Podcast: Delivering Marketing Success Through DataOps

Jonathan Martin, Chief Marketing Office, Hitachi Vantara

Good. Good to be back!

Bill: Back here again to share more insights about the world and life of being a chief marketing officer. So tell me from the view of a chief marketing officer, especially today, why is data so important?

Jonathan:

So, I think we, we in the tech industry, we talk a lot about, you know, going through the second biggest change in technology ever as people get off, you know, lands and desktops and notebooks too, like social media, social, mobile, cloud, AI like machine learning, all of that new stuff. I think in marketing we're going through an even bigger transition and I would say probably the biggest transition since the early 1950s. So since then, since that time, all marketing's really been based on a single concept, which has been called the interrupt, and it's basically just, I'm going to find a way as a marketing person to interrupt whatever you're doing and for a moment in time you're going to pay attention to me. And the more times that I can interrupt you during the day, um, the more people that I can interrupt during a day, slowly over time, my idea ideas become, all your ideas. Now marketing people have got really, really good at this. Um, if you live in Madison, Wisconsin, you'll be interrupted somewhere between two and a half and 3000 times a day. If you've been interrupted, if you are living in times square, No, I have no idea why anyone would live in time square. But if you did, you get interrupted like 13 and a half thousand times a day. So we've got like 50, 60 years to perfect this and whether we're interrupting you with billboards, whether we're into interrupting you with a social media or ad an email, whatever it may be, we got really good at interrupting you. The problem is that if you look all the latest neuromarketing research and people's brains actively screening out those interrupts and all of those are interrupts are triggering people's fight or flight mechanism. Okay. And the lower order of the brain is really just screening them out. So that's why if you, you watch somebody that, or you ask people in a group of 200 people and who watched television last night, five years ago, 150 of them would put land up. Now maybe 25 of them do. Cause people are not watching as much television anymore. But those people, you ask them, do you remember the commercials? And all of them apart from that, one person in the back says, I don't remember anything. And that's because the brain is actively screening out commercials and interrupts and those sort of things. So every technique, every fund, like foundational techniques, all the things that we went to school, all the things that we've been learning are based on this concept of interrupting people. Okay. And so that is incredibly challenging for, for marketing people because the effectiveness of your marketing techniques is deteriorating really every quarter that goes by. What is being replaced with is a set of techniques around engagement. So people respond very, very differently when their first interaction with you. Their first interaction with a brand is really around being educated or being entertained. And so those are the two simplest ways of beginning to establish rapport with an individual, a rapport with an account and is to either say, Hey, you've got a problem. Let me show you a faster way of solving that problem, or you've got a problem. Hey, there's maybe a new way of thinking about this or just quite simply entertaining and making them laugh. And slowly over time if you can continue to educate them, if you can ensure that every interaction with them is meaningful to them, not yet on you, then you begin to establish trust. And, at some point they give you the green light to begin to market to them. But it's super important to be able to engage with them first. Now, the way that you engage with them is also changing a lot. It's becoming increasingly personal. The combination of channels that people are using, it is absolutely unique to the individual. So, some people, their channels might be Instagram and What'sApp, email, other people, it might be what web and traditional media and you know, Facebook. So, the channels that they're using and in particular, the combination of channels is particularly, uh, unique to that individual. So, you can't just say, Hey, we're going to go run an Instagram campaign and expect everybody to engage with you. Simply not going to happen. You have to make sure that you are going across the channels to do that. So, that's the second problem. is that the is around personalization and then the third one is really around kind of the buyer's journey, which is becoming an incredibly complicated thing. So, now if you've got a group of prospects out there we could group potential purchases who are switched off to you, marketing to them who are very particular about the combination of channels that they use to get engaged. That means that the, the buyer's journey is beginning, is going to become incredibly complicated because the way that people use these channels and the way that they string these channels together is incredibly, incredibly unique to them. The one thing that is common is that the first channel or the F sorry, the first engagement that they normally have, the first way they have of engaging with the brand is, is through Google.

Bill:

So describing that the role of a chief marketing officer now involves around engagement, personalization and the customer journey. Sounds like it's data science meets design thinking. Filtered with being driven by data.

Jonathon:

Yeah.

Bill:

Is that a good way to look at it?

