<img height="1" width="1" style="display:none" src="https://www.facebook.com/tr?id=2006193252832260&amp;ev=PageView&amp;noscript=1">

2 Min Read

How to Build a Predictive Customer Model with AI

Featured Image

Wondering how to get started with AI? Take our on-demand Piloting AI for Marketers Series.

Learn More

Data is left behind everyday as consumers navigate the web...

And it can be harvested and used in tandem with AI and machine learning to create rich, predictive customer models for market research.

Keep reading to learn more about how to take advantage of this data, straight from AI market research expert Richard Boyd (@metaversial) of Tanjo Inc.

These insights come from Boyd's AI Academy for Marketers course—How To Use Data Exhaust and Machine Learning to Model Behavior.

 

What You Need to Create a Customer Model

A synthetic customer model is composed of years of data that machine learning can use to predict the actions of your customers.

There are three components you need to create synthetic customer models, according to Boyd.

  1. Data Exhaust: "Data exhaust" is all the data consumers generate online. Brands likely have some of this data already, through their marketing and CRM systems. But this exhaust becomes truly powerful when you layer on third-party data, such as Nielsen purchase data or location services data. 
  2. Machine Learning: Once you have your data exhaust, you also need a robust machine learning model that can use this data to detect useful patterns in the data.
  3. Hypothetical Customer Modeling: It's not enough to detect patterns in customer data exhaust using machine learning. You need to be able to create a hypothetical picture of future customers in order to turn data and insights into action.

Customer Model Considerations

There are a few different ways to think about customer models, says Boyd.

  1. Build a Persona: Use the data you have currently to create a picture of an individual customer using the patterns you detect in your data. Give this persona a background, a set of behaviors, and an interest graph. 
  2. Collect More First-Party Data: When you don’t have enough data, think about launching marketing initiatives that collect more first-party data, like surveys.
  3. Layer in Third-Party Data: You probably won't find all the data you need in one place. Consider layering data from multiple third-party sources to create a single composite picture of a customer. For example, start with census data identifying who lives in the market you are trying to address. Then, layer in interest graph data from social media to determine what these demographics care about. Lastly, use transactional data to identify their purchasing behaviors and preferences.

No model will be 100% accurate. But the right model can be a lot better than what you have today.

How to Use AI for Customer Models

These insights come from Boyd's AI Academy for Marketers course—How To Use Data Exhaust and Machine Learning to Model Behavior.

You can learn more about how to model behavior using machine learning by taking the full course as an AI Academy for Marketers member.

AI Academy for Marketers is our members-only online education platform and community. The Academy features dozens of on-demand courses and certifications taught by leading AI and marketing experts.

The 25+ courses available in AI Academy are complemented by additional exclusive content, including:

  • Live monthly Ask Me Anything sessions with instructors. 
  • The Answering AI series of quick-take videos that provides simple answers to common AI questions.
  • Keynote presentations from the Marketing AI Conference (MAICON).
  • AI Tech Showcase product demos from leading AI-powered vendors.

Individual and team licenses are available. Discounts are offered for students, educators and nonprofits.

Learn more by clicking the button below.

Related Posts

How to Predict Consumer Behavior and Recommend Products with AI

Paul Roetzer | December 18, 2019

Xineoh uses AI, including deep learning, simulation, and optimization algorithms, to predict consumer behavior. Here's how.

How to Boost Ecommerce Sales with AI

Paul Roetzer | November 26, 2019

Zoovu uses AI to drive ecommerce sales. Here's how.

Klevu Uses Artificial Intelligence to Help Ecommerce Stores Sell More

Paul Roetzer | September 5, 2017

Klevu is an AI-powered ecommerce search solution that grows revenue for brands. This interview describes how the company does it.