Menu Optimization for Multi-Profile Customer Systems on Large Scale Data

Jeyhun Karimov (), Murat Ozbayoglu (), Bulent Tavli () and Erdogan Dogdu ()
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Jeyhun Karimov: TU Berlin
Murat Ozbayoglu: TOBB University of Economics and Technology
Bulent Tavli: TOBB University of Economics and Technology
Erdogan Dogdu: Angelo State University

Computational Economics, 2022, vol. 60, issue 1, No 9, 242 pages

Abstract: Abstract Everyday, a majority of the people, most probably several times, use the banking applications through online applications or physical ATM (Automated Teller Machine) devices for managing their financial transactions. However, most financial institutions provide static user interfaces regardless of the needs for different customers. Saving even a few seconds for each transaction through more personalized interface design might not only result in higher efficiency, but also result in customer satisfaction and increased market share among the competitors. In ATM Graphical User Interface (GUI) design, transaction completion time is, arguably, one of the most important metrics to quantify customer satisfaction. Optimizing GUI menu structures has been pursued and many heuristic techniques for this purpose are present. However, menu optimization by employing an exact mathematical optimization framework has never been performed in the literature. We cast the ATM menu optimization problem as a Mixed Integer Programming (MIP) framework. All the parameters of the MIP framework are derived from a comprehensive actual ATM menu usage database. We also proposed two heuristic approaches to reduce the computational complexity. Our solution can be accustomed with ergonomic factors and can easily be tailored for optimization of various menu design problems. Performance evaluations of our solutions by using actual ATM data reveal the superior performance of our optimization solution.

Keywords: HCI design and evaluation methods; Mixed integer programming; Heuristic algorithms; Graphical user interfaces; Interaction design theory; Scalable data processing (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10614-021-10147-0

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