This dataset provides comprehensive customer data suitable for segmentation analysis. It includes anonymized demographic, transactional, and behavioral attributes, allowing for detailed exploration of customer segments. Leveraging this dataset, marketers, data scientists, and business analysts can uncover valuable insights to optimize targeted marketing strategies and enhance customer engagement. Whether you're looking to understand customer behavior or improve campaign effectiveness, this dataset offers a rich resource for actionable insights and informed decision-making.

Key Features:

Anonymized demographic, transactional, and behavioral data. Suitable for customer segmentation analysis. Opportunities to optimize targeted marketing strategies. Valuable insights for improving campaign effectiveness. Ideal for marketers, data scientists, and business analysts.

Usage Examples:

Segmenting customers based on demographic attributes. Analyzing purchase behavior to identify high-value customer segments. Optimizing marketing campaigns for targeted engagement. Understanding customer preferences and tailoring product offerings accordingly. Evaluating the effectiveness of marketing strategies and iterating for improvement. Explore this dataset to unlock actionable insights and drive success in your marketing initiatives!

Premium B2C Consumer Database - 269+ Million US Records

Supercharge your B2C marketing campaigns with comprehensive consumer database, featuring over 269 million verified US consumer records. Our 20+ year data expertise delivers higher quality and more extensive coverage than competitors.

Core Database Statistics

Consumer Records: Over 269 million

Email Addresses: Over 160 million (verified and deliverable)

Phone Numbers: Over 76 million (mobile and landline)

Mailing Addresses: Over 116,000,000 (NCOA processed)

Geographic Coverage: Complete US (all 50 states)

Compliance Status: CCPA compliant with consent management

Targeting Categories Available

Demographics: Age ranges, education levels, occupation types, household composition, marital status, presence of children, income brackets, and gender (where legally permitted)

Geographic: Nationwide, state-level, MSA (Metropolitan Service Area), zip code radius, city, county, and SCF range targeting options

Property & Dwelling: Home ownership status, estimated home value, years in residence, property type (single-family, condo, apartment), and dwelling characteristics

Financial Indicators: Income levels, investment activity, mortgage information, credit indicators, and wealth markers for premium audience targeting

Lifestyle & Interests: Purchase history, donation patterns, political preferences, health interests, recreational activities, and hobby-based targeting

Behavioral Data: Shopping preferences, brand affinities, online activity patterns, and purchase timing behaviors

Multi-Channel Campaign Applications

Deploy across all major marketing channels:

Email marketing and automation

Social media advertising

Search and display advertising (Google, YouTube)

Direct mail and print campaigns

Telemarketing and SMS campaigns

Programmatic advertising platforms

Data Quality & Sources

Our consumer data aggregates from multiple verified sources:

Public records and government databases

Opt-in subscription services and registrations

Purchase transaction data from retail partners

Survey participation and research studies

Online behavioral data (privacy compliant)

Technical Delivery Options

File Formats: CSV, Excel, JSON, XML formats available

Delivery Methods: Secure FTP, API integration, direct download

Processing: Real-time NCOA, email validation, phone verification

Custom Selections: 1,000+ selectable demographic and behavioral attributes

Minimum Orders: Flexible based on targeting complexity

Unique Value Propositions

Dual Spouse Targeting: Reach both household decision-makers for maximum impact

Cross-Platform Integration: Seamless deployment to major ad platforms

Real-Time Updates: Monthly data refreshes ensure maximum accuracy

Advanced Segmentation: Combine multiple targeting criteria for precision campaigns

Compliance Management: Built-in opt-out and suppression list management

Ideal Customer Profiles

E-commerce retailers seeking customer acquisition

Financial services companies targeting specific demographics

Healthcare organizations with compliant marketing needs

Automotive dealers and service providers

Home improvement and real estate professionals

Insurance companies and agents

Subscription services and SaaS providers

Performance Optimization Features

Lookalike Modeling: Create audiences similar to your best customers

Predictive Scoring: Identify high-value prospects using AI algorithms

Campaign Attribution: Track performance across multiple touchpoints

A/B Testing Support: Split audiences for campaign optimization

Suppression Management: Automatic opt-out and DNC compliance

Pricing & Volume Options

Flexible pricing structures accommodate businesses of all sizes:

Pay-per-record for small campaigns

Volume discounts for large deployments

Subscription models for ongoing campaigns

Custom enterprise pricing for high-volume users

Data Compliance & Privacy

VIA.tools maintains industry-leading compliance standards:

CCPA (California Consumer Privacy Act) compliant

CAN-SPAM Act adherence for email marketing

TCPA compliance for phone and SMS campaigns

Regular privacy audits and data governance reviews

Transparent opt-out and data deletion processes

Getting Started

Our data specialists work with you to:

1. Define your target audience criteria

2. Recommend optimal data selections

3. Provide sample data for testing

4. Configure delivery methods and formats

5. Implement ongoing campaign optimization

Why We Lead the Industry

With over two decades of data industry experience, we combine extensive database coverage with advanced targeting capabilities. Our commitment to data quality, compliance, and customer success has made us the preferred choice for businesses seeking superior B2C marketing performance.

