With Versium REACH Demographic Append you will have access to many different attributes for enriching your data.

Basic, Household and Financial, Lifestyle and Interests, Political and Donor.

Here is a list of what sorts of attributes are available for each output type listed above:

Basic: - Senior in Household - Young Adult in Household - Small Office or Home Office - Online Purchasing Indicator
- Language - Marital Status - Working Woman in Household - Single Parent - Online Education - Occupation - Gender - DOB (MM/YY) - Age Range - Religion - Ethnic Group - Presence of Children - Education Level - Number of Children

Household, Financial and Auto: - Household Income - Dwelling Type - Credit Card Holder Bank - Upscale Card Holder - Estimated Net Worth - Length of Residence - Credit Rating - Home Own or Rent - Home Value - Home Year Built - Number of Credit Lines - Auto Year - Auto Make - Auto Model - Home Purchase Date - Refinance Date - Refinance Amount - Loan to Value - Refinance Loan Type - Home Purchase Price - Mortgage Purchase Amount - Mortgage Purchase Loan Type - Mortgage Purchase Date - 2nd Most Recent Mortgage Amount - 2nd Most Recent Mortgage Loan Type - 2nd Most Recent Mortgage Date - 2nd Most Recent Mortgage Interest Rate Type - Refinance Rate Type - Mortgage Purchase Interest Rate Type - Home Pool

Lifestyle and Interests: - Mail Order Buyer - Pets - Magazines - Reading
- Current Affairs and Politics
- Dieting and Weight Loss - Travel - Music - Consumer Electronics - Arts
- Antiques - Home Improvement - Gardening - Cooking - Exercise
- Sports - Outdoors - Womens Apparel
- Mens Apparel - Investing - Health and Beauty - Decorating and Furnishing

Political and Donor: - Donor Environmental - Donor Animal Welfare - Donor Arts and Culture - Donor Childrens Causes - Donor Environmental or Wildlife - Donor Health - Donor International Aid - Donor Political - Donor Conservative Politics - Donor Liberal Politics - Donor Religious - Donor Veterans - Donor Unspecified - Donor Community - Party Affiliation

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

General Information:

Total Rows: 53,503 Total Columns: 20 File Size: ~8.2 MB

Data Types:

Integer: 5 columns Object (String): 15 columns

Column Details:

Customer ID: Unique identifier for each customer (Integer). Age: Age of the customer (Integer). Gender: Gender of the customer (Male/Female) (String). Marital Status: Marital status of the customer (e.g., Single, Married) (String). Education Level: Highest education level attained (e.g., Bachelor's Degree) (String). Geographic Information: Location information (State/Region) (String). Occupation: Customer's profession (e.g., Manager, Entrepreneur) (String). Income Level: Annual income of the customer in local currency (Integer). Behavioral Data: Categorical data on behavior patterns (String). Purchase History: Date of the last purchase (Date format). Interactions with Customer Service: Preferred method of communication with customer service (e.g., Phone, Chat) (String). Insurance Products Owned: Insurance policies owned by the customer (String). Coverage Amount: Total insurance coverage amount (Integer). Premium Amount: Monthly premium payment (Integer). Policy Type: Type of insurance policy (e.g., Family, Group) (String). Customer Preferences: General preferences (e.g., Email, Text) (String). Preferred Communication Channel: Method of communication preferred (e.g., In-Person Meeting, Mail) (String). Preferred Contact Time: Most suitable time for contact (e.g., Morning, Afternoon) (String). Preferred Language: Language preference for communication (e.g., English, French) (String). Segmentation Group: Customer segmentation group assigned (e.g., Segment2, Segment3) (String).

Key Observations: Comprehensive customer segmentation data, ideal for demographic, behavioral, and financial analysis. Mixture of categorical, numerical, and date-related attributes. Useful for marketing analysis, predictive modeling, and customer insights.

Objective: To perform Exploratory Data Analysis (EDA) on the customer segmentation dataset to uncover insights into customer demographics, purchasing behaviors, and transaction patterns. These insights will guide the company in identifying potential segments for targeted marketing.

GapMaps premium demographic 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.

Demographic Data attributes include:

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.

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.

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.

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

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.

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

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

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 AGS Demographic 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

Audience Segmentation for OTT Market Outlook

According to our latest research, the global Audience Segmentation for OTT market size reached USD 5.7 billion in 2024, reflecting robust expansion driven by the proliferation of digital media consumption and advanced data analytics. The market is expected to maintain a strong growth trajectory, registering a CAGR of 14.8% from 2025 to 2033, and is forecasted to reach USD 17.7 billion by 2033. This rapid growth is primarily fueled by the rising adoption of OTT platforms, the increasing importance of personalized content delivery, and the integration of AI-driven segmentation tools into OTT service ecosystems.

