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Introduction

Being insights-driven is a term that is used extensively across multiple industries and business these days. But what does it actually mean and how does a company become insights-driven?

In today’s rapidly changing landscape, marketers and CMOs often struggle to understand how to best translate data into meaningful insights and how to then translate those insights into meaningful actions that will impact performance.

In our latest roundtable discussion, Adverity CEO, Alexander Igelsböck spoke with leading experts Andy Lark, John Veichmanis, and Wes Nichols, about what being insights driven actually means, why it is such a hot topic at the moment, and what marketers and CMOs can do to create an insight-driven culture that provides tangible results.

Meet the Panel


Wes Nichols

Wes Nichols (WN)

Partner at March Capital, an investment company focusing on breakout technology companies. He brings a myriad of experiences having built the world’s largest planning and attributions software company for CMOs called MarketShare.


John Veichmanis

John Veichmanis (JV)

Currently COO at Carwow. John brings 20+ years of experience leading digital transformation to our panel. He has held senior positions at Apple, Skype, and Expedia, focusing on and leveraging marketing data for organizational growth.



Andy Lark

Andy Lark (AL)

Andy is a globally awarded CMO, teacher, and leader, drawing experience at Xero, Foxtel Intel. Andy now runs his own global consultancy enabling digital transformation for the world’s leading enterprises. Andy currently holds a senior marketing position at Dubber.

Moderator:

alex-igelsböck

Alexander Igelsböck (AI)

CEO & Co-Founder at Adverity, Alexander has over 20 years of tech and marketing experience. He is a Forbes Tech Council Member and part of the I-COM Germany Advisory Board.


Key takeaways

Being insights-driven requires putting data at the heart of day-to-day activities. Teams should schedule regular stand-ups that specifically address what their data is telling them.

Data and data-driven insights should not be used to simply justify existing decisions but instead should be the driver of decisions themselves.

CMOs and marketing leaders should foster an insights-driven culture that empowers staff to hypothesise and test their theories against the available data without fear of failure.

CMOs are advised to look for and hire data-driven individuals with an inquisitive nature who are passionate about unearthing insights from their datasets.

Topic 1: Why?

Why insights-driven and why now?


 

AI: John, you were quoted as saying that data is the new marketer’s currency. But people will now be asking; why is being insights-driven more important than that?

JV: It’s a good question. I think that quote is quite old now, but it’s still really relevant, certainly to how I think about building a team and building a culture that can succeed. And over the course of my career, I’ve been looking to see businesses tied to consumers businesses. And the reason that I’ve made those choices in terms of the roles and the businesses is that I wanted to use data to connect to the audiences that we ultimately serve.

The way I’ve always described it to my teams is, if you had $100 and just left that sat in the cupboard, obviously, it depreciates over time - you create no additional value. The great thing about D2C (direct to consumer) business is you quickly collect a huge amount of data and a huge amount of diagnostics about your business that you can use to create additional value.

And I’ve always thought about it in terms of what are we doing to capitalize on this value so that we can compete more effectively. We have to put the data to work in everything that we do. And it’s not the domain of the analytics team or the engineering team - it’s everybody’s job to use the data and building that into the culture of the organization is critical.

If you had a $100 and left that sat in the cupboard it depreciates over time - you create no additional value.

I suppose the most important aspect for me, and ultimately the thing I love doing as a leader, is empowering teams to use data to make decisions quickly, to provide them with more autonomy to take calculated risks and try things.

If we think about cash in the bank and generating interest on that capital, then, to me, creating a culture where people can use data to try things, to question everything that we do, and build not just a better optimization stream, but question the overall strategy and generate new ideas that may impact the business’s future strategic priorities. That’s how you generate a much better return on those assets.

And that was always my sort of point around the currency analogy. I think, certainly over the last 20 years now, my favorite meetings are those where the team is using data to hypothesize and say ‘Well, we discovered this and we think this is why this has happened. We joined the dots together. Now, we’re going to try X, Y, and Z’.

The best thing for me is people come into the business just after university and they’ve got so much autonomy to try things, which probably 20 or 30 years ago they wouldn’t have had. Most of the ideas came top-down but now they’re coming from the entire organization. And that allows you to move at such a fast pace, and innovate, and connect with consumers - and that creates a fulfilling job. It’s a good reason to get out of bed every day, to try new things with a sense of perspective and the confidence that you’re likely to succeed in most cases.

AI: That’s great. Next to creating a fulfilling job, you touched on many, relevant points like putting data to work and empowering the teams, and so on. So, with that, I want to hand it over to Andy. Do you have anything to add on those points? How do you see being insights-driven? Why is it such a hot topic?

