You’re a marketing analyst and you’ve been told by the Chief Marketing Officer that recent marketing campaigns have not been as effective as they were expected to be. You need to analyze the data set to understand this problem and propose data-driven solutions.
The data set
marketing_data.csv consists of 2,240 customers with data on:
- Customer profiles
- Product preferences
- Campaign successes / failures
- Channel performance
- Are there any null values or outliers? How will you wrangle/handle them?
- Are there any variables that warrant transformations?
- Are there any useful variables that you can engineer with the given data?
- Do you notice any patterns or anomalies in the data? Can you plot them?
- What factors are significantly related to the number of store purchases?
- Does US fare significantly better than the Rest of the World in terms of total purchases?
- Your supervisor insists that people who buy gold are more conservative. Therefore, people who spent an above average amount on gold in the last 2 years would have more in store purchases. Justify or refute this statement using an appropriate statistical test
- Fish has Omega 3 fatty acids which are good for the brain. Accordingly, do “Married PhD candidates” have a significant relation with amount spent on fish? What other factors are significantly related to amount spent on fish? (Hint: use your knowledge of interaction variables/effects)
- Is there a significant relationship between geographical regional and success of a campaign?
- What does the average customer look like for this company?
- Which products are performing best?
- Which channels are underperforming?
Bring together everything from Sections 1 to 3 and provide data-driven recommendations or suggestions to your CMO.
Please see the full notebook and project files below for a full report.