One of the most significant growth drivers in the Audience Segmentation for OTT market is the dramatic shift in consumer behavior towards digital streaming services. As traditional media consumption declines, OTT platforms are witnessing exponential user growth, leading to an increased demand for sophisticated audience segmentation tools. These solutions enable OTT providers to analyze vast datasets, extract actionable insights, and deliver hyper-personalized experiences. The evolution of machine learning and artificial intelligence has further enhanced the granularity and accuracy of audience segmentation, allowing platforms to cater to diverse viewer preferences, optimize content recommendations, and boost user engagement. The surge in smartphone penetration and affordable high-speed internet, especially in emerging markets, has also played a pivotal role in expanding the OTT audience base, necessitating more nuanced segmentation strategies.

Another crucial factor propelling market growth is the intensifying competition among OTT platforms. As the market becomes increasingly saturated, providers are leveraging audience segmentation to differentiate their offerings and maximize subscriber retention. Advanced segmentation strategies—spanning demographic, psychographic, behavioral, geographic, and technographic parameters—enable platforms to tailor marketing campaigns, enhance targeted advertising, and minimize churn rates. The integration of real-time analytics and predictive modeling empowers OTT services to anticipate viewer needs, optimize ad placements, and drive higher conversion rates. Moreover, the growing emphasis on privacy-compliant data collection and analysis is fostering trust among users, encouraging them to share more information that can be used to refine segmentation models further.

The ongoing digital transformation across industries has also contributed to the expansion of the Audience Segmentation for OTT market. Enterprises, particularly in the media, entertainment, and advertising sectors, are increasingly adopting advanced segmentation solutions to gain a competitive edge. The proliferation of smart TVs, connected devices, and multi-platform viewing experiences has created new touchpoints for data collection and audience analysis. As OTT platforms continue to diversify their content portfolios and expand into new geographies, the need for localized and contextually relevant segmentation becomes paramount. Regulatory developments, such as data protection laws and cross-border data transfer policies, are shaping the evolution of audience segmentation practices, compelling OTT providers to adopt more transparent and secure methodologies.

Regionally, North America remains the dominant market for Audience Segmentation in OTT, accounting for the largest revenue share in 2024. The region’s advanced digital infrastructure, high internet penetration, and mature OTT ecosystem have facilitated the widespread adoption of segmentation solutions. However, the Asia Pacific region is emerging as the fastest-growing market, driven by rapid urbanization, increasing disposable incomes, and a burgeoning population of digital-first consumers. Europe continues to demonstrate steady growth, supported by robust regulatory frameworks and a strong focus on data privacy. Latin America and the Middle East & Africa are also witnessing increased adoption, albeit at a relatively nascent stage, as OTT platforms expand their reach and tailor their offerings to local preferences.

Segmentation Type Analysis

The Segmentation Type segment plays a pivotal role in the Audience Segmentation for OTT market, encompassing demographic, psychographic, behavioral, geographic, and technographic categorization. Demographic segmentation remains a foundational approach, enabling OTT platforms

Global Market Segmentation Market is segmented by Application (Consumer Goods_Retail_E-Commerce_Technology_Healthcare), Type (Demographic Segmentation_Behavioral Segmentation_Psychographic Segmentation_Geographic Segmentation_Firmographic Segmentation), and Geography (North America_ LATAM_ West Europe_Central & Eastern Europe_ Northern Europe_ Southern Europe_ East Asia_ Southeast Asia_ South Asia_ Central Asia_ Oceania_ MEA)

Percentage of responses in the range 0-6 for 'Worthwhile' by LSOA in the First ONS Annual Experimental Subjective Wellbeing survey, April 2011 - March 2012

The Department for Communities and Local Government (DCLG) has estimated the expected wellbeing of residents at Lower-layer Super Output Area (LSOA) level. The purpose is to illustrate the likely degree of variation between neighbourhoods.

These are modelled estimates for local areas based on national findings from the ONS Annual Population Survey 2011-2012. They are not the actual survey responses of people living in those areas [1]. As such, DCLG encourage local areas to test these expected findings against their own local knowledge and data.

DCLG used CACI’s ACORN geo-demographic segmentation to estimate the likely wellbeing characteristics of each neighbourhood. Analysis of the APS provided a national profile of wellbeing by ACORN Type, with estimates of average subjective wellbeing and low subjective wellbeing for each of the 56 Types. The national profile was then applied to localities, to reflect their composition according to ACORN Type [2].

The method presumes the national profile of wellbeing for the ACORN types is broadly the same in each local authority. For all of the subjective wellbeing measures, DCLG tested this assumption broadly held across the nine regions. As a result, DCLG made a minimal number of adjustments to the profiles for life satisfaction, worthwhile, and happy yesterday, and determined that the method was not robust for modelling anxiety [3].

