Modeling the types of customer connections

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Your community of customers, like any other community, consists of a collection of individual parties that can also be referred to as “actors” who are related to one another. These relationships can be modeled using the graph abstraction, in which every actor is represented as a node, and every connection between two actors is represented as a link or an edge between two nodes.

For example, if one actor is friends with another actor, it would be represented as two nodes with one undirected edge between them.

However, not all relationships are necessarily reciprocal. For example, actor A may be the father of actor B, while actor B is the daughter of actor A. These relationships would have to be modeled slightly differently to show the direction of the relationship with a directed edge between them. This also allows you to refine the nature of the relationships between actors within a community.

This abstraction can be used to represent any relationship among a set of actors, such as households, schoolmates, people who work in the same industry, etc. Not every relationship is between two customers, either. One customer has purchased one of your company’s products – one node represents the customer, the other represents the product, and the edge between them would indicate that the customer purchased that product. In this way we can build up a social network representing the ways that the different actors are connected; this social network model becomes the basis for a number of different analyses for understanding and taking advantage of ways some customers can influence the behaviors of others.

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About Author

David Loshin

President, Knowledge Integrity, Inc.

David Loshin, president of Knowledge Integrity, Inc., is a recognized thought leader and expert consultant in the areas of data quality, master data management and business intelligence. David is a prolific author regarding data management best practices, via the expert channel at b-eye-network.com and numerous books, white papers, and web seminars on a variety of data management best practices. His book, Business Intelligence: The Savvy Manager’s Guide (June 2003) has been hailed as a resource allowing readers to “gain an understanding of business intelligence, business management disciplines, data warehousing and how all of the pieces work together.” His book, Master Data Management, has been endorsed by data management industry leaders, and his valuable MDM insights can be reviewed at mdmbook.com . David is also the author of The Practitioner’s Guide to Data Quality Improvement. He can be reached at loshin@knowledge-integrity.com.

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