AL: I think largely for marketers, data is such a hot topic because most don’t have very much. And we’re living in a new world where you can’t pick up anything without reading about AI, data likes, data-driven businesses, whatever the phrase is today. I think most marketers today are still struggling to get their hands on data.

You know you look at the classic old-world Pareto principle that marketers suffer from. John and I and certainly Wes, we live in the world of what I call emerging growth businesses. We were born out of data. We started based on data. Our business is data. People talk about being in product with companies, actually, most of us who are building sort of weird web 3.0 with web 4.0 companies, we’re not actually building product companies, we’re building data companies that happen to manifest data through products.

But the majority of the world’s marketers aren’t in those businesses, and they’re sitting there and going ‘My budget is locked up in such a way where it’s a classic 80/20 rule, but inverted. About 80% of my budget goes on exactly what I did last year, which was exactly what I did the year before that. And the other 20%, they’re trying to take off me’

I think there’s a systematic issue facing a lot of marketers today, and that’s why it’s a hot topic.

So, it’s incredibly hard for them to suddenly go ‘Oh, yeah, why don’t I go and invest in significant data projects?’ Normally, if you’re in a large company that begins with a McKinsey or Bain, which we would regard in our businesses as being kind of critical to productivity and results.

So, I think it’s a hot topic because most marketers want to use data, but they’re largely using data to justify expenditure, explain expenditure to try and explain results, or they’re trying to use it right up front to devise a strategy in a reasonably ad hoc way. So, I think the challenge for marketers today is rethinking how strategy gets formulated over a 5- or 10-year window.

And most marketers actually live in the job, CMOs, in particular, live in the job for 80 months. So, that’s a really hard time frame for any senior marketer to comprehend.

You’re dealing with this challenge of having to rethink marketing from the ground up as a data-driven business that exists outside of the realms of digital, which is obviously data-intense. But how do you as a true marketer spanning the entire business - pricing, packaging, promotions - use that to inform decisions?

I was sitting with a CMO of a large business the other day and he was busy explaining to me how they’re using data to explain positioning in an ad campaign. And I said, ‘Well, are you trying to use data to explain price and price elasticity to inform better promotions and better distributions, and targeting in markets and revectoring a spend where you can win versus where you can’t win’? And the answer was, ‘I don’t even know where we get that data from’. I think there’s a systematic issue facing a lot of marketers today, and that’s why it’s a hot topic.

AI: You touched also on a few challenging points. First of all, nowadays every company should be, to a certain extent, a data company. Obviously, for us, it’s kind of obvious, but many companies need to transition themselves to be data-driven first. And that is obviously a challenge for many.

On the other hand, the 80/20 rule, is quite challenging. Budgets are cut and you have more constraints while on the other hand, you need to steer very innovative projects. Wes, you were facing that in your company, MarketShare, so, how did you handle the why question?

WN: It’s a great question and it’s an interesting topic because we’ve come a long way as an industry in a relatively short period. In a contrarian way, I believe that the COVID pandemic has actually helped accelerate the digital transformation that needs to happen at most companies by force function. What has been exciting to me is working with CEOs and boards and CMOs around the world for many years now and the recurring theme is that marketing is a cost center that needs to prove itself every day or get marginalized, like Andy was saying, with budgets cuts.

Let me use a pharma company that I worked with a long time ago, 10 years ago, as an example. This company is a large producer of flu medicine and the CEO of that division every year was given a budget based on last year’s flu season and sales. But, last year’s flu season is generally quite different than the next year’s flu season. And so, every year they were either way over budgeted or way under budgeted because it was some stupid allocation, like 4% or 5% allocation of budget.
It wasn’t until we started building out a proper data set of what happened, what variables actually influenced the sale of the product, as opposed to a backward-looking model, that we started to create predictive levers.

We have insights coming out of analytics that can fuel marketing allocation, trade funds, new product development, market development funds.

In this case, we had partnered early with Google on using query volume data to look at indicators. And out of the model, think of it as an investigative or forensic modeling exercise, we were able to find that you could see Google query searches for flu symptoms a few days before the medicine was prescribed.
One thing that came out that was interesting was we saw bus ridership and metro ridership, public transportation numbers, go down several days before the flu symptoms started to be googled. And all of a sudden that gave a very interesting combination of tools that we could use to predictively prescribe marketing money based on markets that we’re starting to see dips in public transportation ridership.