Jonathon:

Yeah. Yes. So data is really become over the last 10 years, like foundational to the mission of a CMO. And I really think you know, the, the role of marketing these days is, is you know, often gets confused with social media and events and all of these things. And obviously those things are important. But, but the purpose of marketing to me is, is really to be the growth catalyst for an organization. If you've got a great product and you've got a great, great site sales team, guess what? You're going to sell some of that product. So the role of marketing has to be on top of that and it has to be able to, to drive growth for an organization. And probably three vectors. One is how do I drive growth in just top line revenue in terms of margin. How do I go attract a greater amount of the available market to me rather than somebody else. And secondly, how do I establish a brand that will allow people to part with more money for similar products, apples, the master at doing this. And then thirdly, how do I go and create a story that, that will show that we're able to drive enterprise value for an organization. So all of these things are very, very dependent upon data. And I use the word dependent very specifically because it is in the absolute worst meaning of the word. We are addicted to data. We are like the crack cocaine uses of data. I am absolutely dependent on data to deliver my mission. And that getting access to more and more data, getting, being able to process data in more like real time will allow me to put the right offer in front of the right person at the right moment. If I can't do that, then I will just simply not punch through the noise.

Bill:

It seems like the, the, the characteristics that's gonna define the successful chief marketing officer of the future is a person who is not only comfortable with data but maybe relishes rolling around a bit in the data.

Jonathon:

Absolutely. I think, you know, the, the five years ago you would meet, meet CMOs and they were either like left brain or right brain, you'd either meet the like super creative like advertising agency person and, or you'd meet the data. Right? And I think today like what we really will, CMOs are becoming more and more centre brained. Like yes, you need to be creative, but you need to be able to quantify the impact of that creativity. Um, I'll give you a great little example. I hope he doesn't mind me sharing this, but at a former company I met with my creative director for the first time and he's at home with me. And you know, the great thing with meeting creative directors is they just have lots of cool stuff to show you. They got that is my video, this and my this and this and this and this. And he said, he lays all this stuff out on the desk. And I was like, wow, right, let's cool stuff. And he's like, yeah, we built like 4,000 assets last quarter. And I was like, wow, for like barely fourth. And he's like, Oh yeah, we're translating to 12 different languages and blah blah. I'm like, wow, 4,000 of anything is a lot, which is the one that moved the needle on revenue the most. And he looked at me like I just stepped off the moon. Just crickets. Just absolute. So like, yes, you need to be creative, but you need to be able to quantify that creativity. But secondly, like a lot of people I think historically have looked at data, people, data scientists as being the nerdy somewhere on the spectrum kind of individuals, um, who are not creative and in my experience, some of the most creative people that I've met, I've actually been the analyst and the data scientists where they're digging in these like huge datasets to try and find gold. And that's a very, very like, creative process to go through that. So I think today like see, CMOs need to be a little, a little of both. They need, they need to be creative, but they need to have like a distinct interest and passion for kind of data and for technology.

Bill:

It's interesting cause it, what really is going to happen here or it's happening is it's putting the CMO right at the junction point of the DataOps conversation. Can, can you share with me when, what do you think about when you hear the word DataOps, what does it mean to you?

Jonathon:

So really like, I'll give you just a super, super practical example and it's based on a conversation that I had with our CRO HBK a couple of days ago. And he came into my office and he just asked a really simple question, which is like, Hey, we think there's possibly an opportunity in storage for mainframe environments. Couple of the other people, people in the market, have their eyes somewhere else. Can we just go find those accounts and find what they bought and see if there's an opportunity and it should be a two minute response. Right? The answer's yes. And it's these accounts and and and…. So we offered the teams and the teams are great here. But, the response in terms of finding all of the data, getting into a format that's correct, being able to do something with the data and basically get the answer to that simple request was going to be measured in a matter of days or maybe even weeks. And if you've met our CRO, he's a little bit more impatient that. So, that problem we are, you see in every customer that we make that it's like, how do I use data for decision support and how do I use data for, um, uh, analysis and prediction of, of, of the future. And how do I do that? In real time, like in the moment. And so lots of organizations want to do that. And what they tend to be challenged with is the same thing that I was just talking about there is they have a bunch of people who want to consume data. Right now that has been people, I think increasingly in the mission in the future we'll see that the people that can see or the things that are consuming data will be machines, not individuals. Then you have another group in an organization that is responsible for "managing the data". And that management of the data, constructs around it, changing very rapidly at the moment. So even like five, six, seven years ago, you, all the data you owned sat in a data centre, right? And you, you know, when somebody said, well, Hey, where's this dataset? You could point to the server or the piece of storage and say like, it's right over that. The world that I'm operating in right now is, yeah, I have a bunch of data in, in Renee's data centres that, that we use. But equally I have data sitting in 50 other clouds, almost 50 other clouds. I have a ton of telemetry data out on the edge in the equipment that we use. Right? And if I'm trying to understand buying behaviour, I need to find a way of identifying where all that data is being able to, to get my arms around it and being a, being able to manage it holistically. So this is taking a lot of the basic concepts of data management but doing it for the AI era. So, that's so that like the ability to kind of store data, the ability to enrich and analyse data, the ability to ultimately monetize data is what we see many, many organizations trying to do. And to support that is a new set of new techniques, new capabilities, new technology that is called DataOps, which is really the, the, the corollary to DevOps for applications. I think the, the worlds taken a pivot the last few years away from a view that's based on storage and network and compute to a world that's based on apps and data. And what DevOps did for apps and the ability to speed the delivery of apps. We is just constant iteration, is happening in the world of data right now with DataOps.

