Context

Problem Statement

Customer Personality Analysis is a detailed analysis of a company’s ideal customers. It helps a business to better understand its customers and makes it easier for them to modify products according to the specific needs, behaviors and concerns of different types of customers.

Customer personality analysis helps a business to modify its product based on its target customers from different types of customer segments. For example, instead of spending money to market a new product to every customer in the company’s database, a company can analyze which customer segment is most likely to buy the product and then market the product only on that particular segment.

Content

Attributes

People

• ID: Customer's unique identifier
• Year_Birth: Customer's birth year
• Education: Customer's education level
• Marital_Status: Customer's marital status
• Income: Customer's yearly household income
• Kidhome: Number of children in customer's household
• Teenhome: Number of teenagers in customer's household
• Dt_Customer: Date of customer's enrollment with the company
• Recency: Number of days since customer's last purchase
• Complain: 1 if the customer complained in the last 2 years, 0 otherwise

Products

• MntWines: Amount spent on wine in last 2 years
• MntFruits: Amount spent on fruits in last 2 years
• MntMeatProducts: Amount spent on meat in last 2 years
• MntFishProducts: Amount spent on fish in last 2 years
• MntSweetProducts: Amount spent on sweets in last 2 years
• MntGoldProds: Amount spent on gold in last 2 years

Promotion

• NumDealsPurchases: Number of purchases made with a discount
• AcceptedCmp1: 1 if customer accepted the offer in the 1st campaign, 0 otherwise
• AcceptedCmp2: 1 if customer accepted the offer in the 2nd campaign, 0 otherwise
• AcceptedCmp3: 1 if customer accepted the offer in the 3rd campaign, 0 otherwise
• AcceptedCmp4: 1 if customer accepted the offer in the 4th campaign, 0 otherwise
• AcceptedCmp5: 1 if customer accepted the offer in the 5th campaign, 0 otherwise
• Response: 1 if customer accepted the offer in the last campaign, 0 otherwise

Place

• NumWebPurchases: Number of purchases made through the company’s website
• NumCatalogPurchases: Number of purchases made using a catalogue
• NumStorePurchases: Number of purchases made directly in stores
• NumWebVisitsMonth: Number of visits to company’s website in the last month

Target

Need to perform clustering to summarize customer segments.

Acknowledgement

The dataset for this project is provided by Dr. Omar Romero-Hernandez.

Solution

You can take help from following link to know more about the approach to solve this problem. Visit this URL

Inspiration

happy learning....

Hope you like this dataset please don't forget to like this dataset

Dataset

This dataset was created by phineas mcclintock

Released under Database: Open Database, Contents: Database Contents

Contents

Dataset

This dataset was created by Preetam_009

Contents

Customer Personality Analysis – EDA Results

  1. Project Goal

The goal of this project is to use numeric-focused Exploratory Data Analysis (EDA) on the Customer Personality Analysis dataset to understand:

Which customer characteristics are associated with higher spending. How these characteristics differ between customers who responded to the last marketing campaign and those who did not.

The main outcome variable is:

Response (0 = no, 1 = yes) – did the customer respond… See the full description on the dataset page: https://huggingface.co/datasets/maigurski/maigurski-customer-personality-assignment1.

The dataset for this project is provided by Dr. Omar Romero-Hernandez.

Objective: Need to perform clustering to summarize customer segments, and predict something that should be interested in.

Metadata: 1. People - ID: Customer's unique identifier - Year_Birth: Customer's birth year - Education: Customer's education level - Marital_Status: Customer's marital status - Income: Customer's yearly household income - Kidhome: Number of children in customer's household - Teenhome: Number of teenagers in customer's household - Dt_Customer: Date of customer's enrollment with the company - Recency: Number of days since customer's last purchase - Complain: 1 if the customer complained in the last 2 years, 0 otherwise

1. Products
2. MntWines: Amount spent on wine in last 2 years
3. MntFruits: Amount spent on fruits in last 2 years
4. MntMeatProducts: Amount spent on meat in last 2 years
5. MntFishProducts: Amount spent on fish in last 2 years
6. MntSweetProducts: Amount spent on sweets in last 2 years

7. MntGoldProds: Amount spent on gold in last 2 years

8. Promotion

9. NumDealsPurchases: Number of purchases made with a discount

10. AcceptedCmp1: 1 if customer accepted the offer in the 1st campaign, 0 otherwise

11. AcceptedCmp2: 1 if customer accepted the offer in the 2nd campaign, 0 otherwise

12. AcceptedCmp3: 1 if customer accepted the offer in the 3rd campaign, 0 otherwise

13. AcceptedCmp4: 1 if customer accepted the offer in the 4th campaign, 0 otherwise

14. AcceptedCmp5: 1 if customer accepted the offer in the 5th campaign, 0 otherwise

15. Response: 1 if customer accepted the offer in the last campaign, 0 otherwise

16. Place

17. NumWebPurchases: Number of purchases made through the company’s website

18. NumCatalogPurchases: Number of purchases made using a catalogue

19. NumStorePurchases: Number of purchases made directly in stores

20. NumWebVisitsMonth: Number of visits to company’s website in the last month

Customer Personality Analysis Dataset from https://www.kaggle.com/datasets/imakash3011/customer-personality-analysis used for Business Intelligence and Data Stewardship

Context: Understanding and analyzing customer personalities is crucial for businesses. It involves delving into the specific needs, behaviors, and concerns of various customer types. By comprehending their ideal customers, companies can tailor products and services accordingly.

