In this project we are going to analyze the "Marketing Campaign Performance ”, which contains detailed information about the performance of various marketing campaigns carried out by a company in Malaysia. The objective is to apply data processing and cleaning techniques, exploratory analysis and visualization to extract valuable insights that can improve the company's marketing strategies.
- Understand and preprocess the dataset: Identify and handle missing values, inconsistencies and outliers.
- Perform exploratory data analysis (EDA): Discover patterns and significant relationships between variables.
- Visualize data: Create informative visualizations using Matplotlib, Seaborn and Plotly.
- Draw conclusions: Interpret results and provide recommendations based on the analysis.
The dataset includes the following features:
- Campaign_ID: Unique identifier of each campaign.
- Channel: Marketing channel used (e.g., Social Media, Email, TV, News, Radio, Billboard).
- Product_Category: Product category (e.g., Electronics, Fashion, Groceries, Automotive, Health).
- Region: Geographic region in Malaysia where the campaign was targeted (e.g., Kuala Lumpur, Penang, Malacca, Johor, Sabah, Sarawak).
- Budget: Allocated budget in Malaysian Ringgit.
- Spend: Actual campaign expenditure in Ringgit Malay.
- Impressions: Number of times the ad was viewed.
- Clicks: Number of clicks the ad received.
- Click Through Rate: Number of clicks the ad received.
- Conversions: Number of successful actions (e.g., purchases, registrations).
- CR: Conversion Rate (Conversion Rate).
- Revenue: Revenue generated by the campaign in Malaysian Ringgit.
- ROI: Return on Investment (Revenue divided by Spend).
- Duration: Campaign duration in days.
- Start_Date: Campaign start date.
- End_Date: Campaign end date.
- Customer_Satisfaction: Customer satisfaction rating (1 to 5).
- Campaign_Effectiveness: Effectiveness of the campaign according to the marketing team (1 to 10).
- Market_Segment: Target market segment (Low, Medium, High).