Popular Products from NewChic.com E-Commerce Website

Product, Brand, and User Interaction Analytics

By Jeffrey Mvutu Mabilama [source]

About this dataset

This dataset offers an inside look at the popular products of the widely-known e-commerce website NewChic.com. With a snapshot from early August 2020, it provides insights into user tastes, which categories attract buyers, trends in fashion and more!

Using this data, you can gain a better understanding of customer preferences for products and optimize your stock accordingly. You can use it to explore what styles are in trend right now (and how long lasting they are) and find out which niches may be most productive. Moreover, you can segment user tastes based on demographic information such as age groups or geographic locations by analyzing SimilarWeb's traffic data for NewChic.

Are you ready to discover new ways to run your business? Take advantage of this resource now and make smarter decisions about product selection and seasonality – the market is constantly changing so don't miss any opportunity that passes by!

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How to use the dataset

This dataset is an excellent resource for anyone interested in understanding the trends and popularity of products sold by e-commerce website NewChic.com. It contains records of customers’ interactions with products, as well as individual product listings with their associated IDs, prices, discounts, images, brand name, likes count and more. In this guide we explore how to use this data to understand customer behaviour and make informed decisions about the effectiveness of sales and promotional activities.

Exploring the Data: Before diving into the analysis process it is important to understand what you have at your disposal - information that is relevant to customer behaviour on a particular e-commerce website. This dataset provides a comprehensive overview that contains detailed product descriptions as well as popularity metrics such as likes counted from customers who have interacted with them online or offline. Firstly browse through categories from which different types of products have been classified into – apparel & accessories being perhaps some of the most popular ones based on historical purchase behaviour seen in other platforms too; other interesting options could include lifestyle items such as electronic gadgets or art prints among many others. It is also noteworthy to note that the dataset includes details like inventory status (how many pieces remain) which allows us to compare promotional efforts in comparison with inventory size - essential knowledge while deciding how much resources should be allocated towards individual marketing campaigns or discounts etcetera without diluting margins too much over time forcing artificial demand increases simply because stock needs clearing out fast but doesn’t necessarily indicate genuine interest from customers which would leadto sustainable long term engagement opportunities

Processing & Analysing Data: Now begins the real work! Using different methods including descriptive statistics/visualizations & predictive modelling techniques benchmark performance/stock levels against user ratings collected by customers allows insights into customer approval ratings giving quick actionable ideas on where adjustments may need attention so changes can be necessary quickly while avoiding any further stock damage due massive overhauls like introducing completely new styles/products leading greater expenses then required initially so instead focus energies toward aligning existing repertoire while focusing positive public opinion providing value at same time reducing overall costs saving vital resources better engage elsewhere later Additionally analyse past purchase histories similar items helps develop effective plans tweak look structure features down level send more appropriate recommendations instead just trying guess previously failed giving real world tangible results making improvements bottom line profit margin every step way monitoring adjustments dynamically drive fresh continual improvement ways Thereafter when try determine whether current instructions still driving shift user engagement could dampen beginning success measure returns alignment adjust accordingly ensure always stay relevant continuously

Research Ideas

• Use this dataset to compare the popularity of different products across countries, and discover which countries have an affinity to particular styles.
• Identify popular brands in different product categories, then use this insigh...

About this dataset

Context

The dataset tabulates the population of University Park by race. It includes the population of University Park across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of University Park across relevant racial categories.

Key observations

The percent distribution of University Park population by race (across all racial categories recognized by the U.S. Census Bureau): 9.99% are white, 84.77% are Black or African American, 0.08% are Asian, 1.29% are some other race and 3.87% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the University Park
• Population: The population of the racial category (excluding ethnicity) in the University Park is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of University Park total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for University Park Population by Race & Ethnicity. You can refer the same here

Welcome to the Retail Sales and Customer Demographics Dataset! This synthetic dataset has been meticulously crafted to simulate a dynamic retail environment, providing an ideal playground for those eager to sharpen their data analysis skills through exploratory data analysis (EDA). With a focus on retail sales and customer characteristics, this dataset invites you to unravel intricate patterns, draw insights, and gain a deeper understanding of customer behavior.

****Dataset Overview:**

This dataset is a snapshot of a fictional retail landscape, capturing essential attributes that drive retail operations and customer interactions. It includes key details such as Transaction ID, Date, Customer ID, Gender, Age, Product Category, Quantity, Price per Unit, and Total Amount. These attributes enable a multifaceted exploration of sales trends, demographic influences, and purchasing behaviors.

