Faraday AI

How to build a consumer marketing AI stack

A comprehensive guide to building your own B2C marketing AI stack

What you'll learn in the guide

Key considerations, pricing guidance, and vendors to consider when building and implementing an in-house AI stack for your B2C marketing team.

Data requirements

All predictions start with prior data. B2C customer lifecycle optimization is no different. The other side of the data coin, consumer data, provides the “raw material” for AI algorithms to find patterns in.

Building an identity graph

With your own data and a rich set of consumer data, your next job is to combine it. Portions of this enriched data will serve as “training data,” the rich observations that machine learning algorithms learn from.

Data discovery and analysis

Data science is an art. Don’t leap headfirst into the machine learning step without developing a deep understanding of the data in play. Carefully consider potential biases.

Machine learning requirements

This is where the magic happens. We'll cover the 5 primary components required to build AI models. Your big cost here will be a data science team to build and supervise the pipeline.

Model validation and deployment

Without careful oversight, models can age out or, worse, be applied to the wrong task. In order to deploy your model to optimize your customer lifecycle, you’ll need to identify how and when it will be used.

Applications

Ad hoc outreach, real-time intelligence, and scheduled evaluation. Understand the use cases, key considerations, and pricing guidance for each application.