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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
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