In 2021, Baby Boomers were the main new car buyers in the United States, representing around ** percent of new car sales. By contrast, Gen X made up the majority of the used car buyers, at close to ** percent of the sales.

Over ** percent of new car buyers in the United States between September 2020 and August 2021 identified as men. By contrast, women only represented ** percent of new car buyers but lead the used car market, amounting to over **** of the used vehicle sales for that same time period.

Graph and download economic data for Expenditures: Vehicle Purchases: Cars and Trucks, New by Race: White, Asian, and All Other Races, Not Including Black or African American (CXUNEWCARSLB0902M) from 1984 to 2023 about asian, white, purchase, trucks, vehicles, expenditures, new, and USA.

This statistic illustrates the share of new vehicles buyers in the U.S. in 2014 and 2015, broken down by ethnicity. In 2015, Hispanic car buyers accounted for some **** percent of new vehicle buyers in the United States.

Detailed Dataset Description: Car Sales Transactional Data This dataset provides a rich, multi-dimensional view of individual car sales transactions, capturing valuable information across customer demographics, car specifications, pricing metrics, payment details, sales performance, and seasonal or regional context. Each row in the dataset represents a single car sale transaction, allowing for granular-level analysis of how various factors influence profitability, sales trends, and customer behavior.

📅 Date & Temporal Context Date: The exact date of the transaction, allowing daily trend analysis.

Sale Year, Month, Quarter, Day of Week, and Season: These columns offer temporal segmentation that helps identify seasonal patterns, monthly performance, and weekday vs weekend trends.

🧑‍💼 Salesperson and Customer Information Salesperson: Identifies the individual responsible for the sale, useful for tracking salesperson performance, commission analysis, and productivity metrics.

Customer Name, Age, and Gender: Offers demographic insights, enabling segmentation by age group and gender, and understanding customer preferences in vehicle types and pricing.

🚗 Vehicle Details Car Make and Model: Specifies the brand and specific vehicle model sold.

Car Year: Indicates the model year of the vehicle, helpful in analyzing the popularity of newer vs older models.

💵 Financial and Sales Metrics Quantity: Number of cars sold in the transaction (typically 1, but can vary in business fleet cases).

Sale Price and Cost: Gross sale price and internal cost incurred by the dealership.

Profit: Computed as the difference between sale price and cost, giving direct insight into transaction-level profitability.

Discount: Shows the discount offered as a decimal (e.g., 0.05 = 5%), aiding in understanding the impact of promotions on sales.

💳 Payment & Incentive Structure Payment Method: Indicates how the customer paid (e.g., Cash, Loan, Credit), helping identify payment preferences over time or across customer types.

Commission Rate & Commission Earned: Details the salesperson's incentive structure and earnings from the sale, supporting analysis of commission efficiency, reward optimization, and employee motivation.

🌎 Geographic Coverage Sales Region: Highlights the physical region where the sale occurred (e.g., Alaska), allowing for regional performance comparison, mapping in BI tools, and assessing market penetration across different areas.

Use Cases and Applications This dataset can be effectively used for:

Business Intelligence Dashboards (e.g., Tableau, Power BI)

Sales & Profitability Analysis

Customer Demographics and Segmentation

Payment Method Trends

Salesperson Performance Monitoring

Seasonal Demand Forecasting

Regional Sales Comparisons

Commission Strategy Optimization

Its wide coverage across multiple dimensions makes it ideal for predictive modeling, trend visualization, and data storytelling in sales, marketing, and operations

Dataset 1: lada_buyers.csv This dataset contains information about Lada car buyers, including demographic data, income, and purchase details. Columns: • purchase_id – Unique identifier for each purchase. • id – Unique identifier for each buyer • Age – Age of the buyer • Sex – Gender of the buyer • Income – Annual income of the buyer (RUB). • Purchase_Date – Date of the car purchase (ranging from January 2021 to December 2023). • Region – Region where the car was purchased (includes all regions of the Volga Federal District in Russia).

Dataset 2: lada_machines.csv This dataset contains details about the purchased Lada cars, including model type, price, engine specifications, and additional options. Columns: • purchase_id – Unique identifier for each purchase, linking to lada_buyers.csv. • Model – Model of the purchased Lada car • Price – Purchase price of the car • Engine_Power – Engine power of the purchased car in horsepower • Transmission – Type of transmission (Manual "MT" or Automatic "AT"). • Fuel_Type – Type of fuel used by the car (Gasoline or Diesel). • Num_Additional_Options – Number of additional options purchased with the car.

