be concerned about ROI, but you're very concerned about incrementality in CAC, things like gross revenue, and customer numbers. And you need the data to help you navigate those decisions to set a benchmark from where you can go, "Right, I know this is what was happening in my business last month, or this is what's happening in the industry now. I want to beat that growth rate. To do that, I'm going to have to invest this, and I know I'm going to take a cut on the bottom line, and I'm happy with that."

If, on the other hand, you're tasked with something like profitability, you'll make different decisions based on margin and the actual efficiency of your spending. And again, you'll look at different benchmarking data to help you make those decisions. The data helps you map and navigate your decision-making framework. And traditionally, the analysts created that kind of map, and the business teams did the navigation. That is now going to be augmented by machine-based learning and machine-based experimentation. And again, having that foundation right at the store track transform stages of the methodology will help you get the most out of the understanding and the activate stages. So it all joins together to create a self-fulfilling cycle, but you need every stage of that cycle to make it work.

Edward:
Yeah, absolutely. And that context is so critical but sometimes forgotten about or a bit overlooked because if we were having this conversation, say, six, 12 months ago, it would be around growth, top line, and so forth. But now, it's really all about efficiency at the moment. So that context changes how you work with the data. And from here, then, the relationship between the CMO and the CFO is, of course, critical for any growing organization. So how do you use data and marketing intelligence to have more effective conversations with the CFO to ensure marketing has a seat at the table to show how marketing impacts business growth? Because I know this is a big challenge for many CMOs and marketing leaders out there.

Gabi Stafford:
Yeah, marketing tends to be something that everybody thinks they can do. So having a clear narrative about why you're focusing and your teams are focusing on what they're focusing on, why you're doing what you're doing now and what your next step is, and how that will impact the business is key for any marketing leader. That narrative will only stand up if you can back it up with data. And you need to do that because marketing can often be the biggest item on the P&L outside SG&A. And if you're managing a global business, that can be spending... I've been in the seat where you're spending millions a day. Your finance partners are going to be your key stakeholders. They will be the people helping you ensure that the marketing team is spending that money to achieve whatever business outcome you want.

Data helps you align those conversations. It creates a common ground for you and finance to have a shared understanding of what is happening day-to-day and how that will impact the midterm and the long-term outlook for your business. And having data help you create that clear narrative around which marketing and finance can align and work together will enhance the overall decision-making of your business because you want marketing and finance to go with a single voice to the board with the narrative of, "This is what we're doing, this is why we're doing it, and this is what we think the result is going to be." Data is the shared understanding between the two, and that's why you both must understand it in the same way; you're both looking at it in the same way, and you have a shared narrative.

Edward:
Yeah, absolutely. And that's going to be a big builder in terms of trust, which is also a critical piece in that relationship between marketing the CFO and the rest of the leadership team, the board, and so forth.

Gabi Stafford:
Trust is a really good point, and from that trust, you can have meaty, meaningful conversations where you're taking the two skill sets together to create the best outcome for your business, and that's why businesses have different organs. Data can be the glue between those organs to ensure everyone has a common understanding of what's happening, sees it the same way, challenges each other the right way, and ultimately come out with the best decision for the business.

Edward:
Yeah, absolutely. So, as a leader, how do you develop a culture of marketing intelligence within the organization?

Gabi Stafford:
So the most important thing is something I've tried to do in every business I've worked in. We focus on it a lot at Supermetrics. It's really important as a leader to create an operating model for your team that sets a clear expectation that data is the foundation of our decision-making. And that means that relying on your gut to set you down a direction is fine, but go and look at the data and see does that back up your gut. And if not, change your decision.

The data will tell you the way to go, and having that data at your fingertips, whether in a visualization tool or a spreadsheet and being data literate enough to know exactly where to get the data you're looking for. Do I go to my visualization tool? Do I write a SQL query? Do I do a pivot in a spreadsheet? Modern marketers need to be that data literate because data is at the core of everything we're doing, and you need to set that expectation with your team. It's art and science. Marketing always has been art and science, but even the art piece will be increasingly data-driven, and your team needs to make that the foundation of everything they do.

Edward:
Yeah, absolutely. And as you said, you've been in marketing for a long time, you've worked both agency side, brand side, you've led a marketing agency, you were a global CMO at Groupon, you're now CMO here at Supermetrics in the B2B space. So these are big marketing organizations. Could you give some examples and share stories of how you've leaned into marketing intelligence throughout your career?

