Buy Identity Graph Data

Identity graph data is information connecting all the devices an individual uses. Identity data APIs are used by marketers and advertisers e.g in omnichannel marketing and cross-device targeting. Datarade helps you find the best cross-device graph data providers and datasets. Learn more →
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Our Data Partners
Adstra Link2Me
by Adstra
1 country covered
Our Persistent ID that enables preconnected data, allowing for an infinite number of attributes from vertical (auto, travel, retail, etc.) and horizontal (be...
20 million businesses mapped
70% match rates against CRM account data with a 10X improvement on results
1 country covered
The industry’s only location based ABM onboarding, measurement and activation solution covering more than 20 million business locations.
1.06B Device to HEM (Hashed Email)
50% Device to HEM (Hashed Email) Matching
107 countries covered
Fresh and accurate identity linkage data that allows first party data owners to extend the reach of their data.
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BIGDBM
Based in USA
BBIGDBM provides software development on top of highly accurate Big Data.
1 Billion
Online & Offline Identifiers
Validated
Identity Resolution Graph
Self-Serve
Data Market
zeotap
Based in Germany
Zeotap is a Customer Intelligence Platform that helps brands understand customers and predict behavior, empowering them to invest in customer relationships a...
50-70%
Match Rate
3Bn+
Data Profiles
150+
Channel Integrations
Adstra
Based in USA
Adstra is the new data model for the data-driven enterprise. Adstra comprises a comprehensive suite of portable data and identity solutions. We can ingest an...
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The Ultimate Guide to Identity Graph Data 2021

Learn everything about Identity Graph Data. Understand data sources, popular use cases, and data quality.

What is Identity Graph Data?

Most consumers today use more than one device to stay connected to the internet. There are thousands of addressable media outlets, with each user using a range of devices.

The entire concept of marketing was simpler in the good old days of television, print, and radio when everything used to be sorted for marketers and all they had a pretty straightforward playground. However, with omnichannel marketing approach today, businesses need to address their consumers on all possible channels - out blogs, social media, review websites, and buying guides and the like.

Until very recently, managing online identity was all about matching customers’ online cookies and other online activities with CRM data and the task was done. However, today, the digital landscape is broader, and this is why their identity across the devices, browsers, video game consoles, and mobile app SDKs needs to be matched to follow the consumer wherever they go, regardless of the device they use.

This is when cross-device identity data comes into the role. Essentially, this data helps businesses in identifying users across various devices with the help of IDs and cross-device graphs.

How does Identity affect consumer behavior?

Defined as the process through which customers select, buy, use, and research goods and services, consumer behavior has grown into a key paradigm area of focus for contemporary marketers. Consumer behavior focuses on the specific activities of consumers in a given marketplace and the core drivers of these undertaken actions. As far as online marketing strategies are concerned, one of the ways to track down consumer behavior in online market places is through the creation of identities for potential customers. Identity affects consumer behavior in the sense that consumer’s habits and trends are used to uniquely identify them.

What is a deterministic Identity Graph?

Deterministic matching refers to the process of matching user profiles with 100% accuracy level by means of key identifiers such as email, phone or logged in username. In this algorithm-based identity graph resolution, multiple device relationships are established by joining them using personally identifiable information (PII). The user’s devices are linked in such a way that they are directly observed by the means of PII to a consumer, putting more emphasis on accuracy and preventing or limiting the false positives. Deterministic matching is an identity graph approach whose desirability by marketers stems from its ability to target only actual buyers. Because deterministic data has a complex collection methodology, on a data marketplace, the price of deterministic identity graph data is typically higher when compared to its corresponding probabilistic-based identity graph.

What is a probabilistic Identity graph?

While a deterministic identity graph presents a user’s highly accurate data based on PII, probabilistic identity graph identifies a user by means of non-subjective methods such as IP address, device type, browsers and the device’s operating system. Technically, probabilistic identity graph formulates device relationships by means of a knowledge base of linkage data and predictive algorithms as the core facet of an identity graph. Probabilistic identity graph achieves tacit grouping of devices by means of fingerprinting, IP address identification for specific machines, screen resolution, OS, GPRS location tracking and Wi-Fi network. If marketers are looking for people who might buy or be interested in a specific product, then probabilistic identify graph is their best bet.

