Are you wearing sun glasses to a 3-D Marketing Segmentation Movie?

For years, marketing professionals have tried to look at customers with a simple filter.  The most common is the customer's business segment.  This data is easy to get and whether it is SIC or ISIC, it allows the business to split its customers/prospects by the line of business that the customer serves.  Another way that the customers are segmented is by their spend on the good or service that a company is providing.  We have all heard about our "large, medium and small customers".  This metric is a bit ironic as it does not measure what the customer could be purchasing but rather what it is purchasing.  For example, a small customer today may have big customer potential if they just increased their spend with a different supplier.  Furthermore, we can get really sophisticated and put them in a 2 by 2 matrix (See diagram below) whereby we look at spend and business segment.  While this gets a bit more granular, it still misses the target.

We have all seen how sophisticated retailers are getting with their ad choices for on line shoppers or how physical retailers are able to look at traffic patterns in a store or even digitally counting how often items are handled.  This is all about knowing enough about the customer's behavior to be able to predict and at times persuade a purchase.  This is really no different in the B2B world.  In the end, our customers are those same shoppers that buy on line or in the stores.  They want to transact/interact with us in different ways and in fact exhibit different behaviors.

Big Data and our capability to capture and store this will drive a change in the way we do business.  Gone are the days of the 2 by 2 matrix.  Today, we need to be capturing our customers behaviors and preferences for how they get data and what type of data they want about our good or service.  If this is not done, your message will just get lost in the "Data noise".  There are several creative ways to get this customer information.  Some are direct and some are less direct.  Once you have the preferences determined, it is much easier to correlate this back to other more simple filters such as business size, industry, digital sophistication, etc.  From first hand experience, I was truly shocked at the power of big data when we stumbled on to a customer trait that we believed was of low value.  When this behavior was discovered, we changed our presentation to focus on this hidden desire and the sales came in exponentially.  I urge you to spend the time to get your customer's preference data (just do not do it in my industry!).