Digital Intelligence: The Strategy For Engagement Success With Data, Analytics & Tech

November 6, 2017

Digital Intelligence: The Strategy for Engagement Success with Data, Analytics & Tech

Slide 1:

John:

It’s an honor and privilege to be here today and to participate in the Analytics Summit. I’m happy to be here. I couldn’t be more proud and pleased to welcome James McCormick from Forrester with us. We’re excited to have you James. You’ve spent many years as an analyst and are really an expert in the start of digital intelligence. Both you and I have seen things evolve over the years, myself being one of the cofounders of Omniture back in 1996. Then after selling Omniture to Adobe, to starting ObservePoint with my current partner and CEO, Rob Seolas. It’s been an incredible ride.

If you think about it, it’s been a huge evolution of technology over the last 20 years. I was wondering, maybe to get things kicked off and started, if you can speak to the evolution of digital intelligence and the aspirations companies have had to be data driven?

James:

Yes, John, and thank you so much for the introduction. It’s very kind of you to invite me today. I’m excited to be presenting alongside yourself and ObservePoint around a topic that I’m really quite passionate about. It’s nice to be able to talk about what I call “Digital Intelligence.” It’s really just digital analytics and how we use that to optimize business decisions and our relationships and engagements with our customers. The subtitle for what we’re going to be talking about today is “The Strategy for Engagement Success with Data, Analytics, and Technology,” and thanks so much for that kind intro, John.

Slide 2:

No great discussion around customer data and intelligence of customers, especially when it comes to them engaging with us, goes without at least a small mention of the age of the customer that we all know we’re in. Just take a quick pause to remember what that is. It’s the age when the customer is more empowered than ever before to engage with us and it’s them, not us, that are dictating that narrative. It reminds us that one of the key things we need to master in this age of the customer is—to entice them, to get them to do the right things for themselves, and for us—we need to be delivering the right experience and the best experience.

And if we’re going to do that in this digitally transforming world and at scale, you really need to be harnessing the information we have on them at these moments of engagement to understand what it is they are and what it is they want. We need to do it at scale. Hence, the need to be good with data, good with the analytics generated insights, and good at delivering these great experiences that we kind of already mentioned. That’s kind of where digital data, customer data, and the age of the customer really meet, is at the point where the data informs those experiences that we want to deliver to entice them.

Slide 3:

Let’s take a quick moment to see how we are evolving around the customer data and the digital data that we have on our customers. If you think about it, it really starts for me in the 1990’s when we had these weird world wide web pages that we would dial into our modems that made this kind of weird kangaroo noise over a period of 30 second, and if we were lucky, a page would download. I remember the first time I saw this and I thought, “Jeez, we are in the digital age.” Because this was the first medium that brands were en masse engaging with customers, or at least from a brand perspective anyway.

Back then, as companies, as enterprises, we didn’t really know what this meant for us. And what does success, from a customer engagement perspective was really perceived through these web server logs that we had. John, as you can remember when you were starting up your companies back then, it was just the amount of hits on the server that really seemed to be of any interest to us as the IT department running the website. I guess it was in the early part of this century, the early part of last decade, when enterprises and brands started taking their websites more seriously and their email engagements more seriously as a real way of understanding their customers during moments of engagement.

And John and a lot of others started up what we call web analytics trend where these vendors and their tools understood these customers where they were engaging with the browsers and their behaviors as they went through the page and where they came from. What became really interesting, it was around about the mid 2000s, when social became more of an interest to brands. It was not just about what customers were doing on our sites, but what they were saying about our properties. And linking what they were saying on the social sites to their behavior on sites became really important. Or what their search terms were on the big search engines out there became more important. So, this kind of link between search engines, between ads, between social, and to our websites became important in this age of digital analytics or digital behavioral analytics came on us.

It was the beginning part of this particular decade, the teens so to speak, where we wanted to start to act upon that data and those insights at the speed of customer. It was really hooking in our optimization systems or personalization systems and our testing systems with our analytics and those programs really started then. And we still are, en masse, really good at that, but we are really in this age of digital intelligence, where we are at the speed of customer engagement using what we know of their engagements to optimize their experience.