So, when shopping for identity graph data, marketers ought to understand their business needs and make a choice between deterministic and probalistic.

Who uses ID Graph Data and for what use cases?

Essentially, ID graph data is used to recognize customers and users across a range of internet-connected devices that they use. This data is then used to obtain critical hidden insights like user behavior, user preferences, and user demographics and so on.

A range of businesses across all major verticals rely on identity graphs during their data onboarding procedures. It helps the comapnies fuel their marketing campaigns, whether it be a tour and travel company, an apparel brand, ecommerce store, or a healthcare company.

Here are a few use cases of cross-device identity data:

Global frequency management
As important it is to stay connected with your customers, it is equally important that you maintain the right balance. You bombard them with too many marketing communications, and the chances are high that they will unsubscribe themselves from your database.

This is when cross-channel identity data provides you with details on all the users you have reached out to on mobile, search, email, video, or display. Most marketers use this data to ensure that they don’t end up communicating the same message to each of these users on different devices.

Sequential messaging
Device-identity data allows you to target a user through different ads on different stages during their consumer journey, regardless of the device they are using to connect online. This optimization is what separates award-winning digital marketers from the rest.

Customer journey modeling
Connecting user identity across platforms helps in understanding the mood of the target audiences. From tracking the last click to the first view, all these activities of a user can be tracked through cross-device identity data.

Which brands use Identity Graph Data?

An identity graph refers to a database that stores all the key identifiers that correlate with specific customers. A given business can possess specific customer data in various systems, such as e-commerce platforms, CRM, email marketing tool or an ad platform. It is the work of an identity graph to analyse this information in these tools and stitch in a single identifiable profile. In data marketplaces, identity graph data vendors collect secure identity data by aggregrating consumers’ online activities. Brands such a Netflix, Amazon Prime, HBO Max and Doordash are known to be among the biggest consumers of either historical or real-time identity graph data for the purpose of tailoring streaming content according to the user information.

What are examples of how Identity Graph Data is used in marketing?

From commercial identity datasets, marketers invest in data acquisition by buying identity graph data for the purpose of enhancing marketing strategies. From identity graphs, marketers are given the opportunity to stitch customer identities and create a ‘single view of customer’ that is considered more precise, up-to-date preview of customer attributes and behaviors. Amazon and Netflix both use identity graphs to enhance their marketing strategies. At Amazon, identity graph data is used to foster customer insights that are necessary towards the delivery of the type and level of personalization to each customer. It is from this data that the company, through big data analytics, has been able to carry out successful consumer targeted marketing strategies.

What is Identity stitching?

Identity stitching refers to the core feature of identity graph data in which multiple information about a person is pulled from a number of platforms and brought together to create a unique identity of that person based on this amassed data. A modern day consumer is most likely to use multiple devices when accessing the internet every day. While it is possible that these consumers will log into these devices, hence identifying themselves, in most cases they do not login, making it hard to identify them from a string of data collected by the device browser’s cookies feature alone. However, enhanced data analysis mechanisms are used to bring together all these consumer data points to create unique profiles for each individual, a process referred to as identity stitching. This consumer data may range from login details such as email, phone numbers, browser cookies and payment data.

What does a cookieless world mean for identity graph data?

Computer cookies consist of information. When internet users visit a website, the website sends the cookie to the computer which is then stored in a file situated within the computer web browser. The core purpose of the computer cookie is to assist the website visited to keep tabs on the users’ activities. Furthermore, websites use cookies to keep a record of site visitor’s login information. Cookies are important components of internet usage, which play a vital role in the collection of internet user data that is used to create identity graphs. As cookie usage declines, data mining companies are turning solely to identity graphs to obtain consumer data for marketing purposes, which has lead to an increase in the cost of identity graph data.

What are typical Identity Graph Data attributes?

