This report describes the work process behind two A/B tests that was developed, launched and analyzed. The tests were designed for the e-merchants Wegot and Cirkusfabriken. The work was carried out at the conversion optimization company Internetexperten. The report reviews the tools used for development, as well as the actual work that the development of the two tests entailed. After that the results of the tests will be described based on how the new design performed on the sections of the sites in the original version that they where compared against. / Den här rapporten redogör för arbetsprocessen kring två stycken A/B test som utvecklas, lanseras samt analyseras. Testerna är framtagna till e-handelsföretagen Wegot och Cirkusfabriken. Arbetet är utfört internt på konverteringsoptimeringsföretaget Internetexperten. Rapporten går igenom vilka hjälpmedel som använts för framtagandet, samt det faktiska arbetet som utvecklingen av de två testerna medförde. Efter detta beskrivs resultatet av hur testerna presterade mot de sektioner av webbplatserna i originalutförande som ställdes mot den nya designen.

This thesis investigates a forecasting approach to clustering device behavior based on multivariate time series data. Identifying an equitable selection to use in conversion optimization testing is a difficult task. As devices are able to collect larger amounts of data about their behavior it becomes increasingly difficult to utilize manual selection of segments in traditional conversion optimization systems. Forecasting the segments can be done automatically to reduce the time spent on testing while increasing the test accuracy and relevance. The thesis evaluates the results of utilizing multiple forecasting models, clustering models and data pre-processing techniques. With optimal conditions, the proposed model achieves an average accuracy of 97,7%.

Presented is implementation of algorithm for alias analysis, which was integrated into LLVM framework. Properties and limitations of various alias analysis algorithms are discussed. Demonstrated are different approaches to working with predicates and integration of these principles with LLVM. One of the outcomes of this master's thesis is design and implementation of algorithm for profile guided if-conversion.

This thesis consists of two parts. The first part is on fractional factorial design, and the second part is on computer experiment. The first part has two chapters. In the first chapter, we use the concept of conditional main effect, and propose the CME analysis to solve the problem of effect aliasing in two-level fractional factorial design. In the second chapter, we study the conversion rates of a system of webpages with the proposed funnel testing method, by using directed graph to represent the system, fractional factorial design to conduct the experiment, and a method to optimize the total conversion rate with respect to all the webpages in the system. The second part also has two chapters. In the third chapter, we use regression models to quantify the model form uncertainties in the Perez model in building energy simulations. In the last chapter, we propose a new Gaussian process that can jointly model both point and integral responses.

This thesis deals with the improving of website business performance through the optimization with a focus on user experience. Its concern is not how to accumulate more traffic to the website but rather how to motivate website users, persuade and help them achieve desired goals. The aim is to introduce the concept of User Experience (UX) and Conversion Optimization (CRO). Further on, to suggest a general process of the website optimization focused on user experience and to apply this procedure in a case study. The contribution of this thesis is to link the UX/CRO concepts that provide users with a positive brand experience and allow organizations a sustainable competitive advantage, differentiation and higher marketing ROI.