Prediction of Customer Transactional Net Promoter Score (tNPS) Using Machine Learning
نویسندگان
چکیده
منابع مشابه
A Survey on Customer Churn Prediction using Machine Learning Techniques
The fast expansion of the market in every sector is leading to superior subscriber base for service providers. Added competitors, novel and innovative business models and enhanced services are increasing the cost of customer acquisition. In such a fast set up, service providers have realized the importance of retaining the on-hand customers. It is therefore essential for the service providers t...
متن کاملThe Net Promoter Score debate and the meaning of customer loyalty
The Net Promoter Score (NPS) is used by many of today’s top businesses to monitor and manage customer relationships. Fred Reichheld and his co-developers of the NPS say that a single survey question is the only loyalty metric companies need to grow their company. Despite its widespread adoption by such companies as General Electric, Intuit, T-Mobile, Charles Schwab and Enterprise, the NPS is no...
متن کاملThe Fallacy of the Net Promoter Score: Customer Loyalty Predictive Model
The Net Promoter Score (NPS) is still a popular customer loyalty measurement despite recent studies arguing that customer loyalty is multidimensional. Therefore, firms require new data-driven methods that combine behavioral and attitudinal data sources. This paper provides a framework that holistically assesses and predicts customer loyalty using attitudinal and behavioral data sources. We buil...
متن کاملStock Price Prediction using Machine Learning and Swarm Intelligence
Background and Objectives: Stock price prediction has become one of the interesting and also challenging topics for researchers in the past few years. Due to the non-linear nature of the time-series data of the stock prices, mathematical modeling approaches usually fail to yield acceptable results. Therefore, machine learning methods can be a promising solution to this problem. Methods: In this...
متن کاملImproving propensity score weighting using machine learning.
Machine learning techniques such as classification and regression trees (CART) have been suggested as promising alternatives to logistic regression for the estimation of propensity scores. The authors examined the performance of various CART-based propensity score models using simulated data. Hypothetical studies of varying sample sizes (n=500, 1000, 2000) with a binary exposure, continuous out...
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: Advances in economics, business and management research
سال: 2022
ISSN: ['2352-5428']
DOI: https://doi.org/10.2991/978-94-6463-080-0_14