Why Explore This Dataset?

• Realistic Representation: Though synthetic, the dataset mirrors real-world retail scenarios, allowing you to practice analysis within a familiar context.
• Diverse Insights: From demographic insights to product preferences, the dataset offers a broad spectrum of factors to investigate.
• Hypothesis Generation: As you perform EDA, you'll have the chance to formulate hypotheses that can guide further analysis and experimentation.
• Applied Learning: Uncover actionable insights that retailers could use to enhance their strategies and customer experiences.

Questions to Explore:

• How does customer age and gender influence their purchasing behavior?
• Are there discernible patterns in sales across different time periods?
• Which product categories hold the highest appeal among customers?
• What are the relationships between age, spending, and product preferences?
• How do customers adapt their shopping habits during seasonal trends?
• Are there distinct purchasing behaviors based on the number of items bought per transaction?
• What insights can be gleaned from the distribution of product prices within each category?

Your EDA Journey:

Prepare to immerse yourself in a world of data-driven exploration. Through data visualization, statistical analysis, and correlation examination, you'll uncover the nuances that define retail operations and customer dynamics. EDA isn't just about numbers—it's about storytelling with data and extracting meaningful insights that can influence strategic decisions.

Embrace the Retail Sales and Customer Demographics Dataset as your canvas for discovery. As you traverse the landscape of this synthetic retail environment, you'll refine your analytical skills, pose intriguing questions, and contribute to the ever-evolving narrative of the retail industry. Happy exploring!

About this dataset

Context

The dataset tabulates the population of Bartlett by race. It includes the population of Bartlett across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Bartlett across relevant racial categories.

Key observations

The percent distribution of Bartlett population by race (across all racial categories recognized by the U.S. Census Bureau): 56.26% are white, 8.48% are Black or African American, 4.10% are American Indian and Alaska Native, 0.34% are Asian, 21.62% are some other race and 9.22% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Bartlett
• Population: The population of the racial category (excluding ethnicity) in the Bartlett is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Bartlett total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Bartlett Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Hartland township by race. It includes the population of Hartland township across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Hartland township across relevant racial categories.

Key observations

The percent distribution of Hartland township population by race (across all racial categories recognized by the U.S. Census Bureau): 94.61% are white, 0.01% are Black or African American, 0.28% are American Indian and Alaska Native, 1.30% are Asian, 0.42% are some other race and 3.38% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Hartland township
• Population: The population of the racial category (excluding ethnicity) in the Hartland township is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Hartland township total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Hartland township Population by Race & Ethnicity. You can refer the same here

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This dataset simulates customer behavior for a fictional telecommunications company. It contains demographic information, account details, services subscribed to, and whether the customer ultimately churned (stopped using the service) or not. The data is synthetically generated but designed to reflect realistic patterns often found in telecom churn scenarios.

Purpose:

The primary goal of this dataset is to provide a clean and straightforward resource for beginners learning about:

• Exploratory Data Analysis (EDA): Understanding customer characteristics and identifying potential drivers of churn through visualization and statistical summaries.
• Data Preprocessing: Handling categorical features (like converting text to numbers) and scaling numerical features.
• Classification Modeling: Building and evaluating simple machine learning models (like Logistic Regression or Decision Trees) to predict customer churn.

Features:

The dataset includes the following columns:

• CustomerID: Unique identifier for each customer.
• Age: Customer's age in years.
• Gender: Customer's gender (Male/Female).
• Location: General location of the customer (e.g., New York, Los Angeles).
• SubscriptionDurationMonths: How many months the customer has been subscribed.
• MonthlyCharges: The amount the customer is charged each month.
• TotalCharges: The total amount the customer has been charged over their subscription period.
• ContractType: The type of contract the customer has (Month-to-month, One year, Two year).
• PaymentMethod: How the customer pays their bill (e.g., Electronic check, Credit card).
• OnlineSecurity: Whether the customer has online security service (Yes, No, No internet service).
• TechSupport: Whether the customer has tech support service (Yes, No, No internet service).
• StreamingTV: Whether the customer has TV streaming service (Yes, No, No internet service).
• StreamingMovies: Whether the customer has movie streaming service (Yes, No, No internet service).
• Churn: (Target Variable) Whether the customer churned (1 = Yes, 0 = No).

Data Quality:

This dataset is intentionally clean with no missing values, making it easy for beginners to focus on analysis and modeling concepts without complex data cleaning steps.