Contact our team to discuss your specific ta...

Bank Customer Segmentation

Most banks have a large customer base - with different characteristics in terms of age, income, values, lifestyle, and more. Customer segmentation is the process of dividing a customer dataset into specific groups based on shared traits.

According to a report from Ernst & Young, “A more granular understanding of consumers is no longer a nice-to-have item, but a strategic and competitive imperative for banking providers. Customer understanding should be a living, breathing part of everyday business, with insights underpinning the full range of banking operations.

About this Dataset

This dataset consists of 1 Million+ transaction by over 800K customers for a bank in India. The data contains information such as - customer age (DOB), location, gender, account balance at the time of the transaction, transaction details, transaction amount, etc.

Interesting Analysis Ideas

The dataset can be used for different analysis, example -

1. Perform Clustering / Segmentation on the dataset and identify popular customer groups along with their definitions/rules
2. Perform Location-wise analysis to identify regional trends in India
3. Perform transaction-related analysis to identify interesting trends that can be used by a bank to improve / optimi their user experiences
4. Customer Recency, Frequency, Monetary analysis
5. Network analysis or Graph analysis of customer data.

Customer Retention with Consumer Edge Credit & Debit Card Transaction Data

Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. CE Transact Signal is an aggregated transaction feed that includes consumer transaction data on 100M+ credit and debit cards, including 14M+ active monthly users. Capturing online, offline, and 3rd-party consumer spending on public and private companies, data covers 12K+ merchants and deep demographic and geographic breakouts. Track detailed consumer behavior patterns, including retention, purchase frequency, and cross shop in addition to total spend, transactions, and dollars per transaction.

Consumer Edge’s consumer transaction datasets offer insights into industries across consumer and discretionary spend such as: • Apparel, Accessories, & Footwear • Automotive • Beauty • Commercial – Hardlines • Convenience / Drug / Diet • Department Stores • Discount / Club • Education • Electronics / Software • Financial Services • Full-Service Restaurants • Grocery • Ground Transportation • Health Products & Services • Home & Garden • Insurance • Leisure & Recreation • Limited-Service Restaurants • Luxury • Miscellaneous Services • Online Retail – Broadlines • Other Specialty Retail • Pet Products & Services • Sporting Goods, Hobby, Toy & Game • Telecom & Media • Travel

This data sample illustrates how Consumer Edge data can be used for customer retention purposes, such as performing a shopper retention analysis over time for a specific company.

Inquire about a CE subscription to perform more complex, near real-time competitive analysis functions on public tickers and private brands like: • Choose a pair of merchants to determine spend overlap % between them by period (yearly, quarterly, monthly) • Explore cross-shop history within subindustry and market share (updated weekly)

Consumer Edge offers a variety of datasets covering the US and Europe (UK, Austria, France, Germany, Italy, Spain), with subscription options serving a wide range of business needs.

Use Case: Competitive Analysis

Problem A grocery delivery brand needs to assess overall company performance, including customer acquisition and retention levels relative to key competitors.

Solution Consumer Edge transaction data can uncover performance over time and help companies understand key drivers of retention: • By geography and demographics • By channel • By shop date

Impact Marketing and Consumer Insights were able to: • Develop weekly reporting KPI's on customer retention for company-wide reporting • Reduce investment in underperforming channels, both online and offline • Determine demo and geo drivers of retention for refined targeting • Analyze customer acquisition campaigns driving retention and plan accordingly

Corporate researchers and consumer insights teams use CE Vision for:

Corporate Strategy Use Cases • Ecommerce vs. brick & mortar trends • Real estate opportunities • Economic spending shifts

Marketing & Consumer Insights • Total addressable market view • Competitive threats & opportunities • Cross-shopping trends for new partnerships • Demo and geo growth drivers • Customer loyalty & retention

Investor Relations • Shareholder perspective on brand vs. competition • Real-time market intelligence • M&A opportunities

Most popular use cases for private equity and venture capital firms include: • Deal Sourcing • Live Diligences • Portfolio Monitoring

Public and private investors can leverage insights from CE’s synthetic data to assess investment opportunities, while consumer insights, marketing, and retailers can gain visibility into transaction data’s potential for competitive analysis, understanding shopper behavior, and capturing market intelligence.

