This notebook will accomplish the following task:
- pandas
- numpy
- matplotlib
- seaborn
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.
- 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?
Please plot and visualize the answers to the below questions.
- Which marketing campaign is most successful?
- What does the average customer look like for this company?
- Which products are performing best?
- Which channels are underperforming?
- The most successful advertising campaign was the most recent campaign (column name: Response), and was particularly successful in Mexico (>60% acceptance rate!)
- Suggested action: Conduct future advertising campaigns using the same model recently implemented in Mexico.
- Advertising campaign acceptance is positively correlated with income and negatively correlated with having kids/teens
- Suggested action: Create two streams of targeted advertising campaigns, one aimed at high-income individuals without kids/teens and another aimed at lower-income individuals with kids/teens
- Suggested action: Create two streams of targeted advertising campaigns, one aimed at high-income individuals without kids/teens and another aimed at lower-income individuals with kids/teens
- The most successful products are wines and meats (i.e. the average customer spent the most on these items)
- Suggested action: Focus advertising campaigns on boosting sales of the less popular items