Remember when you were a kid and you wanted to do something seriously stupid, like dye your hair purple or spend your entire savings on a fad that lasted about eight milliseconds? You always pointed to the kids who were doing it. Your mom always responded with, "If they jumped off a bridge, would you?"
Now you're all grown up, and you know better than to follow just any fad that wanders along. So you held off when big data and predictive analytics strolled onto the scene, because you're a grownup, responsible marketer who never wastes the precious budget on something that's gone with the wind before it gets ramped up good.
But now the duration of a fad has come and gone, yet big data marketing and predictive analytics is still here. That means it isn't just a fad, and your marketing department needs to jump on the wagon soon before you get left behind. How can you get ready for the B2B marketing revolution that's being brought on by data and predictive analytics?
1. Collect a Total View of Customer Touchpoints Across the Organization
If you try to conduct predictive analytics on a small subset of your organization's data, at best you will develop a narrow and limited view of your customers. At worse, the data will be skewed and horribly wrong. Do away with data silos and combine the information from all of the customer touchpoints: billing, tech support, customer service, sales, marketing, etc. Then you will construct a complete, holistic, accurate picture of who your customers are, what they need, and what they respond well (or poorly) to in terms of marketing messages.
2. Don't Forget to Include Relevant Industry Data
If possible, include other relevant data from outside the organization. For instance, social media data, industry trends, and other external data can serve to supplement your in-house data, put it into perspective, and even help you determine what your data needs to be doing for you.
3. Determine Specific Questions to Answer With Predictive Analytics
The way to go wrong with data and predictive analytics is to fail to focus your analytical efforts. The way to do it right is to develop specific questions to answer with the data and then conduct the analytics to answer those questions. For instance, "What actions by the shopper are best indicative of an imminent purchase?" Create questions that you can answer numerically and let the analytics do the answering.
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