The attributes of device identity data are all those parameters which help in identifying a user and confirming their identity.
Some examples could include:

  • Email or physical address
  • Online cookies
  • MAID Device IDs
  • Phone numbers
  • Account usernames
  • IP numbers
    … and essentially anything that can be linked back to the device and its identifier.

How is Identity Graph Data collected?

A typical digital consumer owns multiple connected devices. Tracking a user identity in such a scenario is a tough task.

Majorly, there are two ways through which cross-device tracking is conducted: deterministic and probabilistic tracking.

Deterministic tracking
Here, the identity of a user is tracked using personally identifiable information also called PII. This information includes email addresses, Facebook IDs, and so on. However, tracking users through this method requires a proper setup which giants like Facebook, Google, and Apple possess.

Probabilistic tracking
Quite predictably, this method obtains data on the basis of probability by tracking millions and billions of anonymous data points and tracking them altogether to gain insights on the devices. The elements that are often used to connect the dots include wifi networks, screen resolution, operating systems, and so on.

How to assess the quality of Identity Data?

Assessing the quality of cross-device identity data is fairly difficult. The best way here is to deal with a reputed data vendor who can assure you of the data quality.

Here are a few methods that you can adopt to assess the cross-device identity data quality:

Sample set for testing
Ask for a sample set for testing. This will help you in testing the waters and gauging that the data shared by you is actually authentic and reliable.

Ask for customer referrals
Perhaps, this is one of the best approaches. If customers of a particular data vendor seem to be happy, it is likely that the identity data company provides quality data.

Research and understand data providers’ collection methods
You might also like to have a look at the collection methods used by data vendor for collecting cross-device identity data. As it goes – logical and appropriate data collection methods will yield reliable data.

How Identity Graph Data is typically priced?

The price of cross-device identity data is generally based on the data delivery method:

You can get the data either through cloud storage or through push and pull APIs.

You can also get it delivered in raw form packed in JSON and CSV files.

What are the common challenges when buying Identity Graph Data?

When it comes to extracting identity graph data, there are some challenges that crop up:

Is data authenticated or not?
Needless to say, the profiles of users come in two forms: authenticated and unauthenticated. It is no brainer that the data extracted from authenticated profiles is what you should be after.

This is because the data provided by the authenticated profile is genuine and reliable. On the other hand, the data from non-authenticated profiles is temporary and majorly consists of device-specific IDs and cookies.

Matching of data
As cross-device identity data spans across a number of devices, it is essential to ensure that the user using his laptop is matched to the same identity when he is using his tablet. A mismatch in these coordinates will disturb the purpose of buying cross-device data.

What to ask your Identity Data providers?

Here are a few questions that you may want to ask your cross-device identity data provider:

  • How do you extract the MAID device IDs?
  • How do you test and evaluate your data?
  • How are identity assets outsourced? Are they licensed or owned?
  • How do you ensure the quality of your data?
  • Are both the offline and online identifiers linked?

    Who are the best Identity Graph Data providers?

    Finding the right Identity Graph Data provider for you really depends on your unique use case and data requirements, including budget and geographical coverage. Popular Identity Graph Data providers that you might want to buy Identity Graph Data from are BIGDBM, zeotap, Adstra, BDEX, and Datastream Group.

    Where can I buy Identity Graph Data?

    Data providers and vendors listed on Datarade sell Identity Graph Data products and samples. Popular Identity Graph Data products and datasets available on our platform are Adstra Link2Me by Adstra, 180byTwo - AccountLink - Mobile Account Based Marketing - ABM Cross Channel by 180byTwo, and Lifesight Identity Data by Lifesight.

    How can I get Identity Graph Data?

    You can get Identity Graph Data via a range of delivery methods - the right one for you depends on your use case. For example, historical Identity Graph 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 Identity Graph Data APIs, feeds and streams to download the most up-to-date intelligence.

    What are similar data types to Identity Graph Data?

    Identity Graph Data is similar to Cross-Device Identity Data. These data categories are commonly used for Advertising and Identity Graph Data analytics.

    What are the most common use cases for Identity Graph Data?

    The top use cases for Identity Graph Data are Advertising, Identity Resolution, and Data Onboarding.