The fields available include make, model, year, trim, style, fuel type, MSRP, and many more.

We have developed this file to be tied to our Consumer Demographics Database so additional demographics can be applied as needed. Each record is ranked by confidence and only the highest quality data is used. This file contains over 180 million records in addition to over 1 million+ fresh automotive intender records per day.

Note - all Consumer packages can include necessary PII (address, email, phone, DOB, etc.) for merging, linking, and activation of the data.

BIGDBM Privacy Policy: https://bigdbm.com/privacy.html

In 2021, most of the car buyers in the United States self-identified as white. This included both the new and used car markets, where this ethnic group made up over *********** of the buyers. Contrastingly, respondents self-identifying as Hispanic made up ** percent of the new car buyers.

Snapshot img

The online car buying market share is expected to increase by USD 214.41 million from 2021 to 2026, and the market’s growth momentum will accelerate at a CAGR of 12.4%.

This online car buying market research report provides valuable insights into the post-COVID-19 impact on the market, which will help companies evaluate their business approaches. Furthermore, this report extensively covers the online car buying market segmentations by Class Type (pre-owned and new vehicle) and Geography (North America, Europe, APAC, South America, and Middle East and Africa). The online car buying market report also offers information on several market vendors, including American City Business Journals Inc., Asbury Automotive Group Inc., AutoNation Inc., CarGurus Inc., CarMax Inc., Cars & Bids LLC, Cars.com Inc., Cars24 Services Pvt. Ltd., CarSoup of Minnesota Inc., Carvago, Carvana Co., Cox Enterprises Inc., eBay Inc., Edmunds.com Inc., Hendrick Automotive Group, Lithia Motors Inc., MH Sub I LLC, Miami Lakes Automall, and TrueCar Inc., among others.

What will the Online Car Buying Market Size be During the Forecast Period?

Download Report Sample to Unlock the Online Car Buying Market Size for the Forecast Period and Other Important Statistics

Online Car Buying Market: Key Drivers, Trends, and Challenges

The research studied the historical data considered for years, with 2021 as the base year and 2022 as the estimated year, and produced drivers, trends, and challenges for the global online car buying market.

Key Online Car Buying Market Driver

The increasing adoption of e-commerce and technological advancements in online channels are key factors driving the global online car buying market growth. Technological advancements such as the development of smartphones and rising Internet penetration are spurring the use of e-commerce applications to boost the sales of businesses, while the introduction of hybrid and electric vehicles has changed the buyers' position in the global online car buying market. With the aid of online technology, consumers are learning more about the vehicle, the on-road prices of new automobiles, residual value, third-party profit margins, and other factors for used cars. Additionally, growing urbanization, an increase in Internet connectivity, and the growth of the telecom industry have made it possible for the general public to access information much more easily. Online car dealers are increasingly using these factors to advertise their vehicles and disseminate information about them. The sale process has been streamlined on web platforms, which also makes it possible for more stakeholders to sell and acquire used cars. Thus, the growing e-commerce industry and the increasing adoption of technological advancements by vendors will propel the growth of the global online car buying market during the forecast period.

Key Online Car Buying Market Trend

Easy online financing will fuel the global online car buying market growth. Financing options are widely available on many car-buying websites, which encourages customers to get preapproval for loans before they even start looking for cars on their websites. According to a survey, 71% of customers choose to finance through the site where they purchased their car. These customers are highly satisfied with the financing options available on car-buying websites. Hassle-free loan applications and favorable interest rates attract more customers to opt for online financing options. For instance, AutoNation Inc. provides hassle-free auto financing options for every customer according to his or her needs and requirements. The company offers a wide range of finance programs that makes auto financing simple and clear. To provide a variety of financing and leasing alternatives, AutoNation has partnered with hundreds of banks in the US. Owing to such easy financing options, customers are attracted to online car-buying options. Thus, the availability of hassle-free and paperless online auto finance provided by car-buying websites will fuel the growth of the global online car buying market during the forecast period.