Slide 4:

We are basically exiting what I’ve termed the “Golden Age of Web Engagement,” where for a brief moment in time, we felt we understood our customers on our digital front-ends and we use that understanding to optimize our engagements. Today, it’s been broken, that perception, mainly because mobile engagement has become far more complex and become a more important part of our businesses in driving not only marketing, but product services, etcetera. It’s not just mobile of course, there’s this kind of on-the-horizon opportunity or threat of IoT, of engaging customers on those things that are becoming more important. How do we understand our customers then and how do we act upon that?

Slide 5:

We’re in this age of digital intelligence. What we’ve done at Forrester is look at firms that are really good at being digitally intelligent with their customers and not only using analytics-driven insights to drive great experiences, but they use data and analytics across the entire enterprise to gain competitive advantage. What do we mean insights from business? Let’s look at why these guys are important, why we should even think about them. At Forrester, in a recent report, we looked at firms like these startup firms that we characterize as insights-driven, and we found that they had an annual compound growth rate of around 40 percent. And when we looked at it from a publicly-traded perspective, which tend to be these larger firms, that there was still a hefty 27 and a half percent annual growth on their revenues.

When you think about the global GDP at 3.5, these guys are taking the money away from somebody. At Forrester, we really believe that money, that revenue, has been taken away from the rest of us who aren’t insights-driven. By 2020, these folks will be driving in the order of 1.2, 1.3 trillion dollars per annum, and that revenue is essentially going to be taken away from those who aren’t.

Slide 6:

So, let’s talk about the rest of us who aren’t really what we call insights-driven. There’s no doubt that all of us—if I speak to any kind of VP or Chief Digital Officer or anybody in any of the enterprises I speak to, we say, “Do you want to use your data to generate value?” The answer is yes. Many of our surveys show it. There are ones that say almost three quarters or organizations aspire to be data-driven or want to be data-driven, but in reality, what we find is we’re not good at turning data into action.

Slide 7:

At first, we call that the “data-action divide.” Some of the reasons why we aren’t good at it, is we really aren’t good at the management of data.

Slide 8:

Here’s a report that talks about some of the findings we found in our data readiness survey where we asked data management professionals: “Is data management really important to you and to your business?” what we found is, yes, of course, it’s really important. It’s at least between somewhat important and very important. But actually, you find in the response, on that vertical axis, is: “Do you do this well?” And the answer is between well and not well at all. Nobody is feeling that they’re doing extremely well at this, whether it’s from measuring these data management programs, whether it’s the processes we have around them, whether it’s the technology and the data delivery and use around it, whether it’s around business alignment, we’re just not doing very well, even though we need it to be really important.

Slide 9:

We can find that many of us or most of us, really, are not insights-driven, and most of us are still trying to find that digital intelligence, that ability to link data to action, especially at the speed of doing business, and at the speed of customer engagement.

John:

Those points that you bring up, it’s exactly what we’ve seen as well. While we’ve built the ObservePoint platform to really kind of solve the pain of the data quality issues, so many companies feel the failure in their data management and their data governance practice. We’ve found many of these leaders across the enterprise are uncertain how to continue on or mature their digital intelligence or data management practice. In a report that you published recently, you discuss this extensively and you talk about how mature data intelligence and data governance practices scale in an organization. James, can you talk a little bit about that idea with us?

James:

Yes, thanks for bringing that up John.

Slide 10:

And actually, in that particular report, what we did is we looked at what we call these insights-driven businesses that we talked about. We asked them what they were doing differently to the rest of us. What was is that they were doing? There were about six different things, especially from a digital customer engagement perspective that these insight-driven businesses were doing differently.

