Estimating defection in subscription-type markets: empirical analysis from the scholarly publishing industry

• URL: http://arxiv.org/abs/2211.09970v1
• Date: Fri, 18 Nov 2022 01:29:51 GMT
• Title: Estimating defection in subscription-type markets: empirical analysis from the scholarly publishing industry
• Authors: Michael Roberts and J. Ignacio Deza and Hisham Ihshaish and Yanhui Zhu
• Abstract summary: We present the first empirical study on customer churn prediction in the scholarly publishing industry. The study examines our proposed method for prediction on a customer subscription data over a period of 6.5 years. We show that this approach can be both accurate as well as uniquely useful in the business-to-business context.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present the first empirical study on customer churn prediction in the scholarly publishing industry. The study examines our proposed method for prediction on a customer subscription data over a period of 6.5 years, which was provided by a major academic publisher. We explore the subscription-type market within the context of customer defection and modelling, and provide analysis of the business model of such markets, and how these characterise the academic publishing business. The proposed method for prediction attempts to provide inference of customer's likelihood of defection on the basis of their re-sampled use of provider resources -in this context, the volume and frequency of content downloads. We show that this approach can be both accurate as well as uniquely useful in the business-to-business context, with which the scholarly publishing business model shares similarities. The main findings of this work suggest that whilst all predictive models examined, especially ensemble methods of machine learning, achieve substantially accurate prediction of churn, nearly a year ahead, this can be furthermore achieved even when the specific behavioural attributes that can be associated to each customer probability to churn are overlooked. Allowing as such highly accurate inference of churn from minimal possible data. We show that modelling churn on the basis of re-sampling customers' use of resources over subscription time is a better (simplified) approach than when considering the high granularity that can often characterise consumption behaviour.

Related papers

• Achieving Fairness in Predictive Process Analytics via Adversarial Learning [50.31323204077591]
  This paper addresses the challenge of integrating a debiasing phase into predictive business process analytics. Our framework leverages on adversial debiasing is evaluated on four case studies, showing a significant reduction in the contribution of biased variables to the predicted value.
  arXiv  Detail & Related papers  (2024-10-03T15:56:03Z)
• Learning Augmentation Policies from A Model Zoo for Time Series Forecasting [58.66211334969299]
  We introduce AutoTSAug, a learnable data augmentation method based on reinforcement learning. By augmenting the marginal samples with a learnable policy, AutoTSAug substantially improves forecasting performance.
  arXiv  Detail & Related papers  (2024-09-10T07:34:19Z)
• F-FOMAML: GNN-Enhanced Meta-Learning for Peak Period Demand Forecasting with Proxy Data [65.6499834212641]
  We formulate the demand prediction as a meta-learning problem and develop the Feature-based First-Order Model-Agnostic Meta-Learning (F-FOMAML) algorithm. By considering domain similarities through task-specific metadata, our model improved generalization, where the excess risk decreases as the number of training tasks increases. Compared to existing state-of-the-art models, our method demonstrates a notable improvement in demand prediction accuracy, reducing the Mean Absolute Error by 26.24% on an internal vending machine dataset and by 1.04% on the publicly accessible JD.com dataset.
  arXiv  Detail & Related papers  (2024-06-23T21:28:50Z)
• Optimizing Sales Forecasts through Automated Integration of Market Indicators [0.0]
  This work investigates the potential of data-driven techniques to automatically select and integrate market indicators for improving customer demand predictions. By adopting an exploratory methodology, we integrate macroeconomic time series, such as national GDP growth, into textitNeural Prophet and textitSARIMAX forecasting models. It could be shown that forecasts can be significantly enhanced by incorporating external information.
  arXiv  Detail & Related papers  (2024-05-15T08:11:41Z)
• Predictive Churn with the Set of Good Models [64.05949860750235]
  We study the effect of conflicting predictions over the set of near-optimal machine learning models. We present theoretical results on the expected churn between models within the Rashomon set. We show how our approach can be used to better anticipate, reduce, and avoid churn in consumer-facing applications.
  arXiv  Detail & Related papers  (2024-02-12T16:15:25Z)
• Modeling the Telemarketing Process using Genetic Algorithms and Extreme Boosting: Feature Selection and Cost-Sensitive Analytical Approach [0.06906005491572399]
  This research aims at leveraging the power of telemarketing data in modeling the willingness of clients to make a term deposit. Real-world data from a Portuguese bank and national socio-economic metrics are used to model the telemarketing decision-making process.
  arXiv  Detail & Related papers  (2023-10-30T08:46:55Z)
• Learning for Counterfactual Fairness from Observational Data [62.43249746968616]
  Fairness-aware machine learning aims to eliminate biases of learning models against certain subgroups described by certain protected (sensitive) attributes such as race, gender, and age. A prerequisite for existing methods to achieve counterfactual fairness is the prior human knowledge of the causal model for the data. In this work, we address the problem of counterfactually fair prediction from observational data without given causal models by proposing a novel framework CLAIRE.
  arXiv  Detail & Related papers  (2023-07-17T04:08:29Z)
• Customer Profiling, Segmentation, and Sales Prediction using AI in Direct Marketing [0.0]
  This paper proposes a data mining preprocessing method for developing a customer profiling system to improve sales performance. The main result of this study is the creation of a customer profile and forecast for the sale of goods.
  arXiv  Detail & Related papers  (2023-02-03T14:45:09Z)
• Predictive analytics for appointment bookings [0.0]
  The first model predicts whether a customer will show up for the meeting, while the second model indicates whether a customer will book a premium service. Both models produce accurate results with more than a 75% accuracy rate. This paper offers a framework for resource planning using the predicted demand.
  arXiv  Detail & Related papers  (2022-04-18T14:02:15Z)
• Test-time Collective Prediction [73.74982509510961]
  Multiple parties in machine learning want to jointly make predictions on future test points. Agents wish to benefit from the collective expertise of the full set of agents, but may not be willing to release their data or model parameters. We explore a decentralized mechanism to make collective predictions at test time, leveraging each agent's pre-trained model.
  arXiv  Detail & Related papers  (2021-06-22T18:29:58Z)
• Profit-oriented sales forecasting: a comparison of forecasting techniques from a business perspective [3.613072342189595]
  This paper compares a large array of techniques for 35 times series that consist of both industry data from the Coca-Cola Company and publicly available datasets. It introduces a novel and completely automated profit-driven approach that takes into account the expected profit that a technique can create during both the model building and evaluation process.
  arXiv  Detail & Related papers  (2020-02-03T14:50:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.