In non-contractual freemium and sharing economy settings, a small share of users often drives the largest part of revenue for firms and co-finances the free provision of the product or service to a large number of users. Successfully retaining and upselling such high-value users can be crucial to firms' survival. Predictions of customers' Lifetime Value (LTV) are a much used tool to identify high-value users and inform marketing initiatives. This paper frames the related prediction problem and applies a number of common machine learning methods for the prediction of individual-level LTV. As only a small subset of users ever makes a purchase, data are highly imbalanced. The study therefore combines said methods with synthetic minority oversampling (SMOTE) in an attempt to achieve better prediction performance. Results indicate that data augmentation with SMOTE improves prediction performance for premium and high-value users, especially when used in combination with deep neural networks.
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Mobile digital games are dominantly released under the freemium business model, but only a small fraction of the players makes any purchases. The ability to predict who will make a purchase enables optimization of marketing efforts, and tailoring customer relationship management to the specific user's profile. Here this challenge is addressed via two models for predicting purchasing players, using a 100,000 player dataset: 1) A classification model focused on predicting whether a purchase will occur or not. 2) a regression model focused on predicting the number of purchases a user will make. Both models are presented within a decision and regression tree framework for building rules that are actionable by companies. To the best of our knowledge, this is the first study investigating purchase decisions in freemium mobile products from a user behavior perspective and adopting behavior-driven learning approaches to this problem.
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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes.You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. licence. www.econstor.eu ISSN 1866-3494 Copyright 2016 by ESMT European School of Management and Technology GmbH, Berlin, Germany, www.esmt.org. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicatedAll rights reserved. No part of this publication may be reproduced, stored in a retrieval system, used in a spreadsheet, or transmitted in any form or by any means -electronic, mechanical, photocopying, recording, or otherwise -without the permission of ESMT.Find more ESMT working papers at ESMT faculty publications, SSRN, RePEc, and EconStor. However, they often lack a framework for analysis that goes beyond directly measurable outcomes and focuses on longer term profit. We aim to fill this gap by structuring existing knowledge on freemium pricing into a stylized framework. We apply the proposed framework in the analysis of a field experiment that contrasts three variations of a freemium pricing scheme and comprises about 300,000 users of a software application.Our findings indicate that a reduction of free product features increases conversion as well as viral activity, but reduces usage -which is in line with the framework's predictions.Additional back-of-the-envelope profit estimations suggest that managers were overly optimistic about positive externalities from usage and viral activity in their choice of pricing scheme, leading them to give too much of their product away for free. Our framework and its exemplary application can be a remedy.Keywords: Freemium, pricing, digitization, experimentation 3 Digital products are characterized by high cost to produce the first copy and very low marginal cost of reproduction (Arrow 1962). This particular cost structure has given rise to freemium pricing -i.e., a hybrid pricing scheme that combines free use of a basic version of the product in perpetuity, with premium upgrades that require the payment of a Against this backdrop, we structure relevant literature into a stylized framework of freemium pricing, and use the framework to analyze a large-scale field experiment. The randomize...
Some companies offering online services employ tactics that make it hard for customers to quit their accounts. These tactics are commonly referred to as “dark patterns” and may include hiding the cancelation procedure, asking customers to go through an excessive number of steps to complete the cancelation, or simply not letting customers quit their accounts straight away. Arguably, dark patterns are the result of misaligned incentives between companies and customers as companies can still benefit from their customers’ data even if they no longer use the companies’ services. Against this background, the authors conduct an observational survey of the state of current market practice and call for future research that enhances our understanding of dark patterns, their organizational antecedents, customers’ psychological responses to these tactics, and the wider consequences of dark patterns for firms and markets.
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