That gave this company incredibly cool tools to work with to completely crush the competition, who weren’t thinking of anything like this. They were just riding the wave of a flu season prior and riding the waves and the whims of finance until they had the data and the tools to do something about it.

So, it’s not that data is the new oil, as you hear a lot, it’s more like the insights of the data is the new oil. If you think about the way oil is made, crude oil is pumped out of the ground and it goes to the refinery - in this case, the analytic methods and the data science - then the refined oil or the insights come out of that.

In the oil business, the refined product can be gas, jet fuel, diesel, wax, salt - a bunch of different things comes out of the refinery. And it’s the same for data - we have insights coming out of analytics that can fuel marketing allocation, trade funds, new product development, market development funds.

 


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Topic 2: What?

What does insights-driven mean?


AI: Next to data, what else is needed to be insights-driven? What does an organization need to have?

AL: Well, there’s a lot, I could talk about that for an hour! So, I’ll give you my three snapshot points, and they’re all quite different. If we go back to Wes’ analogy of drilling for oil, you need people who know how to drill for oil, and you need people who actually know how to refine data, and they’re not the same thing.

WN: Or it explodes! It’s a big fire!

AL: Exactly! So, if you can’t get that within marketing, you have to find them within your organization or look outside - I found there are plenty of people around who are willing to help you with lots of data. They normally want money in some other form for advertising or something else. But there is a ton of great people around, like Adverity and others, who can help you.

The second thing I think that many marketers are challenged with is, marketing today has been heavily isolated as a communications discipline, not as a business discipline. And that’s a real tragedy because marketing was always intended to be a true business discipline. So, I think marketing leaders need to renew the curiosity within their organizations for the business, for the customer, for the business model, for what the actual outcome of the business is. Not for what ad campaign or what creative should we do next. I find the perpetual discussions around creativity just banal because I can’t tell you how many lousy advertisers are running brilliantly successful businesses because they’re all very data-driven.

That’d be my second point, the sort of a renewed curiosity in marketing for what marketing is really about - the business they’re in and the real customers.

I can’t tell you how many lousy advertisers are running brilliantly successful businesses because they’re all very data-driven.

The third point I’d make is I think marketing has sort of philosophically got lost. And as it’s philosophically got lost, it has lost its focus on insights. If you look at the two schools of thought out there today, you got Ehrenberg-Bass and that gang at one end of the spectrum advocating a tame view of the market, right. There is only a market, everyone is a buyer. Well, that leads you to use data in all kinds of exciting ways because you are marketing quite differently than if you were at the Mark Ritson school of thought at the other end of the spectrum, which is heavily weighted towards refined segment target positioning.

And in the middle, you’ve got this cacophony of white noise based on the Effie Effectiveness Index and things like that, that anyone in data science would immediately prove to be total garbage and you should just stop reading those reports because they’re rubbish.

It’s amazing to me how many marketers I sit with and when I’m interviewing them, very senior CMOs, I say, ‘Tell me about two really interesting pieces of academic research related to marketing you’ve read in the past year’. And they’ll say ‘I read Seth Godin’s latest book or I watched Gary Banner Chuck’ or something and I’m just like your marketing is a discipline. You should take it seriously.

Accountants can’t be accountants without getting accredited every year. Doctors have to get training every year. But somehow marketers, we can go to school between the ages of 23 and 24 and just stop learning from that point forward. So, I think my third point is renewing a deep-found belief in the profession of marketing and taking the right philosophical lens that will lead you to use insights to support that execution model.

Businesses have a business model, marketers have to have a marketing model, and that will lead you to demand insights that prove your model, not the effectiveness of your advertising, but to prove your model, your business model. I could keep going all day on this, but those are three insights to look at.

AI: Great points. John, how do you see that?

JV: I would completely agree with Andy’s points. I think in terms of building a culture where curiosity is at the core. You want curious minds to help diagnose problems and opportunities. And again, I think marketing very much has become a communications discipline and it’s really interesting.

When I think about performance marketing, for example, if we’re bidding on Google Ads or social media as a platform business, if we see our campaigns starting to deteriorate in terms of their performance, it might be because we’ve used the wrong ads, or the ads are not working quite as effectively as they should do, or we potentially linked to the wrong landing page.

Or there could be a bigger problem deep down in the actual product experience or the availability of a particular product. And I think the biggest opportunity is exactly as Andy described, to make marketing a business discipline again and use data as the connective tissue to make sure that everything you do is aligned.

In terms of building a culture you want curious minds to help diagnose problems and opportunities.

