Uncovering Opportunities and Risks: Applying Predictive Analytics in CSM
32mGeneral2025-01-06
Authors
Madecraft
Full-Service Learning Content Company
Samuel Cummings
Course details
Looking to harness data for better customer success management? In this course, data scientist and customer success leader Sam Cummings will teach you how to use predictive analytics to identify opportunities and mitigate risks in customer success. Learn to collect and prepare data, develop and evaluate predictive models, and monitor and improve your models. After this course, you'll be ready to use predictive analytics to make informed decisions and drive growth in your organization.
Learning objectives
Identify types and sources of customer success management data.
Clean and preprocess customer success management data.
Develop and train predictive models.
Integrate predictive models with customer success management platforms.
Leverage predictive models to predict customer churn and identify upsell and cross-sell opportunities.
Monitor and improve predictive models.
Learning objectives
Identify types and sources of customer success management data.
Clean and preprocess customer success management data.
Develop and train predictive models.
Integrate predictive models with customer success management platforms.
Leverage predictive models to predict customer churn and identify upsell and cross-sell opportunities.
Monitor and improve predictive models.
Skills covered
Data ModelingCustomer Service ManagementCustomer ServiceLimited SeriesData Science
Concepts
0. Introduction
- 01 - Improve customer success management performance with predictive analytics
1. Collect and Prepare Relevant Customer Data
- 02 - Identify types and sources of CSM data
- 03 - Clean and preprocess data
2. Build Predictive Customer Success Management Models
- 04 - Identify the right problem
- 05 - Explore predictive modeling techniques
- 06 - Develop and deploy predictive models
3. Implement Predictive Analytics in Customer Success Management
- 07 - Leverage predictive models
- 08 - Predict customer churn
- 09 - Identify upsell and cross-sell opportunities
- 10 - Customize predictions for different stakeholders
4. Evaluate, Monitor and Improve Predictive Models
- 11 - Monitor and improve models after launch
Conclusion
- 12 - Boost CSM outcomes through predictive analytics