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Last week, I cleaned out my refrigerator—which, in and of itself, would seem to have nothing to do with data. Yet it got me thinking about the relationship between what we have and what we do with it. Customer data, for example, is a lot like food. It can feed decisions and powerful insights, but most of it ends up sitting in storage day after day, doing nothing.
Data warehouses are a lot like giant refrigerators of data. We put everything in there with the idea that maybe we’ll do something with it later. After a few months, we archive it in the “freezer” section. The trouble with this approach is that, just as food isn’t providing its “value” until you consume it, customer data isn’t customer intelligence until you actually use it.
Customer intelligence is actionable data. It’s knowledge that marketers can use right now to create lookalike models for their best customers to build highly targeted campaigns, measure levels of customer engagement (and build customer experiences that will keep customers more fully engaged), optimize ad spend on social media campaigns (e.g., Is a customer more like to convert on an ad in Facebook or Twitter?), identify content categories that were never revealed before, and much more. We call that behavioral data because it focuses on customer behavior rather than just customer data. Only a small percentage of customer data by itself is useful data. You have to take the signals your customers are sending by the actions they are taking and surface behavioral data in order to understand what actions you can take to provide relevant experiences to your customers. That’s not to say that the rest of your data is garbage—a lot of it isn’t—but it’s the behavioral data that represents the real food for thought.
Where do you find the behavioral data that results in real customer intelligence? I’ll tell you. In a customer data platform (CDP). CDPs are sometimes confused with traditional data management platforms (DMPs), but they’re actually quite different. A DMP typically stores massive amounts of data for later analysis, including data generated by third parties. A CDP focuses on first-party behavioral data: from cookies on your website, registrations and other information that you can collect from your customer interactions. This is the choicest cut of your data. It’s unique, highly relevant, and fresh.
CDPs don’t simply collect this customer information, however. Using data science, CDPs can augment this data with previously hidden insights about customers, products, content, and affinities. For example, a CDP might reveal that the affinity between affluent women and sweaters isn’t the price of the sweater but the fabric type, or that men who read articles about fishing are even more interested in articles about steak.
These kinds of customer insights naturally lead to better marketing campaigns, but with a caveat: freshness counts. Instead of just a quarterly review of campaigns, CDPs can review campaign results in real time and adjust the campaign if one aspect is under- or overperforming. This is agile marketing at its best, and in today’s competitive digital market it’s a must-do if brands want to effectively compete for customers and create content that resonates with readers.
As important as having the right data and the right data science is, human intelligence is still a critical part of the marketing equation. AI or machine learning may surface the fact that customers are interested in a certain product, for example, but not realize that a new version of the product is scheduled for launch in three months. This contextual, human intelligence is critical for activating customer intelligence at the right time and in the right manner.
So, in keeping with our food theme, you can think of customer data in a DMP as the meat and potatoes of your marketing, and the customer intelligence in your CDP as the filet mignon.