So, for example, if we’re bidding on customers looking to buy a brand-new Ford Focus, our campaign might start to deteriorate. There’s low availability of that particular model in a particular part of the UK. So, I need to connect the supply side of our house back to the demand side so that we understand how much we should bid on a particular piece of media or potentially should we reallocate some of our spend to a different campaign, depending on what’s actually happening in the business.

It’s those curious minds I suppose that start to make those connections within the business again, as they probably would have been written down 30 years ago. I think we’re starting to build those connective diagnostics back together, and that’s the opportunity.

It’s really hard to do from a data perspective but that is a huge opportunity to run marketing as a business discipline rather than just ‘Let’s throw comms out there and hope that they actually work and if they don’t perhaps there’s something wrong with the ad creative or the media buy’. Well, obviously, diagnosing the effectiveness of a piece of marketing is far more complex than that.

Topic 3: Culture

Creating a data-driven culture


AI: How do you create a data-driven, insights-driven culture in an organization. Wes, when you created MarketShare, how did you create that internal culture?

WN: That’s what’s interesting to me is the importance of culture. As I’ve gotten older, I’ve now done four CEO jobs and three public company boards, and dozens of private company boards and it literally starts at the top. The CEO has to actually get this stuff.

I’ve been in companies, visiting companies whose CEOs don’t have a computer on their desk. They still have their secretaries print out an email for them to write a response on the bottom to be typed back. I mean, you’ve got to run screaming from a business like that if you work for such a company because they are an endangered species.

And I think that the board and thus a CEO have to be data-driven. They have to see a vision of how to disrupt their own business - how to challenge and reposition the business. And to Andy’s point, marketing, and even marketing science, which has been a discipline, marketing science has been a bit of an oxymoron for a long time. If you think about these super simplistic media mix models that I observed back before launching MarketShare and I kept putting myself in the shoes of if I were a CEO of a big company and I’m trying to make decisions on this crap - I’d be out of a job!

The board and CEO have to be data-driven. They have to see a vision of how to disrupt their own business - how to challenge and reposition the business.

Do you remember the old Apple Maps that came out on the iPhone, that it was just garbage compared to Google Maps? And everyone stopped using Apple Maps because they kept driving into the lake because Apple Maps kept telling them the wrong thing. It’s the same thing with bad insights. Not all data, not all data science, not all insights are created equal. And you get what you pay for - that’s the other thing I’ve learned in this world.

You see some really horrible and I’d even go as far as calling it a malpractice level of insights that I’ve seen companies making decisions based on, and it had a negative, materially negative, impact on their business. So, I think a lot of that comes down to is whether the culture of the company is truly committed to excellence in insights and driving data-driven insights.

Actually, I’ve just finished my third Harvard Business Review article, it’s in edit mode right now and Andy is going to be featured as well, and the theme is; okay, now that you have your data and analytics at least you’re starting to think about this, now what technology stack do you need to have inhouse that is geared to helping you be always on, always listening? How do you marry marketing and customer experience together into one singular viewpoint? Because that’s what’s happening. That’s what consumers expect. And what I’m finding is that the companies with the CEOs that get that, are the ones that win and the companies that don’t have that, lose.

 


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So, to answer your question in a very long way, at MarketShare it was self-selective. I mean, we were obviously data-driven, analytics-driven, and if we hired people by mistake that weren’t that way, they were gone very quickly. The culture and the hunger for innovation were key. My co-founders were academics and by having this academic curiosity of ‘Hey, what do we do next? How do we disrupt ourselves? Someone is going to be doing something better than this in a couple of years. What do we need to do to disrupt ourselves?’ - that’s a hard thing to do, particularly when you start making a lot of money in something that’s working.

But you constantly have to challenge that, because for everyone on this call today some kids are sitting in a garage coming up with something that will completely destroy your business. So, once you get ahead of it, figure out what it is. And I think that comes from the culture. At the end of the day, you need data and you need insights to have a seat at the table, to inform, and ultimately make professional decisions. Like an airplane pilot, I don’t view the technology or data replacing the decision-making. I see it as an augmentation of that. That’s where I think AI is like augmented intelligence, as opposed to replacement technology of just pure AI. Because, at the end of the day, these are hard decisions to make. Just like a pilot, you have avionics, and you have traffic collision avoiding systems and other technology that’s giving you information all the time. But, at the end of the day, it’s the pilot making the ultimate decision, the same with the businesses.

AI: I couldn’t agree more in terms of it needs a top-level commitment, but I think even more it needs this kind of attitude to challenge the status quo, and disrupt yourself because that gives you this edge to drive excellence. Just saying I’m committed to insights-driven is not doing the trick. What do you think, John? What does it take to create a data-driven, and insights-driven culture?

