Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management

By G. Linoff, Michael A. Berry. 2004

1,255 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/51bc6bacc1b22d4de6fb70c0da4e767e25c80965

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Jianfeng Gao
2. Li Deng
3. Xiaodong He
4. Zhe Gan
5. Yoshua Bengio
6. Jianshu Chen
7. Dong Yu
8. Yuxi Li

Citations in the corpus

• A comparison of machine learning techniques for customer churn prediction (2015, background): “…representing sets of decisions capable to generate classification rules for a specific data set [33], or as Berry and Linoff noted “a structure that can be used to divide up a large collection of records into successively smaller sets of records by applying a sequence of simple decision rules” [34].”
• Mining Massive Fine-Grained Behavior Data to Improve Predictive Analytics (2016, methodology): “We now present the generalization performance of the different BeSim variants for predicting consumer purchase likelihood, as measured by the area under the ROC curve (AUC) (Fawcett 2006) and the lift over random selection (Linoff and Berry 2011).”
• The effect of ad rank on the performance of keyword advertising campaigns (2013, background): “, the process of getting consumers who come to an online store for potentially only one product to purchase related products ) is a common retail practice ( Berry & Linoff , 2004 ) .”
• Travel photos : motivations, image dimensions, and affective qualities of places (2014, methodology): “Known as association analysis or association rule mining, MBA was introduced in 1993 (Agrawal, Imielinski, & Swami, 1993) to discover customer purchase patterns by determining associations from transaction data (Chen, Tang, Shen, & Hu, 2005; Linoff & Berry, 2011).”
• Recurrent Reinforcement Learning: A Hybrid Approach (2015, background): “For example, Deng et al. (2013) apply a deep network to track belief states in a spoken dialogue system.”
• Using Data Sciences in Digital Marketing: Framework, methods, and performance metrics (2020, methodology): “Depending on the company’s goals when developing DM, different types of patterns can be used to improve these strategies, as well as to enhance the company’s ability to understand and structure the main attributes, features, or variables extracted from companies’ databases (Berry & Linoff, 2004).”