Previously, we saw that brands have a poor understanding of their target consumer, at an individual level, in order to be able to achieve marketing relevance. We also saw that shifting the prediction of relevance, of a marketing message, from the realm of the brand to the realm of the consumer could bring the opportunity to solve this problem of relevance like never before. This new paradigm is what I’m calling the Naytra paradigm.

These decisions could be around whether a marketing message (Ad, offer, product, piece of content, or any piece of marketing information) will be relevant to that consumer. The consumer’s Naytra seamlessly learns from the digital exhaust of the consumer. It wouldn’t have limitations that humans do and could deal with any number of marketing messages from brands.

A predictive model like Naytra is possible due to the science and technology of Data Science and Machine Learning.

Data Science is an emerging discipline that seeks to predict inferences from data, often using machine learning algorithms that automate these predictions. Data science spans the collection of data to the modeling and computation needed to make predictions and therefore, the Naytra paradigm spans across all these capabilities.

Let’s take a look at the Data Science behind Naytra.