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

In this document:

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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 AudienceStream, you have the ability to begin using Predict Machine Learning. 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 can provide you with a source of new insights about which AudienceStream data points are the key drivers to an important user event. You can also use 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 general best practices can 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 any 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 Predict to improve or augment existing audiences and make them more efficient.
  • Ensure that your AudienceStream profile has high-quality attributes to signal target behaviors, such as Badges or Booleans. If they are not 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 jump start your implementation, you can review popular and emerging machine learning use cases being implemented by Tealium customers. From here you can gather ideas or implement proven use cases, such as:

  • 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.