Leveraging entity-resolution to identify customers in 3rd party data
(eVideo)
Contributors
Published
[Austin, Texas] : Data Science Salon, 2020.
Format
eVideo
Physical Desc
1 online resource (1 streaming video file (31 min., 2 sec.))
Status
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Language
English
Notes
General Note
Title from resource description page (Safari, viewed October 29, 2020).
General Note
Place of publication from title screen.
Participants/Performers
Presenter, Kelsey Redman.
Description
"Presented by Kelsey Redman, AVP, Data Science at Comerica Bank. Purchasing 3rd party data on individuals can give great insights on customers, but first we have to know which individuals from that outside data source are actually customers and which are just prospects. Without a unique identifier like SSN or Driver's License number from the 3rd party data, we have to use a combination of name, address, and demographic information to identify the matching customer. Between nicknames, misspelled names and addresses, and family members with similar names all at one address, this quickly becomes a difficult task involving heavy data cleanup and an increasingly complicated series of rules. In this presentation, we demonstrate some techniques to help resolve these entities across data sources by employing the use of supervised classification machine learning techniques to quantify and predict entity 'likeness.' We showcase some of the challenges we faced with exploring other entity resolution methods, with manually labeling a comprehensive training set, and how this approach might extend to solve other data issues."--Resource description page
Citations
APA Citation, 7th Edition (style guide)
Redman, K. (2020). Leveraging entity-resolution to identify customers in 3rd party data . Data Science Salon.
Chicago / Turabian - Author Date Citation, 17th Edition (style guide)Redman, Kelsey. 2020. Leveraging Entity-resolution to Identify Customers in 3rd Party Data. Data Science Salon.
Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)Redman, Kelsey. Leveraging Entity-resolution to Identify Customers in 3rd Party Data Data Science Salon, 2020.
MLA Citation, 9th Edition (style guide)Redman, Kelsey. Leveraging Entity-resolution to Identify Customers in 3rd Party Data Data Science Salon, 2020.
Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.
Staff View
Grouped Work ID
0232e91f-c202-902a-54a7-dc3e2aa4a86c-eng
Grouping Information
Grouped Work ID | 0232e91f-c202-902a-54a7-dc3e2aa4a86c-eng |
---|---|
Full title | leveraging entity resolution to identify customers in third party data |
Author | data science salon |
Grouping Category | movie |
Last Update | 2024-09-06 16:31:08PM |
Last Indexed | 2024-09-21 02:33:18AM |
Book Cover Information
Image Source | default |
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First Loaded | Sep 22, 2024 |
Last Used | Sep 22, 2024 |
Marc Record
First Detected | Jul 29, 2024 04:05:54 PM |
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Last File Modification Time | Sep 06, 2024 04:41:41 PM |
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