Marketing Data Analysis with Excel and Sheets Course

Lesson 1 • What Marketing Data Analysis Means

Defines marketing analytics and distinguishes it from general business analytics. Establishes the analytical mindset required throughout the course.

Lesson 2 • Setting Up Your Analytical Workspace

Configures Excel and Google Sheets for marketing analysis work. Covers file organisation, naming conventions, and version control habits.

Lesson 3 • Data Quality and Integrity Basics

Identifies the most common data quality problems and their impact on analysis. Provides a checklist for evaluating raw data before any calculation.

Lesson 1 • Principles of Tidy Marketing Data

Introduces the tidy-data standard where each variable is a column and each observation is a row. Applies this standard to typical marketing export formats.

Lesson 2 • Handling Dates and Time Periods

Converts date strings into true date values and extracts useful time components. Enables accurate period-over-period comparisons later in the course.

Lesson 3 • Text Cleaning Functions

Uses built-in text functions to standardise campaign names, channel labels, and customer fields. Reduces manual editing time significantly.

Lesson 4 • Validating Cleaned Data

Builds simple validation checks to confirm cleaning was successful. Creates a reusable audit trail for stakeholder transparency.

Lesson 1 • Conditional Aggregation Functions

Applies SUMIF, COUNTIF, AVERAGEIF, and their multi-criteria variants to segment marketing data. Replaces manual filtering with dynamic, formula-driven summaries.

Lesson 2 • Calculating Standard Marketing KPIs

Translates business definitions into spreadsheet formulas for the most common marketing metrics. Builds a reusable KPI formula library.

Lesson 3 • Arithmetic and Percentage Calculations

Builds fluency with the arithmetic operations behind marketing KPIs. Covers growth rates, share calculations, and margin formulas.

Lesson 4 • Dynamic Ranges and Named Ranges

Creates named ranges and dynamic range references to make formulas readable and maintainable. Reduces formula errors when data rows are added.

Lesson 1 • Calculated Fields and Items

Adds custom metric calculations directly inside pivot tables without altering source data. Builds derived KPIs like ROAS and CPA within the pivot.

Lesson 2 • Building Your First Pivot Table

Walks through pivot table creation from a clean marketing dataset. Establishes the rows, columns, values, and filters framework.

Lesson 3 • Pivot Table Best Practices

Covers layout, formatting, and documentation habits that make pivot tables production-ready. Prevents common errors that corrupt reports.

Lesson 4 • Grouping and Segmenting Data

Groups dates, numeric ranges, and text categories to create meaningful marketing segments. Enables channel, campaign, and time-period breakdowns.

Lesson 1 • Choosing the Right Chart Type

Maps common marketing questions to the chart types that answer them best. Prevents misleading visualisations caused by poor chart selection.

Lesson 2 • Formatting Charts for Clarity

Applies formatting rules that reduce chart clutter and direct viewer attention. Aligns visual style with brand and reporting standards.

Lesson 3 • Building Charts from Marketing Data

Creates charts directly from pivot tables and raw data ranges. Covers dynamic chart ranges that update automatically when data changes.

Lesson 4 • Conditional Formatting as a Visual Tool

Uses conditional formatting to highlight performance thresholds, trends, and anomalies in tables. Complements charts with in-cell visual cues.

Lesson 1 • Linking Charts and Metrics to Source Data

Connects dashboard display elements to live calculation sheets using cell references and pivot tables. Ensures all visuals update when source data is refreshed.

Lesson 2 • KPI Scorecards and Summary Tiles

Builds headline metric tiles that show current value, target, and variance at a glance. Uses conditional formatting to signal performance status instantly.

Lesson 3 • Dashboard Maintenance and Refresh Workflow

Establishes a repeatable process for updating dashboards with new data each reporting period. Documents the workflow so any team member can run it.

Lesson 4 • Interactive Controls and Filters

Adds drop-down lists, slicers, and form controls to let users filter the dashboard view. Reduces the need for multiple static report versions.

Lesson 1 • Time-Series and Trend Analysis

Tracks campaign metrics over time to identify trends, seasonality, and anomalies. Uses trendlines and moving averages to smooth noisy data.

Lesson 2 • Reporting Findings to Stakeholders

Translates analytical findings into a concise, insight-led report format. Structures recommendations around data evidence rather than opinion.

Lesson 3 • Channel and Campaign Comparison

Compares performance across channels and campaigns using pivot tables and formulas. Identifies top and bottom performers by multiple KPIs simultaneously.

Lesson 4 • Funnel Drop-Off Analysis

Calculates conversion rates at each funnel stage and identifies where volume is lost. Quantifies the revenue impact of improving each stage.

Lesson 1 • Statistical Summary Functions

Uses AVERAGE, MEDIAN, STDEV, and percentile functions to describe marketing data distributions. Identifies whether averages are representative or misleading.

Lesson 2 • Budget Allocation Optimisation Models

Builds spreadsheet models that allocate budget across channels to maximise a target KPI. Introduces Solver for constrained optimisation problems.

Lesson 3 • Forecasting with Built-In Functions

Applies FORECAST.ETS and FORECAST.LINEAR to project future marketing performance. Evaluates forecast confidence intervals for risk assessment.

Lesson 4 • Correlation and Regression Basics

Measures relationships between marketing inputs and outputs using CORREL and LINEST. Builds simple linear regression models to quantify impact.