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Searching customer patterns of mobile service using clustering and quantitative association rule
4 references·12 citing·55 second hop
Searching customer patterns of mobile service using clustering and quantitative association rule (2007)Searching customer patterns of mobile ser…Customer's time-variant purchase behavior and corresponding marketing strategies: an online retailer's case (2002)Customer's time-variant purch…Market segmentation and the structure of competition: applicability of the strategic group concept for an improved market segmentation on industrial markets (2001)Market segmentation and the s…Segmentation of the on-line shopping market using neural networks (1999)Segmentation of the on-line s…Segmenting the industrial market (1983)Segmenting the industrial mar…The Moderating Role of Personal Innovativeness in Tourists’ Intention to Use Web 3.0 Based on Updated Information Systems Success Model (2022)The Moderating Role of Person…User willingness to purchase applications on mobile intelligent devices: evidence from app store (2019)User willingness to purchase …Segmentation of Brazilian travelers’ mobile purchase behavior (2019)Segmentation of Brazilian tra…Statistics-based CRM approach via time series segmenting RFM on large scale data (2017)Statistics-based CRM approach…Smartphone user segmentation based on app usage sequence with neural networks (2017)Smartphone user segmentation …A data mining approach for turning potential customers into real ones in basket purchase analysis (2015)A data mining approach for tu…Conventionalists, Connectors, Technoisseurs and Mobilarti: Differential profiles of mobile marketing segments based on phone features and postmodern characteristics of consumers (2014)Conventionalists, Connectors,…Behavior scoring model for coalition loyalty programs by using summary variables of transaction data (2012)Behavior scoring model for co…Exploring consumer adoption of new services by analyzing the behavior of 3G subscribers: An empirical case study (2011)Exploring consumer adoption o…A Financial Data Mining Model for Extracting Customer Behavior (2011)A Financial Data Mining Model…An efficient approach for building customer profiles from business data (2009)An efficient approach for bui…Customer pattern search for after-sales service in manufacturing (2008)Customer pattern search for a…Self organizing neural networks for financial diagnosis (1996)From segmentation to fragmentation (1997)Linking innovative product development with customer knowledge: a data-mining approach (2005)Enhancing Knowledge Development in Marketing (2010)The Effects of Social Media Marketing on Online Consumer Behavior (2013)Revealing customers’ satisfaction and preferences through online review analysis: The case of Canary Islands hotels (2019)Customer Satisfaction, Market Share, and Profitability: Findings From Sweden (1994)The American Customer Satisfaction Index: Nature, Purpose, and Findings (1996)Go Downstream: The New Profit Imperative in Manufacturing (1999)Integration of IPA and QFD to assess the service quality and to identify after sales service strategies to improve customer satisfaction – a case study (2016)After-sales services and aftermarket support: a systematic review, theory and future research directions (2019)Assessment of Lean Supply Chain Practices in Indian Automotive Industry (2020)The Crm Handbook: A Business Guide to Customer Relationship Management (2001)Applying data mining to telecom churn management (2005)Credit scoring with a data mining approach based on support vector machines (2006)Customer portfolio analysis using the SOM (2011)A model for bundling mobile value added services using neural networks (2012)Research on Segmenting E-Commerce Customer through an Improved K-Medoids Clustering Algorithm (2022)Introduction to Operations Research (1968)The One to One Future: Building Relationships One Customer at a Time (1995)The Value of Purchase History Data in Target Marketing (1996)The Impact of Customer-Related it Investments on Customer Satisfaction and Shareholder Returns (2006)Applying knowledge engineering techniques to customer analysis in the service industry (2007)Trends, Topics and Under-Researched Areas in CRM Research : A Literature Review (2009)Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management (2004)Application of data mining techniques in customer relationship management: A literature review and classification (2008)Measuring customer loyalty using an extended RFM and clustering technique (2014)International Journal of Engineering Business Management (2016)Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation (1991)Diffusion of innovations. 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this work references cited by second hop

References

  • 1983Segmenting the industrial market173 cited
  • 1999Segmentation of the on-line shopping market using neural networks129 cited
  • 2002Customer's time-variant purchase behavior and corresponding marketing strategies: an online retailer's case74 cited
  • 2001Market segmentation and the structure of competition: applicability of the strategic group concept for an improved market segmentation on industrial markets64 cited

Cited by

  • 2008Customer pattern search for after-sales service in manufacturing58 cited
  • 2009An efficient approach for building customer profiles from business data39 cited
  • 2019User willingness to purchase applications on mobile intelligent devices: evidence from app store34 cited
  • 2017Statistics-based CRM approach via time series segmenting RFM on large scale data31 cited
  • 2011Exploring consumer adoption of new services by analyzing the behavior of 3G subscribers: An empirical case study30 cited
  • 2011A Financial Data Mining Model for Extracting Customer Behavior27 cited
  • 2017Smartphone user segmentation based on app usage sequence with neural networks26 cited
  • 2022The Moderating Role of Personal Innovativeness in Tourists’ Intention to Use Web 3.0 Based on Updated Information Systems Success Model22 cited
  • 2012Behavior scoring model for coalition loyalty programs by using summary variables of transaction data22 cited

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  • 2014Conventionalists, Connectors, Technoisseurs and Mobilarti: Differential profiles of mobile marketing segments based on phone features and postmodern characteristics of consumers16 cited
  • 2015A data mining approach for turning potential customers into real ones in basket purchase analysis11 cited
  • 2019Segmentation of Brazilian travelers’ mobile purchase behavior8 cited