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Enhancing e-commerce customer churn management with a profit- and AUC-focused prescriptive analytics approach
16 references·0 citing·45 second hop
Enhancing e-commerce customer churn management with a profit- and AUC-focused prescriptive analytics approach (2024)Enhancing e-commerce customer churn manag…Marketing innovations during a global crisis: A study of China firms’ response to COVID-19 (2020)Marketing innovations during …Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming (2020)Employees recruitment: A pres…Prescriptive analytics: Literature review and research challenges (2019)Prescriptive analytics: Liter…Business intelligence and analytics use, innovation ambidexterity, and firm performance: A dynamic capabilities perspective (2019)Business intelligence and ana…Customer churn prediction in telecommunication industry using data certainty (2018)Customer churn prediction in …A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees (2018)A new hybrid classification a…Information technology-enabled dynamic capabilities and their indirect effect on competitive performance: Findings from PLS-SEM and fsQCA (2016)Information technology-enable…How Do Firms Adapt to Discontinuous Change? 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References

  • 1997Dynamic capabilities and strategic management34,723 cited
  • 2000Dynamic capabilities: what are they?14,950 cited
  • 2016Information technology-enabled dynamic capabilities and their indirect effect on competitive performance: Findings from PLS-SEM and fsQCA1,035 cited
  • 2006Innovation and knowledge creation: How are these concepts related?585 cited
  • 2006Defection Detection: Measuring and Understanding the Predictive Accuracy of Customer Churn Models574 cited
  • 2018A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees529 cited
  • 2019Prescriptive analytics: Literature review and research challenges501 cited
  • 2020Marketing innovations during a global crisis: A study of China firms’ response to COVID-19463 cited
  • 2011New insights into churn prediction in the telecommunication sector: A profit driven data mining approach460 cited
  • 2016How Do Firms Adapt to Discontinuous Change? Bridging the Dynamic Capabilities and Ambidexterity Perspectives390 cited
  • 2020Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming303 cited
  • 2018Customer churn prediction in telecommunication industry using data certainty251 cited
  • 2014The analytics mandate236 cited
  • 2019Business intelligence and analytics use, innovation ambidexterity, and firm performance: A dynamic capabilities perspective

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221 cited
  • 2016A comparative analysis of data preparation algorithms for customer churn prediction: A case study in the telecommunication industry214 cited
  • 1996Mailing Decisions in the Catalog Sales Industry193 cited
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