Exploring customer specific KPI selection strategies for an adaptive time critical user interface
Year: 2014
Type: conference-paper
Abstract: Rapid growth in the number of measures available to describe customer-organization relationships is presenting a serious challenge for Business Intelligence (BI) interface developers as they attempt to provide business users with key customer information without requiring users to painstakingly sift through many interface windows and layers. In this paper we introduce a prototype Intelligent User Interface that we have deployed to partially address this issue. The interface builds on machine learning techniques to construct a ranking model of Key Performance Indicators (KPIs) that are used to select and present the most important customer metrics that can be made available to business users in time critical environments. We provide an overview of the prototype application, the underlying models used for KPI selection, and a comparative evaluation of machine learning and closed form solutions to the ranking and selection problems. Results show that the machine learning based method outperformed the closed form solution with a 66.5% accuracy rate on multi-label attribution in comparison to 54.1% for the closed form solution.
Authors: Ingo R. Keck, Robert J. Ross
Institution: Dublin Institute of Technology
Language: English
Subfield: Management Information Systems
Domain: Social Sciences
Open Access status: green
Funders: Enterprise Ireland, IDA Ireland
OpenAlex ID: W2011917249