JV: Again, I agree with the points that have already been made and I think there are a number of constituent components. First and foremost, hiring those curious minds as well as having teams that can collaborate and work effectively. Most organizations are heavily matrixed and they tend to all have a set of KPIs for their own particular department or the problem they’re focused on solving. But I think, trying to build an organization where everybody understands the KPIs and key metrics for the entire business, and how they can drive and contribute towards those, is fundamentally important.

And those metrics should be used on a day-by-day basis. We meet every single morning. We’ll spend 10, 15 minutes just walking through the metrics. And if we can see something is starting to move off trend or if there’s an opportunity emerging, we quickly dig into the underlying data and the underlying trend.

Again, you need those that are curious, that can see the opportunity or can see the threat and can take action quickly. And one of the things that’s key to culture is that agility, the desire to make a change quickly rather than sitting on a problem. And again, I always describe when I started working back in ‘95, you get to the end of a quarter, and you’d have a QBR (quarterly business review), and you review everything that you’ve done and learned over the past of the quarter. Well, this is what we learned, these things went not quite to plan so, we’re going to do something slightly different next quarter and the quarter after that.

Data is not there to help you make fast decisions. Data is there to help you make the right decisions.

I encourage my teams not to do that. If we find a problem in a quarter, let’s change direction quickly and adapt to the circumstances and the learnings. And then when we have our QBR, and we still have our QBR, we say this is what we learned and this is what we did during the quarter. And that I think is a very different culture that allows you to really drive a competitive advantage at pace.

Going back to Wesley’s point, there’s competition everywhere and speed to market and agility is one of the biggest benefits of having that data-driven culture that you actually do something with the data quickly. Not to randomly go off strategy but sitting within a strategic framework where you have enough latitude to make changes as you see fit. That just breeds smart people with huge amounts of intellectual horsepower who will deliver brilliant results in that type of environment because they have autonomy, they have the ability to make an impact. We all talk about how do people have an impact on a company, well, this is exactly how from my perspective.

AI: True. You need to create a culture that appreciates the possibility to accelerate and get ahead at the end of the day. And if you don’t appreciate that, you might not be the right person for the job. Andy, what do you think is required to make it work?

AL: I think there’s a few things. I think the first thing marketers need to do is take responsibility for their own careers. If you’re in a company that doesn’t get data, thinks marketing is all about ROI, thinks marketing is based on what the next quarter’s sales are going to be - you need to get out and go work in a real company. Because, companies like that, deserve the marketers they deserve and it ain’t you.

My second point is data is not there to help you make fast decisions or help a business move faster. Data is there to help you make the right decisions and to enable you to build really interesting hypotheses. Data is not there to try and explain why you deserve more or less money. It’s there to explain why outcomes are occurring and how you can achieve better outcomes. And once you get that shift in mindset going, all kinds of weird things happen.

I remember sitting at Xero in London with my head of marketing over there at the time, and said, ‘You know, this Google stuff just doesn’t work, right? It’s just a drug. We are just addicted. We’re just spending and spending and spending. I want you to imagine that I cut Google by 50-60% and gave you all that money to do home advertising and radio. What would you do? How would it work? Do you need 50%? Would you need 50% plus another 50%? Tell me what you need.’ And he went away and worked on it.

We weren’t necessarily going to do it, I just want to know could we do it, what would happen. But, we assembled a lot of data and did it and sales went up, you know, ridiculous. Like double, triple digits, and everything was good. The hypothesis worked. Now, the hypothesis might not have worked as well, which is interesting, but we were in a position where we could recover and walk it back if it wasn’t working because you know what? You can turn Google on and off, but it’s a lot harder to turn out of home on and off.

You believe you should be generating insights for your organization? Ok, open up your calendar and show me how many meetings you’ve got that are about data.

So, I think the second point in making it work is to understand that you’re trying to create an environment where you’re building hypotheses, endlessly testing and learning. A really helpful tip if you’re a marketer and can’t get access to data is to just imagine you do have the data. Just imagine; we have this data and it told us the following, what should we do? And then go and see if that data might exist, because you’ll be surprised how much of the time you’ll find that data actually sitting there.

I call it the Volvo car thing. Someone will say to me ‘I bought a Volvo’ and then all I do is I see Volvo cars driving around for the next 10 days. I think you need this sort of retraining the brain to understand it’s not about ROI, it’s not about justification of marketing, it’s about fundamentally changing the trajectory of the business as a whole. And if you adopt that mindset, you start concocting weird hypotheses and suddenly start seeing data that might have been there all along.

