Draft:Identity graph

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Submission declined on 8 March 2025 by Caleb Stanford (talk). This submission reads more like an essay than an encyclopedia article. Submissions should summarise information in secondary, reliable sources and not contain opinions or original research. Please write about the topic from a neutral point of view in an encyclopedic manner. If you would like to continue working on the submission, click on the "Edit" tab at the top of the window. If you have not resolved the issues listed above, your draft will be declined again and potentially deleted. If you need extra help, please ask us a question at the AfC Help Desk or get live help from experienced editors. Please do not remove reviewer comments or this notice until the submission is accepted. Where to get help If you need help editing or submitting your draft, please ask us a question at the AfC Help Desk or get live help from experienced editors. These venues are only for help with editing and the submission process, not to get reviews. If you need feedback on your draft, or if the review is taking a lot of time, you can try asking for help on the talk page of a relevant WikiProject. Some WikiProjects are more active than others so a speedy reply is not guaranteed. How to improve a draft Wikipedia:Contributing to Wikipedia – a basic overview on how to edit Wikipedia. Help:Wikitext – how to use the markup Help:Referencing for beginners – how to include references Wikipedia:Article development – how to develop your article Wikipedia:Writing better articles – how to improve your article Wikipedia:Verifiability – make sure your article includes reliable third-party sources You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article. Improving your odds of a speedy review To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags. Add tags to your draft Editor resources Easy tools: Citation bot (help) | Advanced: Fix bare URLs Declined by Caleb Stanford 8 months ago. Last edited by Erictleung 5 days ago. Reviewer: Inform author. This draft has been resubmitted and is currently awaiting re-review.

• 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)

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]

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]

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]

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]

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]

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]

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

• Usernames
• Hashed email addresses
• Phone numbers
• Account numbers
• Credit card numbers
• Purchase histories
• Loyalty card numbers
• First-party cookies
• Third-party cookies
• Alternative IDs
• Personally identifiable information (PII)
• Customer ID
• IP addresses
• Identifiers for advertising
• Mobile ad IDs (MAIDs)
• Connected TV ID

• Record linkage
• Privacy-enhancing technologies
• Graph database