Key Online Car Buying Market Challenge

Limited customer awareness and acceptance in semi-urban and rural areas are the major challenges to the global online car buying market growth. Buying a car online is still an urban concept despite its prevalence and its numerous advantages. The acceptance of buying a car through online channels is low in semi-urban and rural areas. Buying cars online has not penetrated a large portion of the population, particularly in developing countries such as India. In emerging economies, including India, China, and Indonesia, a car is considered a status symbol. Thus, customers in such countries generally prefer to buy a car through physical stores where they can physically inspect the features of the car. For the middle-class population, buying a car is a major in

Context

Automobile data holds immense importance as it offers insights into the functioning and efficiency of the automotive industry. It provides valuable information about car models, specifications, sales trends, consumer demographics, and preferences, which car manufacturers and dealerships can leverage to optimize their operations and enhance customer experiences. By analyzing data on vehicle reliability, fuel efficiency, safety ratings, and resale values, the automotive industry can identify trends and implement strategies to produce more reliable and environmentally friendly vehicles, improve safety standards, and enhance the overall value of cars for consumers. Moreover, regulatory bodies and policymakers rely on this data to enforce regulations, set emissions standards, and make informed decisions regarding automotive policies and environmental impacts. Researchers and analysts use car purchase data to study market trends, assess the environmental impact of various vehicle types, and develop strategies for sustainable growth within the industry. In essence, car purchase data serves as a foundation for informed decision-making, operational efficiency, and the overall advancement of the automotive sector.

Content

This dataset comprises diverse parameters relating to car purchases and ownership on a global scale. The dataset prominently incorporates fields such as 'First Name', 'Last Name', 'Country', 'Car Brand', 'Car Model', 'Car Color', 'Year of Manufacture', and 'Credit Card Type'. These columns collectively provide comprehensive insights into customer demographics, vehicle details, and payment information. Researchers and industry experts can leverage this dataset to analyze trends in car purchasing behavior, optimize the customer car-buying experience, evaluate the popularity of car brands and models, and understand payment preferences within the automotive industry.

Dataset Glossary (Column-wise)

• First Name - The first name of the car purchaser.
• Last Name - The last name of the car purchaser.
• Country - The country of residence of the car purchaser.
• Car Brand - The brand or manufacturer of the purchased car.
• Car Model - The specific model of the purchased car.
• Car Color - The color of the purchased car.
• Year of Manufacture - The year the car was manufactured.
• Credit Card Type - The type of credit card used for the car purchase.

Structure of the Dataset

https://i.imgur.com/olZpXsT.png" alt="">

Acknowledgement

The dataset provided here is a simulated example and was generated using the online platform found at Mockaroo. This web-based tool offers a service that enables the creation of customizable mock datasets that closely resemble real data. It is primarily intended for use by developers, testers, and data experts who require sample data for a range of uses, including testing databases, filling applications with demonstration data, and crafting lifelike illustrations for presentations and tutorials. To explore further details, you can visit their website.

Cover Photo by: Freepik

Thumbnail by: Car icons created by Freepik - Flaticon

As of 2023, ** percent of UK respondents preferred to use online tools to neogtiate prices while purchasing their new vehicle. This was the leading online activity among car buyers, followed by completing the transaction, which ** percent of participants reported preferring to do online.

The Japan used car market, valued at approximately ¥15 trillion (assuming a market size "XX" of 15,000 million USD based on current exchange rates and typical market sizing for mature economies) in 2025, is projected to experience robust growth, exhibiting a Compound Annual Growth Rate (CAGR) of 6.28% from 2025 to 2033. This growth is fueled by several key drivers. Increasing vehicle ownership among younger demographics, coupled with a preference for more affordable used vehicles over new cars, especially in light of rising new car prices and economic fluctuations, significantly contributes to market expansion. The rise of online car buying platforms and the expansion of certified used car dealerships offering greater transparency and consumer confidence further accelerate market growth. Moreover, the increasing popularity of SUVs and MPVs is reshaping segmental dynamics, driving demand for these specific used vehicle types. However, potential restraints include fluctuations in the Japanese economy, government regulations impacting vehicle emissions and resale value, and the availability of pre-owned inventory due to chip shortages and supply chain disruptions that have influenced new car production in recent years. Segment analysis reveals a dynamic market structure. While online channels are rapidly gaining popularity, established dealerships maintain a significant share, particularly those offering certified pre-owned vehicles that command premium pricing. The transaction types are diversified, with a balance between full payments and financed purchases. Major players in the market, including PROTO Corporation, Mobilico, carsensor.net, and others, are actively adapting their strategies to cater to evolving consumer preferences, leveraging technological advancements to enhance the buying experience and expand their reach. This competitive landscape is driving innovation and further fueling market growth. The historical period (2019-2024) likely reflected a period of relatively stable growth, followed by acceleration in recent years as previously mentioned factors came into play. Japan Used Car Market: A Comprehensive Forecast & Analysis (2019-2033) This in-depth report provides a comprehensive analysis of the dynamic Japan used car market, projecting its growth trajectory from 2019 to 2033. With a focus on key segments and influential players, this report offers invaluable insights for investors, industry professionals, and anyone seeking a thorough understanding of this multi-billion dollar market. The study encompasses historical data (2019-2024), considers the base year (2025), and provides estimations and forecasts (2025-2033) for the market size in million units. Recent developments include: August 2022: Lexus, the Japanese luxury carmaker, announced a new initiative for the sale and purchase of used Lexus vehicles. The new Lexus Certified Program will allow the existing Lexus owners to sell their vehicles and new buyers to obtain pre-owned vehicles that have passed a rigorous inspection., January 2022: Carused.jp launched a new partner program. As authorized partners of the company, sellers will be certified local agents who will provide the service of importing cars to local customers under the Carused.jp brand.. Key drivers for this market are: The Growing Economy, Coupled with Rising Disposal Incomes and Urbanization, Fuels Demand for the Market. Potential restraints include: Various Regulatory Changes, Safety Standards, and Taxation Policies by the Government may Hamper the Market. Notable trends are: Growing Online Used Car Sales Aiding the Market.

