Predictive Customer Analytics

Learn about the customer life cycle and how predictive analytics can improve the customer journey. Explore using predictive analytics to identify, attract, and retain customers.

Introduction

• The Power of Predictive Analytics
• Expectations and course organization
• How to use the exercise files

1. Customer Analytics Overview

• The importance of customer analytics
• The customer lifecycle
• Apply analytics to the customer lifecycle
• Sources of customer data
• The customer analytics process
• Use case: Online computer store

2. Will You Become My Customer?

• The customer acquisition process
• Find high propensity prospects
• Recommend best channel for contact
• Offer chat based on visitor propensity
• Use case: Determine customer propensity

3. What Else Are You Interested In?

• Upselling and cross-selling
• Find items bought together
• Create customer group preferences
• User-item affinity and recommendations
• Use case: Recommend items

4. How Much Is Your Future Business Worth?

• Generate customer loyalty
• Create customer value classes
• Discover response patterns
• Predict customer lifetime value
• Use case: Predict CLV

5. Are You Happy with Me?

• Improve customer satisfaction
• Predict intent of contact
• Find unsatisfied customers
• Group problem types
• Use case: Group problem types

6. Will You Leave Me?

• Prevent customer attrition
• Predict customers who might leave
• Find incentives
• Discover customer attrition patterns
• Use case: Customer patterns

7. Best Practices

• Devise customer analytics processes
• Choose the right data
• Design data processing pipelines
• Implement continuous improvement

Conclusion

• Next steps and additional resources