Marketing Campaign Results in Marketing campaign data results of 2,240 customers of Maven Marketing, including customer profiles, product preferences, campaign successes/failures, and channel performance.
The proposed data pre-processing includes the following steps. First, clean up the column names by checking the meaning and removing the space if there is any. Second, fix the misspelling and misclassified observations. Third, variables with more than 40% missing values will be removed. Finally, I analyze the numeric variables of the data set to check for any suspicious outliers. To handle this problem, we calculate the statistical values of the data (average, max, min, percentile.., etc). Then we extract the suspected variable that might have potential outliers, we plot the variable using a boxplot to get the threshold that allows us to remove observations with outliers.
marital status, Graduation, and Income are significantly related to the number of web purchases. From the histogram visualization of the number of purchases per category of marital status and education, we can conclude that the graduate married couples have the highest number of web purchases
The 5th marketing campaign was the most successful, this was shown using SQL query
The average customer is aged between 20-40 years, married and graduated from college or university
The best performing product is the wine