Applied Data Science with PythonDS210

Applied Data Science with Python

Course-End Project Problem Statement

Marketing Campaigns

Problem scenario:

Marketing mix stands as a widely utilized concept in the execution of marketing strategies. It encompasses various facets within a comprehensive marketing plan, with a central focus on the four Ps of marketing: product, price, place, and promotion.

Problem objective:

As a data scientist, you must conduct exploratory data analysis and hypothesis testing to enhance your comprehension of the diverse factors influencing customer acquisition.

Data description:

The variables such as birth year, education, income, and others pertain to the first 'P' or 'People' in the tabular data presented to the user. The expenditures on items like wine, fruits, and gold, are associated with Product. Information relevant to sales channels, such as websites and stores, is connected to Place, and the fields discussing promotions and the outcomes of various campaigns are linked to Promotion.

Steps to perform:

1. After importing the data, examine variables such as Dt_Customer and Income to verify their accurate importation.

2. There are missing income values for some customers. Conduct missing value imputation, considering that customers with similar education and marital status tend to have comparable yearly incomes, on average. It may be necessary to cleanse the data before proceeding. Specifically, scrutinize the categories of education and marital status for data cleaning.

3. Create variables to represent the total number of children, age, and total spending.

1. Derive the total purchases from the number of transactions across the three channels.

4. Generate box plots and histograms to gain insights into the distributions and identify outliers. Implement outlier treatment as needed.

5. Apply ordinal and one-hot encoding based on the various types of categorical variables.

6. Generate a heatmap to illustrate the correlation between different pairs of variables.

7. Test the following hypotheses:

1. Older individuals may not possess the same level of technological proficiency and may, therefore, lean toward traditional in-store shopping preferences.
2. Customers with children likely experience time constraints, making online shopping a more convenient option.
3. Sales at physical stores may face the risk of cannibalization by alternative distribution channels.
4. Does the United States significantly outperform the rest of the world in total purchase volumes?

8. Use appropriate visualization to help analyze the following:

1. Identify the top-performing products and those with the lowest revenue.
2. Examine if there is a correlation between customers' age and the acceptance rate of the last campaign.
3. Determine the country with the highest number of customers who accepted the last campaign.
4. Investigate if there is a discernible pattern in the number of children at home and the total expenditure.
5. Analyze the educational background of customers who lodged complaints in the last two years.