Machine Learning For Campaign Management




What you'll learn
How to Build Machine Learning Models for Google Ads Campaign Management
Case Study of 360 degree Customer Marketing and Machine Learning to Boost Sales
Case Study for Google Ads Campaign Management
Case Study for Google Ads Campaign Optimization
Case Study for Google Ads Campaign Selection - Facebook Ads, Google Ads
Case Study for Google Ads Campaign Trends Analysis and Compare Benchmarks Ads
Analyze campaign metrics: Interpret ad spends, keyword performance, and conversions using data visualizations
Predict campaign outcomes: Build ML models to forecast campaign performance and impressions
Apply ML algorithms: Use Random Forest and Gradient Boosting for campaign optimization
Perform cohort analysis: Segment and retain customers with marketing cohort and RFM techniques
Optimize revenue: Compare campaigns to maximize ROI and refine budget allocations
Explain model results: Visualize and interpret trends and outcomes of campaign predictions
Boost profits: Create profit models using SMOTE, cost analysis, and machine learning
Identify campaign trends: Leverage historical data to guide future ad strategies
Create data pipelines: Preprocess, engineer features, and scale datasets for ML models.
Build propensity models: Predict purchase likelihood for targeted marketing efforts
Requirements
Basic Knowledge of Python
Fundamentals of Machine Learning
Description
In the age of data-driven marketing, campaigns thrive on insights and intelligent optimization. This course, Machine Learning for Campaign Management, is designed to empower marketers, data analysts, and aspiring data scientists with the tools and techniques to transform marketing campaigns using machine learning. From campaign trend analysis to revenue optimization, this comprehensive course covers every facet of campaign management.Course Highlights:1. Introduction: Understand your campaign's landscape with an in-depth analysis of Google Ad spends, top-performing keywords, and campaign trends. Learn how to visualize campaign spend results effectively.2. Campaign Prediction Using Machine Learning: Discover the power of predictive models. Learn how to preprocess datasets, build ensemble models, and execute campaign pipelines to anticipate campaign performance and optimize conversion rates.3. Campaign Trend Analysis: Identify and analyze emerging campaign trends. Gain hands-on experience building and visualizing trend models to make informed decisions.4. Campaign Comparison - Revenue Optimization: Master comparative analysis techniques to forecast budget vs. conversion rates and visualize benchmarks to optimize revenue across multiple campaigns.5. Campaign Impression Prediction: Dive deep into data pipelines and build machine learning models using Random Forest and Gradient Boosting to predict impressions for platforms like Instagram, Google, and Facebook.6. Click Prediction Using Random Forest Models: Leverage Random Forest models to predict click rates. Learn to build and execute model pipelines, scale datasets, and deliver actionable insights.7. Marketing Cohort Analysis: Explore cohort analysis to understand customer retention and segmentation. Use advanced techniques like K-Means clustering and RFM (Recency, Frequency, Monetary) scoring to visualize and interpret marketing data.8. Profit Booster Model: Build profit-centric models that incorporate logistic regression, XGBoost, and profit estimation equations. Learn to use SMOTE for handling imbalanced datasets and develop profit curves for enhanced decision-making.9. Propensity Model for Product Purchase: Build propensity models to predict customer purchase behavior and develop targeted marketing strategies.This course blends theoretical knowledge with practical implementations, ensuring that you gain hands-on experience in campaign prediction, optimization, and analysis. By the end of this course, you'll be equipped with the expertise to design data-driven marketing campaigns that achieve maximum profitability and efficiency.Enroll now to transform your approach to campaign management with the power of Machine Learning!