Inspiration:

Understanding customer churn is crucial for many businesses. This dataset provides a sandbox environment to practice the fundamental techniques used in churn analysis and prediction.

About this dataset

Context

The dataset tabulates the population of Chesapeake Beach by race. It includes the population of Chesapeake Beach across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Chesapeake Beach across relevant racial categories.

Key observations

The percent distribution of Chesapeake Beach population by race (across all racial categories recognized by the U.S. Census Bureau): 77.26% are white, 6.64% are Black or African American, 0.67% are American Indian and Alaska Native, 3.14% are Asian, 0.31% are some other race and 11.98% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Chesapeake Beach
• Population: The population of the racial category (excluding ethnicity) in the Chesapeake Beach is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Chesapeake Beach total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Chesapeake Beach Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Newton by race. It includes the population of Newton across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Newton across relevant racial categories.

Key observations

The percent distribution of Newton population by race (across all racial categories recognized by the U.S. Census Bureau): 71.07% are white, 2.24% are Black or African American, 0.37% are American Indian and Alaska Native, 16.60% are Asian, 0.02% are Native Hawaiian and other Pacific Islander, 2.41% are some other race and 7.29% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Newton
• Population: The population of the racial category (excluding ethnicity) in the Newton is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Newton total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Newton Population by Race & Ethnicity. You can refer the same here

Context and Content

The COVID-19 case surveillance system database includes individual-level data reported to U.S. states and autonomous reporting entities, including New York City and the District of Columbia (D.C.), as well as U.S. territories and states. On April 5, 2020, COVID-19 was added to the Nationally Notifiable Condition List and classified as “immediately notifiable, urgent (within 24 hours)” by a Council of State and Territorial Epidemiologists (CSTE) Interim Position Statement (Interim-20-ID-01). CSTE updated the position statement on August 5, 2020 to clarify the interpretation of antigen detection tests and serologic test results within the case classification. The statement also recommended that all states and territories enact laws to make COVID-19 reportable in their jurisdiction, and that jurisdictions conducting surveillance should submit case notifications to CDC. COVID-19 case surveillance data are collected by jurisdictions and shared voluntarily with CDC.

For more information:

https://data.cdc.gov/Case-Surveillance/COVID-19-Case-Surveillance-Public-Use-Data/vbim-akqf

The deidentified data in the public use dataset include demographic characteristics, exposure history, disease severity indicators and outcomes, clinical data, laboratory diagnostic test results, and comorbidities. All data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.

Acknowledgement

https://www.cdc.gov/

Inspiration

• Covid-19 researches e.g. Demographic Trends of COVID-19 cases and deaths

Version 2: Updated with preprocessed , reduced data ("Covid-19_Ver2.gz")

About this dataset

Context

The dataset tabulates the population of Mississippi County by race. It includes the population of Mississippi County across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Mississippi County across relevant racial categories.

Key observations

The percent distribution of Mississippi County population by race (across all racial categories recognized by the U.S. Census Bureau): 57.48% are white, 34.81% are Black or African American, 0.14% are American Indian and Alaska Native, 0.42% are Asian, 0.01% are Native Hawaiian and other Pacific Islander, 1.32% are some other race and 5.83% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Mississippi County
• Population: The population of the racial category (excluding ethnicity) in the Mississippi County is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Mississippi County total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Mississippi County Population by Race & Ethnicity. You can refer the same here

U.S. Mortality and Health Indicators

Impact of Risk Factors on Population Health Outcomes

By Data Society [source]

About this dataset

This dataset provides county-level mortality and health indicators that are useful for measuring the impact of health policies in the United States. It includes data elements and values from over a dozen categories, including Demographics, Leading Causes of Death, Summary Measures of Health, Measures of Birth and Death, Relative Health Importance, Vulnerable Populations and Environmental Health, Preventive Services Use, Risk Factors and Access to Care. Additionally, this dataset offers Healthy People 2010 Targets and US Percentages or Rates for easy comparison across states. With comprehensive information for each county in each indicator domain available here at your fingertips could help you get insight into American population health from the local level like never before. Discover trends on disease outbreaks or immunizations that are unprecedentedly localized with insights from this dataset!

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For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset contains various data elements related to the mortality and health of the US population at various levels such as county, state, etc. This dataset is an ideal source of information for researchers and policy makers who are interested in exploring patterns in the mortality and health of US citizens.

In order to use this dataset effectively, it is important to understand the different indicators included as well as how to interpret these indicators. In this guide we will look at each indicator domain separately so that users can easily identify which relevant data elements they need for their analysis.

