Dataset

This dataset was created by Sai Srinivas 194

Released under Database: Open Database, Contents: Database Contents

Contents

Analysis of ‘Customer Personality Analysis’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/imakash3011/customer-personality-analysis on 28 January 2022.

--- Dataset description provided by original source is as follows ---

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

--- Original source retains full ownership of the source dataset ---

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. It 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 to 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 the customer accepted the offer in the 1st campaign, 0 otherwise AcceptedCmp2: 1 if customer accepted the offer in the 2nd customer accepted the offer in the 2nd campaign, 0 otherwise AcceptedCmp3: 1 if the customer accepted the offer in the 3rd campaign, 0 otherwise AcceptedCmp4: 1 if customer accepted the offer in the 4th customer accepted the offer in the 4th campaign, 0 otherwise AcceptedCmp5: 1 if the 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 catalog NumStorePurchases: Number of purchases made directly in stores NumWebVisitsMonth: Number of visits to the company’s website in the last month Target Need to perform clustering to summarize customer segments.

Inspiration happy learning….

I hope you like this dataset please don't forget to like this dataset

This meta-analysis investigates the relationship between consumer personality traits and consumer satisfaction. The study explores how various personality traits are associated with consumer satisfaction. Specifically, it will examine the strength (and, if possible, direction) of the relationship between different personality traits and satisfaction outcomes, as well as identify potential moderators of this relationship. The findings will provide valuable insights into the role of personality in shaping consumer satisfaction.

Domestic theories of democratization emphasize the role of values, interests, and mobilization/opportunities as determinants of regime change. This article takes a step back and develops a model of national personality and democratization to ascertain the indirect effect of national personality traits on worldwide variation of regime type. In particular, I theorize that personality traits influence a country’s regime type by shaping citizens’ traditional and self-expression values, which, in turn, influence the establishment and consolidation of democratic institutions. Data from McCrae and Terracciano’s assessment of the five-factor model from 47 countries allow me to assess this hypothesis empirically. Results reveal that countries whose societies are high in Openness to experience tend to have more democratic institutions, even after adjusting for relevant confounders: economic inequalities, economic development, technological advancement, disease stress, climate demands, and methodological characteristics of the national sample. Although the effect of Extraversion on a country’s democratic institutions is also significantly positive, the inclusion of confounders weakens the reliability of this association. In an exploration of the mechanisms of these associations, a mediation analysis shows that the relationship between national Openness and democratic institutions is channeled through secular and especially self-expression national values. The same analysis with the effect of Extraversion on democracy indicates that the association between this trait and democracy is only channeled through national self-expression values but not national secular values. In short, this article constitutes a first step toward a more complete understanding of the cross-cultural psychological roots of political institutions.

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.

As a result of changes in customers’ shopping behaviors and a corresponding increase of omnichannel behavior (i.e., a blend of online and in-store shopping channels), a good customer experience (CX) is crucial for retailers’ success. Yet, each shopping channel has its potentials. On the one hand, customers who shop in-store can touch the product and evaluate its quality right at the store. Online, on the other hand, customers do not see the product before actually buying it, hence, customers will have to trust the retailer to deliver the product in an accurate quality. It follows that some people prefer in-store shopping while others prefer to shop online. In this context, we investigate the influences of the customer’s personality traits (Big Five and trust propensity), the Need for touch, and the level of trust towards the retailer, on the in-store and online purchase behavior. To test our hypotheses, we plan to conduct a survey and analyze past purchase behaviors in cooperation with an Austrian Sports and Fashion Retailer.

Raw data and data analysis scripts from the study “Executive functions, Personality traits and ADHD symptoms in adolescents: A mediation analysis”. This study aimed to analyze the associations between performance on cognitive executive function (EF) measures and FFM personality traits in a sample of adolescents with and without ADHD.

Abstract copyright UK Data Service and data collection copyright owner.

The data are from a large Internet-based survey designed and administered in collaboration with the British Broadcasting Corporation (BBC). Between November 2009 and April 2011, 588,014 individuals competed the “Big Personality Test”.

Volunteers were told that the survey was designed to assess personality and that by clicking on the link to proceed to the survey they were giving their consent to participate. Informed consent was not requested from the next of kin, caretakers, or guardians on behalf of minors or children because only individuals 18 and older were eligible to participate. Initiating the survey was used as a record of participant consent. To complete the survey, respondents clicked on a link on the BBC’s Lab UK website.

