When Social Influence Meets Item Inference

Research issues and data mining techniques for product recommendation and viral marketing have been widely studied. Existing works on seed selection in social networks do not take into account the effect of product recommendations in e-commerce stores. In this paper, we investigate the seed selection problem for viral marketing that considers both effects of social influence and item inference (for product recommendation). We develop a new model, Social Item Graph (SIG), that captures both effects in form of hyperedges. Accordingly, we formulate a seed selection problem, called Social Item Maximization Problem (SIMP), and prove the hardness of SIMP. We design an efficient algorithm with performance guarantee, called Hyperedge-Aware Greedy (HAG), for SIMP and develop a new index structure, called SIG-index, to accelerate the computation of diffusion process in HAG. Moreover, to construct realistic SIG models for SIMP, we develop a statistical inference based framework to learn the weights of hyperedges from data. Finally, we perform a comprehensive evaluation on our proposals with various baselines. Experimental result validates our ideas and demonstrates the effectiveness and efficiency of the proposed model and algorithms over baselines.Comment: 12 page

Customer Lifetime Network Value

Today, people are increasingly connected and extensively interact with each other using technology-enabled media. Hence, customers are more frequently exposed to social influence of other customers when making purchase decisions. However, established approaches for customer valuation most widely neglect network effects based on social influence leading to a misallocation of resources. Therefore, following a design-oriented approach, this paper develops a model for customer valuation referred to as the customer lifetime network value (CLNV) incorporating an integrated network perspective. By considering the net network contribution of customers, the CLNV reallocates values between customers based on social influence without changing the overall network value, that is, a firm’s customer equity. Using a real-world dataset of a European online social network, we demonstrate and evaluate the applicability of the CLNV. We show that the CLNV enables a sound determination of both individual customers’ value and firm’s customer equity and supports thorough customer segmentation

Identifying target audiences on social network sites by analyisng user connections : a social network analysis approach for instagram

Social Network Sites offer users and brands a platform to interact by following each other and liking, commenting and sharing of content. This dissertation demonstrates that brands can leverage on rich data emerging from user-user-, user-brand-, and brand-brand-connections on Instagram to identify, understand and target new prospects. The concept of homophily suggests that users are mainly connected to other users they perceive as similar to themselves and to brands they identify with. Taking these insights into account, this dissertation aims to develop an audience selection approach to identify prospects that are likely to be interest in following a focal brand on Instagram. By extracting real network data from Instagram, users were segmented based on their “follow-relationship” to a set of exemplar brands that share a similar image with the focal brand. Four segments were identified and profiled: True-Brand-Lovers, Fashion Seeker, Hidden Treasures and Intangibles. Additionally, by taking secondary layer effects into account, a targeting experiment was conducted on Instagram to examine whether and to what extent resulted segments can be employed to find highly interested prospects. Findings disclosed that new prospects can especially be found by detecting overlapping followers between brands within the set. Moreover, tendencies were found that new prospects can be detected in the secondary layer of existing followers, especially when their connection to the set is taken into account as well. Therefore, the results of this study suggest that taking users affinity to other entities to account can help brands to define more precisely targeting decisions.As redes sociais oferecem a utilizadores e marcas uma plataforma para que interajam. Esta dissertação demonstra que as marcas podem aproveitar a rich data emergente de interações utilizador-utilizador, utilizador-marca e marca-marca no Instagram, para identificar, perceber e visar potenciais clientes. O conceito de homofilia sugere que utilizadores estão principalmente ligados a outros utilizadores que sejam semelhantes a si mesmos e a marcas com que se identificam. Esta dissertação ambiciona desenvolver uma abordagem de seleção de audiência para identificar novos clientes que poderão ter interesse em seguir uma marca no Instagram. Ao extrair dados reais do Instagram, os utilizadores são segmentados com base na sua “follow-relationship” para determinar um conjunto de marcas que partilham uma imagem semelhante com a marca focal. Quatro segmentos foram identificados e divididos: True-Brand-Lovers, Fashion Seeker, Hidden Treasures e Intangibles. Adicionalmente, ao ter em consideração efeitos de segunda camada, uma experiência de targeting foi conduzida no Instagram para examinar se e em que medida os segmentos resultantes podem ser utilizados para descobrir potenciais clientes altamente interessados. Os resultados indicam que potenciais clientes podem ser encontrados particularmente ao detetar seguidores sobrepostos dentro do grupo. Para além disso, foram encontradas tendências que indiciam que potenciais clientes podem ser detetados na segunda camada de seguidores, especialmente quando a sua conexão ao grupo é levada também em conta. Portanto, os resultados deste estudo sugerem que ter em conta a afinidade dos utilizadores a outras entidades pode ajudar as marcas a definirem com mais precisão as suas decisões de targeting