Demographics: The Demographics indicator domain includes data elements related to demographic characteristics such as age composition, gender composition etc. These indicators can be used to explore trends across different parts of the country or identify disparities among populations.

Leading Causes of Death: The Leading Causes of Death indicator domain contains information on fatalities by cause over a set period of time -- either two years or five years depending on availability -- so that researchers can identify causes that pose major threats to public health overall or in more specific regions such as certain counties. It is important to note that these largely report figures based on death certificates which may not always tell an exact story due to reporting inaccuracies caused by both individual factors and registration biases across counties/states over time.

 **Summary Measures Of Health**: The Summary Measures Of Health Indicator Domain includes measures commonly used for gauging overall population health such as birth rates and death rates but also key quality-of-life considerations like prevalence rate physical activity rate . These can be used together with other data sources (such as income info) when analyzing population health outcomes from a broader perspective than individual diseases or conditions would allow for . 

 **Measures Of Birth And Death**: This category provides further insight into the important summary level figures mentioned earlier by providing observations about frequency , timing , type etc where available . Additionally , it offers valuable insights about trends related specifically (among others ) out - migration /in - migration mortality ratio changes/births outside hospitals marriage age / labor force participation trends etc – all essential ingredients when trying solve complex issues related improving public one's life expectancy positively  

 **Relative Health Importance & Vulnerable Populations And Environment Capacity :** This section covers two closely intertwined fields revealing how they interact – socioeconomic status disparities & environment quality – around boundaries & neighborhoods influencing risks factors (not only related medical matters ) aspects such disabilities insurance coverage alcohol use & smoking habits road fatalities veh

Research Ideas

• Using the Health Status Indicators as input features, machine learning models can be built to predict county-level mortality rate, which can then be used as an important indicator for health and medical resource allocation.
• The data can also be used to analyze the social determinants of health in different counties by combining with socioeconomic indicators such as poverty, population density and educational attainment levels.
• Additionally, the dataset could help assess th...

Predicting Portuguese Bank Term Deposit Subscriptions

Identifying Likely Customers for Conversion Optimization

By Huggingface Hub [source]

About this dataset

This innovative dataset is gathered from a Portuguese banking institution's direct marketing campaigns to identify customers who are likely to subscribe to a term deposit, with the ultimate goal of maximizing their conversion rates. With the utilization of telephonic marketing campaigns, this bank has sought out information on individual selection characteristics such as age, job type, marital status, educational level, default history and banking balances that could potentially bring insight into what renders somebody more or less likely to subscribe. The dataset produced provides detailed data on customer contact day and duration in order to answer questions surrounding customer inclination towards the term deposit offers made in these telemarketing campaigns. Furthermore it also considers previous outcomes from similar calls with the same customer as part of its featureset. With all this knowledge at hand we are thus presented with an opportunity to drastically augment conversion success rate through learning which factors yield positive results when attempting to attract new customers for this particular product offering

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For more datasets, click here.

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• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset can be used to gain insights into the direct marketing campaigns of a Portuguese banking institution, and to better understand customer subscription behaviors to their term deposits. The objective is to use this data to predict which customers are most likely to subscribe a term deposit, as well as maximize conversion through this information.

In order to take advantage of the dataset provided, we suggest using tools such as classification and predictive modeling methods. These methods will enable you to perform an assessment of the likelihood of customer subscription, given certain parameters in regards to their self-reported information and interactions with the bank itself. For example, you may run regression models in order determine which charactersitics are significantly correlated with subscription rates so that you can appropriately targeted your effective marketing strategies more accurately. Furthermore, various machine learning techniques could also be employed for building predictive models that scale over time so that customer trends can be identified on a continual basis.

Overall, this dataset is useful for understanding customers’ engagement and how they interact with banking services- it provides valuable insights for predicting whom among potential customers have a tendency towards subscribing terms deposits at banks institutions- For example from exploring duration or day parameters about phone calls or from balance , age or job parameter combo. by doing categorizing potential customers under respective segments according them regarding education level or marital status; This provides ample opportunity con develop custom tailored solutions based on real world data gathering methods and application scenarios like feature scaling etc.. Moreover it is equally applicable large scale cost optimization measures such search engine campaigns – because different categories population demographics exhibit certain trend patterns related offers pertaining interest areas based on past analytics enables instant decision making process at disposal marketers what segment needs more promotion etc.. Welcome Every one And Explore Dataset further !! maximize your profit strategy tailor made real word scenarios…

Research Ideas

• Developing customer segments with similar profiles, and developing targeted campaigns to reach them. This could include gathering customer behaviors, preferences, and other demographic information into categories to help identify the most specific target markets for successful conversion.
• Utilizing machine learning algorithms such as Random Forest or Decision Tree models to create a predictive model that can forecast which customers are likely to subscribe. This could be utilized as a tool when evaluating potential prospects and only targeting those with the highest probability of conversion rate optimization.
• Creating a goal-based system by setting up milestones in an effort to increase customer retention rate or offer existing subscribers further incentives might prove beneficial for better converting campaigns in lower cost marketing efforts over time

Acknowledgements

If you use this dataset in your research, please credit the original a...

