It’s time for a new kind of analytics. It’s time for a fourth dimension. Time to recognize the reality of cause and effect, of unpredictable decisions and uncertainty and motion in marketing. It’s time for a new approach to measuring and improving the way we talk to our people.
It’s time for real customer journey analytics.
Before we go there, reflect where we’ve been. Statistics are always more or less an approximation of reality — sometimes more, often less. Marketing statistics can drive this approximation right up to the border of wishful thinking. The problem is not a lack of analytical ability but rather a lack of information. An inherently unstable sample. A partial picture of an irrational target.
People are hard to influence, confounding to predict, impossible to know. They stop opening your emails and don’t say why. Their lifetime value is rock solid until it’s all wrong. They clear their browser cache and you lose sight of them. And it’s difficult to know just why they don’t drop by more often, or ever, what ad they saw or remembered, what they really think of you, your products, your brand.
Most likely, they don’t think anything. In addition to being conceptually difficult, marketing analytics has the indignity of getting applied to a field that isn’t all that important, really. We’re not finding quarks here or fighting crime. But our task can be just as imposing.
If you’re bored as a marketing analyst then you’re just not awake.
Why a Manifesto?
It’s time to assemble an analytical system that recognizes the reality of digital life. One that combines the best of customer identity resolution, channel and media measurement, static and time-series methods, and text analytics — time to create a single version of the truth at the individual level, across marketing and media, to estimate the impact of our efforts on the things we are trying to do.
It’s time to apply the math and scale of media attribution to things beyond advertising: to site and app engagements, email, social and distributed content, call centers and search.
Today’s marketing analyst uses too many tools. We have to: it’s just the way that it is. These can be divided into five basic types:
- Web and Mobile Analytics — measuring engagement with sites and mobile apps (which themselves often need a different tool)
- Social Analytics — applying text analytics, counting and graph methods to posts
- Media Analytics — attribution and marketing mix modeling (either separately or combined) for measuring the impact of paid media
- Customer Journey Analytics — using CRM and marketing system data to track known individuals over time
- Voice of the Customer Analytics — mining customers’ perceptions and opinions using surveys, text analytics and feedback cues
These systems are all looking at bits and pieces of what to a person — whether called a target or audience or prospect or customer, it’s still a person, people — is a single relationship.
It Gets Worse
Very often, web and mobile analytics is performed by a digital marketing department or agency, social analytics by an intern, media analytics by a high-end platform or consultancy, voice of the customer by the support team, and customer journey analytics by no one or an intrepid CRM or I.T. numerati.
Most likely, these people don’t even recognize one another at the same holiday party.
It’s time to stop the madness, combine the tools, starting with the customer record. We’re getting there: already, web and mobile analytics are coming together, social and voice of the customer. There is a heroic effort under way to build a master customer record using onboarding platforms, cross-device math, APIs and DMPs and integration systems.
And then there is the rise of customer journey analytics itself, which promises to do for marketing what attribution does for media.
Customer Journey Analytics: What is this? It’s actually an interesting and relatively early-stage set of methods and tools that grew out of CRM. Leaders in the space include Thunderhead (“It’s Not Your Journey, It’s Theirs”) and Clickfox, names little known in digital marketing departments. And there’s some promising start-up activity (see Pointillist). These tools build a customer profile based on known attributes — value, loyalty, product preferences, locations — and layer on how they behave over time in digital channels or stores. Any data scientist will see the value of such information, gathered from enough individuals, to marketings’ efforts to improve offers, messages, timing and sales.
It strikes me that customer journey analytics of this type is the future of a combined marketing and media analytics solution. It’s our money shot. Why? Because it incorporates the element of time, the fourth dimension, and it can be adapted to respond rapidly based on triggers. The freshest information is the best, no matter what anyone tells you.
McKinsey has talked about the customer journey. So have we (clients see here). McKinsey has a formal partnership with Clickfox. Our ambition as analysts is not to say that “the funnel is broken” or “customers jump around” — everybody knows that — no, our aim is to encourage dreamers to believe there is a market for coherent analysis. Systems that capture customer-level detail and use time-series analysis. Systems that take the incredible advances made by ad tech players, in infrastructure and stream processing and time-series analysis — that take these advances and apply them directly to the enterprises’ own marketing technologies.
I’m not saying we’re not trying. Just that we’re not trying hard enough. We need a Moon Shot, an Omega Project, a Zero Moment of Truth Machine for Marketing. We need to start thinking about time.