Data Aggregators - Towards a Size Strategy: An Exploratory Study of Digital Platform Evolution Strategies

Abstract

With the rise and intensification of technology in the business context, new forms of businesses have emerged, among those the phenomenon of data aggregators. Although academics have studied related areas such as digital platforms in depth, the field of aggregators is left unexplored. Accordingly, this research project examines the platform evolution strategies of selected data aggregators and analyses how the specific environment has influenced the stated strategies. Correspondingly, this exploratory project uses a multiple-case study approach of three data aggregators: the Danish shared mobility tool Cogo, the Norwegian owned price comparison site Prisjakt and the Swedish API integrator Roaring. Moreover, this thesis proposes an abductive approach, aimed at giving a better understanding of the phenomenon, rather than creating generalisable findings for all data aggregators. Findings indicate that there are preferred approaches for the data aggregators when considering technology and growth strategies. In terms of data collection mechanisms, API integrations is the superior option and is likely to become the dominant design in the industry. Further, a hybrid approach of open and closed innovation where some collaboration occur, is the most suitable innovation strategy. Considering pricing decisions, all aggregators use a form of transaction-based model, however it matters less which side to charge as long as they do not bill all sides of the platform. All these dimensions connect to the choice of growth strategy, where findings show that data aggregators are more likely to choose a size strategy of growing as fast as possible, over an identity strategy of creating a unique positioning in the market. The preference of a size strategy is likely related to that the main success metric of aggregators is the size of the user base and the fact that the presence of network effects can create viral growth. These findings contribute to practical recommendations for the selected case companies and creates a theoretical foundation for data aggregators in the academic area.