Before beginning the survey, respondents were asked to create a BBC ID if they did not already have one. This was used to invite participants to take part in future projects and to prevent individuals from repeat responding – the survey could not be completed more than once with the same ID. After completing the survey, participants received customized feedback about their personalities based on their responses to the survey items.

A primary aim of the data was to map the distribution of personality in Great Britain (GB), so of all the participants who completed the survey, only those who reported living in England, Wales, and Scotland were included. Participants who did not complete the personality measure were also excluded. These selection criteria resulted in a total sample of 386,375 respondents.

In the present dataset, the researchers are sharing select demographic variables and the personality data used for mapping personality variation across GB. The dataset includes postcode sector information, which allows for aggregating responses for 380 Local Authority Districts.

Further information can be found on the BBC Lab UK Big Personality Test webpage.

Some participants also took part in other studies hosted by the BBC’s Lab UK (with the same unique ID) so in principle matching is possible across data sets. A combined dataset containing matched respondents who also completed the BBC Big Money Test is available from the UK Data Archive under SN 8132.


Main Topics:

Main topics of the BBC Big Personality Test, 2009-2011 included:

• personality
• demographics
• location of residence at the postcode district level

This dataset comes from a study conducted in Poland with 44 participants. The goal of the study was to measure personality traits known as the Dark Triad. The Dark Triad consists of three key traits that influence how people think and behave towards others. These traits are Machiavellianism, Narcissism, and Psychopathy. Machiavellianism refers to a person's tendency to manipulate others and be strategic in their actions. People with high Machiavellianism scores often believe that deception is necessary to achieve their goals. Narcissism is related to self-importance and the need for admiration. Individuals with high narcissism scores may see themselves as special and expect others to recognize their greatness. Psychopathy is linked to impulsive behavior and a lack of empathy. People with high psychopathy scores tend to be less concerned about the feelings of others and may take risks without worrying about consequences. Each participant in the dataset answered 30 questions, divided into three sections, with 10 questions per trait. The answers were recorded using a Likert scale from 1 to 5, where: 1 means "Strongly Disagree" 2 means "Disagree" 3 means "Neutral" 4 means "Agree" 5 means "Strongly Agree" This scale helps measure how much a person agrees with statements related to each of the three traits. The dataset also includes basic demographic information. Each participant has a unique ID (such as P001, P002, etc.) to keep their identity anonymous. The dataset records their age, which ranges from 18 to 60 years old, and their gender, which is categorized as "Male," "Female," or "Other." The responses in the dataset are realistic, with small variations to reflect natural differences in personality. On average, participants scored around 3.2 for Machiavellianism, meaning most people showed a moderate tendency to be strategic or manipulative. The average Narcissism score was 3.5, indicating that some participants valued themselves highly and sought admiration. The average Psychopathy score was 2.8, showing that most participants did not strongly exhibit impulsive or reckless behaviors. This dataset can be useful for many purposes. Researchers can use it to analyze personality traits and see how they compare across different groups. The data can also be used for cross-cultural comparisons, allowing researchers to study how personality traits in Poland differ from those in other countries. Additionally, psychologists can use this data to understand how Dark Triad traits influence behavior in everyday life. The dataset is saved in a CSV format, which makes it easy to open in programs like Excel, SPSS, or Python for further analysis. Because the data is structured and anonymized, it can be used safely for research without revealing personal information. In summary, this dataset provides valuable insights into personality traits among people in Poland. It allows researchers to explore how Machiavellianism, Narcissism, and Psychopathy vary among individuals. By studying these traits, psychologists can better understand human behavior and how it affects relationships, decision-making, and personal success.

Self, parent, and peer NEO-FFI personality ratings; undergraduate GPA

Big Five Personality Traits

  OCEAN

Openness Conscientiousness Extraversion Agreeableness Neuroticism

Market Overview The global Personality Assessment Software market is projected to reach USD XXX million by 2033, exhibiting a CAGR of XX% from 2025 to 2033. The growth is driven by increasing demand for data-driven talent management solutions, advancements in artificial intelligence (AI) and machine learning (ML), and the increasing emphasis on employee screening and development. Cloud-based personality assessment software is gaining popularity due to its flexibility, cost-effectiveness, and accessibility. Key market players include Testgorilla, Evalart, eSkill, The Hire Talent, Mercer Mettl, PSI Services, and Thomas International Ltd. Trends and Restraints Emerging trends in the market include the use of AI and ML for automated analysis and scoring, the integration with other HR systems, and the development of personalized and customized assessments. However, the market faces certain restraints such as concerns over data privacy and ethical considerations, the reluctance of some organizations to embrace technology, and the limited availability of reliable assessments in certain regions. North America and Europe are expected to be dominant markets due to their advanced HR practices and high adoption of technology. The Asia Pacific region is expected to see significant growth due to its increasing workforce and focus on talent management.

