Mastering Marketing Data Science

EXERCISE 8.1: MARKETING MIX MODELING (MMM)

Objective: Develop a multiple regression model to understand the impact of various marketing efforts on a hypothetical company's sales.

Tasks:

1. Load the generated data into a DataFrame.
2. Perform exploratory data analysis (EDA) to understand data distributions and correlations.
3. Build a multiple regression model to analyze the influence of each marketing channel on sales.
4. Interpret the coefficients and evaluate the model's performance.

Steps:

1. Loading Libraries:

   First, we need to import the necessary libraries for data manipulation and statistical analysis.

   1. import pandas as pd
   2. import numpy as np
   3. import statsmodels.api as sm

   • pandas is used for data manipulation and analysis.
   • numpy is for numerical operations.
   • statsmodels is for estimating and interpreting models for statistical analysis.
2. Loading the Data:

   Next, we load the generated CSV file into a DataFrame. This is where our MMM data resides.

   4. df = pd.read_csv(‘/mnt/data/marketing_mix_modeling_data.csv’)

   • We use “pd.read_csv” to read the CSV file and load it into a DataFrame named df.
3. Exploratory Data Analysis (EDA):

   Before modeling, it's crucial to understand the data. Let's get a quick overview and check for any anomalies or patterns.

   5. print(df.describe())
   6. print(df.corr())
   

   • df.describe() provides a statistical summary of the DataFrame, including mean, standard deviation, and quartiles.
   • df.corr() calculates the correlation matrix, helping us understand the relationships ...