Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Analysis of topological characteristics of huge online social networking services
Proceedings of the 16th international conference on World Wide Web
Optimal marketing strategies over social networks
Proceedings of the 17th international conference on World Wide Web
Yes, there is a correlation: - from social networks to personal behavior on the web
Proceedings of the 17th international conference on World Wide Web
Planetary-scale views on a large instant-messaging network
Proceedings of the 17th international conference on World Wide Web
Influence and correlation in social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Combining Behavioral and Social Network Data for Online Advertising
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
How much can behavioral targeting help online advertising?
Proceedings of the 18th international conference on World wide web
Large-scale behavioral targeting
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Audience selection for on-line brand advertising: privacy-friendly social network targeting
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Inferring relevant social networks from interpersonal communication
Proceedings of the 19th international conference on World wide web
Influential nodes in a diffusion model for social networks
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
Proceedings of the 12th ACM conference on Electronic commerce
Wisdom of the better few: cold start recommendation via representative based rating elicitation
Proceedings of the fifth ACM conference on Recommender systems
Large-scale behavioral targeting with a social twist
Proceedings of the 20th ACM international conference on Information and knowledge management
Finding the right consumer: optimizing for conversion in display advertising campaigns
Proceedings of the fifth ACM international conference on Web search and data mining
Who drive people to forward information: publisher or spreader?
Proceedings of the Fifth Workshop on Social Network Systems
A Social Network Study of the Apple vs. Android Smartphone Battle
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Predicting purchase behaviors from social media
Proceedings of the 22nd international conference on World Wide Web
Recommending branded products from social media
Proceedings of the 7th ACM conference on Recommender systems
Hi-index | 0.00 |
Online social networks offer opportunities to analyze user behavior and social connectivity and leverage resulting insights for effective online advertising. We study the adoption of a paid product by members of a large and well-connected Instant Messenger (IM) network. This product is important to the business and poses unique challenges to advertising due to its low baseline adoption rate. We find that adoption by highly connected individuals is correlated with their social connections (friends) adopting after them. However, there is little evidence of social influence by these high degree individuals. Further, the spread of adoption remains mostly local to first-adopters and their immediate friends. We observe strong evidence of peer pressure wherein future adoption by an individual is more likely if the product has been widely adopted by the individual's friends. Social neighborhoods rich in adoptions also continue to add more new adoptions compared to those neighborhoods that are poor in adoption. Using these insights we build predictive models to identify individuals most suited for two types of marketing campaigns - direct marketing where individuals with highest propensity for future adoption are targeted with suitable ads and social neighborhood marketing which involves messaging to members of the social network who are most effective in using the power of their network to convince their friends to adopt. We identify the most desirable features for predicting future adoption of the PC To Phone product which can in turn be leveraged to effectively promote its adoption. Offline analysis shows that building predictive models for direct marketing and social neighborhood marketing outperforms several widely accepted marketing heuristics. Further, these models are able to effectively combine user features and social features to predict adoption better than using either user features or social features in isolation.