Most popular use cases among public and private investors include: • Track Key KPIs to Company-Reported Figures • Understanding TAM for Focus Industries • Competitive Analysis • Evaluating Public, Private, and Soon-to-be-Public Companies • Ability to Explore Geographic & Regional Differences • Cross-Shop & Loyalty • Drill Down to SKU Level & Full Purchase Details • Customer lifetime value • Earnings predictions • Uncovering macroeconomic trends • Analyzing market share • Performance benchmarking • Understanding share of wallet • Seeing subscription trends

Fields Include: • Day • Merchant • Subindustry • Industry • Spend • Transactions • Spend per Transaction (derivable) • Cardholder State • Cardholder CBSA • Cardholder CSA • Age • Income • Wealth • Ethnicity • Political Affiliation • Children in Household • Adults in Household • Homeowner vs. Renter • Business Owner • Retention by First-Shopped Period • Churn • Cross-Shop • Average Ticket Buckets

Our consumer data is gathered and aggregated via surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points.

Our comprehensive data enrichment solution includes a variety of data sets that can help you address gaps in your customer data, gain a deeper understanding of your customers, and power superior client experiences. 1. Geography - City, State, ZIP, County, CBSA, Census Tract, etc. 2. Demographics - Gender, Age Group, Marital Status, Language etc. 3. Financial - Income Range, Credit Rating Range, Credit Type, Net worth Range, etc 4. Persona - Consumer type, Communication preferences, Family type, etc 5. Interests - Content, Brands, Shopping, Hobbies, Lifestyle etc. 6. Household - Number of Children, Number of Adults, IP Address, etc. 7. Behaviours - Brand Affinity, App Usage, Web Browsing etc. 8. Firmographics - Industry, Company, Occupation, Revenue, etc 9. Retail Purchase - Store, Category, Brand, SKU, Quantity, Price etc. 10. Auto - Car Make, Model, Type, Year, etc. 11. Housing - Home type, Home value, Renter/Owner, Year Built etc.

Consumer Graph Schema & Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings:

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method on a suitable interval (daily/weekly/monthly).

Consumer Graph Use Cases: 360-Degree Customer View: Get a comprehensive image of customers by the means of internal and external data aggregation. Data Enrichment: Leverage Online to offline consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment Fraud Detection: Use multiple digital (web and mobile) identities to verify real users and detect anomalies or fraudulent activity. Advertising & Marketing: Understand audience demographics, interests, lifestyle, hobbies, and behaviors to build targeted marketing campaigns.

Here's the schema of Consumer Data: person_id first_name last_name age gender linkedin_url twitter_url facebook_url city state address zip zip4 country delivery_point_bar_code carrier_route walk_seuqence_code fips_state_code fips_country_code country_name latitude longtiude address_type metropolitan_statistical_area core_based+statistical_area census_tract census_block_group census_block primary_address pre_address streer post_address address_suffix address_secondline address_abrev census_median_home_value home_market_value property_build+year property_with_ac property_with_pool property_with_water property_with_sewer general_home_value property_fuel_type year month household_id Census_median_household_income household_size marital_status length+of_residence number_of_kids pre_school_kids single_parents working_women_in_house_hold homeowner children adults generations net_worth education_level occupation education_history credit_lines credit_card_user newly_issued_credit_card_user credit_range_new
credit_cards loan_to_value mortgage_loan2_amount mortgage_loan_type
mortgage_loan2_type mortgage_lender_code
mortgage_loan2_render_code
mortgage_lender mortgage_loan2_lender
mortgage_loan2_ratetype mortgage_rate
mortgage_loan2_rate donor investor interest buyer hobby personal_email work_email devices phone employee_title employee_department employee_job_function skills recent_job_change company_id company_name company_description technologies_used office_address office_city office_country office_state office_zip5 office_zip4 office_carrier_route office_latitude office_longitude office_cbsa_code
office_census_block_group
office_census_tract office_county_code
company_phone
company_credit_score
company_csa_code
company_dpbc
company_franchiseflag
company_facebookurl company_linkedinurl company_twitterurl
company_website company_fortune_rank
company_government_type company_headquarters_branch company_home_business
company_industry
company_num_pcs_used
company_num_employees
company_firm_individual company_msa company_msa_name
company_naics_code
company_naics_description
company_naics_code2 company_naics_description2
company_sic_code2
company_sic_code2_description
company_sic_code4 company_sic_code4_description
company_sic_code6
company_sic_code6_description
company_sic_code8
company_sic_code8_description company_parent_company
company_parent_company_location company_public_private company_subsidiary_company company_residential_business_code company_revenue_at_side_code company_revenue_range
company_revenue company_sales_volume
company_small_business company_stock_ticker company_year_founded company_minorityowned
company_female_owned_or_operated company_franchise_code company_dma company_dma_name
company_hq_address
company_hq_city company_hq_duns company_hq_state
company_hq_zip5 company_hq_zip4 co...

