Advanced Analytics, The Modern Marketer’s Best Friend

Advanced data analytics is a game-changer for marketing organizations.  A McKinsey DataMatic study showed that firms in the top quartile of analytics performance were 20 times better at attracting new customers and more than 5 times better at retaining existing customers.

With the rise of the digital ecosystem, marketing analytics seems to have a bright future. The intersection of marketing and analytics has enabled teams to adopt a more customer-centric approach. Examples range from using specific offers to retain existing customers, delivering highly-targeted offers, serving targeted content to prospects, using payment network partnerships to facilitate the delivery of time &location-sensitive offers, and much more.

Realizing all of these goals hinges on customer knowledge. Without inputs on who customers are and how they behave, organizations have no insight on how to leverage them. This is, after all, the Age of the Customer, where consumers are the driving force behind business decisions. Customers no longer blindly accept what’s offered to them - self-education now precedes purchasing decisions. This has forced marketers to re-think how they reach potential customers at all phases of the buyer journey.
Knowing customers is not a new idea, but the concept has evolved in our modern data-driven environment. Customer insights are now the province of Big Data, where consumer behavior, actions, and trends lie hidden in vast quantities of heterogeneous data. Knowing and segmenting your customers is truly a data problem: how do you get marketers to drive their campaigns based on data rather than on gut feeling?

In this whitepaper, we will discuss how advanced analytics have the potential to transform the ways in which segmentation for marketing purposes is accomplished. We’ll start with a look at traditional segmentation methods and then move on to exploring how advanced analytics (model-based segmentation) can change the game. Then we’ll explore a few marketing & analytics use cases in various industries. Lastly, we’ll examine the methodologies needed to implement model based segmentation in the real world.