Predicting Success for Web Product through Key Performance Indicators based on Balanced Scorecard with the Use of Machine Learning
Authors: Salmon, Ioannis 
Anagnostopoulos, Theodoros 
Psarras, Alkinoos 
Psycharis, Panagiotis Nikolaos 
Tagkouta, Eleni 
Issue Date: 1-Jan-2023
Journal: WSEAS Transactions on Business and Economics 
Volume: 20
Keywords: Artificial Intelligence, Artificial Neural Networks, Balanced Scorecard, Business plan, Business strategy, Change management, E-Business, Machine Learning, Product Success, Start-ups
Abstract: 
Machine Learning (ML) can be proved as an important tool in planning better business strategies. For the purposes of the present study, the prospect for the development of an electronic platform by a technology firm providing financial services is explored. The purpose of this article is to demonstrate the ways in which a start-up can predict the success of an online platform prior to its market launch. The prediction is achieved by applying Artificial Intelligence (AI) on Key Performance Indicators (KPIs) derived from the customers’ perspective, as shown in the Balanced Scorecard (BSC). The research methodology was quantitative and online questionnaires were used to collect empirical quantitative data related to bank loans. Subsequently, KPIs were created based on the collected data, to measure and assess the success of the platform. The effectiveness of the model was calculated up to 91.89%, and thus, it is estimated that the online platform will be of great success with 91.89% validity. In conclusion, prediction was found to be crucial for businesses to prevent a dire economic situation. Finally, the necessity for businesses to keep up with technological advances is highlighted.
ISSN: 22242899
11099526
DOI: 10.37394/23207.2023.20.59
URI: https://uniwacris.uniwa.gr/handle/3000/1576
Type: Article
Department: Department of Business Administration 
School: School of Administrative, Economics and Social Sciences 
Affiliation: University of West Attica (UNIWA) 
Appears in Collections:Articles / Άρθρα

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