Snapshot img

Customer Analytics Applications Market Size 2024-2028

The customer analytics applications market size is estimated to grow by USD 16.73 billion at a CAGR of 17.58% between 2023 and 2028. The growth of the market depends on several factors, including the increasing number of social media users, the growing need for improved customer satisfaction, and an increase in the adoption of customer analytics by SMEs. Customer analytics application refers to a software or system that analyzes customer data such as behavioral, demographic, and personal information to gain insights into their behavior, preferences, and needs. It uses various techniques such as data mining, predictive modeling, and statistical analysis to gather information and make informed decisions in marketing, sales, product development, and overall customer management. The goal of a customer analytics application is to enhance customer understanding and improve business strategies by allowing companies to make data-driven decisions and provide personalized experiences to their customers.

What will be the Size of the Market During the Forecast Period?

To learn more about this report, View Report Sample

Market Dynamics

In the evolving internet retail landscape, businesses are increasingly adopting innovative cloud deployment modes to enhance their operational efficiency. Customer Data Platforms (CDPs) like Neustar and Clarity Insight are pivotal in integrating and analyzing customer data to drive personalized experiences and strategic decisions. These platforms leverage cloud deployment modes to offer scalable solutions that support internet retail operations and enhance customer engagement. Data platforms are instrumental in collecting and processing vast amounts of data, providing valuable insights for trailblazers in the industry. By utilizing advanced cloud deployment modes, companies can efficiently manage their data infrastructure and improve their online retail strategies. Integrating Neustar and Clarity Insight into their systems enables businesses to stay ahead of the competition by offering tailored experiences and optimizing their internet retail performance through scalable solutions.

Key Market Driver

An increase in the adoption of customer analytics by SMEs is notably driving market growth. Expanding the efficiency and performance of business operations is critical to achieving the desired set of goals of an organization. Businesses with a customer-centric approach deal with massive amounts of customer data, which is stored, managed, and processed in real-time. SMEs generate numerous forms of customer data related to customer demographics and sales, marketing campaigns, websites, and conversations. Consequently, these businesses must scrutinize all this customer-related data to achieve a competitive edge in the market. SMEs are majorly using these as they enable better forecasting, resource management, and streamlining of data under one platform, lower operational costs, improve decision-making, and expand sales.

In addition, the increase in customer data, along with the companies' need to automate customer data processing, is leading to the increased adoption by SMEs. Hence, customer analytics is being executed across SMEs for better management of their business operations via a centralized management system with enhanced collaboration, productivity, simplified compliance, and risk management. Such factors are the significant driving factors driving the growth of the global market during the forecast period.

Major Market Trends

Advancements in technology are an emerging trend shaping the market growth. AI and ML technologies have revolutionized the way businesses understand and analyze customer data, allowing them to make more informed decisions and deliver customized experiences. Also, AI and ML have played a critical role in fake detection and prevention in the customer analytics market. Algorithms can identify unusual activities that may indicate fraud by analyzing transactional data and behavioral patterns. This allows businesses to secure themselves and their customers from potential financial losses.

Additionally, AI and ML have enhanced customer segmentation capabilities. Businesses can group customers based on their similarities by using clustering algorithms, allowing them to create targeted marketing campaigns for specific segments. This enables enterprises to personalize their messages and offers, resulting in higher customer engagement and conversion rates. These factors are anticipated to fuel the market growth and trends during the forecast period.

Significant Market Restrain

Data integration issues are a significant challenge hindering market growth. To analyze customer data generated from various types of systems, enterprises use these. The expansion in the use of smart devices and Internet penetration is creating huge amounts of dat

Services provided to clients by DFPS may include social casework, case management, and arranging for psychiatric and health evaluation, home care, day care, social services, health care, respite services, and other services.

The APS specialist works with the client to develop a service plan to address identified problems. Safely maintaining clients in the least restrictive environment is a primary goal of APS intervention.

Protective services may be necessary to alleviate or prevent the client from returning to a state of abuse, neglect or financial exploitation. In this case, DFPS may also provide services to a family member or caretaker. (Texas Human Resources Code §48.002(a)(5) and §48.204) Protective services may be delivered in every stage of an investigation

The APS specialist makes all reasonable efforts to resolve problems, including root causes, and stabilizes the client’s condition. Full resolution of a client’s problems is always the goal of APS casework, but it is not always achievable. When full resolution is not a practical goal because of inadequate resources, client resistance, or some other impediment, the APS specialist closes the case when a client’s situation is as close to stable as possible.