Used Cars Dataset

Overview This dataset contains information about used cars in the Indian market, comprising 15,000 entries with 11 detailed attributes. The data appears to be collected up to November 2024, providing a comprehensive view of the second-hand car market in India.

Dataset Features

Brand: Car manufacturer (e.g., Volkswagen, Maruti Suzuki, Honda, Tata)
Model: Specific car model (e.g., Taigun, Baleno, Polo, WRV)
Year: Manufacturing year of the vehicle (ranging from older models to 2024)
Age: Age of the vehicle in years
kmDriven: Total kilometers driven by the vehicle
Transmission: Type of transmission (Manual or Automatic)
Owner: Ownership status (first or second owner)
FuelType: Type of fuel (Petrol, Diesel, Hybrid/CNG)
PostedDate: When the car listing was posted
AdditionalInfo: Extra details about the vehicle
AskPrice: Listed price in Indian Rupees (₹)

Dataset Statistics

• Total entries: 14,993
• Columns: 11
• Memory usage: 1.94+ MB
• Data types: Mixed (int64 and object)

Potential Use Cases

• Price prediction modeling for used cars in India
• Market analysis of different car segments
• Study of depreciation patterns
• Analysis of fuel type preferences
• Understanding transmission preferences in the Indian market
• Examining the relationship between kilometers driven and pricing
• Brand value retention analysis

This dataset would be valuable for data scientists, automotive market analysts, and machine learning practitioners interested in the Indian automotive sector.

Datasys Automotive Demographics provides a detailed view of 60M+ U.S. vehicle owners enriched with age, income, household, lifestyle, and geographic attributes. This dataset enables marketers to understand the people behind the vehicles, build more relevant campaigns, and target based on ownership plus demographic context. It is valuable for insurers, auto dealers, and service providers aiming to reach specific consumer groups with tailored offers.

Used Car Online Marketplace Market Outlook

According to our latest research, the global Used Car Online Marketplace market size reached USD 62.7 billion in 2024, reflecting a robust digital transformation in the automotive resale industry. The market is projected to expand at a CAGR of 10.3% from 2025 to 2033, reaching a forecasted value of USD 151.8 billion by 2033. This significant growth is underpinned by rising consumer confidence in online transactions, enhanced digital infrastructure, and the increasing demand for affordable personal mobility solutions.

One of the primary growth factors driving the Used Car Online Marketplace market is the growing consumer preference for digital-first experiences. As internet penetration deepens and smartphone adoption accelerates globally, more consumers are turning to online platforms to research, compare, and purchase used vehicles. Enhanced transparency, access to comprehensive vehicle histories, and the availability of digital financing and insurance solutions make online marketplaces increasingly attractive. The COVID-19 pandemic further accelerated this shift, as lockdowns and social distancing measures compelled both buyers and sellers to embrace digital channels. Furthermore, the integration of advanced technologies such as artificial intelligence, machine learning, and big data analytics enables platforms to offer personalized recommendations, price optimization, and fraud detection, all of which contribute to building consumer trust and streamlining the buying process.