Overview
Section 1: Introduction
Lecture 1 Overview of Company's Google Ad Spends
Lecture 2 Overview of Company's Google Ad Spends Continued
Lecture 3 Analysis of the Trend Campaigns
Lecture 4 Plot Chart for Top Keywords Campaigns
Lecture 5 Plot Chart for Top Ad Spends on Campaigns
Lecture 6 Visualize Campaign Ad Spend Results
Section 2: Campaign Prediction using Machine Learning
Lecture 7 Campaign Prediction Overview
Lecture 8 Why Perform Campaign Optimization
Lecture 9 Overview of Campaign Conversion Prediction
Lecture 10 Import Datasets
Lecture 11 Perform Data Proceprocessing
Lecture 12 Scale the Dataset
Lecture 13 Build Ensemble Model Prediction
Lecture 14 Execute Campaign Model Pipeline
Lecture 15 Build Campaign Performance
Lecture 16 Run the AI Model
Lecture 17 Campaign Performance Part 1
Lecture 18 Campiagn Performance Results Part 2
Lecture 19 Campaign Performance Results Part 3
Lecture 20 Campaign Performance Results Part 4
Section 3: Campaign Trend Analysis
Lecture 21 Campaign Trends Overview
Lecture 22 Build Campaign Trends
Lecture 23 Visualize Campaign Trends
Section 4: Campaigns Comparison - Revenue Optimization
Lecture 24 Overview of Campaign Comparison
Lecture 25 Revenue Optimization for Campaigns
Lecture 26 Budget Vs Conversion Forecasting Part 1
Lecture 27 Budget Vs Conversion Forecasting Part 2
Lecture 28 Budget Vs Conversion Forecasting Part 3
Lecture 29 Campaign Management
Lecture 30 Campaign Management - Visualize Benchmark Vs Campaigns
Section 5: Campaign Impression Prediction
Lecture 31 Import and Visualize Dataset
Lecture 32 Visualize Correlation between dependent and independent variables
Lecture 33 Build Data Pipeline - drop columns from the dataset
Lecture 34 Build Data Pipeline - Create Other buckets
Lecture 35 Build Data Pipeline - One Hot Encoding
Lecture 36 Build Data Pipeline Continued
Lecture 37 Split Pipeline
Lecture 38 Build ML Model - Random Forest
Lecture 39 Build ML Model - Execute and Review Results
Lecture 40 Save the ML Model - create pickle file
Lecture 41 Print Prediction Results
Lecture 42 Linear Vs Random Forest model results
Lecture 43 Gradient Boosting Model
Lecture 44 Gradient Boosting Model Continued
Lecture 45 Make Predictions for Instagram, Google, FaceBook Ads
Section 6: Click Prediction using Random Forest Machine Learning Model
Lecture 46 Overview of the Click Prediction Model
Lecture 47 Build ML Model Data Pipeline
Lecture 48 Train Test Split the dataset
Lecture 49 Scale the dataset
Lecture 50 Scale the dataset Continued
Lecture 51 Build Model Pipeline
Lecture 52 Build Model Pipeline Continued Part 1
Lecture 53 Build Model Pipeline Continued Part 2
Lecture 54 Execute Random Forest Regressor Model
Lecture 55 Create Model Results
Lecture 56 Test Prediction Results
Section 7: Marketing Cohort Analysis
Lecture 57 Overview of Marketing Cohort Analysis
Lecture 58 What is Cohort Analysis?
Lecture 59 Clean Dataset
Lecture 60 Import Dataset
Lecture 61 Visualize Data
Lecture 62 Remove Outliers
Lecture 63 Kde Plot for Distribution of Unit Price
Lecture 64 Cohort Type Lecture
Lecture 65 Plot Retention Rate of the Customer
Lecture 66 Plot Customer Vs Revenue Chart Part 1
Lecture 67 Plot Customer Vs Revenue Chart Part 2
Lecture 68 Create Pareto Chart Continued
Lecture 69 Aggregate Dataset
Lecture 70 K-Means Clustering Algorithm Part 1
Lecture 71 K-Means Clustering Algorithm Part 2
Lecture 72 K-Means Clustering Algorithm Part 3
Lecture 73 K-Means Clustering Algorithm Part 4
Lecture 74 What is Recency, Frequency, Monetary (RFM) Value
Lecture 75 Prepare RFM Table Part 1
Lecture 76 Prepare RFM Table Part 2
Lecture 77 Build RFM Score
Lecture 78 Visualize RFM Matrix
Section 8: Profit Booster Model
Lecture 79 Build Profit Booster Model
Lecture 80 Overview of Profit Booster Model
Lecture 81 Import and Enrich the Dataset
Lecture 82 Filter the Dataset
Lecture 83 Preprocessing of the Dataset
Lecture 84 Implement SMOTE
Lecture 85 Profit Estimation Equation
Lecture 86 Confusion Matrix - Logistic Regression, XGB Model
Lecture 87 Find Cumulative Cost of Errors
Lecture 88 Build Machine Learning Models - DummyClassifier, XGB Models
Lecture 89 Build Profit Curve and Review Results
Section 9: Propensity Model for Product Purchase
Lecture 90 Introduction
Lecture 91 Import Dataset
Lecture 92 Visualize Data
Lecture 93 Feature Selection Lasso-Ridge Regularization
Lecture 94 Feature Selection and Elimination
Lecture 95 Display Selected Features
Lecture 96 Display Selected Features Continued
Lecture 97 Build Model Pipeline
Lecture 98 Build Model Pipeline Continued
Lecture 99 Build Deep learning Model
Lecture 100 Voting Classifier
Lecture 101 Implement Voting Classifier
Lecture 102 Model Predictions
Beginner Python developer who are ready to Build Machine Learning Apps,Digital Marketers seeking to enhance campaign performance through data-driven insights and predictive modeling,Marketing Analysts who want to leverage machine learning to analyze campaign trends and optimize revenue strategies,Data Scientists interested in applying advanced ML techniques to solve real-world marketing challenges,Business Professionals aiming to improve ad spend efficiency, customer retention, and revenue generation,Students and Beginners exploring how machine learning applies to marketing and campaign management,Entrepreneurs and Small Business Owners looking to optimize their marketing efforts for better ROI


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