The phrase "reasonable effort" implicitly recognizes that:

• personal choice on the part of the client may limit the effectiveness of APS intervention;

• resources available to APS for helping clients are limited; and

• APS cannot remedy all situations.

Counts for FY 2015 and subsequent years cannot be compared to those from prior Data Books, due to changes in the APS casework practice model. Cases with services provided during the investigation may not have a separate service stage.

Clients in validated cases may receive more than one service.

Visit dfps.state.tx.us for information on all DFPS programs.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

GapMaps GIS data for USA and Canada sourced from Applied Geographic Solutions (AGS) includes an extensive range of the highest quality demographic and lifestyle segmentation products. All databases are derived from superior source data and the most sophisticated, refined, and proven methodologies.

GIS Data attributes include:

1. Latest Estimates and Projections The estimates and projections database includes a wide range of core demographic data variables for the current year and 5- year projections, covering five broad topic areas: population, households, income, labor force, and dwellings.

2. Crime Risk Crime Risk is the result of an extensive analysis of a rolling seven years of FBI crime statistics. Based on detailed modeling of the relationships between crime and demographics, Crime Risk provides an accurate view of the relative risk of specific crime types (personal, property and total) at the block and block group level.

3. Panorama Segmentation AGS has created a segmentation system for the United States called Panorama. Panorama has been coded with the MRI Survey data to bring you Consumer Behavior profiles associated with this segmentation system.

4. Business Counts Business Counts is a geographic summary database of business establishments, employment, occupation and retail sales.

5. Non-Resident Population The AGS non-resident population estimates utilize a wide range of data sources to model the factors which drive tourists to particular locations, and to match that demand with the supply of available accommodations.

6. Consumer Expenditures AGS provides current year and 5-year projected expenditures for over 390 individual categories that collectively cover almost 95% of household spending.

7. Retail Potential This tabulation utilizes the Census of Retail Trade tables which cross-tabulate store type by merchandise line.

8. Environmental Risk The environmental suite of data consists of several separate database components including: -Weather Risks -Seismological Risks -Wildfire Risk -Climate -Air Quality -Elevation and terrain

Primary Use Cases for GapMaps GIS Data:

1. Retail (eg. Fast Food/ QSR, Cafe, Fitness, Supermarket/Grocery)
2. Customer Profiling: get a detailed understanding of the demographic & segmentation profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular census block level using all the key metrics
4. Site Selection: Identify optimal locations for future expansion and benchmark performance across existing locations.
5. Target Marketing: Develop effective marketing strategies to acquire more customers.

6. Integrate AGS demographic data with your existing GIS or BI platform to generate powerful visualizations.

7. Finance / Insurance (eg. Hedge Funds, Investment Advisors, Investment Research, REITs, Private Equity, VC)

8. Network Planning

9. Customer (Risk) Profiling for insurance/loan approvals

10. Target Marketing

11. Competitive Analysis

12. Market Optimization

13. Commercial Real-Estate (Brokers, Developers, Investors, Single & Multi-tenant O/O)

14. Tenant Recruitment

15. Target Marketing

16. Market Potential / Gap Analysis

17. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)

18. Customer Profiling

19. Target Marketing

20. Market Share Analysis

Geodemographic Segmentation Data from Caliper Corporation contain demographic data in a way that is easy to visualize and interpret. We provide 8 segments and 32 subsegments for exploring the demographic makeup of neighborhoods across the country.

This dataset contains data on SC clients for each of the twenty-six Local Board (LB) areas in Ontario for the 2015/16 fiscal year, based on data provided to Local Boards and Local Employment Planning Councils (LEPC) in June 2016 (see below for details on Local Boards). These clients have been distributed across Local Board areas based on the client’s home address, not the address of their training institution(s).Different variables in this dataset cover different groups of Second Career clients, as follows:Demographic and skills training variables are composed of all SC clients that started in 2015/16.At exit outcome variables are composed of all SC clients that completed their program in 2015/16.12-month outcome variables are composed of all SC clients that completed a 12-month survey in 2015/16.The specific variables that fall into each of the above categories are detailed in the Technical Dictionary. As a result of these differences, not all variables in this dataset are comparable to the other variables in this dataset; for example, the outcomes at exit data is not the outcomes for the clients described by the demographic variables.Definitions for fields in this layer are available in the abbreviated Technical Dictionary.

Pursuant to Local Laws 126, 127, and 128 of 2016, certain demographic data is collected voluntarily and anonymously by persons voluntarily seeking social services. This data can be used by agencies and the public to better understand the demographic makeup of client populations and to better understand and serve residents of all backgrounds and identities.

The data presented here has been collected through either electronic form or paper surveys offered at the point of application for services. These surveys are anonymous.

