The Experts below are selected from a list of 228 Experts worldwide ranked by ideXlab platform
Sunil Gupta - One of the best experts on this subject based on the ideXlab platform.
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Designing Freemium: Strategic Balancing Growth and Monetization
SSRN Electronic Journal, 2017Co-Authors: Clarence Lee, Vineet Kumar, Sunil GuptaAbstract:Freemium (free + premium) has become the dominant business model among technology start-ups for its ability to acquire and monetize a large customer base with limited marketing resources. Freemium is a hybrid strategy where a firm offers both a perpetually free but limited version of its service, and a premium version with enhanced features that requires a fee. These firms often have a referral program where current customers get additional benefits (e.g., extra storage space for their files) for referring their friends to the firm’s Products. While this strategy allows firms to acquire a large user base at low cost, it needs to be balanced against revenue generation possibilities. Firms need to know if and when customers would upgrade to premium service; how attractive should be the referral offer to current users that encourages them to bring in new customers but may dissuade them from upgrading. Since majority of Freemium customers do not pay anything when they join, it is critical to know their value in the long run. Free customers have value for the firm if in the future either they or their referrals upgrade to premium service.We develop a model based on microfoundations and empirically test it to understand the dynamics of consumers’ plan choice, usage, upgrade, and referral behavior in the Freemium setting using a novel panel data set from a leading cloud-based storage service. We develop and estimate a dynamic structural model that includes both discrete and continuous choices. Using counterfactual analysis, we provide guidelines to companies on how to design their Freemium Product and how to assess the value of their free customers to balance growth and profitability.
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Designing Freemium: Balancing Growth and Monetization Strategies
2015Co-Authors: Clarence Lee, Vineet Kumar, Sunil GuptaAbstract:Freemium (free + premium) has become the dominant business model among technology start-ups for its ability to acquire and monetize a large customer base with limited marketing resources. Freemium is a hybrid strategy where a firm offers both a perpetually free but limited version of its service, and a premium version with enhanced features that requires a fee. These firms often have a referral program where current customers get additional benefits (e.g., extra storage space for their files) for referring their friends to the firm’s Products. While this strategy allows firms to acquire a large user base at low cost, it needs to be balanced against revenue generation possibilities. Firms need to know if and when customers would upgrade to premium service; how attractive should be the referral offer to current users that encourages them to bring in new customers but may dissuade them from upgrading. Since majority of Freemium customers do not pay anything when they join, it is critical to know their value in the long run. Free customers have value for the firm if in the future either they or their referrals upgrade to premium service.We develop a model based on microfoundations and empirically test it to understand the dynamics of consumers’ plan choice, usage, upgrade, and referral behavior in the Freemium setting using a novel panel data set from a leading cloud-based storage service. We develop and estimate a dynamic structural model that includes both discrete and continuous choices. Using counterfactual analysis, we provide guidelines to companies on how to design their Freemium Product and how to assess the value of their free customers to balance growth and profitability.
Clarence Lee - One of the best experts on this subject based on the ideXlab platform.
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Designing Freemium: Strategic Balancing Growth and Monetization
SSRN Electronic Journal, 2017Co-Authors: Clarence Lee, Vineet Kumar, Sunil GuptaAbstract:Freemium (free + premium) has become the dominant business model among technology start-ups for its ability to acquire and monetize a large customer base with limited marketing resources. Freemium is a hybrid strategy where a firm offers both a perpetually free but limited version of its service, and a premium version with enhanced features that requires a fee. These firms often have a referral program where current customers get additional benefits (e.g., extra storage space for their files) for referring their friends to the firm’s Products. While this strategy allows firms to acquire a large user base at low cost, it needs to be balanced against revenue generation possibilities. Firms need to know if and when customers would upgrade to premium service; how attractive should be the referral offer to current users that encourages them to bring in new customers but may dissuade them from upgrading. Since majority of Freemium customers do not pay anything when they join, it is critical to know their value in the long run. Free customers have value for the firm if in the future either they or their referrals upgrade to premium service.We develop a model based on microfoundations and empirically test it to understand the dynamics of consumers’ plan choice, usage, upgrade, and referral behavior in the Freemium setting using a novel panel data set from a leading cloud-based storage service. We develop and estimate a dynamic structural model that includes both discrete and continuous choices. Using counterfactual analysis, we provide guidelines to companies on how to design their Freemium Product and how to assess the value of their free customers to balance growth and profitability.
