The Dynamics of Viral Marketing

We present an analysis of a person-to-person recommendation network, consisting of 4 million people who made 16 million recommendations on half a million products. We observe the propagation of recommendations and the cascade sizes, which we explain by a simple stochastic model. We analyze how user behavior varies within user communities defined by a recommendation network. Product purchases follow a 'long tail' where a significant share of purchases belongs to rarely sold items. We establish how the recommendation network grows over time and how effective it is from the viewpoint of the sender and receiver of the recommendations. While on average recommendations are not very effective at inducing purchases and do not spread very far, we present a model that successfully identifies communities, product and pricing categories for which viral marketing seems to be very effective

Product Variety, Online Word of Mouth and Long Tail: An Empirical Study on the Internet Software Market

This study examines the impact of both demand and supply side factors on long tail and superstar effects in the context of online software download. Our descriptive analysis suggests the coexistence of a steeper head and a longer but slimmer tail. Employing a novel empirical approach via the quantile regression model, we find a significant interaction effect between the demand-side factor (online user reviews) and the supply-side factor (product variety) on users’ software download. The influence of the two factors and their interplay on long tail and superstar effects vary significantly across different product popularity level. The results highlight the importance of incorporating both supply and demand factors in long tail research. The findings also offer an explanation for the mixed results reported in extant studies on the influence of online user reviews

Examining the power-law distribution among eWOM communities: a characterisation approach of the Long Tail

Nowadays electronic word-of-mouth (eWOM) communities symbolise a significant source of information that helps customers to make informed purchasing decisions. Through eWOM communities, a great audience of users is able to acquire knowledge from reviews concerning products and services that are less popular to the majority. The Long Tail effect is a manifestation of such redistribution of demand from popular products to niche products. In this paper, a new methodology that mathematically fits the relationship between the power-law distribution and the Long Tail from an eWOM community is developed. In addition, this paper defines a tool for finding niche products inaccessible through conventional channels. The results are consistent in showing that not all the categories fitting a power-law distribution are characterised by the Long Tail phenomenon, and conversely some of those having a Long Tail do not fit a power-law distribution

Talk up or criticize? Customer responses to WOM about competitors during social interactions

Popular metrics such as the Net Promoter Score (NPS) highlights many benefits of word of mouth (WOM) to firms. Is WOM all it is claimed to be? Building on social identity theory, this research develops a conceptual model of WOM exchange in social settings and tests the model with customer surveys of three service sectors. The findings show that the effects of (1) positive and negative WOM (P/NWOM) received about competitors and (2) perceived presence of critical incidents (PPCIs) on P/NWOM given about own service provider are far from intuitive. Responses to PWOM received counter the suggestions in the NPS literature. The findings also indicate that the best firms can hope for when receiving NWOM about competitors is that their customers remain silent. It is recommended that firms communicate a message that is consistent with the nuanced views expressed by friends in social circles, rather than a uniformly superior positioning

The Hitchhiker\u27s Guide to the Long Tail: The Influence of Online-Reviews and Product Recommendations on Book Sales - Evidence from German Online Retailing

Exploring the long tail phenomenon, we empirically analyze whether online reviews, discussion forums, and product recommendations help to reduce search costs and actually alter the sales distribution in online book retailing. We have collected a data set containing 320,248 observations for 40,031 different books at Amazon.de, each assigned to one of 111 different product categories in our sample. By adopting an innovative approach, we provide the first long tail conversion model for the German online market, based on publicly available sales data. Our results indicate that online reviews and automated product recommendations reduce search costs by facilitating the identification of adequate books and the assessment of their quality. This highlights the relevance of information technology implementation as vital part of the marketing strategy

Critical review of the e-loyalty literature: a purchase-centred framework

Over the last few years, the concept of online loyalty has been examined extensively in the literature, and it remains a topic of constant inquiry for both academics and marketing managers. The tremendous development of the Internet for both marketing and e-commerce settings, in conjunction with the growing desire of consumers to purchase online, has promoted two main outcomes: (a) increasing numbers of Business-to-Customer companies running businesses online and (b) the development of a variety of different e-loyalty research models. However, current research lacks a systematic review of the literature that provides a general conceptual framework on e-loyalty, which would help managers to understand their customers better, to take advantage of industry-related factors, and to improve their service quality. The present study is an attempt to critically synthesize results from multiple empirical studies on e-loyalty. Our findings illustrate that 62 instruments for measuring e-loyalty are currently in use, influenced predominantly by Zeithaml et al. (J Marketing. 1996;60(2):31-46) and Oliver (1997; Satisfaction: a behavioral perspective on the consumer. New York: McGraw Hill). Additionally, we propose a new general conceptual framework, which leads to antecedents dividing e-loyalty on the basis of the action of purchase into pre-purchase, during-purchase and after-purchase factors. To conclude, a number of managerial implementations are suggested in order to help marketing managers increase their customers’ e-loyalty by making crucial changes in each purchase stage

The Influence of Information Overload on the Development of Trust and Purchase Intention Based on Online Product Reviews in a Mobile vs. Web Environment: A Research Proposal

Information overload has been studied extensively by decision science researchers, particularly in the context of task-based optimization decisions. Media selection research has similarly investigated the extent to which task characteristics influence media choice and use. This paper outlines a proposed study, which would compare the effectiveness of web-based online product review systems in facilitation trust and purchase intention to those of mobile product review systems. We propose that since web-based systems are more effective in fostering focus and are less prone to navigation frustration, information overload is less likely influence the extent to which a consumer trusts an online product review

Elite Tweets: Analysing the Twitter Communication Patterns of Labour Party Peers in the House of Lords

The micro-blogging platform Twitter has gained notoriety for its status as both a communication channel between private individuals, and as a public forum monitored by journalists, the public, and the state. Its potential application for political communication has not gone unnoticed; politicians have used Twitter to attract voters, interact with constituencies and advance issue-based campaigns. This article reports on the preliminary results of the research team’s work with 21 peers sitting on the Labour frontbench. It is based on the monitoring and archival of the peers’ activity on Twitter for a period of 100 days from 16th May to 28th September 2012. Using a sample of more than 4,363 tweets and a mixed methodology combining semantic analysis, social network analysis and quantitative analysis, this paper explores the peers’ patterns of usage and communication on Twitter. Key findings are that as a tweeting community their behavior is consistent with others, however there is evidence that a coherent strategy is lacking. Labour peers tend to work in ego networks of self-interest as opposed to working together to promote party polic