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Customer Marketing Campaign Data
Data Science and Analytics
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About
Analysis of a company's ideal customers, designed to enhance marketing campaign effectiveness. It offers insights into customer personalities, helping businesses to better understand their target audience and adapt products to specific needs, behaviours, and concerns of various customer segments. By leveraging this data, companies can identify which customer segments are most likely to purchase a new product, thereby enabling more focused marketing efforts and avoiding inefficient spending on untargeted campaigns.
Columns
- ID: A unique identifier for each customer.
- Year_Birth: The customer's birth year.
- Education: The customer's level of education.
- Marital_Status: The customer's marital status.
- Income: The customer's yearly household income.
- Kidhome: The number of children residing in the customer's household.
- Teenhome: The number of teenagers residing in the customer's household.
- Dt_Customer: The date when the customer enrolled with the company.
- Recency: The number of days that have passed since the customer's last purchase.
- Complain: Indicates whether the customer complained in the last 2 years (1 for yes, 0 for no).
- MntWines: The total amount spent on wine in the last 2 years.
- MntFruits: The total amount spent on fruits in the last 2 years.
- MntMeatProducts: The total amount spent on meat products in the last 2 years.
- MntFishProducts: The total amount spent on fish products in the last 2 years.
- MntSweetProducts: The total amount spent on sweet products in the last 2 years.
- MntGoldProds: The total amount spent on gold products in the last 2 years.
- NumDealsPurchases: The number of purchases made using a discount.
- AcceptedCmp1: Indicates if the customer accepted the offer in the 1st marketing campaign (1 for yes, 0 for no).
- AcceptedCmp2: Indicates if the customer accepted the offer in the 2nd marketing campaign (1 for yes, 0 for no).
- AcceptedCmp3: Indicates if the customer accepted the offer in the 3rd marketing campaign (1 for yes, 0 for no).
- AcceptedCmp4: Indicates if the customer accepted the offer in the 4th marketing campaign (1 for yes, 0 for no).
- AcceptedCmp5: Indicates if the customer accepted the offer in the 5th marketing campaign (1 for yes, 0 for no).
- Response: Indicates if the customer accepted the offer in the last marketing campaign (1 for yes, 0 for no).
- NumWebPurchases: The number of purchases made through the company’s website.
- NumCatalogPurchases: The number of purchases made using a catalogue.
- NumStorePurchases: The number of purchases made directly in stores.
- NumWebVisitsMonth: The number of visits to the company’s website in the last month.
Distribution
The dataset is provided in a CSV format, specifically
marketing_campaign.csv, with a size of 220.19 kB. It contains 29 columns and includes 2240 records, as indicated by the total values for various attributes.Usage
This dataset is ideally suited for:
- Customer Segmentation: Dividing a customer base into distinct groups based on shared characteristics.
- Targeted Marketing Strategy Development: Crafting tailored marketing campaigns that resonate with specific customer segments.
- Product Development and Modification: Informing product changes and new product introductions based on identified customer needs and preferences.
- Customer Behaviour Analysis: Understanding purchasing patterns, engagement with promotions, and interaction with different sales channels.
- Predictive Analytics: Forecasting customer responses to future marketing efforts.
Coverage
The dataset includes demographic information such as birth year, education level, marital status, income, and household composition (number of children and teenagers). Behavioural data spans activity over the last 2 years, covering spending on various product categories, campaign acceptance, and complaints. Website visit data is recorded for the last month. The geographic scope of the data is not specified within the provided information.
License
CC0: Public Domain
Who Can Use It
- Marketing Analysts and Managers: To design and refine marketing campaigns, improve customer targeting, and measure campaign effectiveness.
- Product Development Teams: To understand customer preferences and tailor products to meet specific segment needs.
- Data Scientists and Researchers: For developing machine learning models for customer segmentation, churn prediction, or marketing response prediction.
- Business Strategists: To gain deeper insights into customer base characteristics and inform business decisions.
Dataset Name Suggestions
- Customer Marketing Campaign Data
- Marketing Customer Personality
- Retail Customer Segmentation
- Consumer Behaviour Analytics
- Customer Spend and Campaign Response
Attributes
Original Data Source: Customer Marketing Campaign Data
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