Customer Experience, digital Marketing: Bigger, Better, Stronger, Faster with Big Data AnalyticsI pity the marketing organization. It has to deal with a lot of change.

First it was the pesky empowered consumer. Then it was understanding device proliferation and cross-media consumption. As if that wasn’t enough, what about social, mobile and digital disruption?

Then of course there’s the (ubiquitous) marketing ROI question. Let’s not forget about using marketing automation technology. And now, Big Data analytics. (Not necessarily in this order).

A little too much to handle, don’t you think? No wonder you find the stats about CMO tenures pop up once in a while and the comings and goings of senior marketing executives make the news so frequently.

Big Data analytics is no different from the rest. As with any of these trends, the marketing community first starts with myth busting, then does some show-me-the-money analysis and finally looks for use cases on how the trend can be practically applied in the marketing and customer experience context. Here the focus is not to get into a debate of what Big Data analytics is or isn’t but rather to understand how analytics in the age of Big Data impacts customer experience and marketing processes.

So how does marketing become bigger, better, stronger and faster using analytics?


There is no dearth of customer data now available for marketers to digest. Analyzing all of that data -- at scale -- is a challenge for any organization. Every customer interaction is a chance to know her better, serve her relevant experiences, and build the groundwork for the next interaction. Analytics technologies now have the ability to handle bigger datasets without compromising on speed or accuracy of results.

For example, customer journey analytics or path analysis involves data points from multiple interactions and touch points. Path analysis, traditionally performed through sequence mining, found its early application in the website visitor funnel context. Now the entire customer journey can be analyzed using path analysis to understand what series of events led to a desired outcome.

Another technique called social network analysis (SNA) uncovers relationships between entities or customers in a large network with the goal of identifying influencing nodes of customers. This type of analytics utilizes large amounts of network data with underlying graph database structures. For example, in the telecom industry, SNA is used to identify customers who are influential in a call network with the goal of preempting attrition.


The use of predictive analytics to recommend products, offers and experiences has piqued the interest of marketers in the last couple of years. Marketers want to be able to make better predictions in an attempt to drive in-depth relationships or simply get more efficient with their targeting.