Problem Statement: Customer Personality Analysis assists in understanding customers better, facilitating product modifications that align with different customer segments. Instead of broadly marketing to all customers, this analysis enables targeted marketing towards segments more likely to purchase specific products.

Content: The dataset contains various attributes about customers:

• Personal details like ID, birth year, education level, marital status, income, household composition (children and teenagers), enrollment date with the company, recency of last purchase, and complaints in the last two years.
• Purchase-related data such as expenditure on wine, fruits, meat, fish, sweets, and gold over two years.
• Information about promotions, including the number of discounted purchases and responses to different marketing campaigns.
• Details about the mode of purchases, such as through the website, catalogue, or in-store visits.

Objective: The goal is to perform clustering analysis to categorize and summarize customer segments based on the provided dataset.

Acknowledgement: Credit to Dr. Omar Romero-Hernandez for providing the dataset for this project.

Solution: For more information on the approach to solve this problem, please refer to the provided URL.

Inspiration: Wishing an enjoyable learning experience! If you find this dataset valuable, your likes would be greatly appreciated.

We use the https://www.kaggle.com/datasets/imakash3011/customer-personality-analysis dataset to predict whether customers buy in web, store or by catalog.

Dataset

This dataset was created by Sagar Chhabriya

Released under MIT

Contents

20% validation split of https://doi.org/10.82556/re0n-rc68

Dataset

This dataset was created by Paulo Arruda

Released under Apache 2.0

Contents

Dataset

This dataset was created by Abhijai Rajawat

Released under MIT

Contents

The dataset contains raw data for perceived values for customer predispositions, CEBs, and customer advocacy collected from 380 FinTech app users in south India by administering a survey questionnaire. The dataset thus obtained was cleaned and processed for being used in smartPLS3. Structural Equation Modelling (SEM) using partial least squares (PLS) method was later applied to this dataset in smartPLS3 to test the theoretical model, assess the structural model, and understand the direct and indirect effects of the variables.

Dataset

This dataset was created by Berkin Oktay

Contents

Virtual reality (VR) is a useful tool to study consumer behavior while they are immersed in a realistic scenario. Among several other factors, personality traits have been shown to have a substantial influence on purchasing behavior. The primary objective of this study was to classify consumers based on the Big Five personality domains using their behavior while performing different tasks in a virtual shop. The personality recognition was ascertained using behavioral measures received from VR hardware, including eye-tracking, navigation, posture and interaction. Responses from 60 participants were collected while performing free and directed search tasks in a virtual hypermarket. A set of behavioral features was processed, and the personality domains were recognized using a statistical supervised machine learning classifier algorithm via a support vector machine. The results suggest that the open-mindedness personality type can be classified using eye gaze patterns, while extraversion is related to posture and interactions. However, a combination of signals must be exhibited to detect conscientiousness and negative emotionality. The combination of all measures and tasks provides better classification accuracy for all personality domains. The study indicates that a consumer’s personality can be recognized using the behavioral sensors included in commercial VR devices during a purchase in a virtual retail store.

1) Data Introduction • The (MBTI) Myers-Briggs Personality Type Dataset is a text-based personality analysis dataset that contains posts written by approximately 8,600 users with a simplified MBTI label based on the N (iNtuitive) or S (Sensing) trait of each user.

2) Data Utilization

(1) The (MBTI) Myers-Briggs Personality Type Dataset has characteristics that: • Each row contains a user's N/S label (derived from their MBTI type) and post texts written by that user. • The data consists of natural language written by various individuals with either N or S traits, making it suitable for linguistic style analysis and disposition-based classification.

(2) The (MBTI) Myers-Briggs Personality Type Dataset can be used to: • Develop N/S Trait Prediction Models: Based on users' textual data, machine learning models can be developed to predict whether a user leans toward iNtuitive or Sensing personality traits. • Analyze Language Style and Behavior Patterns: This dataset enables psychological and social media research by examining differences in linguistic characteristics, expression styles, and online behavior patterns between N-type and S-type individuals.

Dataset

This dataset was created by Sai Srinivas 194

Released under Database: Open Database, Contents: Database Contents

Contents

Characteristics of recent work in Image-based personality analysis on social media.

The report offers Personality Assessment Software Market Dynamics, Comprises Industry development drivers, challenges, opportunities, threats and limitations. A report also incorporates Cost Trend of products, Mergers & Acquisitions, Expansion, Crucial Suppliers of products, Concentration Rate of Steel Coupling Economy. Global Personality Assessment Software Market Research Report covers Market Effect Factors investigation chiefly included Technology Progress, Consumer Requires Trend, External Environmental Change.

The aim of the present study was to find acoustic correlates of perceived personality from the speech produced in a formal communicative setting–that of Korean customer service employees in particular. This work extended previous research on voice personality impressions to a different sociocultural and linguistic context in which speakers are expected to speak politely in a formal register. To use naturally produced speech rather than read speech, we devised a new method that successfully elicited spontaneous speech from speakers who were role-playing as customer service employees, while controlling for the words and sentence structures they used. We then examined a wide range of acoustic properties in the utterances, including voice quality and global acoustic and segmental properties using Principal Component Analysis. Subjects of the personality rating task listened to the utterances and rated perceived personality in terms of the Big-Five personality traits. While replicating some previous findings, we discovered several acoustic variables that exclusively accounted for the personality judgments of female speakers; a more modal voice quality increased perceived conscientiousness and neuroticism, and less dispersed formants reflecting a larger body size increased the perceived levels of extraversion and openness. These biases in personality perception likely reflect gender and occupation-related stereotypes that exist in South Korea. Our findings can also serve as a basis for developing and evaluating synthetic speech for Voice Assistant applications in future studies.