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|Title:||Modeling connected customer lifetime value (CCLV) in the banking domain|
|Keywords:||Business Analytics;Big Data;Connected Customer Lifetime Value (CCLV);Social Network Analysis|
|Publisher:||Americas Conference on Information Systems|
|Citation:||AMCIS 2017 - America's Conference on Information Systems: A Tradition of Innovation, 2017, 2017-August|
|Abstract:||Customer Lifetime Value (CLV) has become increasingly important as a marketing metric because of its ability to predict the future profitability of clients, potentially enabling more appropriate marketing strategies. Traditional CLV models, however, do not reflect the (dynamic) networks of business transactions. This research develops a Connected Customer Lifetime Value (CCLV) model based on an empirical analysis of transactions in the financial services domain. The model was applied to a significant number of transactions between firms using a modern open source computing infrastructure (Spark plus Hadoop). We have illustrated the outcomes of the application of the model via a ‘top and bottom 20’ listing of firms in relation to their value network. In positive terms, application of the model allows our research partner to see the network implications of decisions they make with respect to customers and opens up an arena for innovation re network-based products and services.|
|Appears in Collections:||Dept of Computer Science Research Papers|
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