Using machine learning to extract insights from consumer data
Advances in digital technology have led to the digitization of everyday activities of billions of people around the world, generating vast amounts of data on human behavior. From what people buy, to what information they search for, to how they navigate the social, digital, and physical world, human...
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| Main Authors: | , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2022
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| Online Access: | https://ink.library.smu.edu.sg/lkcsb_research/7095 https://ink.library.smu.edu.sg/context/lkcsb_research/article/8094/viewcontent/Chang___Mukherjee_Using_Machine_Learning_Methods_to_Extract_Behavioral_Insights_from_Consumer_Data__v.2022.08.22__FINAL.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | Advances in digital technology have led to the digitization of everyday activities of billions of people around the world, generating vast amounts of data on human behavior. From what people buy, to what information they search for, to how they navigate the social, digital, and physical world, human behavior can now be measured at a scale and level of precision that human history has not witnessed before. These developments have created unprecedented opportunities for those interested in understanding observable human behavior–social scientists, businesses, and policymakers—to (re)examine theoretical and substantive questions regarding people’s behavior. Moreover, technology has led to the emergence of new forms of consumer marketplace— crowdfunding (whereby entrepreneurs obtaining funds from an anonymous online crowd; Mukherjee, Chang, & Chattopadhyay 2019) and crowdsourcing (whereby organizations gather new ideas and business solutions from an anonymous online crowd; Mukherjee, Xiao, Wang, & Contractor, 2018)—which not only details people’s behavior in exchange of products and services but also led to new behavior. |
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