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Designing Freemium: Balancing Growth and Monetization Strategies
2015Co-Authors: Clarence Lee, Vineet Kumar, Sunil GuptaAbstract:Freemium (free + premium) has become the dominant business model among technology start-ups for its ability to acquire and monetize a large customer base with limited marketing resources. Freemium is a hybrid strategy where a firm offers both a perpetually free but limited version of its service, and a premium version with enhanced features that requires a fee. These firms often have a referral program where current customers get additional benefits (e.g., extra storage space for their files) for referring their friends to the firm’s Products. While this strategy allows firms to acquire a large user base at low cost, it needs to be balanced against revenue generation possibilities. Firms need to know if and when customers would upgrade to premium service; how attractive should be the referral offer to current users that encourages them to bring in new customers but may dissuade them from upgrading. Since majority of Freemium customers do not pay anything when they join, it is critical to know their value in the long run. Free customers have value for the firm if in the future either they or their referrals upgrade to premium service.We develop a model based on microfoundations and empirically test it to understand the dynamics of consumers’ plan choice, usage, upgrade, and referral behavior in the Freemium setting using a novel panel data set from a leading cloud-based storage service. We develop and estimate a dynamic structural model that includes both discrete and continuous choices. Using counterfactual analysis, we provide guidelines to companies on how to design their Freemium Product and how to assess the value of their free customers to balance growth and profitability.
Vineet Kumar - One of the best experts on this subject based on the ideXlab platform.
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Designing Freemium: Strategic Balancing Growth and Monetization
SSRN Electronic Journal, 2017Co-Authors: Clarence Lee, Vineet Kumar, Sunil GuptaAbstract:Freemium (free + premium) has become the dominant business model among technology start-ups for its ability to acquire and monetize a large customer base with limited marketing resources. Freemium is a hybrid strategy where a firm offers both a perpetually free but limited version of its service, and a premium version with enhanced features that requires a fee. These firms often have a referral program where current customers get additional benefits (e.g., extra storage space for their files) for referring their friends to the firm’s Products. While this strategy allows firms to acquire a large user base at low cost, it needs to be balanced against revenue generation possibilities. Firms need to know if and when customers would upgrade to premium service; how attractive should be the referral offer to current users that encourages them to bring in new customers but may dissuade them from upgrading. Since majority of Freemium customers do not pay anything when they join, it is critical to know their value in the long run. Free customers have value for the firm if in the future either they or their referrals upgrade to premium service.We develop a model based on microfoundations and empirically test it to understand the dynamics of consumers’ plan choice, usage, upgrade, and referral behavior in the Freemium setting using a novel panel data set from a leading cloud-based storage service. We develop and estimate a dynamic structural model that includes both discrete and continuous choices. Using counterfactual analysis, we provide guidelines to companies on how to design their Freemium Product and how to assess the value of their free customers to balance growth and profitability.
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Designing Freemium: Balancing Growth and Monetization Strategies
2015Co-Authors: Clarence Lee, Vineet Kumar, Sunil GuptaAbstract:Freemium (free + premium) has become the dominant business model among technology start-ups for its ability to acquire and monetize a large customer base with limited marketing resources. Freemium is a hybrid strategy where a firm offers both a perpetually free but limited version of its service, and a premium version with enhanced features that requires a fee. These firms often have a referral program where current customers get additional benefits (e.g., extra storage space for their files) for referring their friends to the firm’s Products. While this strategy allows firms to acquire a large user base at low cost, it needs to be balanced against revenue generation possibilities. Firms need to know if and when customers would upgrade to premium service; how attractive should be the referral offer to current users that encourages them to bring in new customers but may dissuade them from upgrading. Since majority of Freemium customers do not pay anything when they join, it is critical to know their value in the long run. Free customers have value for the firm if in the future either they or their referrals upgrade to premium service.We develop a model based on microfoundations and empirically test it to understand the dynamics of consumers’ plan choice, usage, upgrade, and referral behavior in the Freemium setting using a novel panel data set from a leading cloud-based storage service. We develop and estimate a dynamic structural model that includes both discrete and continuous choices. Using counterfactual analysis, we provide guidelines to companies on how to design their Freemium Product and how to assess the value of their free customers to balance growth and profitability.
Eric Benjamin Seufert - One of the best experts on this subject based on the ideXlab platform.
