Predictive Customer Analytics

Predictive Customer Analytics

1h 37mIntermediate2022-04-12

Authors

Kumaran Ponnambalam

Kumaran Ponnambalam

Working with data for 20+ years

Course details

Use big data to tell your customer's story, with predictive analytics. In this course, instructor Kumaran Ponnambalam teaches you about the customer life cycle and how predictive analytics can help improve every step of the customer journey.

Start off by learning about the various phases in a customer's life cycle. Explore the data generated inside and outside your business, and ways the data can be collected and aggregated within your organization. Then review multiple use cases for predictive analytics in each phase of the customer's life cycle, including acquisition, upsell, service, and retention. For each phase, you also build one predictive analytics solution in Python. In the final videos, Kumaran introduces best practices for creating a customer analytics process from the ground up.

Skills covered

Data ModelingMarketing StrategyPythonMarketingData ScienceOpen SourceOne-Off

Concepts

Introduction

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

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

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

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

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

Are You Happy with Me

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

Will You Leave Me

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

Best Practices

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

Conclusion

  • Next steps and additional resources
40,000 Toman