Each record represents an anonymized demographic profile of an individual applicant for social services, disaggregated by response option, agency, and program. Response options include information regarding ancestry, race, primary and secondary languages, English proficiency, gender identity, and sexual orientation.

Idiosyncrasies or Limitations:

Note that while the dataset contains the total number of individuals who have identified their ancestry or languages spoke, because such data is collected anonymously, there may be instances of a single individual completing multiple voluntary surveys. Additionally, the survey being both voluntary and anonymous has advantages as well as disadvantages: it increases the likelihood of full and honest answers, but since it is not connected to the individual case, it does not directly inform delivery of services to the applicant. The paper and online versions of the survey ask the same questions but free-form text is handled differently. Free-form text fields are expected to be entered in English although the form is available in several languages. Surveys are presented in 11 languages.

Paper Surveys

Are optional

Survey taker is expected to specify agency that provides service

Survey taker can skip or elect not to answer questions

Invalid/unreadable data may be entered for survey date or date may be skipped

OCRing of free-form tet fields may fail.

Analytical value of free-form text answers is unclear

Online Survey

Are optional

Agency is defaulted based on the URL

Some questions must be answered

Date of survey is automated

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Hotel customer dataset with 31 variables describing a total of 83,590 instances (customers). It comprehends three full years of customer behavioral data. In addition to personal and behavioral information, the dataset also contains demographic and geographical information. This dataset contributes to reducing the lack of real-world business data that can be used for educational and research purposes. The dataset can be used in data mining, machine learning, and other analytical field problems in the scope of data science. Due to its unit of analysis, it is a dataset especially suitable for building customer segmentation models, including clustering and RFM (Recency, Frequency, and Monetary value) models, but also be used in classification and regression problems.

This map presents the full data available on the MLTSD GeoHub, and maps several of the key variables reflected by the Second Career Program of ETD.The Second Career program provides training to unemployed or laid-off individuals to help them find employment in high demand occupations in Ontario. The intention of the SC program is to return individuals to employment by the most cost effective path. Second Career provides up to $28,000 to assist laid-off workers with training-related costs such as tuition, books, transportation, and basic living expenses, based on individual need. Additional allowances may be available for people with disabilities, and for clients needing help with the costs of dependent care, living away from home and literacy and basic skills upgrading, also based on individual need. People with disabilities may also be given extensions on training and upgrading durations, to meet their specific needs. Clients may be required to contribute to their skills training, based on the client’s total annual gross household income and the number of household members.About This DatasetThis dataset contains data on SC clients for each of the twenty-six Local Board (LB) areas in Ontario for the 2015/16 fiscal year, based on data provided to Local Boards and Local Employment Planning Councils (LEPC) in June 2016 (see below for details on Local Boards). These clients have been distributed across Local Board areas based on the client’s home address, not the address of their training institution(s).Different variables in this dataset cover different groups of Second Career clients, as follows:Demographic and skills training variables are composed of all SC clients that started in 2015/16.At exit outcome variables are composed of all SC clients that completed their program in 2015/16.12-month outcome variables are composed of all SC clients that completed a 12-month survey in 2015/16.The specific variables that fall into each of the above categories are detailed in the Technical Dictionary. As a result of these differences, not all variables in this dataset are comparable to the other variables in this dataset; for example, the outcomes at exit data is not the outcomes for the clients described by the demographic variables.About Local BoardsLocal Boards are independent not-for-profit corporations sponsored by the Ministry of Labour, Training and Skills Development to improve the condition of the labour market in their specified region. These organizations are led by business and labour representatives, and include representation from constituencies including educators, trainers, women, Francophones, persons with disabilities, visible minorities, youth, Indigenous community members, and others. For the 2015/16 fiscal year there were twenty-six Local Boards, which collectively covered all of the province of Ontario. The primary role of Local Boards is to help improve the conditions of their local labour market by:engaging communities in a locally-driven process to identify and respond to the key trends, opportunities and priorities that prevail in their local labour markets;facilitating a local planning process where community organizations and institutions agree to initiate and/or implement joint actions to address local labour market issues of common interest;creating opportunities for partnership development activities and projects that respond to more complex and/or pressing local labour market challenges; andorganizing events and undertaking activities that promote the importance of education, training and skills upgrading to youth, parents, employers, employed and unemployed workers, and the public in general.In December 2015, the government of Ontario launched an eighteen-month Local Employment Planning Council pilot program, which established LEPCs in eight regions in the province formerly covered by Local Boards. LEPCs expand on the activities of existing Local Boards, leveraging additional resources and a stronger, more integrated approach to local planning and workforce development to fund community-based projects that support innovative approaches to local labour market issues, provide more accurate and detailed labour market information, and develop detailed knowledge of local service delivery beyond Employment Ontario (EO).Eight existing Local Boards were awarded LEPC contracts that were effective as of January 1st, 2016. As such, from January 1st, 2016 to March 31st, 2016, these eight Local Boards were simultaneously Local Employment Planning Councils. The eight Local Boards awarded contracts were:Durham Workforce AuthorityPeel-Halton Workforce Development GroupWorkforce Development Board - Peterborough, Kawartha Lakes, Northumberland, HaliburtonOttawa Integrated Local Labour Market PlanningFar Northeast Training BoardNorth Superior Workforce Planning BoardElgin Middlesex Oxford Workforce Planning & Development BoardWorkforce Windsor-EssexMLTSD has provided Local Boards and LEPCs with demographic and outcome data for clients of Employment Ontario (EO) programs delivered by service providers across the province on an annual basis since June 2013. This was done to assist Local Boards in understanding local labour market conditions. These datasets may be used to facilitate and inform evidence-based discussions about local service issues – gaps, overlaps and under-served populations - with EO service providers and other organizations as appropriate to the local context.Data on the following EO programs for the 2015/16 fiscal year was made available to Local Boards and LEPCs in June 2016: Employment Services (ES)Literacy and Basic Skills (LBS) Second Career (SC) ApprenticeshipThis dataset contains the 2015/16 SC data that was sent to Local Boards and LEPCs. Datasets covering past fiscal years will be released in the future.Terms and Definitions

