Dell's SupportAssist customer adoption model: enhancing the next generation of data‐intensive support services

Abstract

We developed a decision support system to model, analyze, and improve market adoption of Dell's SupportAssist program. SupportAssist is a proactive and preventive support service capability that can monitor system operations data from all connected Dell devices around the world and predict impending failures in those devices. Performance of such data-intensive services is highly interconnected with market adoption: service performance depends on the richness of the customer database, which is influenced by customer adoption that in turn depends on customer satisfaction and service performance-a reinforcing feedback loop. We developed the SupportAssist adoption model (SAAM). SAAM utilizes various data sources and modeling techniques, particularly system dynamics, to analyze market response under different strategies. Dell anticipates improving market adoption of SupportAssist and revenue from support services, as results of using this analytical tool.

Sociotechnical interdependencies and tipping‐point dynamics in data‐intensive services

“…While this may be true in some cases, we note the dynamic nature of value in data‐intensive services. The value generated by data‐intensive services increases as the volume of raw data increases (Ghaffarzadegan et al ., 2017) and declines if the resources and capabilities erode (Rahmandad and Repenning, 2016). In these services, data can become outdated or fail to generate new information.…”

“…McKinsey Quarterly notes that data‐driven, real‐time customization has led to new business opportunities around collecting and packaging data for clients (Brown et al ., 2011). In this paradigm of what are called data‐intensive services , data collected from current and potential end users at a massive scale are the dominant ingredient of a firm's end product, and the added value often comes in the form of customized information (Davenport and Kudyba, 2016; Ghaffarzadegan et al ., 2017).…”

“…data resulting from their interactions with the website, past purchases, and “likes” and “votes”) are central in producing what existing customers and potential customers with similar characteristics desire. Thus, value chains of data‐intensive services are cyclical rather than linear, with customized outputs leading to customer interactions with the service, which in turn provides inputs to generate more and higher‐quality services (Davenport and Kudyba, 2016; Ghaffarzadegan et al ., 2017). However, most conventional approaches in organizational scholarship still draw a clear boundary between value generation at the operational level and market adoption.…”

On the differences between theoretical and applied system dynamics modeling

“…Other journals have a stronger focus on practical implications, and applied research is published in such journals (e.g., Jalali and Kaiser, ). System Dynamics Review publishes both types of articles (recent examples of applied research published in System Dynamics Review are Ghaffarzadegan et al, , and Kapmeier and Gonçalves, ). All in all, we conclude that there are enough reasons to justify a distinction between theoretical and applied SD modeling.…”

“…System dynamics (SD) is a versatile method and is used in very different settings, from highly applied (e.g., Cooper, 1980;McCarthy et al, 2014;Ghaffarzadegan et al, 2017) to highly theoretical (e.g., Sastry, 1997;Sterman and Wittenberg, 1999;Gambardella et al, 2017). More generally, there are three broad categories of SD research: methodological, theoretical, and applied modeling.…”