Big Data and the Problem of Plenty

Big Data marketing is the new black for marketers. While it unlocks many possibilities, it also brings about a pleasant problem: the problem of plenty. Running Big Data marketing campaigns results is an exponential growth in the number of leads.

Running the Big Data analytics at a deeper level would separate the good from the bad: hot leads, which the marketers can pursue immediately, and horrible leads, such as spammers and bots, which may safely be ignored. However, there would still be considerable number of “in-between” leads, which, in today’s highly competitive business environment, a marketer should not ignore.

The solution to this problem lies outside the scope of Big Data. It depends on defining what constitutes a “lead” in the first place. Defining what qualifies as a lead should ideally be a joint exercise between sales and marketing; balancing what sales wants against what marketing is in a position to deliver. If the Big Data analytics throws up more leads than the marketer can possibly handle, then the need of the hour is to fine-tune the definition — to cull out the best of such leads, or the leads that are most likely to convert.

Fine-tuning the lead definition, however, doesn’t necessarily mean abandoning the leads that would otherwise qualify, save for the inability of the marketing team, for whatever reasons, to nurture them. With the ultimate goal of any organization to cultivate the maximum number of possible leads, one option is to integrate marketing automation and continue to engage and nurture such suboptimal leads. Big Data again has a major role here to provide customized insights into such suboptimal leads, so that the marketing automation software can deliver the relevant content to them, and thereby increase the chances of these leads converting, even as the marketers spend minimal time and effort on such leads.

How do you define a lead?