The DOI matches its Crossref record.
Customer-based perceptual map as a marketing intelligence source
Abstract
Marketing intelligence is adopted by most global firms to support decision-making, identify potential opportunities and plan appropriate strategies. This paper concentrates on customers' perceptions as a rich, systematic, objective, and intelligent source that supplies strategic marketing planning knowledge. This idea is executed through distilling a perceptual map from mining customers' perceptions via data mining techniques and tools. In this paper, a practical intelligence framework is proposed to integrate marketing resources and information systems techniques in order to maintain a deep understanding of the soundness of data. After application on a real case study for fast food restaurants brands in Jordan, the proposed framework has proven to display promising results.
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“Daabes and Kharbat (2017) demonstrate how data mining techniques can be used to distill a customerbased perceptual map, as an alternative to marketer knowledge, from mining customers ' perceptions.”
From A strategic framework for artificial intelligence in marketing (2021) · cites this paper as Daabes and Kharbat (2017)
Citations
Cited by
A strategic framework for artificial intelligence in marketing
Huang, M.-H., & Rust, R. T. (2021) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-020-00749-9 · Cited by 2,059
Morimura, F., & Sakagawa, Y. (2022) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2022.103193 · Cited by 61
Hyper-Personalization: An AI-Enabled Personalization for Customer-Centric Marketing
Desai, D. (2021) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/978-1-7998-7959-6.ch003 · Cited by 42
Marketing intelligence: Innovation ability to anticipate global competition
Aripin, Z., Suganda, U. K., & Kusumah, A. Z. (2022) · International Journal of Research in Business and Social Science (2147-4478) · doi:10.20525/ijrbs.v11i1.1589 · Cited by 12
Marketing insight from consumer reviews: Creating brand position through opinion mining approach
YİLMAZ, M. K., & Karakoç, H. T. (2023) · Telematics and Informatics Reports · doi:10.1016/j.teler.2023.100094 · Cited by 10
Metadata
- Authors
- Daabes, A. S. A., & Kharbat, F. F.
- Affiliations
- Al Ain University (AE)
- ORCID
- Kharbat, Faten F.
- Venue
- International Journal of Economics and Business Research
- Publisher
- Inderscience Publishers
- Published
- 2017
- Volume
- 13
- Issue
- 4
- Pages
- 360
- ISSN
- 1756-9850 (ISSN-L)
- 1756-9869
- OpenAlex
- W4250419040
- Cited by
- 14
- References
- 0
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 1 · 2023: 2 · 2022: 3 · 2021: 1 · 2020: 2
- FWCI
- 1.09
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Economics and Business Research (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Strategy and Management › Competitive and Knowledge Intelligence
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Market Behavior and Pricing
- Keywords
- market intelligence, consumer perception, data mining, strategic marketing plan, marketing information systems, fast food restaurants, Jordan
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- This page
- CoChat. (2026). Citation check: Customer-based perceptual map as a marketing intelligence source. https://app.cochat.ai/papers/10.1504/ijebr.2017.084381
- The paper
- Daabes, A. S. A., & Kharbat, F. F. (2017). Customer-based perceptual map as a marketing intelligence source. International Journal of Economics and Business Research. https://doi.org/10.1504/ijebr.2017.084381
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