Bill:

Do you feel then that DataOps is or supports a culture of rapid exploration testing and learning from your data?

Jonathon:

Absolutely. Yeah, absolutely. And that's what, that's what we need, right? I can't ask a question and get an answer back or sometimes actually, sometimes three weeks is just fine. But if I'm trying to run like a real time operating environment and making sure in real time and placing the right content through the right channel to the right person in the right moment, that's a real time decision support system. That's what we need.

Bill:

Do you think there are any sort of cultural challenges to a marketing organization as you transition more away from, I'm going to call them campaigns to buying behaviours?

Jonathon:

Yeah, massively. Massively so. So you know, I think everyone made the shift a few years ago towards like, you know, personas and so that's certainly a key piece of it. And, but the big shift is from marketing using data to measure history. They've got really good the last 10 years of telling you how the world was a minute ago or an hour ago or last week or last year. So that tactical kind of rear view mirror kind of stuff, marketing's got really, really great at in a way that I think probably few other functions in an organization have. The goal now is to really shift the mindset and say, Hey, the reality of the situation that we're in right now is there's never been more things to spend your marketing dollars on than there are today. There's so many options out there, but equally I don't think there's ever been as much scrutiny from the CFO and the CEO on the return that we're getting for those dollars. And so being able to, to use in particular predictive analytics, things like basket analysis to understand the, the programs, the campaigns, the triples, the combinations, all of these elements in the marketing mix that will provide the highest return has never been more important to marketing people. And that's, that's the shift, it's like, not being like, Hey, we're going to go run a campaign. We're going to like, we're going to build some creative and we're going to put the channels together and put some dollars behind it and go run it, now it's like, Hey, let's go model what that outcome might look like based on all the previous buying behaviour that we can get to by gathering your telemetry data, your social data, any kind of video analysis from events and the things to do with RFID as well as your digital data, historical sales database, blending, all of that sort of stuff together to say, Hey, if we model placing this piece of content through this channel at this moment in the sales cycle, it accelerates the sales cycle by three days rather than 2.2 days. And, so when you actually go to put real money behind campaigns, it is like as efficient as possible.

Bill:

So, not only has marketing become much more measurable, which historically it has not been yet, but it's also becoming much more predictable, which is hard to do with behaviours. It's really hard to predict behaviours, but you're really transitioning from an environment where you're modelling behaviours in order to predict likely activities so you can prescribe ways to reach that customer more quickly.

Jonathon:

Yeah, and I think like the simplest way of saying it is like marketing really used to focus on things, metrics like customer lifetime value and now they're focusing things like expected lifetime value. Or predicted lifetime value.

Bill:

Yes. Sounds great. Jonathan. We know we have this, this customer engagement methodology we call SEAM, which is really about how do we help customers navigate up the transformation model to get more value out of their data, right. Storage to enrichment to activate, to monetize. What kind of role does marketing play in helping to drive our customers to navigate that SEAM journey?

Jonathon:

Yeah, so absolutely I think, I think you know that that's the, the stairway to value as we call it, through, you know, how do we store, how do we enrich, how do we like analyse and how do we monetize the, all of this data. I think for me and probably many other business leaders, it actually, it starts with monetize. It starts with like how am I, what am I trying to do? And it really comes down to always want a two things. How do I either A] use this data to help me reduce costs, become more efficient, automate whatever it may be, or how do I use this data to find new value in some way? And, so I picked my topic, I put my subjects, I'm like, okay, we're going to go monetize around, create, creating new value in new opportunity in this space, might be, I don't know, new products introduction.

