Proceedings of the 52nd International Academic Conference, Barcelona

HOW DATA ANALYTICS DRIVE SHARING ECONOMY BUSINESS MODELS?

SORAYA SEDKAOUI, RAFIKA BENAICHOUBA

Abstract:

Several studies and reports published by Mckinsey, Gartner, Cesco, PwC, etc., confirm that data analytics offers companies more value and allows them to the creation of new and innovative ideas. This is why the data-driven approach has been the subject of considerable publicity in recent years. This approach has given rise to the emergence of many business models, all of which have created their own way of doing things. This is the case of many emergent business models who have noticed that several assets (goods or services) are not exploited effectively by the parties that hold them. We buy many products that we use only for a certain period and then put them aside. What if we could find one or more people who might need it? This is the question that these innovative business models had taken into account. They could see potential monetary benefits in these different resources, simply by facilitating their sharing. Some succeed by bursting the value chains and shaking up the established players: Uber for taxis, BlaBlaCar for interurban carpooling, Airbnb for accommodation, etc., and this is, of course, just the beginning, because the trend is accelerating. These are fascinating ideas that have led to the emergence of the sharing economy. But, one thing is clear, the ideas created by Uber, Airbnb, BlaBlaCar, etc. cannot be realized without determining what allows their development (how?) and, of course, the target (for whom?). These companies use the data to determine what to develop and target, to create untapped sharing market opportunities. Many researchers have found the potential of large amounts of data produced and collected by many sharing platforms. The analysis of these quantities not only helps to promote the performance of these models or operationalize their activities, but also to predict economic results such as inflation, unemployment, housing prices, etc. All sharing platforms and applications rely on data and analysis to develop practices and determine who to target. These data are increasingly used today because of the conjunction of a number of factors, such as: • The constant decrease in data storage costs; • The increase of the computing power; • The production of large amounts of data, which is largely unstructured and requires different operating techniques and which cannot be preceded by traditional methods. Being able to generate value, in the context of the sharing economy, and make big data more profitable is based on the ability of companies to analyze the amount available data. The challenge, therefore, lie in the ability to extract value from the amount volume of data produced in real-time continuous streams with multiple form and from multiple sources. In another word, the key to explore data and uncover secrets from it is to find and develop applicable ways in such a way to extract knowledge that can conduct any business project strategies. Indeed, recent years have been marked by the use of very advanced methods and computer tools previously reserved only for large companies. This has facilitated access to a large number of ways to create innovative ideas. Therefore, in this paper the following research question will be answered: How the sharing economy companies use data and advanced analytics to boost their business models? Through this question, we recall the context of big data and analytics, their importance in sharing economy context, their challenges and the role they mutually plays to create new opportunities for sharing economy companies. We will, through this paper, see how sharing economy business models use data analytics to generate value.

Keywords: Data analytics, big data, sharing economy, platforms, business model, innovation

DOI: 10.20472/IAC.2019.052.057

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