Feedback on the neighbourhood estimates and requests for further details of the methodology can sent to wellbeing@communities.gsi.gov.uk.

In October, DCLG will be producing wellbeing profiles to enable users to apply the same methodology using geo-demographic classifications: Experian’s MOSAIC and ONS’s Output Area Classification (OAC).

[1] This is because sample sizes from the APS do not permit reliable estimates of subjective wellbeing below the 90 unitary authorities and counties reported in the First ONS Annual Experimental Subjective Well-being Results.

[2] ACORN is a segmentation based on shared characteristics of people’s life-stage, income, profession and housing, as well as characteristics of places including whether they are urban, suburban or rural. Each respondent on the APS had been classified into one ACORN Type, based on the full postcode in which they live – approximately 16 addresses.) ACORN provided estimates of the population in each ACORN Type in each LSOA and local authority district.

[3] These adjustments were made only where there was reliable evidence (based on samples of more than 100 respondents) from APS that the national wellbeing ACORN profile was substantially different from the regional one, and where the implications for neighbourhood maps would be highly geographically clustered.

Percentage of responses in the range 0-6 for 'Happy Yesterday' by LSOA in the First ONS Annual Experimental Subjective Wellbeing survey, April 2011 - March 2012

The Department for Communities and Local Government (DCLG) has estimated the expected wellbeing of residents at Lower-layer Super Output Area (LSOA) level. The purpose is to illustrate the likely degree of variation between neighbourhoods.

These are modelled estimates for local areas based on national findings from the ONS Annual Population Survey 2011-2012. They are not the actual survey responses of people living in those areas [1]. As such, DCLG encourage local areas to test these expected findings against their own local knowledge and data.

DCLG used CACI’s ACORN geo-demographic segmentation to estimate the likely wellbeing characteristics of each neighbourhood. Analysis of the APS provided a national profile of wellbeing by ACORN Type, with estimates of average subjective wellbeing and low subjective wellbeing for each of the 56 Types. The national profile was then applied to localities, to reflect their composition according to ACORN Type [2].

The method presumes the national profile of wellbeing for the ACORN types is broadly the same in each local authority. For all of the subjective wellbeing measures, DCLG tested this assumption broadly held across the nine regions. As a result, DCLG made a minimal number of adjustments to the profiles for life satisfaction, worthwhile, and happy yesterday, and determined that the method was not robust for modelling anxiety [3].

Feedback on the neighbourhood estimates and requests for further details of the methodology can sent to wellbeing@communities.gsi.gov.uk.

In October, DCLG will be producing wellbeing profiles to enable users to apply the same methodology using geo-demographic classifications: Experian’s MOSAIC and ONS’s Output Area Classification (OAC).

[1] This is because sample sizes from the APS do not permit reliable estimates of subjective wellbeing below the 90 unitary authorities and counties reported in the First ONS Annual Experimental Subjective Well-being Results.

[2] ACORN is a segmentation based on shared characteristics of people’s life-stage, income, profession and housing, as well as characteristics of places including whether they are urban, suburban or rural. Each respondent on the APS had been classified into one ACORN Type, based on the full postcode in which they live – approximately 16 addresses.) ACORN provided estimates of the population in each ACORN Type in each LSOA and local authority district.

[3] These adjustments were made only where there was reliable evidence (based on samples of more than 100 respondents) from APS that the national wellbeing ACORN profile was substantially different from the regional one, and where the implications for neighbourhood maps would be highly geographically clustered.

Global Customer Segmentation Market is segmented by Application (Retail_E-commerce_Healthcare_Education_Consumer Goods), Type (Demographic Segmentation_Behavioral Segmentation_Psychographic Segmentation_Geographical Segmentation_Technographic Segmentation), and Geography (North America_ LATAM_ West Europe_Central & Eastern Europe_ Northern Europe_ Southern Europe_ East Asia_ Southeast Asia_ South Asia_ Central Asia_ Oceania_ MEA)

This dataset was sourced from KPMG AU's Data Analytics virtual internship course on Forage

Sprocket Pvt Ltd is a client of KPMG AU. Sprocket is a bike and bike accessories retail business. They need to find the right customer segment to target for marketing to boost revenue. The following dataset is of their customer demographics for the past 3 years.

The original dataset of 3 separate sheets of Customer demographic, Transactions, and Customer Addresses was fully cleaned and merged using a power query. Data types of columns were changed, and values of certain columns which had illegal values were corrected using a standard approach. This final master dataset can be used for customer segmentation projects using clustering methods.

