Customer purchase prediction in electronic markets from clickstream data using the Oracle meta-classifier

Fatemeh Ehsani () and Monireh Hosseini ()
Additional contact information
Fatemeh Ehsani: K. N. Toosi University of Technology
Monireh Hosseini: K. N. Toosi University of Technology

Operational Research, 2024, vol. 24, issue 1, No 10, 32 pages

Abstract: Abstract Electronic commerce has brought about a fundamental change in marketing atmosphere. Marketers strive to gain a competitive advantage by providing exciting electronic platforms to attract visitors. Suppliers must comprehend and fulfill their buyers’ demands to increase their income and profit. Analyzing users’ behavior and predicting their purchase attitudes significantly influences marketing strategies. A comprehensive prediction of customers’ purchase intention is crucial to developing good marketing strategies, which may trigger much greater purchase amounts. Marketing analysts utilize various data mining approaches to identify consumer traits and intentions, helping marketing analyzers distinguish complex consumption patterns. This paper aims to recommend an ensemble meta-classifier algorithm for predicting customer purchase intentions in electronic markets using their clickstream data. To achieve this, we performed the RFECV procedure to select the most practical features, thus achieving the highest accuracy. Additionally, we applied hyperparameter optimization and tuning to select the best parameters for prediction. Furthermore, we chose to use Oracle as a static ensemble meta-classifier method. The results demonstrate that using the Oracle algorithm in combination with supervised classifiers and RFECV feature selection achieves higher precision and a lower error rate compared to conventional classifiers.

Keywords: Customer behavior; Purchase prediction; Electronic marketing; Oracle meta-classifier (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s12351-023-00813-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:operea:v:24:y:2024:i:1:d:10.1007_s12351-023-00813-6

Ordering information: This journal article can be ordered from
https://www.springer ... search/journal/12351

DOI: 10.1007/s12351-023-00813-6

Access Statistics for this article

Operational Research is currently edited by Nikolaos F. Matsatsinis, John Psarras and Constantin Zopounidis

More articles in Operational Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().