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Why are marketers still awful at what they do?

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Why are marketers still awful at what they do?

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Every day we get to see misdirected, mindless marketing material. You’d think with all of the conversation about Big Data and analytics, we’d be past that by now, but it doesn’t feel like we are. Why isn’t Big Data changing our world in more visible ways? Why aren’t analytics being used to target the right messages to the right people? Skepticism is understandable without much evidence of great miracles and wonders. Heck, I’m a skeptic.

But I’m not skeptical about the science. The real problem and cause for skepticism is that most marketers still haven’t figured out how to take advantage of data, technology and techniques that are readily available. For a variety of reasons, including investment in obsolete marketing platforms, poor understanding of analytics and Big Data, and lack of inertia, marketers are struggling to catch up to consumers.

Hey, if everyone is awful, there’s not much incentive to be better, n’est-ce pas?

Modern Marketing 101

It doesn’t have to be too hard to understand. Rapidly increasing amounts of data and easy-to-use analytics allow a couple of things that every marketer can adopt. First, they allow marketers to do more to make customers loyal. While so much has been spent in the past to find a customer, having a combination of demographic and personal data allows today’s marketer to do more to keep and delight customers. Loyalty programs are an excellent tool for doing this and most marketers fundamentally get this, even if their loyalty programs aren’t very sophisticated.

But marketers have to get beyond the plastic card and points model. It’s way too transactional and limiting.

Uplift modeling

Things get much more interesting when marketers use historical customer data, enriched demographic data (available from sources like Axciom), and apply visual analytics.  Rather than merely keeping the customer, uplift modeling is built on the premise that certain customers are more persuadable than others, on one hand, and that some might actually react negatively and increase customer departures on the other. The math of customer analytics bears this out.

Screenshot 2014-01-05 13.59.57

Pitney Bowes Retention Marketing

This idea is based on the premise that doing something or nothing to target customers will have differing effects. Those who will respond negatively to targeting are called “Sleeping Dogs,” those who would be retained regardless are “Sure Things,” those who won’t respond either way are “Lost Causes,” and the target audience, the one that will respond positively is “Persuadable.”

The trick, then, is to have enough of the right data, updated as close to the moment of decision as possible, to target the customers that will have the most positive effect and wisest spend.

While not new, uplift modeling isn’t as widespread as it should be for something that seems like a no-brainer. Marketers are still struggling with the data necessary to feed the analytics and the analytics skills to make sense of the data. The good news is that newer generations of analytics tools are making both the data and the skills easier to come by. Will that turn the tide and reduce the amount of poor marketing we see? It will when competition drives marketers in this direction.


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