This dataset provides comprehensive behavioral segmentation and persona classification for subscribers, integrating usage patterns, device types, service preferences, spending tiers, demographics, and predictive propensity scores. It is ideal for targeted marketing, personalized product recommendations, and advanced customer analytics to drive engagement and retention.

This dataset is an enhanced version of the popular Mall Customers dataset, created to provide a richer and more realistic resource for practicing data science and machine learning. The original dataset contained only basic demographic details (age, gender, income) and a spending score. While useful for simple clustering, it lacked the variety of features needed for deeper analytics.

To address this, we have extended the dataset with synthetic but logically consistent features such as:

Age Group (binned categories for demographic analysis)

Estimated Savings (derived from income and spending patterns)

Credit Score (influenced by income and spending behavior)

Loyalty Years (approximate measure of customer relationship length)

Preferred Category (simulated shopping preference: Luxury, Budget, Fashion, Electronics)

These enhancements make the dataset more versatile for tasks like clustering, classification, regression, and customer segmentation.

The inspiration behind this dataset is to bridge the gap between a toy dataset and real-world business data. By including features commonly used in retail, marketing, and financial analytics, this dataset provides learners with an opportunity to:

Practice unsupervised learning (customer segmentation, market basket analysis).

Apply supervised learning (predicting credit score, category preference, or savings).

Explore feature engineering and visualization techniques for business insights.

Whether you are a beginner exploring K-means clustering or an advanced practitioner testing classification models, this dataset offers a well-rounded playground for experimentation.

This dataset comprises a meticulously structured collection of customer-related information designed for efficient machine learning applications. It consists of three primary folders—offers, customers, and events—each containing valuable data that enable detailed analysis of customer behavior, response to promotional offers, and overall engagement over a 30-day period.

Offers The offers folder contains comprehensive details on various promotional offers that were sent to customers within the 30-day timeframe. Each offer is uniquely identified by an offer_id, which serves as the primary key. Offers are categorized into three distinct types:

BOGO (Buy One, Get One): A customer must purchase a specific product to receive another for free. Discount: A direct discount applied to purchases, incentivizing spending. Informational: Provides details about a promotion without requiring any spending or offering a direct reward. Each offer has specific requirements and rewards:

difficulty: The minimum amount a customer must spend to qualify for the offer. reward: The monetary reward (in USD) received upon successful completion of the offer. duration: The number of days a customer has to complete the offer after receiving it. channels: The marketing channels used to send the offer, which may include email, mobile app notifications, social media, or direct mail. By analyzing the offers dataset, businesses can assess the effectiveness of different promotional strategies and optimize future campaigns.

Customers The customers folder contains demographic information for each member in the dataset. Each customer is uniquely identified using customer_id, which acts as the primary key. The dataset includes the following attributes:

became_member_on: The date (formatted as YYYYMMDD) when the customer created their account. This information helps track customer loyalty and tenure. gender: The customer's gender, categorized as (M)ale, (F)emale, or (O)ther. This allows for demographic segmentation and targeted marketing analysis. age: The customer’s age, useful for analyzing purchasing patterns and offer preferences across different age groups. income: The estimated annual income of the customer (in USD), enabling insights into spending behavior based on economic status. With this dataset, machine learning models can predict customer preferences, segment users into meaningful groups, and tailor offers based on demographic factors.

Events The events folder logs customer activity throughout the 30-day period, capturing interactions with offers and transactions. Each record is associated with a specific customer_id, serving as a foreign key to link activities to individual users. The dataset includes:

event: A categorical description of the customer's interaction. The possible events include:

Transaction: A recorded purchase made by the customer. Offer Received: A notification that an offer was sent to the customer. Offer Viewed: The customer actively opened and engaged with the offer. Offer Completed: The customer fulfilled the necessary conditions to claim the offer's reward. value: A dictionary of values linked to the event, which varies depending on the type of activity:

For transactions, value represents the amount spent by the customer. For offers received, viewed, or completed, value contains the corresponding offer_id. time: A numerical indicator representing the number of hours passed in the 30-day observation window (starting from 0). This allows for tracking customer engagement over time and understanding behavioral trends.

By analyzing the events dataset, businesses can gain insights into customer interactions, measure the success of promotional offers, and identify patterns in spending behavior. Machine learning models can leverage this data to predict which offers will be most effective for different customer segments.

WorldView segments has been developed to segment the global population into 10 consistent consumer types by analysing data including: demographics, value orientation, attitudes, consumer behaviour and consumption volume. The segments have been identified and validated in detailed international primary reserach. They enable the identification of customer target groups and the segmentation of markets consistently across multiple countries. The data is built using a combination of WorldView Demographics enhanced with consumer survey panel data across a number of regions where available.