Address Data: Best Address Datasets & Databases

Address Data Explained

Address data is a list of accurate and validated address information. It commonly includes house number, street name and type, city, state, and ZIP Code in a standardized order. Address data sometimes is converted to geocoding - a textual expression of spatial location - for easy mapping of a target audience.

Address datasets data are often used to enrich customer identity data, which is crucial in business to provide the best customer experience. It’s also used for a range of different use case across various industries - which we’ll go into further down in this guide to address data 2023.

How is Address Data collected?

When collecting address data, online forms are commonly used to extract information. The collected data is compared against reliable existing address data from an authoritative database - for example, the government’s postal service or any licensed sourced organizations.

Then a validation process is involved to match the accuracy of the inputted locations with its existing counterpart. Collected data is checked and updated for accuracy to avoid duplication, incomplete address, invalid and improper format, and outdated addresses.

What are the typical attributes of Address Data?

The typical attributes of an address dataset include:

Street Address:

The physical location of a property or residence, including the street name and number.

City: The name of the city or municipality where the property or residence is located.

State:

The name or abbreviation of the state or province where the property or residence is located.

Zip Code:

The postal code for the property or residence, used for mail delivery and location identification.

Country:

The name of the country where the property or residence is located.

Latitude and Longitude:

The geographic coordinates of the property or residence, used for mapping and location-based services.

Type of Property:

The type of property, such as residential, commercial, or industrial.

Ownership Information:

Information about the ownership of the property, including the owner’s name and contact information.

Property Value:

The estimated value of the property, used for real estate and insurance purposes.

Property Characteristics:

Information about the property’s characteristics, such as the number of bedrooms, bathrooms, and square footage.

Demographic Information:

Information about the population in the area, such as age, income, and education level.

Geographical Boundaries:

The boundaries of a specific area or region, such as a neighborhood, city, or county.

Property Sales History:

Information about the sale history of the property, including the purchase price and date of sale.

Property Tax Information:

Information about the property’s tax assessment and payment history.

Property Development and Zoning:

Information about property development plans, zoning regulations, and land use restrictions.

Generally, a comprehensive address dataset includes various attributes that provide information about the physical location of a property or residence, as well as information about the population in the area, property ownership, sales history, tax information, and development plans.

By understanding these attributes, businesses can use address data to target potential customers, improve customer service, and make informed decisions about real estate investments and development plans.

What is Address Data used for?

Address datasets are used by a variety of companies and job roles for various purposes, including location-based marketing campaigns, address validation, lead generation, cold prospecting, shipment and delivery, mail campaigns, audience segmentation, political campaigning and voter outreach, and customer services. Here is a guide explaining the types of companies and job roles using address datasets for these use cases:

Location-based marketing campaigns:
Companies that offer location-based services, such as restaurants, retailers, and entertainment venues, use address datasets to target potential customers in specific areas. Job roles that may use address datasets for this purpose include marketing managers, digital marketers, and social media specialists.

Address validation:
Companies that require accurate and up-to-date addresses for customer records or shipments use address datasets for address validation. Job roles that may use address datasets for this purpose include customer service representatives, shipping and logistics personnel, and database administrators.

Lead generation:
Companies that need to generate leads for sales or marketing purposes use address datasets to identify potential customers. Job roles that may use address datasets for this purpose include sales representatives, business development managers, and marketing specialists.

Cold prospecting:
Companies that engage in cold calling or cold emailing to generate new business use address datasets to identify potential prospects. Job roles that may use address datasets for this purpose include sales representatives, business development managers, and telemarketing specialists.

Shipment and delivery:
Companies that require accurate and up-to-date addresses for shipping and delivery purposes use address datasets to ensure timely and accurate delivery. Job roles that may use address datasets for this purpose include shipping and logistics personnel, customer service representatives, and warehouse managers.

Mail campaigns:
Companies that use direct mail campaigns to promote their products or services use address datasets to target potential customers. Job roles that may use address datasets for this purpose include marketing managers, direct mail specialists, and database administrators.

Audience segmentation:
Companies that need to segment their customer base for targeted marketing or personalized communication use address datasets to identify customers in specific areas. Job roles that may use address datasets for this purpose include marketing managers, data analysts, and customer service representatives.

Political campaigning and voter outreach:
Political campaigns and organizations use address datasets to identify potential supporters and voters. Job roles that may use address datasets for this purpose include campaign managers, outreach coordinators, and data analysts.

