This article provides a descriptions of various approaches you can use to define your strategy and goals before you begin creating and training models.

In this article:

Table of Contents Placeholder

The variety of approaches you can use to define your strategy and goals using Machine Learning is virtually endless. For example, you can augment existing use cases, create exploratory models, or create specific use cases for targeted findings.

Implementation

If you have Tealium AudienceStream with data collection enabled, you have the ability to begin using Tealium Predict. There are no further requirements. For additional information, see Prerequisites.

As a general approach to get started, train one or more models and review the Included Attributes on the Training Details report.

Predict Included Attributes.jpg

The details in this report provide you with a source of new insights about which Tealium AudienceStream data points are the key drivers to an important user event. You can also use Tealium Predict as a tool for learning more about your data, your implementation, and as a source of information for new ideas.

Best Practices

The following proven best practices provide more robust results:

  • Define the problem you want to solve and goals to achieve resolution.
  • Establish best practices regarding collecting and cleaning your data before you begin. For more information, see Data Readiness.
  • During your data readiness stage, join siloed datasets and consider other characteristics of your organization's data that can be refined.
  • Where possible, use longer date ranges for training.
  • Consider ways to use Tealium Predict to improve or augment existing audiences and make them more efficient.
  • Ensure that your Tealium AudienceStream profile has high-quality attributes to signal target behaviors, such as Badges or Booleans. If none are present, create the attributes as soon as possible to allow more time for data to accumulate.
  • Deploy your model in a production environment or real-world application.
  • Evaluate how well your model is working in production and return to the training and testing stages as needed for improvements.

Use Cases

To help gather ideas to start your implementation or to implement proven use cases, review the following articles about popular and emerging machine learning use cases being implemented by Tealium customers.

  • Proactive Marketing: Machine Learning-Powered Segmentation
  • Marketing Efficiency: Improve Purchase or Conversion Rate
  • Customer Retention: Reduce Churn
  • Customer Experience: Funnel Optimization
  • Predictive Analytics: Get Customer Insights and Validate Assumptions

For detailed information, see Tealium Predict ML: Top 5 Emerging Machine Learning Use Cases with Customer Data.