Draft:Identity graph

Unified customer profile From Wikipedia, the free encyclopedia

An identity graph (also known as ID graph, identity spine, or identity network) is a database that links customer identifiers across different sources to create a unified profile in order for businesses and marketing to understand their customers holistically.[1] These databases are maintained by identity providers and used by businesses to help personalize advertisements based on individual devices.[2]

  • Comment: The topic is probably notable, but there are almost no sources currently in your draft that are both independent and reliable. Blog posts are not a reliable source, and any blog posted by a company that offers identity graph services (e.g. AWS) is not independent. MCE89 (talk) 10:23, 14 December 2025 (UTC)
  • Comment: Can you provide context for where this concept came to be, why it is important? In what field is this studied? Who is using it and why? What other concepts does it relate to? "Graph databases" should be wikilinked. Caleb Stanford (talk) 21:22, 8 March 2025 (UTC)

To accomplish identity resolution across different sources to generate the identity graph, either deterministic or probabilities methods, or a combination of both, are used.[3] Identity graphs are one type of cookie alternative.[4]

Motivation

Marketers will use different tools to better understand their audience market. But these tools don't typically interoperate with each other, which creates a fragmented view of the customer audience from which marketers can learn from. Identity graphs were created because of the increasing number of platforms that customers have access to and to help businesses personalize interactions with customers using all of these access points.[5] Some example identifiers that can be linked together are usernames, phone numbers, purchase histories, and loyalty card numbers.[6]

Examples

There are examples of corporations using identity graphs to help improve their businesses. Netflix and Amazon are able to recommend more relevant shows and products using browser history across devices.[1] International shoe retailer Clarks used Wunderkind's identity network to deanonymize 32% of their website traffic, which brought in twelve times more visitors to the retailer's website and 5.5 times more revenue growth.[7] Programmatic media partner MiQ collaborated with Experian to help their identity graph create a 64% increase in reaching audiences through universal IDs and adding 6.5 devices to each matched IP address.[8]

Applications

Using identity graphs, businesses are able to achieve the following:[1][6][3][9]

  • Personalized and improved customer service
  • Cross-device attribution
  • Personalized in-app experiences
  • Deliver effective promotions using context-aware messaging
  • Precise and personalized marketing campaigns
  • Reach non-logged-in audiences
  • Increase customer engagement and revenue
  • Marketing attribution
  • Audience segmentation by brand
  • Early adopter path to purchase insights
  • Identify look-alike customers

A more complete identity graph for customers helps machine learning algorithms to analyze seasonality, cross-category purchases, churn risk, price sensitivity, and in-store predictions.[6]

Creation

Identity graphs are generally created in three steps:[10]

  1. Ingest event data from different identifiers
  2. Train and build a machine learning model
  3. Construct the graph

The identifiers are clustered at either the household or individual level. Deterministic and probabilistic identity resolution is then done to unify the identifiers.[10] Identity graphs are typically built using graph databases.[1][11][12]

Identifiers

Identity graphs are built up from a number of identifiers, such as:[6][3][10]

See also

References

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