I worked with Wes when he was at MarketShare and it was revealing the weird stuff they started throwing out of data sets for us. It was like we’d always done mall advertising in Queensland, Australia and all the data pointed out the fact that mall advertising was a complete waste of time. And we should’ve been doing it out of home and around the airports. You know, no one ever, in 30 years, at that bank had said stop doing in mall and do more out of home.

So, you need people who just challenge things. There’s a great quote from this guy Jim Rohn. He’s one of these motivational speakers and he says, ‘There are only two things that hold us back in life, the routine of the present and the regret of the past.’ I’d argue that data is the weapon to challenge the routine of the present and all of those around you with the regret of the past. So, it’s a long answer again, but those are some of the things I would look at.

AI: So, it’s about facilitating a culture that challenges status quos, gives you the ability to run experiments, test hypotheses. Having the willingness and the team aspiration to try out new things. Challenging the status quo and not saying, ‘Yeah, we’ve always done it that way, that’s why we keep doing it that way’. Having this insights-driven culture leads you to challenge, you always try to advance and you always try to accelerate.

AL: John made a good point I think. People talk to me a lot about culture and I completely agree with the whole culture thing. But I also don’t buy the whole culture argument around anything can’t survive without effective culture. Much like Wes does when he looks across an executive desk and goes, there’s no computer there, there’s a pad of paper, and a lot of emails he’s scribbling on, leave the building right now, we’ll never sell to this person.

What I say to people is ‘Do you believe data is really important? Okay, do you believe you should be generating insights for your organization? Okay, open up your calendar and show me how many meetings you’ve got that are actually about data and how much of that time are you drilling into and trying to understand data. This week? Ah, there’s no time. What about next week? There’s no time.’

So, it’s actually not the organization. It’s not the culture. It’s not any of that. You’ve decided that it’s not important. But you went to a conference or attended a webinar and you suddenly had an epiphany. But I promise you, within two days you’ll all settle back into the routine of the present and the regret of the past. Unless you actually go, ‘right, from this point forward four hours of my week I’m just going to go hunting for data, exploring data, analyzing data’. Maybe not even inside the organization, it could be outside the organization. But, as soon as you instill that simple discipline as an individual, everything starts to change. I promise you. Everything will start to change.

Go and hang out in businesses that you think are using data cleverly for two or three days and see how they’re doing it.

WN: So, what I think you’re talking about Andy is initiative. I mean, many of the people on this call are not going to be able to go in and change the CEO or change the culture or anything like that. But, what they can do is show some initiative. Read a book on the weekends about this stuff. Maybe think outside the box and go, ‘Hey, maybe I’ll try something. Or maybe I’ll see if I can maybe bring in an intern who’s a math person and put them to work and see what they can come up with’. You know, all low to no-cost activities. And, oh, by the way, maybe you should push for change. Little change.

I’ll give you an example. In my very first job, I was helping launch a bank called Capital One in the States. And in the marketing department, there was no fax machine. And this was early on, obviously well before email, but I knew fax machines were pretty logical. And I just kept pushing for it. Let’s go get a fax machine. I’ll take care of it, I’ll go buy it, I’ll set it up. Let me just go do that. And, in the end, we finally did it and it completely changed my workflow. It made me look more productive because I got more work done.

Then in the first company, I was working for after that, email. I set the first email account for the company in 1990 because it was just logical. Like why write stuff up by hand when you can have your email? Just like stupid stuff, but I look back now and think, that was me practicing some initiative and being a tech weeny, trying new things that really were easy, something I could do within the sphere of my influence, but that actually had a material impact on making me perform better, therefore making me look better in the organization.

And I think anyone on this phone call could do something like that. Let’s start gathering data. We’ve got all the devices. We’ve got a lot of data. Are there some interesting little things that we could try to experiment with that could help make our life easier or smarter or more efficient? And that will help us shine from a career standpoint.

AL: I think John had a key point, which those of us that are in growth businesses, like John and I’m in, we have daily stand-ups. So, I’m looking at the digital person. I’m looking at marketing. I’m looking at the people running my Salesforce for instance. And I’m like one step away when someone goes, ‘Gee, we didn’t see a lot of leads yesterday’. ‘Well, was the website with the lead forms working?’ ‘Yes, the lead forms were working’. ‘Great. Marketing person, what happed with our paid and displayed yesterday?’ ‘Yeah, we had some problems.’ ‘Okay. Well, there we go! And what are we going to do about that?’

