Dark Mode
Automotive Customer Segment Prediction
Retail & Consumer Behavior
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
This dataset provides key information about potential customers, enabling an automobile company to predict the correct customer segment for new markets [1, 2]. The company intends to enter new markets with existing products (P1, P2, P3, P4, and P5) [1]. Based on intensive market research, the behaviour of customers in these new markets is similar to their established market [1]. In their existing market, the sales team has successfully classified customers into four distinct segments (A, B, C, D), utilising a strategy of segmented outreach and communication that proved highly effective [1]. The goal is to apply this proven strategy to 2627 identified new potential customers, leveraging this dataset to assign them to the appropriate segment [1].
Columns
- ID: A unique identification number for each customer [2].
- Gender: Specifies the gender of the customer. In the provided sample, 54% are Male and 46% are Female [3].
- Ever_Married: Indicates the marital status of the customer (yes/no). Data shows 58% are recorded as 'true' (married), 40% as 'false' (not married), with 2% missing values [4].
- Age: The age of the customer in years. Ages range from 18 to 89, with a mean age of 43.6 years and a standard deviation of 17 [4].
- Graduated: Denotes if the customer is a graduate (yes/no). Approximately 61% are graduates ('true'), 38% are not ('false'), and 1% of values are missing [5].
- Profession: Describes the customer's profession. 'Artist' is the most common at 31%, followed by 'Healthcare' at 16%, with other professions making up 54% [5].
- Work_Experience: The customer's work experience in years. Values range from 0 to 14 years, with a mean of 2.55 years and a standard deviation of 3.34 [6].
- Spending_Score: Represents the customer's spending range. 'Low' is the most frequent at 62%, 'Average' accounts for 24%, and 'Other' for 15% [6].
- Family_Size: The number of family members for the customer, including the customer themselves. Family sizes range from 1 to 9, with a mean of 2.83 and a standard deviation of 1.55 [7].
- Var_1: An anonymised categorical variable for the customer. 'Cat_6' is the most common at 64%, 'Cat_4' at 15%, and 'Other' categories at 22% [7].
- Segmentation (target): This is the target variable, representing the customer's classified segment (A, B, C, or D) [8]. Segment A accounts for 32% of customers, Segment D for 29%, and other segments for 39% [7].
Distribution
The dataset is typically in CSV format [9]. It comprises 2627 valid rows, representing new potential customers, and features 11 distinct columns [1, 3, 8]. The sample file, 'Test.csv', has a size of 131.25 kB [8].
Usage
This dataset is ideal for:
- Predicting customer groups for new customers in expanding markets [2].
- Facilitating segmented outreach and communication strategies [1].
- Supporting an automobile company's strategic entry into new markets [1].
- Developing machine learning models for customer classification.
Coverage
The dataset focuses on demographic and behavioural attributes of individual customers [2]. It includes details such as gender, marital status, age, educational background, profession, work experience, spending habits, and family size [2]. While the sources refer to "new markets", no specific geographic locations or time ranges are provided [1]. Data availability for most variables is high, with some minor missing values for 'Ever_Married', 'Graduated', 'Profession', 'Work_Experience', 'Family_Size', and 'Var_1' [4-7].
License
CC0: Public Domain
Who Can Use It
- Automobile companies aiming to expand their market reach and implement targeted sales strategies [1].
- Sales and marketing teams looking to segment customers and personalise communications [1].
- Data scientists and machine learning engineers tasked with building predictive models for customer segmentation [2, 8].
- Market researchers interested in understanding customer behaviour patterns and market dynamics [1].
Dataset Name Suggestions
- Automotive Customer Segment Prediction
- New Market Customer Classification
- Vehicle Buyer Behaviour Dataset
- Automobile Customer Targeting Data
- Customer Segmentation for Automotive Entry
Attributes
Original Data Source: Automotive Customer Segment Prediction
QUALITY