What is an identity graph?
The concept of an identity graph, also known as an ID graph or identity resolution, is a data structure that combines customer profiles across multiple devices and touchpoints into a single, unified view. This is a critical tool in the field of marketing, as it allows businesses to understand and target their customers more effectively. It is a complex concept that involves various aspects, including data collection, data matching, data consolidation, and privacy considerations.
Identity graphs are built using a combination of deterministic and probabilistic data matching techniques. Deterministic matching involves linking identities based on known, verified information, such as a customer’s login details. Probabilistic matching, on the other hand, uses statistical algorithms to link identities based on less certain data, such as device usage patterns or IP addresses. The resulting identity graph provides a comprehensive view of a customer’s interactions with a business, across multiple channels and devices.
Understanding the Components of an Identity Graph
An identity graph is composed of several key components, each of which plays a crucial role in its functionality. These components include identifiers, data sources, data points, and the relationships between them. Understanding these components is essential to understanding how an identity graph works.
Identifiers are unique pieces of information that can be used to distinguish one individual from another. These can include things like email addresses, social media profiles, device IDs, and more. Data sources are the places where these identifiers are collected, such as websites, apps, CRM systems, and third-party data providers. Data points are the individual pieces of information associated with each identifier, such as a customer’s purchase history or browsing behavior. The relationships between these components are what form the ‘graph’ part of an identity graph, linking together identifiers, data sources, and data points to create a comprehensive view of a customer.
Identifiers
Identifiers are the backbone of an identity graph. They are unique pieces of information that can be used to distinguish one individual from another. There are two main types of identifiers used in identity graphs: deterministic identifiers and probabilistic identifiers.
Deterministic identifiers are pieces of information that can definitively identify an individual, such as a login ID or email address. These identifiers are typically collected when a customer interacts directly with a business, such as by making a purchase or signing up for a newsletter. Probabilistic identifiers, on the other hand, are less certain. They are inferred based on patterns of behavior, such as device usage or browsing patterns. While probabilistic identifiers are less accurate than deterministic ones, they can still provide valuable insights into customer behavior.
Data Sources
Data sources are the places where identifiers and data points are collected. These can include a wide variety of platforms and systems, such as websites, mobile apps, CRM systems, and third-party data providers and website visitor identification tools. The quality and reliability of a data source can greatly impact the accuracy of an identity graph.
First-party data sources, such as a business’s own website or CRM system, are typically the most reliable, as they provide direct, verified information about customers. Third-party data sources, such as data brokers or ad networks, can provide additional insights, but their reliability can vary. It’s important for businesses to carefully vet their data sources to ensure the accuracy of their identity graphs.
Building an Identity Graph
Building an identity graph involves several key steps, including data collection, data matching, data consolidation, and privacy considerations. Each of these steps is critical to creating an accurate and useful identity graph.
Data collection is the first step in building an identity graph. This involves gathering identifiers and data points from various data sources. The more data a business can collect, the more comprehensive its identity graph will be. However, it’s also important to ensure that this data is accurate and reliable, as inaccurate data can lead to a misleading identity graph.
Data Matching
Data matching is the process of linking together identifiers and data points to create a unified view of a customer. This is typically done using a combination of deterministic and probabilistic matching techniques.
Deterministic matching involves linking identities based on known, verified information. For example, if a customer uses the same email address to make a purchase on a website and to sign up for a newsletter, a business can deterministically match these two activities to the same individual. Probabilistic matching, on the other hand, involves using statistical algorithms to link identities based on less certain data. For example, if a customer frequently visits a website from the same IP address, a business might probabilistically match this activity to that individual.
Data Consolidation
Data consolidation is the process of combining all of the matched identifiers and data points into a single, unified view of a customer. This is what forms the ‘graph’ part of an identity graph.
During data consolidation, businesses must take care to ensure that they are not duplicating data or creating false connections. This requires careful data management and quality control. Once the data has been consolidated, the resulting identity graph can be used to gain insights into customer behavior and to inform marketing strategies.
Using an Identity Graph
Once an identity graph has been built, it can be used in a variety of ways to inform marketing strategies. These can include audience segmentation, personalized marketing, cross-device tracking, and more.
Audience segmentation involves dividing a business’s customer base into distinct groups based on their behaviors, preferences, and characteristics. An identity graph can provide the detailed, individual-level data needed to create these segments. Personalized marketing involves tailoring marketing messages to individual customers based on their behaviors and preferences. An identity graph can provide the insights needed to create these personalized messages.
Cross-Device Tracking
Cross-device tracking involves tracking a customer’s interactions with a business across multiple devices, such as a desktop computer, smartphone, and tablet. This can provide a more comprehensive view of a customer’s behavior and preferences, as it takes into account all of the different ways a customer might interact with a business.
An identity graph is a key tool for cross-device tracking, as it can link together identifiers from multiple devices to create a unified view of a customer. This can allow businesses to understand how customers move between devices, which can inform strategies for multi-device marketing campaigns.
Privacy Considerations
While identity graphs can provide valuable insights into customer behavior, they also raise important privacy considerations. Businesses must be careful to respect their customers’ privacy when collecting and using data for an identity graph.
This includes obtaining proper consent for data collection, ensuring the security of collected data, and providing transparency about how data is used. Businesses must also comply with relevant privacy laws and regulations, which can vary by region and industry.
Data Security
Data security is a critical consideration when building and using an identity graph. Businesses must take steps to ensure that the data they collect is stored securely and is protected from unauthorized access.
This can include implementing strong encryption, using secure data storage solutions, and regularly auditing data security practices. Businesses must also have plans in place for responding to data breaches, should they occur.
Transparency and Consent
Transparency and consent are also important considerations when using an identity graph. Businesses must be clear with their customers about what data they are collecting, how they are using it, and who they are sharing it with.
This includes providing clear, easy-to-understand privacy policies, obtaining explicit consent for data collection and use, and giving customers the ability to opt out of data collection if they choose. By being transparent and respectful of their customers’ privacy, businesses can build trust and foster positive customer relationships.
Conclusion
In conclusion, an identity graph is a powerful tool for understanding and targeting customers in the field of marketing. By combining data from multiple sources and linking it together into a unified view of a customer, businesses can gain deep insights into customer behavior and preferences.
However, building and using an identity graph also requires careful consideration of data accuracy, data security, and privacy. By understanding these considerations and implementing best practices, businesses can use identity graphs effectively while also respecting their customers’ privacy.
