Pricing Models for IoT and AI-Enhanced Sensor Monitoring Apps

Gabriella Stephanie Siregar; Benny Tjahjono; Dibyo Widodo; Naufal Rashad  Aryaputra; Shidqi Bintang Etsandini; Alodia Shakira Putri

doi:10.1109/TEMSCON-ASPAC62480.2024.11024655

Pricing Models for IoT and AI-Enhanced Sensor Monitoring Apps

Gabriella Stephanie Siregar, Benny Tjahjono, Dibyo Widodo, Naufal Rashad Aryaputra, Shidqi Bintang Etsandini, Alodia Shakira Putri

Centre for E-Mobility and Clean Growth

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

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Abstract

The paper presents the development of a pricing strategy for a company that develops a sensor monitoring app leveraging IoT and AI technologies to enhance operational efficiency and connectivity. In alignment with the company's client objectives of predicting failures and improving operational engagement, the app's development followed a structured process. This process encompassed a Customer Value Proposition Workshop, Scenario and Affordance Analyses, Client Validation, Features Analysis, and Pricing Strategy. Through these steps, critical customer needs were identified and integrated into the app's features, supported by iterative feedback from the company's engineering department. Affordance theory was adopted as the theoretical lens and was proven instrumental in identifying how IoT can achieve operational goals by guiding the app's feature development. The pricing strategy employed value-based bundling and psychological pricing to enhance market appeal. This research highlights the transformative potential of IoT and AI in engineering consultancy, driving tailored solutions and operational excellence.

Original language	English
Title of host publication	2024 IEEE Technology & Engineering Management Conference - Asia Pacific (TEMSCON-ASPAC)
Publisher	IEEE
Pages	1-7
Number of pages	7
ISBN (Electronic)	979-8-3315-1588-1
ISBN (Print)	979-8-3315-1589-8
DOIs	https://doi.org/10.1109/TEMSCON-ASPAC62480.2024.11024655
Publication status	E-pub ahead of print - 13 Jun 2025
Event	2024 IEEE Technology & Engineering Management Conference - Asia Pacific (TEMSCON-ASPAC) - Denpasar, Indonesia Duration: 25 Sept 2024 → 27 Sept 2024

Conference

Conference	2024 IEEE Technology & Engineering Management Conference - Asia Pacific (TEMSCON-ASPAC)
Country/Territory	Indonesia
City	Denpasar
Period	25/09/24 → 27/09/24

Bibliographical note

This document is the author’s post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.

Keywords

AI
IoT
affordance theory
engineering consultancy
pricing strategy
sensor monitoring

ASJC Scopus subject areas

Engineering (miscellaneous)
Management of Technology and Innovation
Strategy and Management
Artificial Intelligence
Computer Networks and Communications
Computer Vision and Pattern Recognition
Information Systems and Management

Access to Document

10.1109/TEMSCON-ASPAC62480.2024.11024655

Siregar2025AAMAccepted author manuscript, 663 KB

Cite this

Siregar, GS , Tjahjono, B, Widodo, D, Aryaputra, NR, Etsandini, SB & Putri, AS 2025, Pricing Models for IoT and AI-Enhanced Sensor Monitoring Apps. in 2024 IEEE Technology & Engineering Management Conference - Asia Pacific (TEMSCON-ASPAC). IEEE, pp. 1-7, 2024 IEEE Technology & Engineering Management Conference - Asia Pacific (TEMSCON-ASPAC), Denpasar, Indonesia, 25/09/24. https://doi.org/10.1109/TEMSCON-ASPAC62480.2024.11024655

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AB - The paper presents the development of a pricing strategy for a company that develops a sensor monitoring app leveraging IoT and AI technologies to enhance operational efficiency and connectivity. In alignment with the company's client objectives of predicting failures and improving operational engagement, the app's development followed a structured process. This process encompassed a Customer Value Proposition Workshop, Scenario and Affordance Analyses, Client Validation, Features Analysis, and Pricing Strategy. Through these steps, critical customer needs were identified and integrated into the app's features, supported by iterative feedback from the company's engineering department. Affordance theory was adopted as the theoretical lens and was proven instrumental in identifying how IoT can achieve operational goals by guiding the app's feature development. The pricing strategy employed value-based bundling and psychological pricing to enhance market appeal. This research highlights the transformative potential of IoT and AI in engineering consultancy, driving tailored solutions and operational excellence.

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KW - affordance theory

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ER -

Pricing Models for IoT and AI-Enhanced Sensor Monitoring Apps

Abstract

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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