Gabi Stafford:
So there's one example I'll give because it also relates to the last question. We had a very large global marketing team in Groupon. At one point, it was over 300 people. And for anyone who dealt with audience selection or audience targeting, basically, anyone who was running audience target activities, we ensured that even if they were a graduate coming in with a marketing degree, they could run SQL so that they could have instant access to the audience that they wanted to run now. That was in a B2C business where things like the weather impacted us hugely, and you might want to run a specific campaign on a specific day, and you didn't have time to wait for your data engineer to run your audience. So that's one way that having data skills can help with your data intelligence.

In more meaningful ways, there was a time in Groupon where again, huge global B2C business, we were spending a lot obviously on search advertising. We changed from a couple of data refreshes a day to real-time data going into the ad platforms. And we saw a double-digit, high double-digit impact in performance, from having just two or three data refreshes a day into the ad platforms to having real-time data going in. And when you're operating at scale, that makes a huge impact on the bottom line.

And then the other place that's been interesting for me over the course of my career to watch how marketing intelligence has changed and continues to evolve and yet stayed the same has been when you think about incrementality, attribution, and things like MMM. Back in the 80s and even the early 90s, MMM was still huge as a way to figure out how particularly your offline spend, predominantly offline , spending was working. TV, radio, print, is that working for me? What am I doing? That's come full circle because we've gone through a journey from last-click attribution for... MMM was junked, everything was about the last click, then everything was about first [inaudible 00:26:11], everything was about multi-touch, then a world... Sorry, where that attribution doesn't hold anymore because of cookies, we're back to first-party data.

And remember, at the very start of this conversation, when we started talking about traditional marketing intelligence, it was first-party data gathered by humans. Now it's first-party data gathered by machines, and that enables us to have a whole new kind of MMM that we can do at scale to measure how we're doing and MMM for measurement but also to point to the next step. The next step can happen at scale by machines executing and learning and growing and coming back and telling you this is what's actually happening in your market right now.

And for me, to have seen that evolution over the course of the last 25 years has just made being a marketing professional really exciting. I think it's one of the reasons I'm still in marketing after all this time because it's constantly changing.

Edward:
Yeah, that's for sure. If you're a marketer and have a career in marketing, it never gets boring. There's always something-

Gabi Stafford:
Never gets boring.

Edward:
New that comes along. When you figure something out, everything changes. And as you said, we've seen the shift back to MMM after the promises of multi-touch attribution never fully realized themselves. So the piece you also said about first-party data being key here is so important. Again, I think we can't emphasize that enough. It's really the foundational piece of that. And if we go back to the marketing intelligence methodology, that's where the connect, store, transform piece comes in that you have that single source of truth.

And so this has been a great chat, a lot of very, very interesting points to go away and think about and act upon, but any final tips or thoughts on how our listeners can start developing their marketing intelligence?

Gabi Stafford:
There are two things to think about in order to take advantage of marketing intelligence and use it to help you run your business. And first is to find ways to make data easily accessible for you and your teams and develop an operating model with data at its core. Ensure your team is data literate and also ensure that they're keeping their skills up to date. And as marketers, we don't need to know how AI works. That's an argument that nobody knows how AI works, but we need to know about the impact it's going to have on how we do our jobs. So you need to instill that belief in the power of your marketing team, keeping their data skills up to date.

And then the next one is really related to the first. Right now, you need to be prepared for our industry to change quickly over the next 24 months as we rely more and more on AI to help with our decision-making. It's going to happen; it's inevitable at this point, but resign yourself to a certain amount of uncertainty as we all navigate this new path and work together to make sure that your business has a really strong data foundation from which you can take advantage of the improved speed of decision-making that AI will bring to ultimately help your business achieve its goals.

So I think there's a lot of fear about AI and what is going to happen. For marketing, it will always be about achieving the business goals. Data is increasingly going to be the foundation of that, and really, they're the two things I'd focus on right now.

Edward:
Great stuff. Well, Gabi, I have to say thank you so much for joining us on the podcast.

Gabi Stafford:
It's an absolute pleasure, Edward; I'll chat with you anytime.

Edward:
Yes, I'll hold you for that because we're going to have to get you back on it.