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. How could the martech world address this challenge?

Let’s step back a bit and think about what brands really need. Do they need data, or do they need answers? Direct answers such as whether or not a consumer is interested in a product or service they are offering. They need answers that are much more reliable than the poor relevance guesses they are coming up with their little sliver of a consumer’s data.

Suppose we went back to the old days where the seller could directly ask a potential buyer if the buyer was interested in the product the seller was offering. The buyer would provide a direct answer instead of the seller needing to guess or predict.

What if something like this could be done in the digital world where we shifted from brands trying to guess the relevance of the product on behalf of the consumer, to directly asking the consumer whether they found it relevant?

Well the obvious problem here is consumers couldn’t possibly give this kind of attention to brands today.

But, could technology help here?

Could there be a predictive model that could represent an individual consumer and predict answers to requests from brands their behalf? If this predictive model knew all the consumer’s digital activity, perhaps it could make much better predictions about how the consumer would respond to messages from brands.

What this does is that the prediction of relevance shifts from the realm of the brand ..

To the realm of the consumer ..

If such a model existed, then a brand could, for example, send an offer to a consumer’s model and get a response on her level of interest on that offer. And only based on a positive level of interest the brand could show her the offer, The brand would know what is relevant to the consumer, and prevent the consequences of misunderstanding the consumer.

This will solve the biggest roadblocks to predicting relevance:

• Sparse, stale consumer data: There will be no need for brands to collect
  consumer digital exhaust because brands will no longer need to be predicting relevance. And with seamless tools that I will detail further, consumers will have the ability to collect their own digital exhaust and keep it up-to-date.
• Consumer Privacy: Consumer will have complete privacy of their data because they will not need to share their data with any external entity for marketing purposes. Even to their own model, the consumer will have complete control to decide what gets in and what stays out.
• Relevance Measurement: Relevance prediction in the consumer’s realm brings opportunities for better measurements within predictive models. Also, the creation of a feedback loop between the consumer and her model, as I will come to explain, will create an optimization cycle that isn’t possible in the current paradigm.
• Segmentation Technology: Having the complete digital exhaust of a consumer, accessible in a single place, opens up entirely new possibilities for deep predictive modeling that will make it possible to get geometrically higher accuracy of predictions.

This new martech paradigm could be a solution to the relevance problem. I’m calling this 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.

We will see how the Naytra Paradigm will transform brand-consumer interaction, as well as the massive benefits that it will bring to the digital marketing world, but before that, let’s take a look at the Data Science behind Naytra.