How to Become a Data-Driven Marketer in 2017?

As with any new year, there have been several articles posted about resolutions going into 2017. Most of the lists for marketers include a move towards more data-driven methods. However, like many resolutions, a lot of the prescriptions toward this end are vague and treated as afterthoughts, or a box to be checked. Yet “sexier” topics like virtual and augmented reality, artificial intelligence, bioinformatics, and neuroscience are spoken of at length, with excitement and reverence. For example, this recent article, after reviewing some of these headline-grabbing topics, notes: “Finally – and I’ll make this quick as it’s not the most riveting of topics – Data and Analytics is a continuing emphasis for retailers.”

So here is my own New Year’s resolution: I want to make predictive analytics as cool and sexy as every other shiny tech “toy” that retailers (and other consumer-facing firms) are investing heavily in.

Analytics – particularly well-validated predictive ones – offer far more potential to consumer-facing businesses than all of the new toys everyone is so excited about combined. However, if articles like this one are any indication, the year ahead in analytics is going to be very similar to the one past, as marketers marvel at the amount of data available and wring their hands about how to put it to practical use.

Below are three simple steps you can take to become a data-driven marketer in 2017:

1.     Do not engage in any marketing activities that cannot be measured. That means not implementing a new technology like virtual reality, or even doing something as mundane as launching a new social media initiative, if you cannot quantify its potential effect on your business. Relentlessly analyze the effect all your efforts have on customer acquisition and retention. Once the effort is launched, measure and monitor it closely to make sure it is contributing to your overall goals with a positive ROI. And if it’s not—stop doing it.

2.     Remember that actions speak louder than numbers. It’s one thing to be able to point to a spreadsheet and show your team all the measurements you’re taking on your campaigns, but the more important step is translating those numbers into action. Your newest Facebook ad generated 1,000 new customer acquisitions? Great! How much did those customers spend after clicking on it? How are you going to follow up with those customers? How are you going to change the go-forward strategy based on your findings? How are you going to measure the value of those customers to make sure that your ROI is positive overall on Facebook as a channel? Take the number and follow it through to the goals you are trying to achieve.

3.     Take the focus off the product and look at your customers. I wrote an article for Ad Age recently in which I talked about the need for companies to abandon their traditional product-focused approaches in favor of customer centricity. In it I talked about a radical decision gaming company Electronic Arts (EA) made in the 90’s. They went from encouraging developers to create anything they wanted to focusing on what a valuable EA customer actually looked like. In doing so they found an entire customer segment that would buy every version of every game across their sports line, from Madden NFL to FIFA. While the company was criticized for “selling out,” they have enjoyed record company profits by using the data to hone in on what would make them successful.

Let’s face it: predictive analytics isn’t going away. So stop pushing it off and deal with it. Not only will it drive more growth for your business, but it will put you in a better position to implement — and accurately measure the impact of — all those other shiny new emerging technologies that are much more “riveting” to talk about.

About the Author: Peter S. Fader is the Frances and Pei-Yuan Chia Professor of Marketing at the Wharton School of the University of Pennsylvania. His expertise centers around the analysis of behavioral data to understand and forecast customer shopping and purchasing activities. In addition to his various roles and responsibilities at Wharton, Professor Fader is also co-founder of Zodiac, a predictive analytics firm that aims to make top-notch customer valuation models and insights easily accessible to a broad array of data-driven organizations.

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