Bill:

Sure. Production. Yup.

Jonathon:

So, first step you need to do is go to all the other way, all the way down to the bottom of the stairway and which is really to look at where is my data stored. And, as we mentioned earlier, the data that I want to get access to is spread in a very, very distributed environment. The second problem that I have is that this data is very, very diverse. It's not just structured and unstructured data, it's data that is coming from, you know, sensors on storage arrays, telemetry type data. It's data coming from Facebook and from Twitter and from social media. It's video-based data from events. It's traditional systems and ERP and CRM data. It's my data, you know, from the Adobe suite. Um, it's data from Bombora and Lean data and all these different systems that we're using. So, we really are, you know, trying to get our arms around data that is coming from a, an edge-to-multi-cloud world. It's coming from, you know, all the way on the edge, on a, on a multiple data centres and you know, 50-ish clouds. So, first step and you know, working with Renee is getting our arms around, first of all, finding the data like that. A lot of GDPR is forcing a lot of people to go find their data. Like, where does this data actually reside? Um, and GDPR is, as you know, is no longer a European phenomenon. It's being introduced in Latin America. It's probably going to be introduced very quickly in California. It's rapidly becoming a worldwide phenomenon. So first thing is like find all of that data and get it in place. Get into management. Second thing then is like, how do I enrich the data? Uh, if I have contact information for, for Bill Schmarzo and I want to go enrich it, I can enrich it in multiple different ways. I can enrich it by going out to third parties like Hoovers and Dunn and Bradstreet and getting more data about Bill Schmarzo from those things. But I can also go into social media and you know, you're, you seem pretty active on Twitter, so I can probably find a lot about your buying preferences. Yup. Certainly, I know that you're a walker. So, if I'm selling sneakers, I can enrich my data set with lots more third-party data. Once I've done that, then I want to be able to, you know, inside this, this enterprise data platform, to be able to hand it over to the analysts. Traditionally that's been data scientists and I always think of data scientists. A lot of people think data science is like one person. I always think it's like, it's actually really two people. It's like statistician and a modeler. And when you put those two things, two people together, like you kind of get a data scientist. So, I want them to go and, and work on the data. And really like if you look at like Mike Foley who you'll also meet somewhere in this, you may already have met him. His goal is really, is only to two things for me is number one, mind the data for recommendations. The only, you can recommend my anything in marketing. It can be media mixed optimization, it can be subject titles on an email, it can be flow traffic and media placement at an event. I don't care. What I do care about is one, the volume of recommendations that Mike is making. And then secondly, how many of those recommendations are implemented by the rest of the team. That's the only thing that we measure him on. And so by doing all that analysis, we'll find hopefully the way of creating new value. So we bring new revenue into the organization or we find a way of becoming more efficient and cutting costs, which generates more cash flow, which allows us to go and do the next set of projects. And so this is, it's not a really a stairway, it's more of a flywheel and the flywheel as it throws off more and more cash begins to spin faster and faster and faster, faster. So, that's what we're trying to do. The idea of how do I do data management in this edge-to-multi-cloud era, how do I enrich it with as many different data sources as I can? How do I run analysis on it? And yes, right now it's been done by Mike, but increasingly Mike and his team are being all augmented by machines. So increasingly that that is machine learning kind of problems and how do I monetize data is something that every CMO on the planet faces, but to be honest, that's the fundamental problem that every organization, every C level executive is trying to solve, which is really how do I either increased revenue or create efficiencies and reduce costs. It's the fundamentals of IT.

Bill:

I'm expecting to see a change in some of our strategy and moving away from stairway to value to flywheel of value. I'd like that. The more it iterates, the more money you make, it's like a printing press. It throws off cash. All right, well thank you Jonathan, and I thank the audience for listening to our podcast here. We've got a deep dive on DataOps and what DataOps means to a chief marketing officer and the role and importance of data and analytics and the future of marketing. Thanks again, John.

I hope you enjoyed this podcast and you certainly want to come back the next one as we talk again to more organizations about how they're leveraging dataOps to drive value out of their data. If you want to learn more about Hitachi Vantara, track us on Twitter @HitachiVantara, or if you want to follow me, follow me @Schmarzo. I'm the only one on Twitter. Thanks for your time. Until next time, Cheers.