About this dataset

Context

The dataset tabulates the population of Gillsville by race. It includes the population of Gillsville across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Gillsville across relevant racial categories.

Key observations

The percent distribution of Gillsville population by race (across all racial categories recognized by the U.S. Census Bureau): 89.52% are white, 1.90% are Black or African American, 1.27% are some other race and 7.30% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Gillsville
• Population: The population of the racial category (excluding ethnicity) in the Gillsville is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Gillsville total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Gillsville Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Kaplan by race. It includes the population of Kaplan across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Kaplan across relevant racial categories.

Key observations

The percent distribution of Kaplan population by race (across all racial categories recognized by the U.S. Census Bureau): 78.15% are white, 10.79% are Black or African American, 0.02% are Asian, 5.46% are Native Hawaiian and other Pacific Islander, 1.17% are some other race and 4.40% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Kaplan
• Population: The population of the racial category (excluding ethnicity) in the Kaplan is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Kaplan total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Kaplan Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Early by race. It includes the population of Early across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Early across relevant racial categories.

Key observations

The percent distribution of Early population by race (across all racial categories recognized by the U.S. Census Bureau): 75.91% are white, 3.48% are Black or African American, 1.23% are Asian, 0.60% are some other race and 18.77% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Early
• Population: The population of the racial category (excluding ethnicity) in the Early is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Early total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Early Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of St. Olaf by race. It includes the population of St. Olaf across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of St. Olaf across relevant racial categories.

Key observations

The percent distribution of St. Olaf population by race (across all racial categories recognized by the U.S. Census Bureau): 100% are white.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the St. Olaf
• Population: The population of the racial category (excluding ethnicity) in the St. Olaf is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of St. Olaf total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for St. Olaf Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Edgefield County by race. It includes the population of Edgefield County across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Edgefield County across relevant racial categories.

Key observations

The percent distribution of Edgefield County population by race (across all racial categories recognized by the U.S. Census Bureau): 59.43% are white, 31.44% are Black or African American, 0.21% are American Indian and Alaska Native, 0.53% are Asian, 0.17% are Native Hawaiian and other Pacific Islander, 2.15% are some other race and 6.05% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Edgefield County
• Population: The population of the racial category (excluding ethnicity) in the Edgefield County is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Edgefield County total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Edgefield County Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Glen Cove by race. It includes the population of Glen Cove across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Glen Cove across relevant racial categories.

Key observations

The percent distribution of Glen Cove population by race (across all racial categories recognized by the U.S. Census Bureau): 62.75% are white, 5.07% are Black or African American, 1.02% are American Indian and Alaska Native, 4.89% are Asian, 0.11% are Native Hawaiian and other Pacific Islander, 12.13% are some other race and 14.02% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Glen Cove
• Population: The population of the racial category (excluding ethnicity) in the Glen Cove is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Glen Cove total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Glen Cove Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Littleton town by race. It includes the population of Littleton town across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Littleton town across relevant racial categories.

Key observations

The percent distribution of Littleton town population by race (across all racial categories recognized by the U.S. Census Bureau): 92.82% are white, 0.12% are American Indian and Alaska Native, 2.19% are some other race and 4.87% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Littleton town
• Population: The population of the racial category (excluding ethnicity) in the Littleton town is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Littleton town total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Littleton town Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Temecula by race. It includes the population of Temecula across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Temecula across relevant racial categories.

Key observations

The percent distribution of Temecula population by race (across all racial categories recognized by the U.S. Census Bureau): 57% are white, 4.28% are Black or African American, 0.55% are American Indian and Alaska Native, 11.54% are Asian, 0.16% are Native Hawaiian and other Pacific Islander, 9.79% are some other race and 16.69% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Temecula
• Population: The population of the racial category (excluding ethnicity) in the Temecula is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Temecula total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Temecula Population by Race & Ethnicity. You can refer the same here