We’ll summarize them here in this slide. What we saw was, first of all, that they had a strategic approach when it came to the use of digital data to understand the customers and use that understanding to optimize whatever it was that they were doing. What does this strategic approach mean? First of all, is they ha this top-down approach where their leadership was invested. When I say leadership, I mean not just the CMO, not just the CIO, I mean the CEO, I mean the product teams, the support teams, the business teams were really guided from the top down to leverage what they knew of the customers to optimize whatever they were doing. Most importantly, they were coordinated around centers of excellence, around an approach to data in the way that they were organized. That was the first, and one of the most important differentiators we found.

We found that from an ownership perspective, the way that they organized and the skill that they had and the way that these skills coordinated with each other around digital intelligence were something special and different. And we’ll talk about that, but essentially, they were coordinated in their approach. Thirdly, they were really great at data management. They like the rest of us, saw that data management was important to attaining their business objectives and they weren’t only aware of the important, but they also had the skills the ownership structure, the leadership support to be good at it as well.

One of the other things is that they measured what was important. Many of us—and John, I’m sure you guys are aware of it—many of the enterprises I deal with on a day-to-day basis have got these—I wouldn’t call them legacy tools—but these tools that they’ve had for a while now that they have just used to measure stuff. It’s almost as if they’ve just out-of-the-box started to measure stuff. They didn’t realize nobody’s taking a concentrated look on; is this stuff I’m measuring really important to my business today. Really what these insights-driven businesses are doing is they’re good at keeping on top of it. They measure what is important to their business and they measure what business success is and they move towards that.

Fifthly, they had the right technology, but also more important than that, they were coordinated around the use of technology. They synchronized their procurement of the various product they bought so that the sum of the parts was greater than the parts themselves. They were really good at that, and again, we’ll talk about that later. Then ultimately, they realize that the whole point around technology and the data that we have about our customers, is to essentially optimize whatever they’re doing to improve on whatever it was the business what trying to achieve at their first touch points or decision points and to continually do that. That’s why we call that optimization. It’s that continuous optimization and that capability.

Slide 11:

So, as I mentioned, I want to talk a little bit about how these insights organizations are operating differently to us. Many of us, when it comes to the coordination around analytics and especially digital analytics, we’re on that bottom left-hand side organization methodology. We have these kind of businesses, each owning their own particular analytics around each particular channel that they engage the customers with. What do I mean? You might have one of your businesses or brand have their own email capability and their own web capability or say mobile app or social analytics capability. Then some of the other teams have their own organizations. Typically, you see this in large—CPGs can go this way, some of these large multi-brand retailers go this way. It’s really not great because you’re spreading the expertise around, and in terms of understanding your customer, the data management capabilities around that and dispersing it. You don’t have any sort of coordination.

That middle organization is where you have various organizations and lines of business that hook into the same email analytics team or the same social analytics team or the same web analytics team or the same app analytics team. At least there is some sense of coordination there where the email center of excellence will guide the standards around email, the web around web, etcetera, but there’s still the lack of coordination.

Where we find the insights-driven businesses do really well is that top right-hand side where we have the centers of excellence or at least a central coordination around the various lines of business and around the various analytics teams. They espouse best practice, they provide training, they define standards, and they coordinate wherever possible. Some of these huge organizations, clearly there’s going to be some differentiation in the way that you engage and understand your customers and even in the technology stack, but at least what these do is they optimize wherever possible and they make maximum use of their technology and their data and insights. From an organization perspective, that is one of the things we’ve learned over the last three or four years whilst analyzing these new types of home that we see.

Slide 12:

The other thing is that they took a pretty holistic view when it came to measurement and KPIs. Many of us will measure, say our web analytics, for instance the visitor and what the visitor is doing, their behaviors, and then we might look at the lifetime metrics and we’ll look at perhaps some kind of conversion metrics or voice of the customer metrics. And we’ll have all these things, but all these metrics and KPIs and this data flow are in isolation. What these insights-driven businesses do is they’re really good at balancing off and bringing together their KPIs and metrics around this formula that we see.

They realize that the perceived value that they provide their customers and the firms and the brands are really a function of the experiences that they provide their customers and the expectation of their customers during those moments of engagement. What they really are good at doing is seeking to create this holistic view of all the metrics that they collect and all those KPIs that they measure the success of the business on and bring them together. That was one of the great things that they did.

