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Customer Segmentation for Marketing Intelligence Enabled Hyper-Personalisation: Exploring Mixed Method Approach

https://doi.org/10.2139/ssrn.4776540
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inferredcites →https://doi.org/10.1002/mar.21587openalex-extracted
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inferredcites →https://doi.org/10.1002/mar.21908openalex-extracted
Marketing analytics: The bridge between customer psychology and marketing decision‐making
inferredcites →https://doi.org/10.1007/978-3-642-15606-9_1openalex-extracted
Marketing and Artificial Intelligence: Great Opportunities, Reluctant Partners
inferredcites →https://doi.org/10.1007/s10551-021-04843-yopenalex-extracted
Leveraging Artificial Intelligence in Marketing for Social Good—An Ethical Perspective
inferredcites →https://doi.org/10.1007/s11747-019-00696-0openalex-extracted
How artificial intelligence will change the future of marketing
inferredcites →https://doi.org/10.1007/s11747-020-00749-9openalex-extracted
A strategic framework for artificial intelligence in marketing
inferredcites →https://doi.org/10.1007/s40745-022-00428-2openalex-extracted
An Improved K-means Clustering Algorithm Towards an Efficient Data-Driven Modeling
inferredcites →https://doi.org/10.1016/j.eswa.2006.10.012openalex-extracted
Market segmentation based on hierarchical self-organizing map for markets of multimedia on demand
inferredcites →https://doi.org/10.1016/j.eswa.2008.04.003openalex-extracted
Classifying the segmentation of customer value via RFM model and RS theory
inferredcites →https://doi.org/10.1016/j.ijresmar.2019.09.001openalex-extracted
Retailing and retailing research in the age of big data analytics
inferredcites →https://doi.org/10.1016/j.jbusres.2020.11.034openalex-extracted
Hyper-personalization, co-creation, digital clienteling and transformation
inferredcites →https://doi.org/10.1016/j.jclepro.2020.125242openalex-extracted
Artificial intelligence-enabled environmental sustainability of products: Marketing benefits and their variation by consumer, location, and product types
inferredcites →https://doi.org/10.1016/j.jjimei.2021.100050openalex-extracted
Turning the blackbox into a glassbox: An explainable machine learning approach for understanding hospitality customer
inferredcites →https://doi.org/10.1016/j.phpro.2012.03.206openalex-extracted
A Clustering Method Based on K-Means Algorithm
inferredcites →https://doi.org/10.1016/j.techfore.2021.121289openalex-extracted
Assessing an on-site customer profiling and hyper-personalization system prototype based on a deep learning approach
inferredcites →https://doi.org/10.1016/j.technovation.2021.102348openalex-extracted
“Baby, you can drive my car”: Psychological antecedents that drive consumers’ adoption of AI-powered autonomous vehicles
inferredcites →https://doi.org/10.1016/j.tourman.2014.07.011openalex-extracted
Variety-seeking: Using the CHAID segmentation approach in analyzing the international traveler market
inferredcites →https://doi.org/10.1016/s0167-8116(02)00080-0openalex-extracted
Retail segmentation using artificial neural networks
inferredcites →https://doi.org/10.1057/s41270-022-00158-7openalex-extracted
Integrated customer lifetime value (CLV) and customer migration model to improve customer segmentation
inferredcites →https://doi.org/10.1057/s41270-023-00235-5openalex-extracted
How can algorithms help in segmenting users and customers? A systematic review and research agenda for algorithmic customer segmentation
inferredcites →https://doi.org/10.1080/09537287.2022.2083524openalex-extracted
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inferredcites →https://doi.org/10.1080/19368623.2020.1781733openalex-extracted
Accommodation eWOM in the sharing economy: automated text comparisons from a large sample
inferredcites →https://doi.org/10.1108/02634509810244390openalex-extracted
Market segmentation: strategies for success
inferredcites →https://doi.org/10.1108/09604521111127956openalex-extracted
Beyond satisfaction
inferredcites →https://doi.org/10.1108/apjml-07-2021-0492openalex-extracted
Profiling the online premium brand consumers based on their fashion orientation
inferredcites →https://doi.org/10.1108/bij-01-2021-0009openalex-extracted
Emerging trends in digital transformation: a bibliometric analysis
inferredcites →https://doi.org/10.1108/bij-07-2021-0415openalex-extracted
Journey of customers in this digital era: Understanding the role of artificial intelligence technologies in user engagement and conversion
inferredcites →https://doi.org/10.1108/bij-10-2021-0621openalex-extracted
Customer experiences in the era of artificial intelligence (AI) in context to FinTech: a fuzzy AHP approach
inferredcites →https://doi.org/10.1108/eb045693openalex-extracted
Market Segmentation — A Strategic Necessity?
inferredcites →https://doi.org/10.1108/eum0000000004695openalex-extracted
Market Segmentation: A Review
inferredcites →https://doi.org/10.1108/ijbm-03-2017-0058openalex-extracted
Profiling emerging market investors: a segmentation approach
inferredcites →https://doi.org/10.1111/ijcs.12899openalex-extracted
Online consumer shopping behaviour: A review and research agenda
inferredcites →https://doi.org/10.1166/asl.2016.7985openalex-extracted
Review on Customer Segmentation Technique on Ecommerce
inferredcites →https://doi.org/10.1177/002224297604000408openalex-extracted
Approaches to Market Segmentation Analysis
inferredcites →https://doi.org/10.1177/002224299005400307openalex-extracted
Double Jeopardy Revisited
inferredcites →https://doi.org/10.1177/0022243719852959openalex-extracted
When Words Sweat: Identifying Signals for Loan Default in the Text of Loan Applications
inferredcites →https://doi.org/10.1300/j366v06n03_02openalex-extracted
Approaches to Customer Segmentation
inferredcites →https://doi.org/10.1362/026725700785100479openalex-extracted
Market Segmentation by Cohorts: The Value and Validity of Cohorts in America and Abroad
inferredcites →https://doi.org/10.1504/ijbe.2009.023796openalex-extracted
Machine learning methods for the market segmentation of the performing arts audiences
inferredcites →https://doi.org/10.1504/jbm.2018.141263openalex-extracted
The Amazon Go Concept: Implications, Applications, and Sustainability
inferredcites →https://doi.org/10.1509/jm.15.0414openalex-extracted
Creating Enduring Customer Value
inferredcites →https://doi.org/10.17010/ijom/2022/v52/i5/169416openalex-extracted
Digital Banking : A Meta-Analysis Approach
inferredcites →https://doi.org/10.2307/258554openalex-extracted
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Social Networks Marketing and Consumer Purchase Behavior: The Combination of SEM and Unsupervised Machine Learning Approaches
inferredcites →https://doi.org/10.3390/electronics9081295openalex-extracted
The k-means Algorithm: A Comprehensive Survey and Performance Evaluation
inferredcites →https://doi.org/10.3390/joitmc8010012openalex-extracted
E-Commerce Customers Behavior Research Using Cohort Analysis: A Case Study of COVID-19
inferredcites →https://doi.org/10.6347/jbm.201803_24(1).0004openalex-extracted
The Amazon Go Concept: Implications, Applications, and Sustainability
inferredcites →https://doi.org/10.9781/ijimai.2019.05.002openalex-extracted
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inferredcites →https://doi.org/10.5465/amr.1989.4308371openalex-snapshot
What Constitutes a Theoretical Contribution?
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