Marketing Analytics with Python
Marketing Analytics is an intermediate-level course of 5 lessons (about 1 h 27 min), with 15 exercises and 15 quiz questions. Prerequisites: Business Metrics and KPIs. Exercises run in the browser, with nothing to install.
- Business analytics
- Intermediate
- 5 lessons
- 1 h 27 min
- 15 exercises
- Exercises run in the browser
The course in brief
This course applies data analysis to the questions a marketing team asks: where do visits come from, which channel deserves which share of sales, where do customers drop off, which campaigns are really profitable. You work with pandas on small fictional datasets, then learn to present honest results: a clearly stated attribution model, test uncertainty, explicit alerts.
What you will be able to do
- Normalize UTM parameters and group traffic into channels using documented rules
- Compute and compare attribution models (first click, last click, linear, U-shaped)
- Build a strict conversion funnel and locate the drop-off step by segment
- Evaluate a campaign's profitability on margin and measure its incremental effect
- Analyze a campaign A/B test and build a weekly dashboard with alerts
Syllabus: 5 lessons
- Understand where marketing data comes from (sessions, touchpoints, conversions)
- Read and normalize the UTM parameters of a URL
- Group source / medium pairs into channels with explicit rules
- 02
Campaign Attribution: Sharing the Credit for a Conversion
- Explain why several channels contribute to the same conversion
- Compute first-click, last-click, linear, and U-shaped (position-based) attributions
- Compare the models on a set of journeys with pandas and draw cautious conclusions
- 03
Conversion Funnel: Measuring Drop-Off Step by Step
- Build a conversion funnel from events
- Distinguish step rate, overall rate, and drop-off
- Compare a funnel across segments to locate the step to improve
- 04
Advertising ROI: Breakeven, Incrementality, and Marginal Return
- Compute the breakeven ROAS and the margin-based ROI of each campaign
- Measure a campaign's incremental effect with a control group
- Reason about marginal return to reallocate a budget
- 05
Campaign A/B Tests and the Marketing Dashboard
- Analyze a campaign A/B test (email subject line, ad) with a two-proportion test
- Build a weekly campaign dashboard with changes and alerts
- Present marketing results by stating the attribution model, period, and uncertainty
Final project
Marketing Performance Audit of an Online Store
Using fictional data generated in Python (sessions with UTMs, touchpoints, funnel events, spend per campaign, and results of a test with a control group), carry out a store's marketing audit: tagging quality, breakdown by channel, comparison of two attribution models, funnel by device, profitability on margin, and incremental effect. Conclude with a budget reallocation proposal justified by marginal return.
Deliverables
- A reproducible Python script or notebook (fixed random seed) that generates the data and performs all the analyses
- A UTM tagging control table (non-compliant values, share of direct traffic)
- A table comparing revenue by channel under two attribution models
- The strict funnel by device with the priority step identified
- A profitability table by campaign (ROAS, threshold, ROI on margin) and the iROAS calculation
- A one-page note with the proposed reallocation and its limits
Assessment criteria
- The grain of each table is identified and respected in the calculations
- The sum of attribution credits is checked and the model is always stated
- Ratios are computed on sums and step rates are distinguished from overall rates
- Profitability is judged on margin and the difference between attributed revenue and incremental revenue is explained
- Recommendations rely on marginal return and mention uncertainty
Frequently asked questions
Do I need to know how to program to follow this course?
You need the basics of pandas (filtering, grouping, aggregating) and the indicators from the Business Metrics and KPIs course (DA2). The exercises stay short and each calculation is explained first.
Does the course explain Google Analytics 4?
The course builds on GA4 concepts (events, sources, funnels) and offers a task in a reporting tool, but most of the work is done in Python so you understand the calculations these tools perform.
Which attribution model should I choose?
No rule-based model is absolutely true. The course teaches you to compare several to spot opening and closing channels, then to measure a channel's real effect with an incrementality test.
Is the data used real?
No, all datasets are fictional and built in the code, so that every result is verifiable.
This course, told for your job
Say what you want to be able to do: “Marketing Analytics” takes its place in your path, with examples from your field. The concepts stay those of the course.