Background: As the requirements for the modern-day comforts are raising from day to day, the great evolution in the field of lawn-mowers is recorded. This evolution made companies produce a fleet of lawn-mowers(commercial, house-hold) for different kinds of usages. Despite the great evolution and market in this field, to the best of our knowledge, no effort was made to understand customer usage by analysis of real-time usage of lawn-mowers. This research made an attempt to analyse the real-time usage of lawn-mowers using techniques like machine learning. Objectives: The main objective of the thesis work is to understand customer usage of lawn-mowers by analysing the real-time usage data using machine learning algorithms. To achieve this, we first review several studies to identify what are the different ways(scenarios) and how to understand customer usage from those scenarios. After discussing these scenarios with the stakeholders at the company, we evaluated a suitable scenario in the case of lawn-mowers. Finally, we achieved the primary objective by clustering the usage of lawn-mowers by analysing the real-world time-series data from the Controller Area Network(CAN) bus based on the driving patterns. Methods: A Systematic literature review(SLR) is performed to identify the different ways to understand customer usage by analysing the usage data using machine learning algorithms and SLR is also performed to gain detailed knowledge about different machine learning algorithms to apply to the real-world data. Finally, an experiment is performed to apply the machine learning algorithms on the CAN bus time-series data to evaluate the usage of lawn-mowers into various clusters and the experiment also involves the comparison and selection of different machine learning algorithms applied to the data. Results: As a result of SLR, we achieved different scenarios to understand customer behaviours by analysing the usage data. After formulating the best suitable scenario for lawn-mowers, SLR also suggested the best suitable machine learning algorithms to be applied to the data for the scenario. Upon applying the machine learning algorithms after making necessary pre-processing steps, we achieved the clusters of usage of lawn-mowers for every driving pattern selected. We also achieved the clusters for different features of driving patterns that indicate the various characteristics like a change of intensity in the usage, rate of change in the usage, etc. Conclusions: This study identified customer behaviours based on their usage data by clustering the usage data. Moreover, clustering the CAN bus time-series data from lawn-mowers gave fresh insights to study human behaviours and interaction with the lawn-mowers. The formulated clusters have a great scope to classify and develop the individual strategy for each cluster formulated. Further, clusters can also be useful for identifying the outlying behaviour of users and/or individual components.

A successful mobile app monetization strategy is the foundation of any sustainable future business. App developers, in this regard, face the demanding challenge of building, maintaining and monetizing this strategy respectively. Factors, such as users' increasing unwillingness to pay for an app, impacts monetization methods negatively which makes current monetization strategies ever more challenging. Particularly for temporary apps, this phenomenon is ever influential. This research therefore addresses how companies can maximize the monetization of users if the usage of the app is limited by time. The researchers examined existing literature on app monetization and discovered that no research has been conducted on temporary apps yet, which highlights a specific research gap in a changing business environment. By conducting expert interviews on app monetization in combination with a multiple case study, investigating four temporary apps, this research found out that temporary apps do not monetize differently than non-temporary apps. This paper uncovered that there is a trend happening within the mobile app monetization industry that shifts from user-based monetization, where the user pays for the app, towards a partner-based monetization strategy. In this regard, external companies provide the revenue for the app. Particularly interesting is the potential of mobile data monetization, which is invisible for the user, thus providing a valuable strategy for the company. Comprising all executed research and insights gathered, the paper built the Mobile App Monetization Model. It examines the challenges and opportunities companies face during their monetization and which success and goal metrics are influential in their decision-making. It summarizes the current most important topics in the mobile app monetization field.