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Understanding service retention within and across cohorts using limited information

Schweidel, David A.; Fader, Peter; Bradlow, Eric T. - In: Journal of marketing 72 (2008) 1, pp. 82-94

Persistent link: https://www.econbiz.de/10003643026

Understanding Service Retention Within and Across Cohorts Using Limited Information

Schweidel, David A.; Fader, Peter; Bradlow, Eric - 2012

services offered by a telecommunications provider, mirroring the format commonly used to forecast future service churn (and to … make managerial decisions). Across all seven services, the inclusion of promotional effects always improves the forecast … accuracy of retention behavior, while including cross-cohort effects does not significantly improve it. In five of the services …

Persistent link: https://www.econbiz.de/10014064243

A Dynamic Allocation-Execution Decision Method Based on In-App Customer Lifetime Value Prediction

Zhao, Yuanzun; Jin, Tao; Lin, Qian - 2023

One important problem in the marketing and user growth for businesses is to infer the marginal gains of specific campaigns or strategies, which is crucial for the decision makers to determine whether to carry on or terminate certain policies. However, in most cases the return of gaining...

Persistent link: https://www.econbiz.de/10014359427

Survey of Model Selection and Model Combination

Xu, Mingyang; Golay, Michael - 2011

A general statistical modeling problem is that given a class of competing models and new data, how one can improve the overall model performance. In general, there exist two solutions for this problem, namely model selection and model combination. Model selection is to select a single best model...

Persistent link: https://www.econbiz.de/10014187010

Robust Optimization of Forecast Combinations

Post, Thierry - 2018

A methodology is developed for constructing robust forecast combinations which improve upon a given benchmark specification for all symmetric and convex loss functions. The optimal forecast combination asymptotically almost surely dominates the benchmark and, in addition, minimizes the expected...

Persistent link: https://www.econbiz.de/10012922712

Nonparametric Tests for Superior Predictive Ability

Arvanitis, Stelios - 2020

Abstract A nonparametric method for comparing multiple forecast models is developed and implemented. The hypothesis of Optimal Predictive Ability generalizes the Superior Predictive Ability hypothesis from a single given loss function to an entire class of loss functions. Distinction is drawn...

Persistent link: https://www.econbiz.de/10012851326

A bibliometric analysis of machine learning econometrics in asset pricing

Zapata, Hector O.; Mukhopadhyay, Supratik - In: Journal of risk and financial management : JRFM 15 (2022) 11, pp. 1-17

Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better...

Persistent link: https://www.econbiz.de/10013475217

Is price a significant predictor of the churn behavior during the global pandemic? : a predictive modeling on the telecom industry

Shukla, Vishal; Prashar, Sanjeev; Pandiya, Bhartrihari - In: Journal of revenue and pricing management 21 (2022) 4, pp. 470-483

Persistent link: https://www.econbiz.de/10013390631

Excited and aroused: the predictive importance of simple choice process metrics

Mueller, Steffen Q.; Ring, Patrick; Fischer, Maria - 2020 - Version: December 2020

We conduct a lottery experiment to assess the predictive importance of simple choice process metrics (SCPMs) in forecasting risky 50/50 gambling decisions using different types of machine learning algorithms as well as traditional choice modeling approaches. The SCPMs are recorded during a fixed...

Persistent link: https://www.econbiz.de/10012427354

I will survive : predicting business failures from customer ratings

Naumzik, Christof; Feuerriegel, Stefan; Weinmann, Markus - In: Marketing science 41 (2022) 1, pp. 188-207

Persistent link: https://www.econbiz.de/10012820507