CRM Analytics Course

Lesson 1 • What CRM Systems Do

Defines CRM architecture, modules, and data capture mechanisms. Connects system structure to the analytics opportunities explored throughout the chapter.

Lesson 2 • Analytics Value in CRM Contexts

Explains why analytics transforms raw CRM data into business decisions. Frames the ROI argument used to justify analytics investment.

Lesson 3 • Analytics Maturity in Organizations

Maps the progression from descriptive reporting to predictive strategy within CRM teams. Sets expectations for skill development across the course.

Lesson 4 • CRM Data Types and Structures

Distinguishes structured, semi-structured, and unstructured CRM data. Prepares students to handle diverse data formats in later analytical work.

Lesson 1 • Data Governance Frameworks

Establishes policies, roles, and accountability structures for ongoing data quality. Connects governance to sustainable analytics programs.

Lesson 2 • Data Cleansing Techniques

Covers systematic methods for correcting, standardizing, and deduplicating CRM records. Directly enables accurate metric calculation in later chapters.

Lesson 3 • Privacy and Compliance Fundamentals

Addresses consent management, data minimization, and customer rights within CRM systems. Ensures analytics work respects applicable privacy obligations.

Lesson 4 • Diagnosing Data Quality Issues

Identifies the most common CRM data defects and their business impact. Grounds students in quality assessment before introducing remediation techniques.

Lesson 1 • Service and Support Reporting

Covers case volume, resolution time, and customer satisfaction metrics in CRM service modules. Connects service data to overall customer health analysis.

Lesson 2 • Sales and Pipeline Reporting

Focuses on opportunity stage analysis, win/loss ratios, and forecast accuracy reports. Provides the sales analytics foundation for advanced pipeline work later.

Lesson 3 • Filtering, Grouping, and Sorting Data

Teaches conditional filters, field groupings, and sort logic to isolate relevant data. Builds precision in report construction used throughout the course.

Lesson 4 • Dashboard Design Principles

Applies data visualization best practices to CRM dashboard layout and component selection. Ensures dashboards communicate insights rather than just display data.

Lesson 1 • Segmentation Strategy Fundamentals

Defines segmentation goals, criteria types, and business alignment. Establishes the strategic rationale before introducing technical methods.

Lesson 2 • Behavioral and Engagement Profiling

Uses interaction history, product usage, and channel preference data to build rich customer profiles. Deepens segment precision beyond demographic attributes.

Lesson 3 • RFM Analysis in CRM

Applies Recency, Frequency, and Monetary scoring to rank and group customers by value. Produces immediately usable segments for marketing and retention campaigns.

Lesson 4 • Clustering Techniques for Segmentation

Introduces k-means and hierarchical clustering as data-driven alternatives to rule-based segments. Bridges descriptive analytics to predictive methods introduced later.

Lesson 1 • Predictive CLV Modeling

Applies probabilistic and regression-based approaches to forecast future customer value. Extends historical CLV into forward-looking strategic planning.

Lesson 2 • Building a Historical CLV Model

Constructs CLV from transaction history using average order value, purchase frequency, and retention rate. Provides a baseline model before introducing predictive extensions.

Lesson 3 • CLV Segmentation and Tiering

Uses CLV scores to create value tiers that guide differentiated service and marketing investment. Connects CLV output to the segmentation skills from the previous chapter.

Lesson 4 • Monitoring and Updating CLV Models

Establishes refresh cadences, drift detection, and retraining triggers for CLV models. Ensures models remain accurate as customer behavior evolves.

Lesson 1 • Model Evaluation and Threshold Setting

Applies precision, recall, AUC-ROC, and lift charts to assess churn model performance. Guides threshold selection to balance intervention cost and missed churn.

Lesson 2 • Churn Definition and Measurement

Establishes voluntary vs. involuntary churn, churn rate formulas, and cohort-based measurement. Creates the precise definitions needed for reliable model targets.

Lesson 3 • Churn Prediction Model Building

Trains logistic regression, decision tree, and ensemble models on labeled churn data. Produces a scored customer list ready for retention action.

Lesson 4 • Churn Drivers and Feature Engineering

Identifies behavioral, transactional, and service signals that precede churn. Translates raw CRM data into predictive features for model training.

Lesson 1 • Quota and Territory Analytics

Uses historical performance and market potential data to set equitable quotas and territories. Connects forecasting outputs to sales planning decisions.

Lesson 2 • Forecast Accuracy and Continuous Improvement

Tracks forecast bias, mean absolute error, and accuracy trends to improve future projections. Establishes a feedback culture between analytics and sales leadership.

Lesson 3 • Pipeline Health Analysis

Evaluates pipeline coverage, stage conversion rates, and deal aging to diagnose sales performance. Builds on pipeline reporting skills from the descriptive analytics chapter.

Lesson 4 • Forecasting Methods in CRM

Compares weighted pipeline, category-based, and AI-assisted forecasting approaches. Equips students to select the right method for their organization's data maturity.

Lesson 1 • Measuring Analytics Program ROI

Quantifies the business impact of CRM analytics initiatives using revenue, cost, and efficiency metrics. Enables ongoing justification of analytics investment to leadership.

Lesson 2 • Building a CRM Analytics Roadmap

Structures a phased analytics roadmap aligned to business priorities and data maturity. Translates technical capability into a stakeholder-ready strategic plan.

Lesson 3 • Next-Best-Action and Personalization

Applies decision logic and model scores to recommend the optimal next interaction for each customer. Advances from descriptive insight to prescriptive, real-time action.

Lesson 4 • Attribution Modeling for CRM

Evaluates first-touch, last-touch, and multi-touch attribution models to credit revenue across channels. Informs budget allocation and campaign investment decisions.