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Chapter 8 – Growth
Freemium Economics, 2014Co-Authors: Eric Benjamin SeufertAbstract:This chapter, “Growth,” explores all means by which a Freemium Product can expand its user base. The chapter begins with an introduction to strategic growth, which is a data-driven approach to optimizing a Product’s reach through market research, and the degree to which users are retained in the earliest stages of Product interaction. This retention focus is often accomplished with the onboarding funnel (sometimes called the tutorial funnel); onboarding is a highly analytical, highly granular process to ensure that the first few minutes of a user’s introduction to a Product are optimized for retention. The chapter then leads to a discussion on paid user acquisition, which is the means by which a Product is advertised. This section outlines the various components of an advertising system: the advertising exchange, the supply-side platform, and the demand-side platform. Paid search is also described in detail. The chapter concludes with a description of alternative user acquisition techniques and the means by which they are employed in Freemium Products. Alternative user acquisition encompasses traditional media advertising, search engine optimization, and the three forms of non-paid user acquisition: discovery, cross-promotion, and virality.
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Freemium Economics: Leveraging Analytics and User Segmentation to Drive Revenue
2013Co-Authors: Eric Benjamin SeufertAbstract:Freemium Economics presents a practical, instructive approach to successfully implementing the Freemium model into your software Products by building analytics into Product design from the earliest stages of development. Your Freemium Product generates vast volumes of data, but using that data to maximize conversion, boost retention, and deliver revenue can be challenging if you don't fully understand the impact that small changes can have on revenue. In this book, author Eric Seufert provides clear guidelines for using data and analytics through all stages of development to optimize your implementation of the Freemium model. Freemium Economics de-mystifies the Freemium model through an exploration of its core, data-oriented tenets, so that you can apply it methodically rather than hoping that conversion and revenue will naturally follow Product launch. By reading Freemium Economics, you will: Learn how to apply data science and big data principles in Freemium Product design and development to maximize conversion, boost retention, and deliver revenue Gain a broad introduction to the conceptual economic pillars of Freemium and a complete understanding of the unique approaches needed to acquire users and convert them from free to paying customers Get practical tips and analytical guidance to successfully implement the Freemium model Understand the metrics and infrastructure required to measure the success of a Freemium Product and improve it post-launch Includes a detailed explanation of the lifetime customer value (LCV) calculation and step-by-step instructions for implementing key performance indicators in a simple, universally-accessible tool like Excel
Shankarananda, Pronitha Mysore - One of the best experts on this subject based on the ideXlab platform.
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Empirical study and business model analysis of successful Freemium strategies in digital Products
Massachusetts Institute of Technology, 2015Co-Authors: Shankarananda, Pronitha MysoreAbstract:Thesis: S.M. in Management Research, Massachusetts Institute of Technology, Sloan School of Management, 2015.Cataloged from PDF version of thesis.Includes bibliographical references (pages 54-58)."Freemium" has become a popular business model for digital and internet startups to acquire customers quickly and monetize with limited marketing resources. In this model, basic functionalities and features of the Product are offered free of cost and advanced features are monetized. Typically firms rely on the size of the free user network since the conversion rate in these models is very low (2%-7%). This thesis analyzes successful business models and uses predictive analytics on a dataset from a Freemium Product to determine critical success factors. Empirical study is conducted on 79,033 total users and 4,217 premium subscribers of Last.fm, a Musica as a service (MaaS) Product which employs the Freemium pricing model, to predict the probability of a user being a subscriber. Classification and regression trees (CART) with k-fold cross validation is used to model on the training data and is validated on the test dataset to understand the influence of demographic, engagement, retention and social factors on subscribers. Five successful premium companies Linkedln, Zynga, Evernote, Spotify and DropBox are studied to understand the company background, value proposition and Freemium model. The analyses was based on secondary case studies and information. The CART model designed yielded an accuracy of 74.84%, sensitivity of 67.6%, false negative rate (FNR) of 32.4% and Area under the curve (AUC) of 0.7474937. On fine-tuning the penalty matrix, FNR is reduced to 22% and sensitivity increased to 78% with an accuracy of 65.8%. The empirical study showed that age (demographic factor), playcount (engagement factor) and registration unixtime (retention) were the most significant variables in predicting outcome. Study of successful firms showed that Freemium is not a one-size-fits-all strategy for internet startups. The strategy needs to be crafted specifically for each firm. Many factors go into formulating this plan including a strong Product catalogue, focused customer acquisition engine, referrals, social and community features, continuous testing, data driven approach and commitment to continuous innovation.by Pronitha Mysore Shankarananda.S.M. in Management Researc