Slide 13:

Then just as a tactical perspective, they were really good at managing their technology and managing their data layers, etcetera, especially on their websites around things like tag management, just some of the basics they did very well. They managed those scripts, those pixels on their sites very well so that they deployed the technologies correctly and in the r right kind of way. Also, they standardized their data layer, those tag managers standardized their data layer. One of the good things about having a good tag management capability or practice is that it improves the quality of analytic that you provide just as much as the insights you provide. Just the basics they were doing well.

Slide 14:

And actually, at Forrester, one of the things we find is that there is almost a maturity curve that aligns with our digital intelligence maturity. As we mature our tag management practice, we find that firms have an ever-increasing maturity around digital intelligence. So just doing some of the basic right can go some way in helping us advance and try and become an insights-driven firm.

John:

That’s really interesting because obviously there’s been a huge increase in the marketing tech stack. You see from what used to be just a few hundred vendors offering technologies to now thousands and thousands of companies. There’s a lot of noise, I think, out there in just the solutions. I think you can talk about, or just as a question: how you see that stack fitting together and helping people achieve success with all the noise and everything else that’s out there?

James:

That’s one of my key passions at Forrester is trying to track the technology, trying to make sense of it, trying to come up with a strategic view rather than this kind of tactical view that many of us have.

Slide 16:

One of the pieces of research that have come out of Forrester recently is this concept of linking our systems of engagement with our systems of insights. Obviously, with other systems as well like automation and systems of record. But really what I find is useful when coming up with an insights-driven strategy, especially around technology, is to think about all your systems of engagement—things like your ecommerce system, perhaps your point of sales, your websites, your mobile sites, all these systems, your social systems—anything that we have that engages with customer that are in some way digitized in terms of the experience we deliver and the data we collect. And if we say just as a general strategy, “It’s a really good idea if we hook these into what we refer to as our systems of insights.”

These systems that collect this information and make sense of it from a modeling perspective and analyze it for things like next best action, things like attribution insights, customer segmentation, etcetera. Imagine if we had a general strategy saying that going forward, we want to make sure these things stick together. What we find is that these insights-driven businesses are really good at having this top-down strategy, this holistic strategy to do this.

Slide 17:

What does that mean then when it comes to digital analytics and digital intelligence stack and the software and product technologies that are out there? First of all when it comes to the systems of engagement, what we do is we place these optimization capabilities that we have—so things like behavioral targeting, personalization tools that we have, whatever—but we can place that within the optimization layer, which sits within systems of engagement—things like online testing, A/B testing, multivariate testing, split testing, etcetera—all can fit within there because they’re all data-driven and they sit within the systems of engagement and they help us understand what the optimal engagement is.

And of course, ecommerce friends amongst us will be more familiar than most around the recommendations engines that we have that sit at the point of engagement and tell us what next product or best offer or content to offer the customer at that point of engagement bearing in mind we know who that customer is, what she has consumed in terms of content, product, etcetera. Those kind of sit there.

Then we have the data and analytics capability that we use in our digital analytics stack that sits with our digital systems of insight. Many of us will recognize a lot of these tools and approaches and practices. Things like if we look at the analytics here; app analytics, cross-channel attribution, digital performance management, sometimes referred to as APM, web analytics, voice of customer, special analytics, etcetera. Those all sit there. Underneath that sits our data management capabilities around customer data platforms, customer CRM systems, data warehousing around digital data, tag management, DMPs, etcetera. Those all sit there.

What we found is that the insights-driven businesses are really good at bringing all of these things together and from a holistic view in terms of the architecture that they’re trying to create.

Slide 18:

Just as an aside, one of the things we’ve done is looked at these various technologies to try to make sense of the value that they provide. And you can see—this is what we call a tech radar, where we look at the left-hand side, there’s that vertical axis, we look at the business value. On the horizontal, x axis we look at the ecosystem phase of a particular technology. You can see that for instance, web analytics is in an equilibrium phase, in other words, it’s stabilize out. It’s not in that exponential growth phase at the moment, but it’s still very important to firms as it’s in that equilibrium phase on the blue one, which by the way is the high-value product curve. Things like tag management as we mentioned earlier is on the medium value, it’s in the equilibrium phase as well, etcetera.

