Marketing Analytics & A/B Testing With Excel Python Powerbi

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