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Uri Eick

dblp:05/6683 · DBLP profile ↗
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3ranked-venue papers
0as first author
0since 2021 · last 2003
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 3Artificial intelligence and machine learning · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
2 papers
Data mining · 68% Machine learning and data management · 32%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational finance and economics · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data mining › customer relationship management
customer retention
0.012002
Customer lifetime value modeling and its use for customer retention planning · KDD 2002
Data mining › predictive modeling
classification
0.012001
Evaluation of prediction models for marketing campaigns · KDD 2001
Machine learning and data management
model evaluation
0.012001
Evaluation of prediction models for marketing campaigns · KDD 2001

Methods — techniques the papers use, named apart from their topics

confidence intervals · 0.0
YearPublicationVenuePosition
2003 Customer Lifetime Value Models for Decision Support
Saharon Rosset, Einat Neumann, Uri Eick, Nurit Vatnik
Data Min. Knowl. Discov.3
2002 Customer lifetime value modeling and its use for customer retention planning
abstract
We present and discuss the important business problem of estimating the effect of retention efforts on the Lifetime Value of a customer in the Telecommunications industry. We discuss the components of this problem, in particular customer value and length of service (or tenure) modeling, and present a novel segment-based approach, motivated by the segment-level view marketing analysts usually employ. We then describe how we build on this approach to estimate the effects of retention on Lifetime Value. Our solution has been successfully implemented in Amdocs' Business Insight (BI) platform, and we illustrate its usefulness in real-world scenarios.
Saharon Rosset, Einat Neumann, Uri Eick, Nurit Vatnik, Yizhak Idan
KDD3
2001 Evaluation of prediction models for marketing campaigns
abstract
We consider prediction-model evaluation in the context of marketing-campaign planning. In order to evaluate and compare models with specific campaign objectives in mind, we need to concentrate our attention on the appropriate evaluation-criteria. These should portray the model's ability to score accurately and to identify the relevant target population. In this paper we discuss some applicable model-evaluation and selection criteria, their relevance for campaign planning, their robustness under changing population distributions, and their employment when constructing confidence intervals. We illustrate our results with a case study based on our experience from several projects.
Saharon Rosset, Einat Neumann, Uri Eick, Nurit Vatnik, Yizhak Idan
KDD3