Measuring Customer Similarity and Identifying Cross-Selling Products by Community Detection
نویسندگان
چکیده
منابع مشابه
Optimal customer selection for cross-selling of financial services products
A new methodology, for optimal customer selection in cross-selling of financial services products, such as mortgage loans and non life insurance contracts, is presented. The optimal cross-sales selection of prospects is such that the expected profit is maximized, while at the same time the risk of suffering future losses is minimized. Expected profit maximization and mean-variance optimization ...
متن کاملIdentifying Innovators for the Cross-Selling of New Products
With recent advances in information technology, most companies are amassing extensive customer databases. The wealth of information in these databases can be useful in identifying those customers most likely to purchase a new product and in predicting when this adoption may take place. This can assist database marketers in determining when individuals should be targeted for the promotion of a n...
متن کاملIdentify Cross-Selling Opportunities Using Customer Growth Model
This report presents our solution to PAKDD 2007 Data Mining Competition, which details the steps to develop the customer growth model to identify cross-selling opportunities. This model is able to score the propensity of a credit card customer to take up a home loan with the finance company. We first describe the data preparation steps in detail. Then, the home loan customer growth model is pro...
متن کاملSelecting prospects for cross-selling financial products using multivariate credibility
Fredrik Thuring, Jens Perch Nielsen, Montserrat Guillén, Catalina Bolancé * Cass Business School, City University, London, United Kingdom, [email protected] (corresponding author) Cass Business School, City University, London, United Kingdom, [email protected] Department of econometrics, RISC-IREA, University of Barcelona, Spain, [email protected] Department of econometrics, RI...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Big Data
سال: 2021
ISSN: 2167-6461,2167-647X
DOI: 10.1089/big.2020.0044