NOC – The National Organizational Classification (NOC) is an occupational classification system developed by Statistics Canada and Human Resources and Skills Development Canada to provide a standard lexicon to describe and group occupations in Canada primarily on the basis of the work being performed in the occupation. It is a comprehensive system that encompasses all occupations in Canada in a hierarchical structure. At the highest level are ten broad occupational categories, each of which has a unique one-digit identifier. These broad occupational categories are further divided into forty major groups (two-digit codes), 140 minor groups (three-digit codes), and 500 unit groups (four-digit codes). This dataset uses four-digit NOC codes from the 2011 edition to identify the training programs of Second Career clients.Notes

Data reporting on 5 individuals or less has been suppressed to protect the privacy of those individuals.Data published: Feb 1, 2017Publisher: Ministry of Labour, Training and Skills Development (MLTSD)Update frequency: Yearly Geographical coverage: Ontario

Baseline socio-demographic and clinical characteristics of 326 participants at Princess Diana Memorial Health Centre IV, Soroti District, April to June 2018.

Overview socio-demographics study sample of the control study (percentages).

Abstract

Rates of overweight, obesity, and chronic diseases such as cardiovascular diseases, hypertension, type 2 diabetes and certain cancers (bowel, lung, prostate and uterine) are on the rise in most sub-saharan Africa (SSA) countries like kenya. These increases can be largely attributed to the shift toward unhealthy diet patterns and increased access to processed foods that are high in fat, sugar, and sodium. The influx of supermarkets in east africa and the replacement of traditional foods for processed foods places this region in a vulnerable position for greater increases in chronic disease rates. Consumer purchasing history from supermarkets can provide valuable insight to food intake over time and the present and future effects on chronic diseases. Purchasing data from supermarkets is available yet underutilized in SSA.

The study aimed to harmonize and increase accessibility to grocery data, use statistical methods to explore purcharing patterns and predict the effects of nutrition on chronic diseases, and inform policy on the various influences on consumer purchases.

A further objective was to examine changes in food purchasing and nutritional composition before, during and after the COVID-19 pandemic restrictions.

Geographic coverage

County coverage: Nairobi

Analysis unit

Supermarket transaction records.

Universe

The survey covers transaction records of individuals who made purchases in supermarkets.

Sampling procedure

The study is a cross-sectional exploratory study with a phased approach employing quantitative secondary data collection from a third-party information management solution provider. The third party provider employs an open integrated point of sale and store information retail system that connects retail touch points and sales channels in several counties in Kenya.

Sampling was conducted after a census of all supermarkets subscribed to the third party system was done. Only those counties with supermarkets subscribed to the platform were sampled. A sample of large, medium sized and small supermarkets were selected to participate in the study. The supermarket sizes were determined as follows; large supermarkets ( supermarkets with a cumulative total of more than 8 branch networks). Medium size supermarkets will be those with 3-8 branch networks in the counties and smaller supermarkets are those with 1-2 branch networks.

Grocery data was received from 2 supermarket chains each with 1 branch.

Sampling deviation

Not Applicable

Mode of data collection

Other [oth]

Research instrument

A standardized form was developed to guide in extration of information from 3rd party information provider for supermarket purchase data. Variables of interest includes supermarket name, supermarket branch, location of supermarket, invoice id, customer id, customer demographics (gender, age), date and time of purchase, product name purchased, unit price per item, number of items purchased, payment method used by customer for purchase etc.