We’ve done studies as to the importance of these digital analytics and digital optimization, essentially digital intelligence technologies and the value they have. Just a few summaries from this particular slide, we see that web analytics, as long as it’s been with us—it’s been with us for decades now—is still really at the core of digital intelligence practice and it’s still a core technology. App analytics is complex and its use is growing, so what we mean by that is understanding customers as they consume apps is really more than app behavior or app store analytics, it’s also session-based replay, there’s a bunch of stuff—there’s actually about 10 sub-technologies just in that category alone.

Then of course, you see things like special analytics and IoT analytics on the creation and growth phase. These are new technologies that are still maturing that will become more important to us going forward. That’s how we try and make sense of the technology areas around insights, particularly, around insights and intelligence.

Slide 19:

John:

One thing that reminded me of, just as a funny little insight, you said that web analytics has been with us for a while now, actually you might not know this, but this last month, October was the 20-year anniversary of the great Super Stats, which eventually became Omniture, which eventually became Adobe Analytics. Just a funny little side note.

James:

Happy birthday to you John. That’s amazing. 20 years is not a long time, especially, as human beings, the digital engagement is really a new thing. But things have moved on and what I find is even the practice of web analytics has moved on, but when you look at what has become of Omniture today and the other vendors around that era, it’s very different. But still, at its core is digital analytics. There’s just a lot more to that.

John:

We launched with five reports, so it’s come a long way.

James:

Well done there. So just in terms of an overarching approach to all this stuff, what I find is it’s all very well talking about collecting data and managing the data, and that’s very important, as well as making sense of it and analyzing it. But ultimately, there needs to be an objective, a business object to all this. I don’t need to tell anybody this on this call, right? But the way that I express that business objective is what we call optimization. We need to optimize whatever it is, whether it’s marketing, acquisition marketing, conversion marketing, retention marketing, whether it’s product consumption, whether it’s support, whether it’s point of sale efficiencies, whatever—we are optimizing it.

Because we are in the process of digitizing these touch points, we have an opportunity to continue to optimize. Not just a project to change the website and walk away, adjust the ATM and walk away. We can use these data systems and analytics systems to continuously do that. We need this overarching strategy that leverages every customer interaction to evolve the understanding that we have of our customers so that we can evolve the treatment or experience that we are providing our customer. Then it’s rinse and repeat, that we need to continually do this. We find that insights-driven firms, the ones I’ve already talked about that are gaining this competitive advantage, are hyper good at doing this, and they’re hyper good at doing this continuously, and they do this at scale in order to evolve and optimize their customer experiences. So, they have this differentiation in the marketplace and are eating the revenue of those of us who aren’t insights-driven.

Slide 20:

One of the ways we can start to move towards what these guys are doing with optimization technologies—it’s another dimension to think about these things—is to think of it in terms of two dimensions. One is the intelligence method that we have to digitally understand our customer to optimize the engagement. Is it business rules driven? For instance, is it a personalization technology such as maybe your content management system that you say, “John, I know where you live—Alabama—and therefore give us the stars and stripes. James, coming in from West London, give him the Union Jack,” or whatever. So that kind of sits there. In the advanced analytics when it comes to intelligence methods, that would take that to a totally different level. It would look at how they reacted when John and James saw that specific content and adjust accordingly and continually learn and adjust.

Then you get to the level of automation where it’s kind of, “Oh okay, we’re just going to set this rule and walk away and the system is just going to do it.” It’s going to be more automatic in nature in the way it applies. It makes more sense as I build out the capabilities that we all have today. So, I’ll just build the whole slide out and what we have is, in the bottom left-hand side, things like A/B testing, traditional segmentation, which are very manual tasks and business rules driven. Still manually driven, but advanced analytics driven, are things like multivariate testing, segmentation discovery using cluster analytics is that.

