Marketing Analytics & A/B Testing With Excel Python Powerbi
Published 6/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.52 GB | Duration: 3h 27m
Analyzing marketing campaign performance, web traffic, customer demographics, customer retention, CLV, CVR, A/B testing
What you'll learn
Learn the basic fundamentals of marketing analytics and A/B testing
Learn about important marketing metrics, such as conversion rate, customer acquisition cost, ROI, click through rate, and customer lifetime value
Learn how to analyze marketing campaign performance
Learn how to calculate ROI and compare initial marketing budget vs actual spend
Learn how to analyze customer retention
Learn how to analyze customer lifetime value
Learn how to analyze web traffic data
Learn how to analyze web conversion rate
Learn how to conduct customer segmentation analysis using unsupervised machine learning
Learn how to perform A/B testing with SciPy
Learn how to predict customer churn using CatBoost Classifier
Learn how to predict customer lifetime value using MLP Regressor
Learn how to visualize customer demographics data using PowerBI
Learn how to visualize marketing campaign performance data using PowerBI
Learn how to visualize web traffic data using PowerBI
Requirements
No previous experience in marketing analytics is required
Basic knowledge in Microsoft Excel and Python
Description
Welcome to Marketing Analytics & A/B Testing with Excel, Python, PowerBI course. This is a comprehensive project based course where you will learn how to analyze marketing data, evaluate marketing campaign performance, segment customer data, and run effective A/B testing. This course is a perfect combination between marketing and data analysis, making it an ideal opportunity to practice your statistical skills while improving your technical knowledge in digital marketing. In the introduction session, you will learn the basic fundamentals of marketing analytics and A/B testing, such as getting to know marketing campaign key metrics and workflow. Then in the next section, we will start analyzing marketing data using Microsoft Excel. In the first section, we are going to analyze marketing campaign performance by calculating key metrics such as conversion rates, click through rates, and engagement scores across different channels to understand which campaigns perform best. Then, we are going to segment customer data based on purchase behavior and demographics to help tailor more effective marketing strategies for specific groups. After that, we are going to calculate return on investment by comparing planned marketing budgets with actual spending and sales revenue to evaluate the financial efficiency of each campaign. Next, we are going to conduct a basic A/B test by comparing different campaign versions, like email subject lines or landing pages, and measure results such as open and conversion rates to determine which version performs better. Then, we are also going to analyze customer retention by tracking repeat purchases over time to understand customer loyalty. Following that, we are going to estimate Customer Lifetime Value by using metrics like purchase history, tenure, total spend to help us to assess the long term value of our customers. Afterward, in the next section, we are going to analyze web traffic data using Python by evaluating total sessions, bounce rates, and session durations to understand how users interact with a website. Then, we are also going to calculate web conversion rates to identify how many visitors complete desired actions, such as signing up or making a purchase. Following that, we are going to segment customer data using hierarchical clustering based on behavior and transaction history to identify meaningful groups that can be targeted more effectively. We are going to run A/B testing using SciPy specifically, we will perform statistical tests to compare control and test groups, helping us make decisions based on data. In the next section, we are going to predict customer churn using CatBoost. This machine learning model will analyze factors like tenure, balance, and usage patterns to predict if the customer is more likely to leave or stay. After that, we are going to predict Customer Lifetime Value using the Multi Layer Perceptron Regression model to forecast future customer worth based on purchase history and total spend data. Lastly, at the end of the course, we are going to visualize marketing data using Power BI. We are going to visualize marketing campaign performance, customer demographics, and web traffic data using pie charts, bar charts and scatter plots.Before getting into the course, we need to ask this question to ourselves, why marketing analytics is very important? Well, here is my answer, marketing analytics helps businesses turn marketing data into actionable and valuable insights that enable better decision-making, campaign optimization, and customer targeting. It also helps companies to allocate their budgets more effectively, improve ROI, and gain a competitive edge by understanding what truly drives customer engagement and conversions.Below are things that you can expect to learn from this course:Learn the basic fundamentals of marketing analytics and A/B testingLearn about important marketing metrics, such as conversion rate, customer acquisition cost, ROI, click through rate, and customer lifetime valueLearn how to analyze marketing campaign performanceLearn how to calculate ROI and compare initial marketing budget vs actual spendLearn how to analyze customer retentionLearn how to analyze customer lifetime valueLearn how to analyze web traffic dataLearn how to analyze web conversion rateLearn how to conduct customer segmentation analysis using unsupervised machine learningLearn how to perform A/B testing with SciPyLearn how to predict customer churn using CatBoost ClassifierLearn how to predict customer lifetime value using MLP RegressorLearn how to visualize customer demographics data using PowerBILearn how to visualize marketing campaign performance data using PowerBILearn how to visualize web traffic data using PowerBI
Overview
Section 1: Introduction to the Course
Lecture 1 Introduction
Lecture 2 Table of Contents
Lecture 3 Whom This Course is Intended for?
Section 2: Tools, IDE, and Resources
Lecture 4 Tools, IDE, and Resources
Section 3: Introduction to Marketing Analytics & A/B Testing
Lecture 5 Introduction to Marketing Analytics & A/B Testing
Section 4: Analyzing Marketing Campaign Performance
Lecture 6 Analyzing Marketing Campaign Performance
Section 5: Segmenting Customer Data
Lecture 7 Segmenting Customer Data
Section 6: Calculating ROI & Comparing Initial Marketing Budget vs Actual Spend
Lecture 8 Calculating ROI & Comparing Initial Marketing Budget vs Actual Spend
Section 7: Conducting Basic A/B Testing
Lecture 9 Conducting Basic A/B Testing
Section 8: Analyzing Customer Retention
Lecture 10 Analyzing Customer Retention
Section 9: Analyzing Customer Lifetime Value
Lecture 11 Analyzing Customer Lifetime Value
Section 10: Analyzing Web Traffic Data
Lecture 12 Analyzing Web Traffic Data
Section 11: Analyzing Web Conversion Rate
Lecture 13 Analyzing Web Conversion Rate
Section 12: Customer Segmentation Analysis with Unsupervised Machine Learning
Lecture 14 Customer Segmentation Analysis with Unsupervised Machine Learning
Section 13: Performing A/B Testing with SciPy
Lecture 15 Performing A/B Testing with SciPy
Section 14: Predicting Customer Churn with CatBoost Classifier
Lecture 16 Predicting Customer Churn with CatBoost Classifier
Section 15: Predicting Customer Lifetime Value with MLP Regressor
Lecture 17 Predicting Customer Lifetime Value with MLP Regressor
Section 16: Visualizing Customer Demographics Data with PowerBI
Lecture 18 Visualizing Customer Demographics Data with PowerBI
Section 17: Visualizing Marketing Campaign Performance Data with PowerBI
Lecture 19 Visualizing Marketing Campaign Performance Data with PowerBI
Section 18: Visualizing Web Traffic Data with PowerBI
Lecture 20 Visualizing Web Traffic Data with PowerBI
Section 19: Conclusion & Summary
Lecture 21 Conclusion & Summary
Digital marketers who are interested in turning marketing data into actionable and valuable business insights,Data analysts who are interested in analysing and visualising marketing data using Excel, Python, and Power BI
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