Sales Analytics Course

Lesson 1 • Sales Data Sources and Types

Maps the landscape of internal and external data sources available to sales teams. Explains structured vs. unstructured data relevance.

Lesson 2 • The Sales Analytics Workflow

Outlines the end-to-end process from question formulation to insight delivery. Provides a repeatable framework applied in later chapters.

Lesson 3 • What Sales Analytics Means

Defines sales analytics and distinguishes it from general business intelligence. Establishes the vocabulary used throughout the course.

Lesson 1 • CRM Data Architecture Basics

Explains how CRM systems store sales data and how records relate to each other. Provides context for extracting clean, usable datasets.

Lesson 2 • Identifying and Fixing Data Issues

Teaches detection and correction of duplicates, missing values, and outliers. Directly improves the accuracy of all analyses built on this data.

Lesson 3 • Data Governance for Sales Teams

Establishes ownership, access controls, and audit processes for sales data. Ensures data integrity is maintained over time across the organisation.

Lesson 4 • Data Collection Best Practices

Covers methods for capturing complete and consistent sales data at the source. Reduces downstream cleaning effort through upstream discipline.

Lesson 1 • Descriptive Statistics for Sales

Applies measures of central tendency and dispersion to sales metrics. Provides a statistical baseline for comparing performance across segments.

Lesson 2 • Trend and Seasonality Analysis

Identifies directional trends and recurring seasonal patterns in sales time series. Supports accurate target-setting and resource planning.

Lesson 3 • Correlation and Relationship Exploration

Measures statistical relationships between sales activities and outcomes. Guides hypothesis formation for predictive modelling in later chapters.

Lesson 4 • Exploratory Visualisation Techniques

Uses charts and graphs to surface patterns quickly during the exploration phase. Builds visual intuition before formal modelling begins.

Lesson 1 • Funnel Optimization Strategies

Translates funnel diagnostics into prioritised actions for sales managers. Connects analytical findings to coaching and process improvement decisions.

Lesson 2 • Pipeline Structure and Terminology

Defines pipeline stages, opportunity attributes, and weighted value concepts. Establishes the shared language used in all pipeline analyses.

Lesson 3 • Pipeline Health Diagnostics

Assesses pipeline balance, aging deals, and stage distribution for risk signals. Enables proactive management before revenue shortfalls materialise.

Lesson 4 • Conversion Rate Analysis

Calculates stage-to-stage conversion rates and identifies where deals stall. Pinpoints the highest-leverage improvement opportunities in the funnel.

Lesson 1 • Regression-Based Forecasting

Uses linear and multiple regression to model relationships between leading indicators and revenue. Introduces statistical forecasting without requiring advanced maths prerequisites.

Lesson 2 • Forecasting Fundamentals

Explains forecast types, accuracy metrics, and the cost of forecast error. Sets expectations for what good forecasting looks like in practice.

Lesson 3 • Forecast Accuracy and Improvement

Measures forecast performance over time and diagnoses systematic errors. Builds a continuous improvement loop for forecasting processes.

Lesson 4 • Pipeline-Based Forecasting

Derives revenue forecasts directly from pipeline stage probabilities and values. Integrates pipeline analytics skills from the previous chapter.

Lesson 1 • Quota Setting and Attainment Analysis

Explains quota allocation methods and analyses attainment distributions across reps. Identifies whether quotas are calibrated fairly and realistically.

Lesson 2 • Rep-Level Performance Diagnostics

Breaks down individual rep performance into activity, pipeline, and outcome layers. Enables targeted coaching by isolating the root cause of underperformance.

Lesson 3 • Team and Territory Performance

Compares performance across teams, regions, and territories to surface structural advantages or gaps. Informs resource allocation and territory design decisions.

Lesson 4 • KPI Framework Design

Guides selection of leading and lagging indicators aligned to sales strategy. Prevents metric overload by prioritising the most predictive measures.

Lesson 1 • Upsell and Cross-Sell Analytics

Identifies expansion revenue opportunities within the existing customer base. Quantifies the revenue impact of product attach and upgrade motions.

Lesson 2 • Customer Lifetime Value Analysis

Calculates and interprets CLV to prioritise acquisition and retention investments. Links CLV to sales strategy and resource allocation decisions.

Lesson 3 • Revenue Mix and Concentration Risk

Analyses revenue distribution across customers, products, and channels for concentration risk. Supports strategic decisions about diversification and dependency reduction.

Lesson 4 • Customer Segmentation for Revenue

Applies RFM and value-based segmentation to rank customers by revenue potential. Directs sales effort toward the segments with the highest return.

Lesson 1 • Churn Prediction Modeling

Builds models that identify at-risk customers before they churn using behavioural signals. Extends the retention analytics from Chapter 7 with predictive capability.

Lesson 2 • Model Deployment and Monitoring

Covers the process of putting models into production and tracking their ongoing accuracy. Ensures predictive tools remain reliable as market conditions change.

Lesson 3 • Predictive Modeling Foundations

Introduces supervised learning concepts applied to sales use cases. Bridges statistical knowledge from earlier chapters to machine learning methods.

Lesson 4 • Deal Outcome Prediction

Predicts win or loss probability for open opportunities using pipeline attributes. Improves forecast accuracy and guides deal-level coaching decisions.