I’ve had the good fortune to travel almost literally around the world over the past six months meeting with Adobe Marketing Cloud customers on several continents. Each market and each customer is different, facing its own unique challenges and applying digital marketing best practices in its own way.
One thing has been the same everywhere I’ve been: Forward-thinking digital marketers are asking more of their data than ever before. That part probably isn’t shocking. What has been something of an epiphany to me, however, is that these marketers are simultaneously realizing that visitor-based data will never be as powerful in optimizing customer experience and marketing campaigns than customer-based data will be.
What is customer analytics and how is it different than digital analytics?
Almost invariably, your customers do not see your brand as a collection of channels: web, mobile, call center, point of sale, etc. You are one brand, and customers expect to be known however they interact with you. Customer analytics seeks to allow marketers to have that same perspective on customers. Ben Gaines is Ben Gaines, whether I am marketing to him via direct mail, sending him a weekly e-mail newsletter, targeting him with a personalized experience on the web, or answering his support question. Your analytics can and should reflect this truth. If they do not yet, they definitely will in the not-too-distant-future.
Here’s a recent, basic example of what I mean: I a while ago took my daughter on a short trip to Boston, where her grandmother lives. I had booked my ticket online, using a credit card. But I added my daughter to my itinerary by calling one of the airline’s phone agents. If an analyst is looking at what I’ve done as a web visitor alone, he’s missing a huge opportunity. I’m not just a business traveler, like my web visitor profile might suggest; I’m also a dad who is always interested in messages about cheap fares to family destinations. But you only get to know that by looking at me at a higher level than the web visitor. A good analyst would see that I have a higher propensity to travel to Boston with a companion; when going to other cities, it’s just me and the web is my preferred channel for interaction. But the airline knows much more about me—and has a much greater opportunity to market to me intelligently—when they take my whole set of interactions into account.
On the web, I’m a visitor. But everywhere, across all channels, I’m a customer. A web visit is just one way I happen to interact with your brand. That’s the key principle; that’s the right parent countable for marketers.
Let me also be clear in stating that I’m not downplaying the importance of the web and web data. I’ve been a digital analyst and a product manager developing digital analytics tools. Great digital analysis generates tremendous ROI and grows businesses. But I also see organizations looking to make their data power content optimization, or remarketing, or merchandising, etc. What will be more effective in reaching your customers: conclusions based on one channel of interaction, or conclusions grounded in the complete customer interaction map? You can still do your content analysis, your navigation analysis, your campaign analysis, etc.—everything you, as a digital analyst, know and love. But you can do it from the top of Everest, with the whole vista laid out before you in addition.
How is this different than BI and/or Hadoop?
Traditional BI technologies, and more recently Hadoop, are great at a lot of different things. Hadoop and map/reduce store large amounts of data really, really well. I am a huge believer in big data.
Where they fall down for marketers—and especially digital marketers—is the requirement for exploratory analysis. Big data for marketers, as some have termed it, demands the ability to iterate through an analytical thought process very quickly.
One of the ways that advanced analysts achieve this today is by focusing on data sets and specific data elements that are relevant to understanding the customer. A common approach with big data is to shove everything under the sun into Hadoop. But a good data architect can help you focus such you have the right data that you need in order to understand the customer journey.
Hadoop and similar big data technologies will continue to play a major role in the growth of organizations. But there is the need for another layer of analytics that provides both the speed and flexibility that marketing, and the analysts serving marketing, will need in order to achieve the kind of relationships with their customers that they’re clamoring for today.
How will customer analytics change in the future?
As a digital analyst, it’s easy to sort of “tune out” the conversation around big data. And if you don’t understand the role of customer analytics in marketing, it’s easy to believe that it’s this other thing that “isn’t you.” You’re the owner of digital analytics. Six months ago, I would have agreed with you. But having sat with advanced analysts and heard them talk about where they’re headed, I’ve come to believe in the vision wholeheartedly. And the best part is that we’re quickly approaching some major technology and industry/skill shifts that will make customer analytics more possible and more accessible to you than ever before.
You’ve probably read about the proliferation of data. That is certainly one of the key drivers of the shift I’m expecting. But perhaps even more significant are hardware improvements that will make it easier for you to explore large amounts of customer data quickly. In-memory analytics is great today, but as memristors move from theory to reality, the amount of data you’ll be able to store in memory for fast retrieval will blow the doors off of everything we do in analytics today.
Now imagine what the emerging field of data science can do with the kind of fast-input/fast-output customer analytics systems I’m envisioning. Think about something like k-means clustering (and other terms I only sort of understand), sitting on top of your customer data, followed by sending those clusters to a tool like Adobe Target or Adobe Neolane for personalized, targeted interactions online and offline. But it isn’t just for data scientists; the visual query concept—where your selections and clicks and exploration of data changes your queries on the fly without you needing to know how to program—makes these kinds of insights accessible to those of us who do not have Ph.Ds. Until a few months ago, I had never seen data interacted with like this. It’s a phenomenally exciting time for marketers.
The Adobe Analytics team believes firmly in a future where you see, market to, and interact with your customers the way they see, buy from, and interact with your brand: as a single entity. Our Adobe Analytics Premium solution offers true best-in-class customer analytics today, in an environment where you an interact with data in ways that I’ve never seen anywhere else. It’s worth noting that even customers who only put web data into our data workbench are amazed by the predictive and statistical modeling that we’ve added, as well as simply the flexibility to delve into their data that customer analytics tools provide. I know that sounds like that contradicts everything I’ve been saying in this post about customer data, but for the cautious digital analyst (like I was), it’s a start!
If you’re like me, and you were once scared of Adobe Insight (now Adobe Analytics Data Workbench), it’s time to get over the fear. I’ve sat with analysts and watched how they can iterate through a thought process across multiple channels and have derived insights (no pun intended) more quickly and powerfully than anywhere else I’ve seen. But I’m not writing this in order to sell you Adobe Analytics; I’m writing this in order to share with you what I’ve seen as I’ve met with advanced analytics practices all over the world, and what I believe is the future of most digital analysts.
The thing is that once you’ve seen what a sharp analyst can do for his or her marketing team with customer analytics, it’s hard to be satisfied with “just” the web anymore. I guess you could say that I’ve become converted to customer analytics. It’s the future of marketing analytics, and, for me at least, there’s no looking back.