Secondary data collected will not be identifiable as it will be anonymized at the supermarket and client level.

The standardized form is provided as external resources data.

The standardized form is provided as external resources data. V1-V27 the questions are found in the “Study abstraction tool” V28-V30 are generated food classifications (user developed) and are not in any resource V31 the questions are found in the “NOVA-Classification-Reference-Sheet” V32-V59 the questions are found in the “Kenya Food Composition Tables 2018”

Cleaning operations

Not Applicable

Response rate

Not Applicable

Sampling error estimates

Not Applicable

Demographic characteristics, health and functional status of home care clients comparing those with and without DSI across multiple countries.

Description: The questions contained in SASAS questionnaires one and two for 2007 were asked of a half sample of approximately 3500 respondents each. The data set contains 2907 records and 116 variables. Topics included in the questionnaires are: democracy, intergroup relations, public services, moral issues, crime, voting, demographics and other classificatory variables. Rotating modules are: child poverty, poverty, household expenditure, women, childcare and work (client module)climate change / global warming, soccer world cup, service delivery, Batho Pele principles, International Social Surveys Programme (ISSP) module: leisure time and sport and smoking and tobacco behaviour (client module). Abstract: The primary objective of the South African Social Attitudes Survey (SASAS) is to design, develop and implement a conceptually and methodologically robust study of changing social attitudes and values in South Africa. In meeting this objective, the HSRC is carefully and consistently monitoring and providing insight into changes in attitudes among various socio-demographic groupings. SASAS is intended to provide a unique long-term account of the social fabric of modern South Africa, and of how its changing political and institutional structures interact over time with changing social attitudes and values. The survey has been designed to yield a national representative sample of adults aged 16 and older, using the Human Sciences Research Council's (HSRC) second Master Sample, which was designed in 2007 and consists of 1000 primary sampling units (PSUs). These PSUs were drawn, with probability proportional to size from a pre-census 2001 list of 80780 enumerator areas (EAs). As the basis of the 2007 SASAS round of interviewing, a sub-sample of 500 EAs (PSUs) was drawn from the second master sample. Three explicit stratification variables were used, namely province, geographic type and majority population group. The survey is conducted annually and the 2007 survey is the fifth wave in the series. To accommodate the wide variety of topics included in the survey, two questionnaires are administered simultaneously. Apart from the standard set of demographic and background variables, each version of the questionnaire contained a harmonised core module. The questions contained in the core modules of the two SASAS questionnaires (demographics and core thematic issues) were asked of 7000 respondents, while the remaining rotating modules were asked of a half sample of approximately 3500 respondents each. The core module remains constant for with the aim of monitoring change and continuity in a variety of socio-economic and socio-political variables. In addition, a number of themes are accommodated in rotation. The rotating element of the survey consists of two or more topic-specific modules in each round of interviewing and is directed at measuring a range of policy and academic concerns and issues that require more detailed examination at a specific point in time than the multi-topic core module would permit. Topics included in the questionnaires are: democracy, national identity, public services, moral issues, crime, voting, demographics and other classificatory variables. Rotating modules are: child poverty, poverty, household expenditure, climate change / global warming, Soccer World Cup, service delivery, Batho Pele principles and smoking and tobacco behaviour. International Social Survey Programme. (ISSP web page:www.issp.org/) The International Social Survey Programme (ISSP) is run by a group of research organisations, each of which undertakes to field annually an agreed module of questions on a chosen topic area. SASAS 2003 represents the formalisation of South Africa's inclusion in the ISSP, the intention being to include the module in one of the SASAS questionnaires in each round of interviewing. Each module is chosen for repetition at intervals to allow comparisons both between countries (membership currently stands at 48) and over time. In 2007, the chosen subject was the leisure time and sport and the module was carried in version two of the questionnaire (Qs.1-60). This data can be accessed through the ISSP data portal (see link above).

Key Features: • Enriches CRM and first-party data with verified demographic attributes • Supports both hashed and unhashed email formats • Privacy-compliant and sourced from permission-based datasets • Coverage available across key APAC markets

Use Cases: • Enhance customer profiles with age, gender, and lifestyle indicators • Build detailed personas for refined audience segmentation • Power personalization engines with enriched user data • Boost acquisition and retention strategies with smarter targeting

Key Attributes Available (varies by region): • Age • Gender • Location (City, State, Country) • Household Composition • Income Bracket • Interests & Lifestyle Indicators

Data Format: Hashed (SHA-256) & Unhashed Emails

Data Delivery: SFTP

Perfect For: • CRM Managers • Data & Analytics Teams • Marketing Automation • Ad Tech / Martech Providers • Media Agencies

*Significant difference between phases at p