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Paper Type
Complete
Description
Digital environment has transformed the work environment of marketing firms. Traditional marketing processes are repetitive, lack coordination, and are not dynamic or data-driven. To compete effectively in the digital world, marketing firms need a digital marketing analytics operations platform (DMAOP) optimized for data analytics, workflow, team coordination, and quick decision-making. We developed a DMAOP using a design science research model known as ADR. DMAOP uses data analytics leading to marketing decisions that create value with intelligent robotic processes assisting marketing practitioners in executing marketing actions. DMAOP is customized to a user's skill level, supporting marketing functions, advanced analytics, preserving privacy, and episodic reporting for users without technical skills. This study contributes to descriptive knowledge problem space by identifying the key aspects of DMAO. It also contributes to the "elaborated ADR" model with a new cycle, "personalization". Finally, it contributes to prescriptive knowledge and DMAOP instantiation with design science for digital platforms.
Paper Number
1633
Recommended Citation
Babu, Xavier and Motiwalla, Luvai F., "Designing a Digital Marketing Analytics Operations Platform using Action Design Research" (2023). AMCIS 2023 Proceedings. 16.
https://aisel.aisnet.org/amcis2023/conf_theme/conf_theme/16
Designing a Digital Marketing Analytics Operations Platform using Action Design Research
Digital environment has transformed the work environment of marketing firms. Traditional marketing processes are repetitive, lack coordination, and are not dynamic or data-driven. To compete effectively in the digital world, marketing firms need a digital marketing analytics operations platform (DMAOP) optimized for data analytics, workflow, team coordination, and quick decision-making. We developed a DMAOP using a design science research model known as ADR. DMAOP uses data analytics leading to marketing decisions that create value with intelligent robotic processes assisting marketing practitioners in executing marketing actions. DMAOP is customized to a user's skill level, supporting marketing functions, advanced analytics, preserving privacy, and episodic reporting for users without technical skills. This study contributes to descriptive knowledge problem space by identifying the key aspects of DMAO. It also contributes to the "elaborated ADR" model with a new cycle, "personalization". Finally, it contributes to prescriptive knowledge and DMAOP instantiation with design science for digital platforms.
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