Advanced CX Metrics & Customer Success Course

Lesson 1 • The Customer Journey as Measurement Context

Maps metric collection points to journey stages. Connects measurement timing to data relevance and accuracy.

Lesson 2 • Core Satisfaction and Loyalty Metrics

Introduces CSAT, NPS, and CES with their formulas and use cases. Provides the baseline metrics referenced in every subsequent chapter.

Lesson 3 • What CX Metrics Actually Measure

Distinguishes perception, behavior, and outcome metrics. Sets the conceptual framework used throughout the course.

Lesson 4 • Data Sources and Collection Methods

Surveys, interaction logs, behavioral analytics, and third-party reviews as data inputs. Establishes data literacy needed for later analysis chapters.

Lesson 1 • Validity and Reliability in CX Data

Applies construct validity and test-retest reliability concepts to CX instruments. Prevents measurement error from corrupting strategic decisions.

Lesson 2 • Survey Instrument Design

Covers question wording, scale selection, and order effects. Directly determines data quality for all downstream analysis.

Lesson 3 • Governance and Data Quality Controls

Establishes data ownership, collection standards, and audit processes. Creates the infrastructure for trustworthy longitudinal metric tracking.

Lesson 4 • Frequency and Timing of Measurement

Determines optimal survey cadence and trigger logic. Balances data freshness against survey fatigue and response rates.

Lesson 1 • Correlation and Driver Analysis

Identifies which operational variables most strongly predict satisfaction scores. Connects statistical output to actionable CX improvement priorities.

Lesson 2 • Hypothesis Testing and Significance

Covers t-tests, chi-square, and p-value interpretation for CX comparisons. Enables evidence-based claims about metric changes and segment differences.

Lesson 3 • Descriptive Statistics for CX

Mean, median, distribution shape, and variance applied to score data. Provides the analytical baseline before inferential methods are introduced.

Lesson 4 • Segmentation and Cluster Analysis

Groups customers by behavior and attitude using clustering techniques. Enables targeted interventions based on statistically distinct customer profiles.

Lesson 1 • Segmenting Health Score Models

Builds separate models for different customer tiers, industries, or use cases. Prevents one-size-fits-all scores from masking segment-specific risk.

Lesson 2 • Selecting and Weighting Input Signals

Evaluates product usage, support activity, engagement, and financial signals. Teaches evidence-based weighting using correlation with retention outcomes.

Lesson 3 • Calibrating and Validating Health Scores

Tests score accuracy against historical churn and expansion data. Ensures the model predicts outcomes rather than reflecting activity volume.

Lesson 4 • Operationalizing Health Scores in CS Workflows

Integrates health scores into playbooks, alerts, and QBR preparation. Converts model output into daily CSM actions and escalation triggers.

Lesson 1 • Feature Engineering for Churn Models

Creates predictive variables from raw usage, support, and engagement logs. Determines model accuracy more than algorithm choice in most CX contexts.

Lesson 2 • Predictive Churn Modeling Techniques

Applies logistic regression, decision trees, and survival analysis to churn data. Builds on regression skills from Chapter 3 with churn-specific applications.

Lesson 3 • Churn Taxonomy and Root Cause Analysis

Distinguishes voluntary, involuntary, and silent churn with their distinct drivers. Frames root cause analysis as the prerequisite to effective prediction.

Lesson 4 • Retention Intervention Design

Matches intervention type and timing to predicted churn probability bands. Converts model scores into structured save plays with measurable outcomes.

Lesson 1 • Cost of Churn and Retention ROI

Quantifies revenue lost to churn and compares it to retention program costs. Builds the business case for CS investment using defensible financial logic.

Lesson 2 • Forecasting Revenue from CX Data

Uses health scores and churn probabilities to build revenue forecasts. Integrates CX analytics into the financial planning process.

Lesson 3 • Customer Lifetime Value Modeling

Builds CLV models using retention rate, margin, and discount rate inputs. Establishes the financial foundation for all revenue-impact calculations.

Lesson 4 • CX Investment ROI Frameworks

Links CX improvement initiatives to revenue, cost reduction, and risk mitigation. Enables practitioners to justify CX budgets with quantified financial outcomes.

Lesson 1 • Role-Based Reporting Architecture

Designs separate views for CSMs, managers, and executives with appropriate metric depth. Prevents information overload while ensuring each role has actionable data.

Lesson 2 • Real-Time vs. Periodic Reporting

Determines which metrics require live feeds versus scheduled reports. Balances infrastructure cost against the operational value of data freshness.

Lesson 3 • Closing the Loop with Reporting

Connects dashboard insights to action workflows and outcome tracking. Ensures reporting drives behavior change rather than passive observation.

Lesson 4 • Dashboard Design Principles

Applies data visualization best practices to CX metric displays. Ensures dashboards communicate insight rather than overwhelming with raw numbers.

Lesson 1 • Aligning Metrics to Business Strategy

Maps CX metrics to corporate OKRs, revenue goals, and risk priorities. Ensures the measurement program earns executive sponsorship and sustained investment.

Lesson 2 • Stakeholder Influence and Metric Adoption

Applies change management techniques to drive metric adoption across departments. Addresses resistance, competing priorities, and data literacy gaps.

Lesson 3 • Metric Governance and Standardization

Establishes definitions, ownership, and change-control processes for all metrics. Prevents metric proliferation and conflicting definitions across business units.

Lesson 4 • Continuous Improvement of the Metrics Program

Establishes review cycles to retire obsolete metrics and adopt emerging ones. Keeps the program relevant as business models and customer behaviors evolve.