Personality Assessment Solutions Market size was valued at USD 696.55 Million in 2023 and is projected to reach USD 1,662.01 Million by 2031, growing at a CAGR of 11.5% from 2024 to 2031.

Global Personality Assessment Solutions Market Outlook

The evolution of the global Personality Assessment Solutions market has been marked by several key events and milestones that have shaped its trajectory and growth over the years. One of the earliest milestones in the market’s evolution can be traced back to the development of foundational personality theories and frameworks by renowned psychologists such as Carl Jung, Gordon Allport, and Raymond Cattell in the early to mid-20th century. These theories laid the groundwork for the conceptualization and measurement of personality traits, paving the way for the development of modern personality assessment tools and methodologies. Another significant milestone in the market’s evolution occurred with the introduction of standardized personality assessment instruments such as the Myers-Briggs Sales Channel Indicator (MBTI) in the mid-20th century.

The widespread adoption of the MBTI in various organizational settings, including, career counseling, and team development, helped popularize personality assessments and demonstrate their utility in understanding individual differences and preferences. The advent of digital technology in the late 20th and early 21st centuries marked another pivotal moment in the market’s evolution. The development of online assessment platforms, digital psychometric tests, and data analytics capabilities revolutionized the way personality assessments were administered, analyzed, and interpreted.

This technological advancement not only enhanced the accessibility and scalability of personality assessments but also paved the way for greater customization and personalization of assessment solutions to meet the unique needs and preferences of organizations and individuals. Furthermore, regulatory developments and ethical considerations have played a significant role in shaping the evolution of the market. The implementation of regulations such as the General Data Protection Regulation (GDPR) and the Americans with Disabilities Act (ADA) has led to increased scrutiny and accountability in the use of personality assessment solutions, prompting vendors to prioritize data privacy, security, and ethical standards in the design and implementation of their products and services.

Companion DATA of the paper "Using social media and personality traits to assess software developers’ emotions" submitted to the IEEE Access journal, 2022.

The folders contain:

/analysis
analyzed_tweets_by_psychologists.csv: file containing the manual analysis done by psychologists
analyzed_tweets_by_participants.csv: file containing the manual analysis done by participants
analyzed_tweets_by_psychologists_solved_divergencies.csv: file containing the manual analysis done by psychologists over 51 divergent tweets' classifications

/dataset
alldata.json: contains the dataset used in the paper

/notebooks
General - Charts.ipynb: notebook file containing all charts produced in the study, including those in the paper
Statistics - Lexicons and Ensembles.ipynb: notebook file with the statistics for the five lexicons and ensembles used in the study
Statistics - Linear Regression.ipynb: notebook file with the multiple linear regression results

Statistics - Polynomial Regression: notebook file with the polynomial regression results
Statistics - Psychologists versus Participants.ipynb: notebook file with the statistics between the psychologists and participants manual analysis
Statistics - Working x Non-working.ipynb: notebook file containing the statistical analysis for the tweets posted during work period and those posted outside of working period

/surveys
Demographic_Survey.pdf: survey inviting participants to enroll in the study. We collect demographic data and participants' authorization to access their public Tweet posts
Demographic_Survey_answers.xlsx: participants' demographic survey answers
ibf_pt_br.doc: the Portuguese version of the Big Five Inventory (BFI) instrument to infer participants' Big Five polarity traits
ibf_answers.xlsx: participantes' and psychologists' answers for BFI

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We have removed from dataset any sensible data to protect participants' privacy and anonymity.
We have removed from demographic survey answers any sensible data to protect participants' privacy and anonymity.