Customer services:
Companies that need to provide customer services, such as support or technical assistance, use address datasets to verify customer addresses and contact information. Job roles that may use address datasets for this purpose include customer service representatives, technical support specialists, and database administrators.

Real estate:

Real estate companies can use address datasets to identify potential properties for investment or development, as well as to analyze market trends and property values. Job roles that may use address datasets for this purpose include real estate agents, brokers, and analysts.

Property development:

When it comes to property development, address data can be used to identify potential properties for investment or development, as well as to analyze market trends and property values. It can help identify areas with high demand for certain types of properties, such as residential or commercial, and inform decisions about where to invest resources. Additionally, address data can be used to evaluate the potential impact of development on the local community, such as changes to traffic patterns or access to services and amenities.

By understanding the different use cases and job roles using address data today, organizations and businesses can identify the most appropriate ways to use address datasets for their specific needs.

How can a user assess the quality of Address Data?

A high-quality address data provider will have data which can be corroborated against the specific country’s postal authority, other government agencies, and third parties such as the United States Postal Service, Canada Post, and Royal Mail for UK, which have complete verified address databases.

Besides address verification, high-quality data providers should ensure that their DaaS is accompanied with a process that eases customer experience like type-ahead verification, which allows the user to instantly validate their external data.

Another top-quality measurement to keep in mind is how often providers update and clean their data. Address data can be updated on a daily basis; with outdated data, you could potentially miss thousands of lead opportunities. Before you decide, it’s always a good idea to ask whether you can sample a provider’s address data, and read reviews left for the provider in question.

How can I buy an Address Database?

When buying an address database from an external data provider, there are several pricing models to consider. Each model has its own advantages and disadvantages, depending on the specific needs of the business. Here are the different pricing models offered by most address data providers on Datarade Marketplace:

Subscription-based pricing
Subscription-based pricing is a common pricing model for address databases. With this model, businesses pay a monthly or annual fee for access to the database. The fee typically depends on the size of the database and the level of access needed. This model can be cost-effective for businesses that require regular access to address data.

Pay-per-use pricing
Pay-per-use pricing is another pricing model for address databases. With this model, businesses pay for each record or query they make in the database. This model can be more cost-effective for businesses that only require occasional access to address data.

Flat-rate pricing
Flat-rate pricing is a pricing model in which businesses pay a fixed fee for access to the entire database. This model can be cost-effective for businesses that require regular access to a large amount of address data.

Custom pricing
Custom pricing is a pricing model in which businesses negotiate a price with the data provider based on their specific needs. This can include factors such as the size of the database, the level of access needed, and the frequency of use. This model can be more flexible for businesses with unique needs.

Volume-based pricing
Volume-based pricing is a pricing model in which businesses receive discounts for purchasing larger amounts of data. This can be a cost-effective option for businesses that require a large amount of address data.

So there are several pricing models to consider when buying an address database from an external data provider. The best pricing model will depend on the specific needs of your business, including the size of the database required, the frequency of use, and the level of access needed. By understanding the different pricing models, businesses can make an informed decision and choose the model that best fits their needs and budget.

Where can I buy Address Data?

Data providers and vendors listed on Datarade sell Address Data products and samples. Popular Address Data products and datasets available on our platform are IPinfo.io Custom IP Address Database On Demand | Global Coverage, Tailored IP Address Data by IPinfo, DrivenIQ Email Database - Email Address Data - 1.5+ Tillion US email records for acquisition and/or Licensing by DrivenIQ, and BIGDBM US LiveDB HEM<>IP Address Data by BIGDBM.

How can I get Address Data?

You can get Address Data via a range of delivery methods - the right one for you depends on your use case. For example, historical Address Data is usually available to download in bulk and delivered using an S3 bucket. On the other hand, if your use case is time-critical, you can buy real-time Address Data APIs, feeds and streams to download the most up-to-date intelligence.

What are similar data types to Address Data?

Address Data is similar to Identity Graph Data, Phone Number Data, Cross-Device Identity Data, MAID - Hashed Email Data, and Identity Linkage Data. These data categories are commonly used for Marketing and Identity Resolution.

What are the most common use cases for Address Data?

The top use cases for Address Data are Marketing, Identity Resolution, and Data Onboarding.