Writing Resources
Customer Data Matching Styles using Machine Learning What is customer data matching Customer data matching tries to solve the problem of identifying mapping customer details that are located in different databases to unify them for future marketing purposes In simple words it is the process of identifying pairs of customer records that refer to all his activities transactions Also this is often referred to record matching linkage as it drills down to record level i e to a customer individual identity who does transaction and herein the real challenge is to identify all records referring to the same customer individual identity across different data sources Business Uses Customer data matching has several business uses as listed below 1 It helps for efficient customer targeting e g building relevant cohorts segments 2 It helps in correction update and de duplication across multiple data sources to provide a harmonized global record for all future marketing purposes 3 It helps in having higher quality data for attribution modelling that helps to identify path between sales and conversions 4 Also helps in deeper analytical insights around customer behavior that help in generating appropriate measure of returns on the different marketing channels spent 5 And many more Methods Ways of matching styles There exists broadly two ways of customer data matching styles i deterministic style and 2 probabilistic style Deterministic style Matching records to obtain a unique identification
