Identity graph

What is an identity graph?

An identity graph is a customer profile comprising all the personal and behavioral customer data specific to that customer. Organizations gather this data with various methods:

• Customers provide names, email addresses, and firmographic information in exchange for access to gated content.

• An e-commerce platform may gather historical data related to product purchasing or loyalty memberships. 

• Analytics tools can provide behavioral data about a customer’s journey through a website.

• Customers’ device locations can provide geographical information 

• Social networks can provide a wealth of personal data from their users.

Identity graphs — sometimes called “identity spines” or “identity charts” — are stored in an online or cloud-based database, sometimes called a data warehouse. Each customer’s data is compiled in a separate table or node in that database. 

Identity graphs and identity resolution

The customer data stored in identity graphs is compiled by deterministic and probabilistic identity resolution, also called deterministic matching and probabilistic matching. These methods use algorithms and machine learning to determine whether data from different sources — and across different devices — relates to the same person.

The benefits of identity graphs for marketers

Because comprehensive identity graphs provide a 360-degree view of individual customers and prospects, they help marketers build customer personas and user profiles that inform:

• Audience segmentation and personalized experiences

• Customer journey mapping

• Hyper-targeted messaging

• Real-world and digital advertising

The customer identity data for identity graphs can be collected continually, making real-time marketing tactics more effective. Such tactics include:

• Geo-targeting, which enables brands to display in-app ads on mobile devices when someone is near one of their retail stores. Geo-targeting can also help brands target ads in social media feeds of people who are located in an area they provided service.s

• User journey orchestration, allowing marketers to remap a user journey by tracking the customer’s channel or website interactions. 

• Ad retargeting, which uses tracking pixels to enables brands to place ads — often in real time — in the social media feeds of people who have recently interacted with products on their website.

• Cross-channel marketing, including  in-app promotion, which places upsell ads for a freemium app or for related products within the app’s interface based on how the user is interacting with app.

Identity graphs can also improve return on ad spend (ROAS) by targeting advertising more efficiently — it helps direct ads at the right people with relevant content. 

Finally, by tracking information about marketing preferences, such as opting out of promotional emails, identity graphs can help improve compliance with regulations on collecting and using personal data.

Examples of personalization using identity graphs

Marketers can employ identity graphs to:

• Inform the recommendation engines -For example, e-tailers use recommendation engines, (which employ collaborative filtering and automatic topic modeling) to suggest products. These suggestions may be for products similar to the ones a customer has already purchased or products purchased by people with characteristics similar to that customer.

• Match embedded ads to the right user - For example, a streaming service may use these ads to target content to viewers most likely to be receptive to those shows or movies.

• Enable successful loyalty programs - For example, an airline can manage rewards programs more efficiently by matching discount offers with eligible customers.

• Targeting upgrade ads to owners of specific products - For example, a telecom company could target plan or phone upgrades, and a technology company could use identity graph data to better target computer hardware upgrades. 

Other use cases for identity graphs

Identity graphs are increasingly used for a wide range of applications beyond marketing. Identity graphs can: 

• Help financial services companies like banks, credit cards, and payment services with real-time fraud detection by comparing previous banking or purchasing behavior with each transaction and halting transactions that fall outside their typical patterns.

• Enable streaming services to handle cross-device IDs and access management by tracking user characteristics more comprehensively, providing or preventing access to the same content on multiple devices simultaneously.

• Simplify IT management for telecom companies by making the structure of complex networks easier to map and understand.

• Manage supply chains for retailers by tracking the relationships among delivery schedules, product availability, manufacturing schedules, and related factors.

How to create an identity graph

Identity graphs are built using four basic steps, each of which is supported by complex concepts rooted in computer and data science:

1. User data is collected from a variety of sources, devices, and analytics tools.

2. The data is consolidated into a single customer profile using an identity resolution framework — deterministic matching, probabilistic matching, or a combination of the two.

3. The identity graph is expanded and updated over time as more data about the user is collected from additional touchpoints and devices.

4. The identity graphs are stored in an online database or a data cloud

Organizations can expand the scope of an identity graph and refine its accuracy by marrying the data it collects (first party data) with third party data available from identity graph vendors. This process is known as data synchronization. 

Third party identity graphs enhance the accuracy with which customer data from different sources is combined, improving identity resolution. Identity graphs can even establish relationships among different customers by gathering data about different users of the same device, a practice known as context switching. 

Privacy-compliant identity graphs

More browsers and platforms are turning off third party cookies to comply with new data privacy laws and regulations. As this continues, the value of third party identity providers will increase, as it will be harder for customers to collect data through their own cookies and third party identity providers use other techniques to gather the information. 

By taking first party data and layering third party data related to a specific market or product, marketers can continue employing programmatic advertising without the use of cookies.

Choosing an identity graph vendor

An identity graph must accurately determine who a customer is or what they are like. Otherwise, it can derail marketing campaigns and return on ad spend (ROAS). This accuracy depends on the data source and quantity, and how well that data is linked in the identity graph.

When choosing an identity graph company, consider the following variables:

Data quality

Reliable identity graph vendors will partner with well-known global advertising technology companies and data providers. The most trustworthy vendors will also be transparent about the source of the information they sell. You can also check with independent ratings agencies that provide background on data vendors.

Compliance with data privacy laws and regulations

Proof of compliance with data privacy laws, including GDPR in Europe and CCPA in the United States, is essential. Because large technology companies like Apple and Google have begun blocking third party cookies, you should work with a vendor that can build graphs using cookie-free data collection. 

Scale

The more data a company has, the more customer profiles your identity graph will generate. The data should come from various sources and include all the essential data types: demographic, behavioral, historical, and geographic.

Relevance

If your personalization and targeting strategies rely on a specific type of user characteristic, make sure that data is collected by the vendor you choose and provided in the dataset you purchase. Continuous data collection is key to ensuring your targeting and personalization remain accurate and successful. 

Price

Vendor pricing varies widely, can be based on different factors about the data, and can even change based on the location of your company.

Customer support

Vendors provide varying degrees of service — or may provide none at all. The best identity graph vendors provide tech support and strategic advice about how to best use their data to build out your campaigns and achieve your business goals. 

Identity graph vs. identity resolution solutions

Organizations can purchase personal data about their customers from a wide variety of vendors. Some, like social or advertising networks, create identity graphs from this data and sell the graph database itself rather than just the data. 

While such solutions provide a large quantity of data, they usually don’t offer transparency into where they collected the data. Additionally, these vendors may not offer much flexibility in how the data can be sorted, exported, and applied. The data dumps may even be static, with no ability to update the data as customer characteristics change over time. These solutions may also be expensive. 

Choosing a provider that builds identity graphs via identity resolution solutions from data collected ethically and stored in a data warehouse or data cloud is a safer and more useful alternative.