Then you get into the right-hand side, that automation capability, which is rules-based targeting where it is business rules based, but it is automatically applied. Then you get around to the top right-hand side where a lot of these use things like machine learning to continually learn from the data. A lot of them have been around for awhile, such as programmatic buying, recommendations, etcetera. The difference between those systems and today is that they were these black box systems that had these set models that rarely only the vendor themselves understood and they weren’t very open. Quite recently, the pervasiveness of these techniques or advanced analytics or the many techniques are far greater and far more widely distributed and far more open and ready for the data scientists within our firms to tweak and manage for our own use case.

Just a couple of notes here, manual and rules-driven approaches are still very important to us. They’re not going away. But to scale, you must consider automated, processes driven by advances analytics. Why’s that? Guess what, the number of digital points of engagement and the number of times we engage with our customers digitally is exponentially growing. The information that we have is exponentially growing. What we need to do with that, the demands of that are exponentially growing, but the number of people we will have to manage that is not. So, we simply have to be looking for those advanced analytics automated processes to engaging with our customers and understanding them better.

How’s that John, did I answer your question?

John:

Yes, that was really good. And obviously, it’s very complicated what people are doing and they’re trying to wrangle complex organizations in many situations and as they’re addressing those different areas—of having a strategic approach or having strong data governance in those processes, sense of ownership—all these things that they’re working on, a lot of them probably feel lost in how they’re moving forward. Maybe you can share an example of an organization who has had success and is doing a lot of these things and doing it well.

James:

Sure, I’m very happy to. As I kind of mentioned, at least for a number of years now, and at least in the last 18 months, we’ve really been hyper focused on weeding out these firms who are doing well and asking them what it is they’re doing differently.

Slide 22:

One of my favorites is, as I’ve talked about a few times in the past, is my conversation with Eric Colson, who is the Chief Algorithmic Officer at Stitch Fix. One of the things that’s interesting with Stitch Fix, what they do, they are a fashion company. They send clothing items out, they are a clothing retailer, but they package it up and the customer’s all have an agreement with Stitch Fix to every month or every three months, to send up to about five different items of clothing. The customer is entitled to send back whatever he or she does not want to keep. It’s imperative of Eric and his team to really understand the customer, what her preferences might be and what kind of sticks.

It’s a hyper data intensive task. They have a machine learning algorithm that they put that into to learn, so that the stylist, who’s essentially the business expert, she or he can work with that machine to make these clothing items relevant to their customers so she keeps them. Obviously the higher the retention of the items, the better they do. That’s kind of one thing. The interesting thing is that the procurement team caught onto this whole thing. They then started communicating with Eric to say, “Hey, can’t we use this machine to help us understand what stock items we should have in stock to we could reduce the amount of unnecessary stock that we have and also to buffer up on that stock that we predict demand?”

The procurement team use the exact same algorithm to help them to have one of the lowest cost of stock in the industry, according to Eric. Really kind of interesting insights-driven and digitally intelligent firm there. So, one of my favorites.

Slide 23:

Another one, I think many of us have been students in the past and needed a student loan and if you remember your late teens or early twenties and how responsible we all were with finances. I know I wasn’t, so I wasn’t a great person to loan to. You give me a loan and I’d be down at the pub having a few pints. But Earnest is in that business of identifying students that are probably more reliable than me in terms of returning their student loans or repaying their student loans. What they do is attract student borrowers that many other firms that use credit scoring only would consider bad. So, a bad student from a credit worthiness perspective would look bad, but try and find out if they’re really good and to use other types of data other than credit scoring to understand that.

Maybe an 18-year-old who doesn’t have a great credit history because they haven’t’ loaned anything before, but looking at a bunch of other pieces of data, they might actually be good. Trying to identify those good-looking customers that actually are baddies and those bad-looking customers who are actually goodies. Also, to identify when is the most important moment to engage them from a marketing and also from a support and product delivery perspective. And of course, they use a bunch of customer data.

