Toward an extended framework of exhaust data for predictive analytics: An empirical approach

Daniel O'Leary

Intelligent Systems in Accounting, Finance and Management, 2024, vol. 31, issue 2

Abstract: We investigate applying and extending an exhaust data framework, using an empirical analysis to explore and compare different predictive analytic capabilities of both internal and external exhaust data for estimating sales. We use internal exhaust data that explores the relationship between app usage and web traffic data and estimation of sales and find the ability to predict sales at least 4 days ahead. We also develop predictive models of sales, using external data of Google searches, extending the previous research to include additional macroeconomic Google variables and Wikipedia pageviews, finding that we can predict at least 4 months ahead, suggesting a portfolio of exhaust data be used. We introduce the roles of internal and external exhaust data, direct and indirect exhaust data and transformed exhaust data, into an exhaust data framework. We examine what appear to be different levels of information fineness and predictability from those exhaust data sources. We also note the importance of the types of devices (e.g., mobile) and the types of commerce (e.g., mobile commerce) in creating and finding different types of exhaust. Finally, we apply an existing exhaust data framework to develop macroeconomic data exhaust variables, as the means of capturing inflation and unemployment information, using Google searches.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1002/isaf.1554

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:wly:isacfm:v:31:y:2024:i:2:n:e1554

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=1099-1174

Access Statistics for this article

More articles in Intelligent Systems in Accounting, Finance and Management from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().