Portfolio Project: Optimizing marketing spend using Market Mix Modeling (MMM)

What you'll be able to do

• What is Market Mix Modeling?
• Various features that could be used for MMM
• How to construct Ad Stock variables
• How to perform EDA for Market Mix Modeling
• Linear and Non-Linear modeling approaches
• Budget optimization strategies with fully solved Python code

1. 01Sec 1: Introduction2 lessons · 15 min

   1. Problem StatementPreview13:38
   2. Download Resources1:00
2. 02Sec 2: Understanding Data4 lessons · 29 min

   1. Packages usedPreview3:04
   2. Load DataPreview10:58
   3. Data Cleaning Part - 1Preview9:21
   4. Data Cleaning Part - 2Preview5:37
3. 03Sec 3: Feature Engineering10 lessons · 42 min

   1. Feature Engineering Part 1 - List price, payday, etcPreview6:20
   2. Outlier Treatment7:21
   3. Data Preprocessing - Week finder3:33
   4. Adstock variables5:55
   5. EMA and SMA variables2:53
   6. NPS Score1:58
   7. Sales Calendar1:53
   8. Climate Data7:16
   9. Payday week and Holiday week4:14
   10. Course Review1:00
4. 04Sec 4: Exploratory Data Analysis (EDA)3 lessons · 40 min

   1. EDA Insights Part 124:49
   2. EDA Insights Part 28:06
   3. EDA Insights Part 37:13
5. 05Sec 5: Model Building6 lessons · 56 min

   1. Approach 1 - Building Additive models13:41
   2. Stepwise model selection logic Theory11:27
   3. Approach 2 - Customized stepwise selection - Code Demo10:07
   4. Multicollinearity problems5:02
   5. Model building for Home Audio and Camera Accessory5:25
   6. Approach 3 - Building Multiplicative models9:58
6. 06Sec 6: Distributive Lags Models5 lessons · 42 min

   1. Approach 4 - Koyck Model of Distributed Lags21:38
   2. Determining the net effect of Influencers - Elasticity2:25
   3. Koyck Model part 23:39
   4. Approach 5 - Distributive Lag Model (Additive)6:29
   5. Approach 6 - Distributive Lag Model (Multiplicative)7:31
7. 07Sec 7: Non-Linear Modeling with GAM3 lessons · 23 min

   1. Introduction to GAM modelling - Theory8:59
   2. Build GAM Models Part 1 - Code Demo11:17
   3. Build GAM Models Part 2 - Code Demo2:38
8. 08Sec 8: Budget Optimization Strategies3 lessons · 14 min

   1. Strategy 1 - Unconstrained Budget optimization one lever at a time (Demo)10:08
   2. Strategy 2 - Budget optimization with multiple levers (Demo)1:31
   3. Strategy 3 - Optimize multiple channels in presence of total budget constraint2:46

What you need

• Basics of Python
• Foundational knowledge of Data Science
• High school maths

Who this course is for

• Data Science Aspirants
• Data Science Professionals
• Software/Data engineers interested in quantitative analysis
• Professionals working with large datasets
• Data analysts, economists, researchers

Your instructor

Prabhakaran S

Principal Data Scientist

My name is Selva, and I am super excited to mentor you on this course!

I head the Data Science team for a global Fortune 500 company and over the last 10 years of my data science experience I've deployed 20+ global products. I'm also the Founder and Chief Author of Machine Learning Plus, which has over 4M annual readers.

I specialize in covering the in-depth intuition and maths of any concept or algorithm. And based on my existing student requests, I've put up the series of courses and projects with detailed explanations, just like on-the-job experience. Hope you love it!

from 3,965 ratings

4.92

learners

57K+

courses

85