Another key driver is the expanding inventory diversity and value-added services offered by online platforms. Unlike traditional dealerships, online marketplaces aggregate listings from OEMs, third-party dealers, and individual sellers, providing a vast selection of vehicles across various segments, fuel types, and price points. This aggregation not only increases the likelihood of buyers finding a suitable vehicle but also fosters competitive pricing. Additionally, platforms are investing heavily in value-added services such as certified pre-owned programs, home delivery, digital documentation, and after-sales support. These services address common pain points associated with used car purchases, such as concerns over vehicle condition, paperwork, and logistics, further incentivizing consumers to transact online.

The market is also benefiting from favorable macroeconomic trends and evolving consumer attitudes toward vehicle ownership. With inflationary pressures and economic uncertainties impacting disposable incomes, many consumers are opting for used vehicles as a cost-effective alternative to new cars. This trend is particularly pronounced among younger demographics, urban dwellers, and small businesses seeking affordable mobility solutions. Moreover, the proliferation of financial products tailored for used cars, including loans, leasing, and subscription models, is making ownership more accessible. The entry of established OEMs into the online resale space, either through proprietary platforms or partnerships, is further legitimizing the market and driving innovation in customer experience.

From a regional perspective, Asia Pacific is emerging as the fastest-growing market for used car online marketplaces, driven by rapid urbanization, a burgeoning middle class, and increasing digital literacy. North America and Europe also hold substantial shares, supported by mature automotive markets, high internet penetration, and a strong culture of vehicle ownership. Latin America and the Middle East & Africa, while currently smaller, are exhibiting promising growth trajectories as digital ecosystems mature and consumer trust in online transactions deepens. Regional dynamics such as regulatory frameworks, vehicle import/export policies, and local consumer preferences continue to shape market evolution and competitive strategies.

Vehicle Type Analysis

The Vehicle Type segment in the Used Car

The data is from an online survey administered to a representative population of former and prospective car buyers in Norway. This dataset contains the discrete choice experiment (DCE) data collected as part of the survey, stored in NLogit format.

Codebook:

Option - 1 (car one); 2 (car two); 3 (neither) Choice - respondent choice Treated - 0 (non-monetary framing); 1 (monetary framing) Safety - % of max Euro NCAP rating Energy - litres per 10km Capacity - litres of boot capacity Cost - car price in NOK ID - respondent identifier

This research set out to examine the role that monetary running cost information can play in terms of highlighting the fuel efficiency of new vehicles. Specifically, this study involved the distribution of a split sample (control/treatment) discrete choice experiment to a representative sample of the Norwegian car buying population, via an online survey undertaken in late 2017. This survey was distributed to over 1000 individuals representing a cross section of the Norwegian population in all regions of the country.

Prior to the distribution of the survey, a series of focus groups identified safety rating and luggage space as the most important attributes to include in the experiment, in addition to the research parameters of interest: purchase price and energy efficiency. Attribute levels were selected to reflect those currently present in the Norwegian automobile market, see Table 1. A fractional factorial design, utilising the JMP software package, generated 32 unique choice pairs. To prevent respondent fatigue, these pairs were split across four survey blocks, so that each respondent faced only eight choices. These eight choices were presented in either the control or treatment format, with each respondent only receiving choices in a single format to avoid any framing contamination effects. Therefore, there were eight versions of the survey in total, four control and four treatment blocks.

In the control version of the experiment, the attributes were displayed in a simplified version of how they are currently displayed on new cars in Norway. In the treatment version, the energy consumption variable was augmented with a monthly running cost estimate, displayed in terms of Norwegian Kroner (NOK). Both the treatment and control images also contained a graphic with the vehicle’s environmental rating (A-G), as mandated under current EU and Norwegian legislation. The rating is based on CO2-emssions, which is proportional to fuel consumption when fuel type is constant. In this study, all vehicles considered used gasoline.

The findings from our analysis of the data suggest that with the addition of running cost estimates, individuals’ WTP for more efficient vehicles can be significantly increased, in the case of this research by up to 28%.

This dataset is a national, VIN-resolved automotive file containing detailed vehicle attributes, ownership signals, and linked consumer demographics. Every row is anchored by a full 17-character VIN, allowing precise matching, decoding, and enrichment across insurance, lending, automotive analytics, marketing, and identity-resolution workflows. The file covers 387M+ U.S. vehicles across all major OEMs, model types, and price tiers.

The dataset includes vehicles from domestic manufacturers (e.g., Ford, GM, Stellantis) as well as foreign/import brands (e.g., Toyota, Honda, BMW, Mercedes, Hyundai, Kia). The manufacturerbased field clearly identifies where the OEM is headquartered, supporting segmentation such as domestic vs foreign, mainstream vs luxury, SUV vs sedan, gas vs hybrid vs electric, and new vs used ownership patterns.