No description was included in this Dataset collected from the OSF

Friendship establishment was analyzed using constructs from social cognitive theory (self-efficacy and personality traits) and social network theory (reciprocity and triad closure). In further studies, we investigated the effect of personality traits, interpersonal self-efficacy, and network structure on the establishment of friendships. In this study, we used social network analysis method and exponential random graph model (ERGM). The following findings are reported. First, the friendship network of college students had small group characteristics, and the formation of this small group was more based on personality complementarity than similarity. The homogeneity hypothesis of personality was not tenable. Secondly, individuals with dominance or influence personality traits and high interpersonal self-efficacy were more likely to be in the center of the friendship network. Furthermore, personality traits and interpersonal self-efficacy may have interactive effects on the formation of friendship networks. Popularity and activity effects existed in friendship networks, but the reciprocal relationship based on personality traits was not verified. The balance structure can easily explain the agglomeration of friendships in a small range, indicating that small groups of friendships prefer a two-way circular close relationship. Finally, the formation of a friendship network includes the comprehensive process of individual characteristics and endogenous tie formation, which helps us to understand the social population structure and its process over a wider range.

Companion DATA

Title:

Using social media and personality traits to assess software developers’ emotions

Authors:

Leo Moreira Silva

Marília Gurgel Castro

Miriam Bernardino Silva

Milena Nestor Santos

Uirá Kulesza

Margarida Lima

Henrique Madeira

Journal:

PeerJ Computer Science

The folders contain:

/analysis

analyzed_tweets_by_psychologists.csv: file containing the manual analysis done by psychologists

analyzed_tweets_by_participants.csv: file containing the manual analysis done by participants

analyzed_tweets_by_psychologists_solved_divergencies.csv: file containing the manual analysis done by psychologists over 51 divergent tweets' classifications

/dataset

alldata.json: contains the dataset used in the paper

/ethics_committee

committee_response.pdf: contains the acceptance response of Research Ethics and Deontology Committee of the Faculty of Psychology and Educational Sciences of the University of Coimbra.

committee_submission_form.pdf: the project submitted to the committee.

consent_form.pdf: declaration of free and informed consent fulfilled by participants.

data_protection_declaration.pdf: personal data and privacy declaration, according to European Union General Data Protection Regulation.

/notebooks

General - Charts.ipynb: notebook file containing all charts produced in the study, including those in the paper

Statistics - Lexicons and Ensembles.ipynb: notebook file with the statistics for the five lexicons and ensembles used in the study

Statistics - Linear Regression.ipynb: notebook file with the multiple linear regression results

Statistics - Polynomial Regression.ipynb: notebook file with the polynomial regression results

Statistics - Psychologists versus Participants.ipynb: notebook file with the statistics between the psychologists and participants manual analysis

Statistics - Working x Non-working.ipynb: notebook file containing the statistical analysis for the tweets posted during work period and those posted outside of working period

/surveys

Demographic_Survey.pdf: survey inviting participants to enroll in the study. We collect demographic data and participants' authorization to access their public Tweet posts

Demographic_Survey_answers.xlsx: participants' demographic survey answers

ibf_pt_br.doc: the Portuguese version of the Big Five Inventory (BFI) instrument to infer participants' Big Five polarity traits

ibf_answers.xlsx: participantes' and psychologists' answers for BFI

Experiment Protocol.pdf: file containing the explanation of the experiment protocol.

We have removed from dataset any sensible data to protect participants' privacy and anonymity.

We have removed from demographic survey answers any sensible data to protect participants' privacy and anonymity.

Multiple linear regression analysis assessing the relationships between the independent variables considered and social support.

Samples relating to 12 analyses of lay-theories of resilience among participants from USA, New Zealand, India, Iran, Russia (Moscow; Kazan). Central variables relate to participant endorsements of resilience descriptors. Demographic data includes (though not for all samples), Sex/Gender, Age, Ethnicity, Work, and Educational Status. Analysis 1. USA Exploratory Factor Analysis dataAnalysis 2. New Zealand Exploratory Factor Analysis dataAnalysis 3. India Exploratory Factor Analysis dataAnalysis 4. Iran Exploratory Factor Analysis dataAnalysis 5. Russian (Moscow) Exploratory Factor Analysis dataAnalysis 6. Russian (Kazan) Exploratory Factor Analysis dataAnalysis 7. USA Confirmatory Factor Analysis dataAnalysis 8. New Zealand Confirmatory Factor Analysis dataAnalysis 9. India Confirmatory Factor Analysis dataAnalysis 10. Iran Confirmatory Factor Analysis dataAnalysis 11. Russian (Moscow) Confirmatory Factor Analysis dataAnalysis 12. Russian (Kazan) Confirmatory Factor Analysis data