Types of customer data: Definitions, value, examples
Types of customer data serve distinct purposes. Identity data, descriptive data, attitudinal data, behavioral data defined, with examples.
Data analytics has hit a plateau.
A 2015 Sloan Management study found that, less and less, companies believe analytics gives them a competitive edge, from 67 percent reported in 2012 down to 61 percent in 2014.
With more data scientists, more technology, and more of everything else, shouldn’t businesses be getting more of an edge? Why are we getting less?
I’ll tell you why: We’ve done the easy stuff. We’ve brought metrics into marketing and operations. We’ve begun measuring everything instead of shooting blindly in the dark. We’ve picked the low-hanging fruit. Now it’s time to roll our sleeves up and make the hard marketing decisions.
Types of customer data serve distinct purposes. Identity data, descriptive data, attitudinal data, behavioral data defined, with examples.
Familiar with dark matter—the invisible material in our universe that keeps everything from tearing apart?
Physicists estimate it outweighs visible matter (like planets, stars, and moons) six-to-one. That is, we can only see a fraction of what exists. The vast majority of everything is invisible—not just to the naked eye, but to any device humans have created so far. In fact, the only reason we know dark matter exists is because we can measure its effect on the rest of the universe.
You know it’s there (you collected it in the first place, right?), but it’s useless. And that’s the difference between dark matter and dark data. You can see the effect of dark matter. Dark data has no effect—and that’s the problem.
Customer data management best practices allow businesses to fortify their commitment to positive relationships. The potential for growth, in commerce and trust, is massive.
We don’t need more data. We need more decisions.
Collecting more data won’t help if it just gets stored away in a drawer or on a server somewhere. More dark data won’t shed light on your challenges or deliver additional customer insight. Hiring even more analysts and data scientists won’t solve anything if it just opens up the fire hydrant even more – and the marketing tech of the future will make this task more and more easy.
Peter Drucker said, “The business enterprise has two—and only two—basic functions: marketing and innovation […] all the rest are costs.” You probably use data to try to cut costs (like a logistics company optimzing drivers’ routes to decrease delivery time and fuel consumption) or to sell more (like a/b tests to see which email gets the most opens).
That’s the low-hanging fruit. We’re in an age now where we have to move past that. We have to use data to construct the customer experience—and then figure out how to optimize it. The bad news is, you’re going to have to kill your silos.
Improving CX through customer data makes intuitive sense, but having a tool and using it effectively are two very different things. CDPs are the data remedy we need.
What!? Kill our product department? Reorganize the entire business around functions instead of products?
I know. The idea of restructuring your entire business sounds impossible.Thousands of people. Millions of man-hours. Possibly lots of dollars.
You have to do it. Switching from delivering a product to delivering an experience is the next “big thing.” We live in the age of commoditization. Nearly every product or service has been turned into a commodity that nearly anyone can offer. It’s a race to the bottom to see who can sell the most widgets for the lowest price.
Yet, a 2015 Harvard Review study that surveyed best-in-class companies versus ‘underachievers’ found that the underachievers’ number one obstacle to improving the customer experience was organizational silos. I’ll tell you why.
All the data that you need is sitting in different marketing departments, various customer-service departments, and multiple operational departments scattered throughout all the silos of your company.
You have to break down those silos and walls, to reach across to rival managers, and to jointly leverage the dark data sitting useless around the company.
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Once you and your peers—or rivals, depending on how deeply ingrained the silo culture is in your company—make the decision to work together, it’s surprisingly easy to get it done.
State-of-the-art tech exists that lets you bring data together from across multiple platforms, sources, and dynamics so that you can start wringing competitive value from your previously dark data almost immediately.
The first step (overcoming those operational silos) is a big one. But my experience with our clients has been that, once they take that step, they’re always glad they did.
You don’t need more hard data. You need more hard decisions.
This post originally appeared on Campaign Asia and is republished with permission.