So, you’ve got to create a safe culture where everyone is comfortable saying, ‘Yeah, it broke yesterday. And I was at the wheel when we drove the car into the wall.’

So, I think what Wes and John are getting at is just look for minor shifts and you’ll get there. I don’t think enough marketers go exploring other businesses. Go and hang out in businesses that you think are using data cleverly for two or three days and see how they’re doing it. We’ve all got open doors. Right? We’ll all help each other out.

Topic 4: Barriers

What are the barriers to success? 


AI: John, in your journey from Farfetch to Carwow, what barriers have you come across in building an insights-driven culture?

JV: I think both Farfetch and Carwow are businesses built on data. And they use data to better serve their customers, their platforms. So, their customers are end consumers buying products and also the wholesale and brand partners that are distributing on either Farfetch or Carwow platforms. I think probably the biggest difference is about our position on the maturity curve. Carwow is a smaller business than Farfetch. Farfetch is around 4,000 people, there is expertise in every single team that is using data to aid decision-making and even automating some of those decisions now.

So, using data science to proactively make a decision using a machine rather than a human. And those capabilities are built into every single department. There’s no single analytics department or data science team – they are built into the product team, into the marketing team, into the HR team. Carwow is not yet at that stage because we’re a smaller organization. So, we’re having to have a more of a central service approach if you like. And I think that does impact the culture a little bit because your pace of change isn’t quite as fast as it might be.

But for both, just the type of people we’ve hired and are continuing to hire have that hunger for building businesses on strength of insights and starting to build connective tissue between all of the different functions and the different data points, that’s the bit that I’m really interested in because that’s where you see tremendous value. The other point I was going to make goes back to one point that Andy touched on around leadership in this environment – and that’s around humility.

As a leader, you want somebody to be able to stand up in a meeting and say, ‘I found this and I made a wrong turn’ and be open about that. As long as we learn from it and we optimize and build on that learning, it’s totally fine to say, ‘I messed up here, I was trying something or something didn’t work’. And I think as a leader that humility is important.

As a leader, you want somebody to be able to stand up in a meeting and say, ‘I found this and I made a wrong turn’ and be open about that.

The other thing for me is now I’m getting old and a lot of my ideas are sometimes the crappiest ideas in the room. And often the data proves that. Again, having that humility to say, ‘it’s not a good idea, let’s try some new things’, it’s really important. And that is quite different to culture from even the last decade.

Humility is so important because the data will also shine a light on some of the decisions that I’ve made that perhaps weren’t that good. And I’ve got to stand up and say, ‘I failed and I’m happy to admit to that’.

AI: Andy, from your perspective, what are the typical barriers you faced when you need certain digital transformation projects around those cases?

AL: I think the biggest barrier is in organizations that haven’t adopted some kind of philosophical standpoint on where data fits into their strategy, decision-making process, or their business model. So, they’re all over the map, which means one day the data says it’s going to be sunny. The next day the data says expect earthquakes. The next day the data says to expect a tsunami. The next day the data says there are polar bears on the lawn. And so, every day it’s something new, you know. They’re just all over the map.

It’s a real challenge to assemble data if you don’t have some kind of overarching hypothesis. So, an example there would be, you know, working with a major bank that was assembling all those data, massive amounts of data, signals from customers, both in their base, but outside of the base. The cool thing about data is you can look really, really clever in front of really important people without actually being clever.

The downside of that is you don’t actually understand how stupid you’re being in your decisions until it’s too late. So, being clever because you’ve spotted something doesn’t mean that it actually matters, you know. And I think that’s another phenomenon you’ve just got to watch.

It’s really hard if you don’t have the philosophy. And a philosophy needs to be centered around moments. We need to be mining for moments, moments that then trigger downstream decisions. And if we can identify moments, we can then architect to meet the customer in the moment before they quite realize they need us, but we can see they’re going to need us. And that changes everything. So, you’ve got to have that philosophy. You’ve got to be able to assemble mountains of data and mine it against the philosophy for what you believe informs those downstream decisions.

Marketing is an operating expense. If you look at the most successful IPOs of the past 24 months, if you look at their sales and marketing expenses, they are running at about 100% or more of revenue.

Otherwise, you just end up with this cacophony of decisions trying to be made all day long. And it’s like oh, a polar bear! Oh, a tsunami! Oh, umbrellas, we need umbrellas! And, you can’t run a business this way.

The final point I’d make is, frankly, the amount of education that has to go on with CFOs that marketing is not about ROI.

