Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Online social networks offer opportunities to analyze user behavior and social connectivity and leverage resulting insights for effective online advertising.  study the adoption of a paid product by members of a large and ll-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.  find that adoption by highly connected individuals is correlated with their social connections (friends) adopting after them. Hover, 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.  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  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 por of their network to convince their friends to adopt.  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.
['Rushi P. Bhatt', 'Vineet Chaoji', 'Rajesh Parekh']
I am writing a research paper on a idea Predicting product adoption in large-scale social networks
[{"from": "human", "value": "I am writing a research paper on a idea Predicting product adoption in large-scale social networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Online social networks offer opportunities to analyze user behavior and social connectivity and leverage resulting insights for effective online advertising.  study the adoption of a paid product by members of a large and ll-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.  find that adoption by highly connected individuals is correlated with their social connections (friends) adopting after them. Hover, 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.  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  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 por of their network to convince their friends to adopt.  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."}]