Did the data-driven era miss an exit?

Analytics

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people crossing in crosswalk

According to headlines, martech embodies technical sophistication, touting thousands of companies fueled by sumptuous features like artificial intelligence, virtual reality, personalization and more. Yet, a recent study found that what marketers really want is more, high-quality demographic data. I can’t help but think, did the data-driven era miss an exit?

The modern marketer can use thousands of pieces of consumer information — demographics, purchases, hobbies, social activity, geolocation etc.—but many marketing efforts are still based on the same basic demographics of age, gender and income. The status quo may be considered the easier, more risk-averse route. But if we stay in autopilot, marketers will slowly lose speed, falling behind for failure to embrace new technology in the spirit of innovation.

Time for an oil change of our way of thinking

First of all, I don’t propose we do away with demographic data, as it is incredibly powerful. But it only represents a sliver of the information available about a person. Furthermore, the accuracy of demographic data is already in question, and the cost of its poor quality can add up for marketers.

If a marketer over-relies on only a few demographic data points when defining an audience, accuracy of those specific data points is mission critical. But with a more comprehensive approach to defining an audience, where thousands of data points are considered, no single data point is at risk of toppling the operation.

No junkers, no hidden gems

There are hundreds of data aggregators in the martech ecosystem, specializing in CPG, behavioral, online, offline data and more, and many claim theirs is higher quality than others. These claims should make marketers wary. For example, I recently read an article claiming online purchase data is the ideal source of truth because it’s tied to a tangible transaction. But for a retailer that wants to drive new, incremental purchases, an advertisement targeted to someone who already bought is not ideal. In this instance, the quality of the behavioral data is a moot point because it doesn’t align with the retailer’s objectives. Further, when marketers have thousands of data points at their disposal, it seems risky to put so much weight into a single point.

Get into gear

Too often marketers question accuracy and quality before knowing what they want to achieve. Take, for example, a TV manufacturer that wants to find new customers. Using recent online purchases, even if the data is 100 percent accurate, won’t find many new customers. Would an audience of men that’s 100 percent accurate fair much better? Or an audience of people who live within a 5-mile radius of an electronics store?

Accuracy is important. But an audience with perfect accuracy that isn’t tied to campaign objectives won’t drive performance.

What if, instead, the manufacturer considered past purchases as one piece of the puzzle — a way to group its valuable customers. Then it could consider the thousands of additional data points available about those customers to find the commonalities that comprehensively represent the ideal customer. This audience mitigates the risk that a single data point’s

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