Vehicle & VIN Attribute Coverage

Each record contains core vehicle details:

vin – Full 17-character Vehicle Identification Number

year – Model year

make / model – OEM brand and specific model name

manufacturer / manufacturerbased – Company name and domestic/foreign origin

fuel – Fuel type (gas, diesel, hybrid, EV, flex-fuel)

style – Marketing style (SUV, crossover, coupe, convertible, etc.)

bodytype / bodysubtype – Body classification such as SUV, sedan, pickup, hatchback

class – Market class (mainstream, luxury, premium, truck, etc.)

size – Compact, mid-size, full-size, etc.

doors – Number of doors

vechicletype – Passenger car, light truck, SUV, etc.

enginecylinders – Cylinder count

transmissiontype / transmissiongears – Automatic, manual, CVT, and gear count

gvwrange – Gross Vehicle Weight Rating (light duty vs heavy duty)

weight / maxpayload – Weight/payload estimates

trim – Detailed trim level

msrp – Original MSRP for pricing tiers and value modeling

validated / rankorder – Internal quality indicators

These fields support risk modeling, valuation, depreciation curves, fleet analysis, replacement cycles, and comparisons across domestic and foreign OEMs.

Ownership Signals & Lifecycle Indicators

The dataset includes rich ownership timing and household-level automotive information:

purchasedate – Date the vehicle was obtained, enabling:

Tenure modeling

Trade-in prediction

Lease/loan lifecycle analysis

Service interval modeling

purchasenew – Purchased new vs used

number_of_vehicles_in_hh – Total vehicles linked to the household

validated – Confirmed record flag

These attributes power auto replacement models, refinance targeting, multi-vehicle household insights, and OEM loyalty analytics.

Consumer Identity & Address Standardization

Each VIN record is linked to standardized consumer and household metadata:

consumer_first / consumer_last / consumer_suffix – Owner name fields

consumer_std_address – USPS-style standardized address

consumer_std_city / consumer_std_state / consumer_std_zip – Clean geographic identifiers

consumer_county_name – County for underwriting and geo-risk segmentation

consumer_std_status – Address quality/verification status

consumer_latitude / consumer_longitude – Geocoded coordinates for mapping, heatmaps, and risk scoring

This enables identity resolution, entity matching, household-level modeling, and geographic segmentation.

Consumer Demographics & Economic Indicators

The auto file connects vehicles to extensive demographic and lifestyle fields, including:

consumer_income_range – Household income band

consumer_home_owner – Homeowner vs renter

consumer_home_value – Home value range

consumer_networth – Net worth category

consumer_credit_range – Modeled credit tier

consumer_gender / consumer_age / consumer_age_range – Demographic segment fields

consumer_birth_year – Year-of-birth

consumer_marital_status – Single/married

consumer_presence_of_children / consumer_number_of_children – Household composition

consumer_dwelling_type – Housing type

consumer_length_of_residence / range – Stability indicator

consumer_language, religion, ethnicity – Cultural/language segments

consumer_pool_owner – Lifestyle attribute

consumer_occupation / consumer_education_level – Socioeconomic indicators

consumer_donor / consumer_veteran – Contribution and service attributes

These fields enable hyper-granular segmentation, lifestyle-based modeling, wealth indexing, market analysis, and insurance/lending underwriting.

Phone, Email & Contact Intel

Each record may include up to three phones and three emails:

consumer_phone1/2/3 – Contact numbers

consumer_linetype1/2/3 – Wireless, landline, VOIP

consumer_dnc1/2/3 – Do-Not-Call indicators

consumer_email1/2/3 – Email addresses

This supports compliant outreach, multi-channel activation, CRM enrichment, and identity graph expansion.

Primary Use Cases Insurance & Risk Modeling

VIN decoding, ownership tenure, household economics, and geo data support auto underwriting, pricing, rating territory analysis, and fraud screening.

Auto Finance, Lending & Refinance

Model trade-in window...

Nearly ************** of U.S. car buyers who had purchased a car in the four months preceding an August 2024 survey had monitored the value of their vehicle exclusively online or from the comfort of their own homes. Researching vehicles was also a popular activity to do from home, with nearly **** of respondents doing so exclusively online.

A comprehensive dataset on consumer car finance trends and statistics in the United Kingdom for the year 2025.