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If you’re a marketer, then you’re in the business of finding potential customers and delivering solutions to them at the moment they need them.

You know the challenge: how do you find real prospects without wasting your resources chasing non-prospects?

If you were to do this on a one-to-one basis, you would learn all about a person in order to determine whether they will have an inclination to buy your product or service. In other words, you would gather data and analyze it.

Let’s make this personal- imagine, you’re at a bar seeking a potential mate. You size up the crowd and eliminate those who fail a basic test like age. Maybe you like neck tattoos.

You strike up a conversation with someone who seems to be a good fit for you. You discover they are smart and funny. You learn a little about them- where they live, where they are from, what they do for a living, etc.. You get a sense of their personality.

You examine the variables and determine your chances. At this point it’s a preliminary determination. You need more data. You go out a few times. You share some interests and you also notice some potential issues. You check their Facebook page and Twitter account. You ask your friends to find out what they can. Data collection works best cross-platform.

After a few dates you have enough data to begin analysis, at least preliminarily. Do these differences reduce the odds of success or are they just noise? You decide you can get past the musical differences. Then you start to discover new data. Say, they drink too much. You do a bit of regression analysis. That often correlates with financial issues; violence; issues with the law, like DUI; and irresponsibility. That changes the calculus.

Congratulations, you just did data modeling.

In the marketing sphere, data modeling occurs without the date, and thousands of people at a time. As in real life, the more data the better. Ad Genius employs 750 big data variables. Together, they provide a finely-drawn buying persona of each individual prospect.

It’s also more scientific. Math and engineering replace gut feeling. That may explain why data modeling has a better track record than dating.

The science of data modeling creates a prototype that represents the existing customer, and finds the next customer or influencers with high probability. Analyzing the data is key. An integrated marketing-analytics approach provides the most well-rounded results to drive growth.

Says the consulting behemoth McKinsey & Co., “Our review of more than 400 diverse client engagements from the past eight years, across industries and regions, found that an integrated analytics approach can free up some 15 to 20 percent of marketing spending. Worldwide, that equates to as much as $200 billion that can be reinvested by companies or drop straight to the bottom line.”

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