Strategic corporate communication in the digital age

This chapter presents a systematic review of over thirty (30) types of online marketing methods. It describes different methods like email marketing, social network marketing, in-game marketing and augmented reality marketing, among other approaches. The researchers discuss that the rationale for using these online marketing strategies is to increase brand awareness, customer centric marketing and consumer loyalty. They shed light on various personalization methods including recommendation systems and user generated content in their taxonomy of online marketing terms. Hence, they explain how these online marketing methods are related to each other. The researchers contend that the boundaries between online marketing methods have not been clarified enough within the academic literature. Therefore, this chapter provides a better understanding of different online marketing methods. A review of the literature suggests that the ‘oldest’ online marketing methods including the email and the websites are still very relevant for today’s corporate communication. In conclusion, the researchers put forward their recommendations for future research about contemporary online marketing methods.peer-reviewe

Customer lifetime network value: customer valuation in the context of network effects

Nowadays customers are increasingly connected and extensively interact with each other using technology-enabled media like online social networks. Hence, customers are frequently exposed to social influence when making purchase decisions. However, established approaches for customer valuation mostly neglect network effects based on social influence. This leads to a misallocation of resources. Following a design-oriented approach, this paper develops a model for customer valuation referred to as the customer lifetime network value (CLNV) incorporating an integrated network perspective. By considering the customers' net contribution to the network, the CLNV reallocates values between customers based on social influence. Inspired by common prestige- and eigenvector-related centrality measures it incorporates social influence among all degrees of separation acknowledging its viral spread. Using a real-world dataset, we demonstrate the practicable applicability of the CLNV to determine individual customers' value

A Systematic Review of Consumer Behaviour Prediction Studies

Due to the importance of Customer behaviour prediction, it is necessary to have a systematic review of previous studies on this subject. To this effect, this paper therefore provides a systematic review of Customer behaviours prediction studies with a focus on components of customer relationship management, methods and datasets. In order to provide a comprehensive literature review and a classification scheme for articles on this subject 74 customer behaviour prediction papers in over 25 journals and several conference proceedings were considered between the periods of 1999- 2014. Two hundred and thirty articles were identified and reviewed for their direct relevance to predicting customer behaviour out of which 74 were subsequently selected, reviewed and classified appropriately. The findings show that the literature on predicting customer behaviour is ongoing and is of most importance to organisation. It was observed that most studies investigated customer retention prediction and organizational dataset were mostly used for the prediction as compared to other form of dataset. Also, comparing the statistical method to data mining in predicting customer behaviour, it was discovered through this review that data mining is mostly used for prediction. On the other hand, Artificial Neural Network is the most commonly used data mining method for predicting customer behaviour. The review was able to identify the limitations of the current research on the subject matter and identify future research opportunities in customer behaviour prediction

A Systematic Review of Consumer Behaviour Prediction Studies

Due to the importance of Customer behaviour prediction, it is necessary to have a systematic review of previous studies on this subject. To this effect, this paper therefore provides a systematic review of Customer behaviours prediction studies with a focus on components of customer relationship management, methods and datasets. In order to provide a comprehensive literature review and a classification scheme for articles on this subject 74 customer behaviour prediction papers in over 25 journals and several conference proceedings were considered between the periods of 1999-2014. Two hundred and thirty articles were identified and reviewed for their direct relevance to predicting customer behaviour out of which 74 were subsequently selected, reviewed and classified appropriately. The findings show that the literature on predicting customer behaviour is ongoing and is of most importance to organisation. It was observed that most studies investigated customer retention prediction and organizational dataset were mostly used for the prediction as compared to other form of dataset. Also, comparing the statistical method to data mining in predicting customer behaviour, it was discovered through this review that data mining is mostly used for prediction. On the other hand, Artificial Neural Network is the most commonly used data mining method for predicting customer behaviour. The review was able to identify the limitations of the current research on the subject matter and identify future research opportunities in customer behaviour prediction.Keywords: Consumer Behaviour, Prediction, Statistics, Data Mining, Dataset, Customer Relationship Management, Literature Revie

Managing Corporate Reputation in the Blogosphere: The Case of Dell Computer

none4The emergence of the blogosphere has created new challenges for large companies in the management of their corporate reputations, since grass roots blogs can generate negative perceptions about a firm and then spread them rapidly and widely. The blogosphere has also created new opportunities for firms to enhance their reputations, because the informal and personal communication that occurs on blogs may generate significant positive ‘ Internet word of mouth ’ . This paper examines the interaction between the blogosphere and a leading technology company, Dell Computer, over a critical two-year period. Our approach combines two novel techniques: automated mining of blog entries, enabled by parsing software, which generates semantic analysis and network maps of the relevant blog entries; and netnography, a method derived from ethnography for analyzing Internet-based discussions. This study shows that many established reputation management approaches, which were developed during the era of mass media, need to be reshaped to meet new realities in the age of Web 2.0.Pasquale Del Vecchio; Robert Laubacher; Valentina Ndou; Giuseppina PassianteDEL VECCHIO, Pasquale; Robert, Laubacher; Ndou, Valentina; Passiante, Giuseppin