Yes, they do use the credit scoring, but they also stay connected via social and email and they’re constantly monitoring the way the communication is going with the students and they’re continuously learning and creating these segments of customers and they’re predicting what these segments—how good they are at repaying their loans. They collect all of the data and record their decisions around that.

And they are very insights-driven. They create this high-fidelity view of the customers and the decisions they make with each different customer and prospective customer and they’re continuously learning. They gain a competitive advantage from that. One of the things we asked them is what are you doing differently besides having this great continuously optimization, machine learning algorithm.

One of the things we find is it’s very much a leadership driven thing. It’s not some poor marketing guy who wants to be data-driven or poor product guy who’s trying to bring it in. it’s really from a very senior perspective, so Louis Baryl was the CEO there really pushing it downward and saying we really need to do this stuff to make it a competitive strategy and a differentiator for us. Those are some of the things we learned and some great examples. Of course, there are plenty of others, Tesla, Amazon, Groupon, then some smaller companies. So really some interesting stories out there.

John:

Those are some great examples. We’re getting near the end of our time today, so maybe in closing you can talk about where we’re going. I mentioned the past there with some old-school Super Stats, but where is it we’re going with the digital intelligence industry and where you think we’ll be by the end of this year and into 2018?

James:

Where we’re going is really interesting.

Slide 25:

For many of us, our imaginations run wild in terms of where we’re going, but some of the things that are definitely going to happen this year and beyond can be summarized in maybe three areas. One is the context of the data that we will be using. The other one is the priority for the enterprise. And thirdly, the role of things like machine learning, AI, and Big Data when it comes to understanding customers. In our recent report from a context perspective what we said was that mobile is becoming the center point of understanding our customers during their moments of engagement because mobile is a multichannel view of the customer from email, social, the websites they’re on, etcetera, the apps, you name it. Mobile is helping us create a better omni-channel view of our customer.

That omni-channel that we’ve craved and wanted for many years is starting to provide, so when a customer engages, we have a much better context around where they’ve been where they come from, that sort of thing. And also, things like location in terms of context as well. So not only what did they do in a previous channel or previous digital engagement before they arrived here and what were they doing when they’re here, but also where were they? How fast were they going? Were they near that theater, that retailer at the time? Is this time to now send them a voucher, etcetera. I know for instance, Starbucks announced at the beginning of the year that this is where they’re going with their mobile app, which launched about 18 months, a few years ago around mobile payment and that kind of thing. That kind of thing is becoming mainstream this year and beyond.

When it comes to the priority of customer insights, it’s becoming an enterprise priority, but what do we mean? This year is the first year that most enterprises—and it’s by a small majority, about 51, 21 percent of us—but most of us will have Chief Data Officers. In other words, we are taking insights seriously and it’s becoming more of a leadership thing. Last year it was a minority, something about 40 percent. This year is the first time we can say it’s the majority. Same when it comes to our Big Data initiatives, trying to make sense as a collective whole of all the various types of data we have about our customers, our businesses, and bring those together. Really big programs starting to happen and the majority of us are starting to do that this year.

When it comes to artificial intelligence, the interesting thing is last year when we asked only 10 percent of enterprises invested in AI in 2016. In 2017, at least—even if it’s a small program, a midsized program, even large programs—over half will be investing in AI this year. Few will by the data that we are liberating by those other programs. The interesting thing is from a technology vendor perspective, they are and already have invested a lot in AI cognitive computing, especially when it comes to their business applications to help the business get more out of say, an analytics system, so they don’t have to rely on the say, web or mobile analytics person social analytics person, be it ecommerce or whatever to get that information. They can get it themselves through the AI. Those are some of the trends.

John:

Things are definitely getting more and more complex as we’re moving forward. These ideas sparked why I even started ObservePoint. We have this evolving industry and we’re trying to empower people to govern and trust the data that they’re working with. Thank you so much James for being with us today. We appreciate it.

James:

I appreciate the invite John, it’s a pleasure.

John:

Thank you.

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