Tactics within marketing need to demonstrate ROI. Did that campaign work? What was the return on it? Within campaigns or certain media spend, you might go looking for ROI. But, marketing is an operating expense. If you look at the most successful IPOs of the past 24 months, if you look at their sales and marketing expenses in their IPO filings, they are running at about 100% or more of revenue. 100% or more. These are companies that are delivering the most value back to shareholders.

How does that work when most people sitting in marketing are running at 3% of revenue? How are you going to compete with someone if they’re spending 150,000 times more than you? That third barrier is really real. And if anyone wants them, I can send you some great papers that have been written on why ROI is dead. I’m happy to share them with you* because I think the re-education of those core business functions around the board as Wes alluded to, the CFO in particular and also the CEO, is crucial. And if not, you have to go work for a guy like John.

Topic 5: Round-up

Final thoughts and key recommendations


AI: As we come to the end, what are your concluding thoughts? What is your advice for businesses and marketers?

WN: Andy and I both know another CMO friend named Denise Karkos who used to be the CMO of TD Ameritrade and now of a company called SiriusXM Radio. And I remember Denise was one of these very competitive, very inquisitive people, she just wanted to keep on finding new insights. Her board was comprised of a bunch of hedge fund people and her goal was to out-finance them in board meetings when she went in there to talk about marketing.

She knew that when she went in, they’d all be thinking, ‘okay, here’s some fluffy bullshit about brand value, sentiment, NPS, clicks, likability.’ But, she went in there talking about returns on invested capital and things that actually mattered to the business and this helped elevate her organizationally because she knew what she was talking about.

The same with Kristin Lemkau at JP Morgan Chase, the CMO. She did such a good job of doing exactly what I just described that she’s now the CEO of JP Morgan Wealth globally. If you zoom way out and ask, how many marketers do you know who’ve become CEOs of big companies? It’s shockingly low because of what we’re talking about. So, if you really want a career in this, it’s really important to embrace that.

But, the last point out I want to make, is that marketing shouldn’t be the only area you think about data. I mean, there is data for every decision and organization. One of the big automotive companies I worked with actually uses analytics and data to make decisions on what factories to open, what their forecast is, what they report to Wall Street, which country should get what level of investment, where the next dollar or euro or pound should go.

So, it’s super strategic if you get this right and you have the right capabilities in place.
And that gets back to having the right data stack, the right technology, sucking in the data from all the different places, and remembering that you can go broke saving money on analytics. I can’t say that enough!

Go and invest in people around you who are really smart at this. Take a chunk of your budget and say, ‘What can you guys help me with?'

 

AL: First, go and learn about data. The brilliant thing about education today is the world is flat. There is no end of amazing data courses out there. When I first met Wes, he exposed to me how little I understood about data. And so, I immediately went and enrolled in all kinds of courses, MIT, Stanford, all kinds of things. Just these sort of executive program things. And, man, it just blew my mind. So, go teach yourself everything there is to learn.

Second, reach out. Reach out to people like Alexander, to John, to Wes, to me. Reach out to others. Do staycations for three or four days. See how others are doing it, adopt their methods. Learn like crazy.

Third, it starts with you. So, just set aside time to go do it and mess with data. But don’t get despondent with the organizational malaise that will probably surround some of you in terms of the slowness of organizations. This takes time.

The people that have been trying to solve data in most of these large businesses have been at it for 10 or 15 years and they’re only about 25% of the way there. It takes time. Just breathe. Understand it’s going to take time. Every month you discover something new. You start building a system of record for data. You start building new data relationships inside your business. You start networking around third-party and outside data sources. It makes a difference.

And my final point is, probably the best investments I’ve made as a CMO with data companies have been going and working with people like Wes or Alexander. You know, go take a chunk of your budget and say, ‘Look, I only have this much, but what can you guys help me with?’ Just go and invest in people around you who are really smart at this. If nothing else, the learning gets more valuable than the outcome they may be able to deliver to you.

JV: I think Andy’s advice is great. I think the one point I’d make is, sitting in a high-growth business, my philosophy is if you want to grow next year at 40, 50%, then you need a huge arsenal of incremental initiatives in order to do that.

So, to Andy’s point going, learning, sitting down with people that are doing it, taking inspiration and ideas, and then just get started.

A really important point, you’ve got to try and do some of this yourself in terms of getting stuck into the data. And that doesn’t mean you’ve got to sit learning SQL or Python, but just start to dig into what it can do and set some meaningful targets that you want to hit.
But start with a small problem. And then as the organization builds capabilities and know-how, keep pushing for more.