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
It is the simplest and works as follows if we have variety of user database who are basically our customers and we keep track of them via transactions database as transactions are done and via email database as and when email campaigns are sent Now if you have few deterministic values such as unique customer names or ids and we match accordingly it is called as deterministic matching E g Customer ID in Transaction Data Base is 01001 and email as jay dey gmail further if email database has similar email by jay dey gmail com both will be merged to have a unified ID This is simplest as you are picking a single deterministic value that is assumed to be unique across records and match all records sharing the same value as matching However there are several pitfall of this approach as it cannot find similarities between partial or quasi identifier values while comparing the records always need full values and no way these can be leveraged for matching records with typographical and phonetic errors that occupy majority in the real world scenarios Probabilistic style Matching records based on inferences about likely probabilities of agreement and disagreement between a range of matching variables data science way
It is based on machine statistical learning and consider the frequency of values within the records that are in a certain range of distribution which characterize the individual customer person as singular In this method all records from different databases are matched against each other to obtain a score that presents itself with the confidence level about the records that are likely to match or not It uses fuzzy logic popularly known as fuzzy matching and standardized values to arrive at a composite weight of a match or non match Currently it is in this area where data scientists are investing and employing advance machine learning ML models as the need for processing tremendous volumes of data records along with higher accuracy of such matching is the need of hour Machine Learning Customer Data Matching As traditional probabilistic styles are mostly concentrated to find the similarities between records mainly using approximate string comparison functions there adoption to new volume and variety of data were not yielding expected results Hence to improve data matching quality various machine learning techniques have been developed in the recent past Though a wide variety of supervised and unsupervised techniques have been developed supervised occupies prominence among them Supervised learning necessitate a labelled training data set which require some in front additional effort In below we have presented what all machine learning algorithms have been used in customer data matching
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