In recent times, almost all organizations run campaigns (though they differ in their size, channel, type, audience, etc.,) all are aimed either loosely or strictly for demand generation in short/long term. Now due to variety, veracity and variability of such campaigns, campaign data lacks a unified view that can straightforwardly provide the set of metrics or values that are needed for qualified leads. It needs to be derived or identify the right set of data points from each channel / type, analyze what they mean (or measure against the goals), and then use that knowledge towards receptive marketable actions. Artificial Intelligence process involving unsupervised machine learning techniques help us arrive at better decisions reducing gap between desired and actual outcomes.
This blog/post attempts to explain in simple terms how to approach such diversified campaign data for marketing qualified leads scoring, it has two steps 1) identifying recurring patterns in the data (unsupervised machine learning), and 2) strategize similar segments for scoring using relative ranking.
Views expressed here are from author’s industry experience. Author trains/consults on Machine (Deep) Learning applications; for more details reach out at https://www.linkedin.com/in/pradeepmavuluri/
