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Paper Type
Complete
Abstract
Product management in manufacturing companies continues to change. Traditionally reliant on experience and intuition, product managers are facing a transformative era driven by digitalization. The growth in available data, coupled with an expanding spectrum of data sources, is opening up opportunities for data-driven decision-making in product management. For example, the needs of customer groups can be better understood through the analysis of social media data. This paper addresses the current lack of a comprehensive overview of use cases for analyzing data in product management. Through five workshops and 21 interviews with product management experts from manufacturing companies, we identified 63 use cases for analyzing data in product management. This paper aims to provide a valuable reference for practitioners seeking to enhance their analytical capabilities, fostering a more data-driven approach to product management.
Paper Number
1781
Recommended Citation
Grigoryan, Khoren; Fichtler, Timm; Asmar, Laban; Kühn, Arno; and Dumitrescu, Roman, "63 Use Cases for Analyzing Data in Product Management of Manufacturing Companies" (2024). AMCIS 2024 Proceedings. 7.
https://aisel.aisnet.org/amcis2024/dsa/dsa/7
63 Use Cases for Analyzing Data in Product Management of Manufacturing Companies
Product management in manufacturing companies continues to change. Traditionally reliant on experience and intuition, product managers are facing a transformative era driven by digitalization. The growth in available data, coupled with an expanding spectrum of data sources, is opening up opportunities for data-driven decision-making in product management. For example, the needs of customer groups can be better understood through the analysis of social media data. This paper addresses the current lack of a comprehensive overview of use cases for analyzing data in product management. Through five workshops and 21 interviews with product management experts from manufacturing companies, we identified 63 use cases for analyzing data in product management. This paper aims to provide a valuable reference for practitioners seeking to enhance their analytical capabilities, fostering a more data-driven approach to product management.
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