Portfolio Project: Optimizing marketing spend using Market Mix Modeling

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Section 1: Introduction

  1. Problem Statement
  2. Download Resources

Section 2: Understanding Data

  1. Packages used
  2. Load Data
  3. Data Cleaning Part - 1
  4. Data Cleaning Part - 2

Section 3: Feature Engineering

  1. Feature Engineering Part 1 - List price, payday, etc
  2. Outlier Treatment
  3. Data Preprocessing - Week finder
  4. Adstock variables
  5. EMA and SMA variables
  6. NPS Score
  7. Sales Calendar
  8. Climate Data
  9. Outlier Treatment
  10. Payday week and Holiday week

Section 4: Exploratory Data Analysis (EDA)

  1. EDA Insights Part 1
  2. EDA Insights Part 2
  3. EDA Insights Part 3

Section 5: Model Building

1 Approach 1 - Building Additive models 2. Stepwise model selection logic Theory 3. Approach 2 - Customized stepwise selection - Code Demo 4. Multicollinearity problems 5. Model building for Home Audio and Camera Accessory 6. Approach 3 - Building Multiplicative models

Section 6: Distributive Lags Models

  1. Outlier Treatment
  2. Approach 4 - Koyck Model of Distributed Lags
  3. Determining the net effect of Influencers - Elasticity
  4. Koyck Model part 2
  5. Approach 5 - Distributive Lag Model(Additive)
  6. Approach 6 - Distributive Lag Model(Multiplicative)

Section 7: Non-Linear Modeling with GAM

  1. Introduction to GAM modelling - Theory
  2. Build GAM Models Part 1 - Code Demo
  3. Build GAM Models Part 2 - Code Demo

Section 8: Budget Optimization Strategies

  1. Strategy 1 - Unconstrained Budget optimization one lever at a time (Demo)
  2. Strategy 2 - Budget optimization with multiple levers(Demo)
  3. Strategy 3 - Optimize multiple channels in presence of total budget constraint
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