Proceedings Article10.1109/ICAIIT.2019.8834571
Predictive Analytics for Predicting Customer Behavior
Asniar,Kridanto Surendro +1 more
- 13 Mar 2019
- Vol. 2019, pp 230-233
41
TL;DR: This paper tries to propose predictive analytics to predict customer behavior by using behavior informatics and analytics approach so that deeper insight into customer behavior can be obtained to support predictive analysis in order to improve business decision making.
read more
Abstract: The development of the internet has caused digitalization of data which opens up big data opportunities. Digital data in large numbers leaves traces of what customers see, what they read, their involvement and behavior, judgment, about their interests and preferences so as to provide a large amount of data that can be mined for learning experiences. The big data value lies in the results of analysis and predictions or actions taken from the results of the analysis and prediction. Predictive analytics is data utilization, statistical algorithms, and machine-learning techniques to identify possible trends, events, and behaviors in the future based on historical data. This paper tries to propose predictive analytics to predict customer behavior by using behavior informatics and analytics approach so that deeper insight into customer behavior can be obtained to support predictive analysis in order to improve business decision making.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Artificial Intelligence in Costumer Acquisition
Mustapha Elhissoufi,Lhoussaine Alla +1 more
TL;DR: This chapter explores AI's applicability in customer acquisition via bibliometric analysis, revealing accelerated research since 2018, American institutional dominance, and a trend towards decentralized research in marketing and computer science journals.
19
Mobile Customer Behaviour Predictive Analysis for Targeting Netflix Potential Customer
Suryadi Tanuwijaya,Andry Alamsyah,Maya Ariyanti +2 more
- 03 Aug 2021
TL;DR: In this paper, the authors used machine learning predictive analytic methodology to profile and predict potential customers of one of the VOD platforms, Netflix, for personalizing marketing targets, which can be used by the mobile operator to target potential customers with effective promotional or product offering by personalized marketing approach based on the behavioral pattern and customer needs.
9
Value creation through marketing data analytics: The distinct contribution of data analytics assets and capabilities to unit and firm performance
TL;DR: In this paper , the authors take an information value chain approach to theorize about how quality data and IT-enabled data analytics sensing capability in the marketing unit relate differently to the unit performance as well as to firm-level performance.
9
Artificial Intelligence in Marketing Communication: A Comprehensive Exploration of the Integration and Impact of AI
Hafize Nurgül DURMUŞ ŞENYAPAR
TL;DR: A paradigm transition in customer engagement and behaviour analysis is revealed, highlighting AI’s role in providing profound insights and facilitating real-time interactions in marketing communications.
7
Predicting Consumer Behaviour with Artificial Intelligence
Prakash Assistant,Sunitha Devi,S.Malli Babu,Kumbala Pradeep Reddy,P.Pavan Kumar,Mankala Satish +5 more
- 07 Oct 2023
TL;DR: A comprehensive knowledge of the present environment is provided via a rigorous examination of existing literature and case studies, paving the way for future research and practical applications in the field of consumer behaviour prediction.
6
References
Data Science: A Comprehensive Overview
TL;DR: A comprehensive survey and tutorial of the fundamental aspects of data science: the evolution from data analysis to data science, the data science concepts, a big picture of the era of Data Science, the major challenges and directions in data innovation, the nature of data analytics, new industrialization and service opportunities in the data economy, the profession and competency of data education, and the future of Data science as discussed by the authors.
328
Data Science: A Comprehensive Overview
TL;DR: This article provides a comprehensive survey and tutorial of the fundamental aspects of data science: the evolution from data analysis to data science, the data science concepts, a big picture of the era of dataScience, the major challenges and directions in data innovation, the nature of data analytics, new industrialization and service opportunities in the data economy, the profession and competency of data education, and the future of datascience.
In-depth behavior understanding and use: The behavior informatics approach
TL;DR: The approach of behavior informatics is proposed, in order to support explicit and quantitative behavior involvement through a conversion from source data to behavioral data, and further conduct genuine analysis of behavior patterns and impacts.
Market segmentation through data mining: A method to extract behaviors from a noisy data set
TL;DR: The authors solved the problem by applying data mining methods to identify behavior patterns in historical noisy delivery data and revealed behavior patterns and subsequent market segmentation are suitable for strategic decision-making.
49
Predictive analytics in data science for business intelligence solutions
Parth Wazurkar,Robin Singh Bhadoria,Dhananjai Bajpai +2 more
- 01 Nov 2017
TL;DR: This paper provides a conceptual decision making process for data using predictive analysis to maximize the success ratio for handling large dataset.
42