The Big, Fat Data Forage for

Mobile Marketers

Marketing has evolved leaps and bounds since the days of traditionally defining target markets. It is beyond demographics and definitely beyond macro segmentation. Those were the days when information was limited and marketers hardly knew their customers well. Most marketing was based on surveys and even on marketers’ assumption of customer profile and customer psyche.

In the digital and mobile marketing era, the trouble is that there is so much of customer information available from so many different sources, that marketers are often overwhelmed by this information, and marketing strategy on a whole seems mislaid.

How to best use customer information

The first and foremost thing marketers need to do is access what data is relevant to them and what is not. This, in itself is a mammoth of a job. For instance, all the information about customer’s whereabouts, spending habits, social circle, internet usage pattern, all of this and more is available with marketers. But so much of information does not really mean anything until someone joins the dots and creates insights that actually come in handy as knowledge-base for marketers.

Joining dots, can be a very expensive and time consuming task that a select few big organizations can achieve fully. And when customers start aggregating, the level of precision required and granularity can become quite daunting. To achieve a certain level of datamining, the systems and technology required have to be very sophisticated, and the derivation and the analysis have to be very precise. Achieving this for billions of potential customers out there can be exorbitant and daunting, both at the same time. For small entrepreneurial firms and startups, it makes sense to just focus on the data that is directly relevant. For instance, GeoQpons uses geofencing to get real-time customer location, and based on this information, the app suggests retail stores that have a sale or discount on. Moreover, based on history, the intuitive app suggests stores and merchandise based on customer liking.

Another important insight is that there are no categories. There are fluid lines that pretty much don’t exist. From elders to kids, everyone in the family is making decisions like which car to buy or where to go for holidays, and the worst part being that the marketer does not know who’s the influencer and who’s really the decision maker. So it’s not a simple demographic categorization that would work here but a geodemographic, social breadcrumbs and psychographic analysis, all put together will give some insights about whom to target and how.