This master thesis deals with e-commerce of entrance tickets. Current reservation and sale systems of entrance tickets are described, advantages and risks of those systems are described from the customer point of view and management point of view. Improvement of e-commerce of entrance tickets implemented in Brno City Theatre is suggested based on detailed analysis of internal and external factors, which are defined with usage of SLEPT, SWOT analysis and Porter model of competition.

The work presented in this study is based on the desire of cutting consumer costs related to purchase of airfare tickets. In detail, the study has investigated whether it is possible to classify optimal purchase decisions for specific flight routes with high accuracy using machine learning models trained with basic data containing only price and search date for a given date of departure. The models were based on Random Forest Classifier and trained on search data up to 90 days ahead of every leave date in July 2016-2018, and tested on the same kind of data for 2019. After preparation of data and tuning of hyperparameters the final models managed to correctly classify optimal purchase with an accuracy of 88% for the trip Stockholm-Mallorca and 84% for the trip Stockholm-Bangkok. Based on the assumption that the number of searches correlates with demand and in turn actual purchases, the study calculated the average expected savings per ticket using the model on the specific routes to be 21% and 17% respectively. Furthermore, the study has also examined how a business model for price comparison could be reshaped to incorporate these findings. The framework was set up using Business Model Canvas and resulted in the recommendation of implementing a premium service where users would be given the information wether to buy or wait based on a search. / Arbetet presenterat i studien är baserat på målet att sänka konsumentkostnader relaterat till köp av flygresor. Mer specifikt har studien undersökt huruvida det är möjligt att predicera optimala köpbeslut för specifika flygrutter med hjälp av maskininlärningsmodeller tränade på grundläggande data innehållande endast information om pris och sökdatum för varje givet avresedatum. Modellerna baserades på Random Forest Classifier och tränades på sökdata upp till 90 dagar före avresa för varje avresedag i juli 2016–2018, och testades på likadan data för 2019. Efter förberedelse av data och tuning av hyperparametrar lyckades modellerna med en träffsäkerhet på 88% respektive 84% predicera optimalt köp för rutterna Stockholm-Mallorca respektive Stockholm-Bangkok. Baserat på antagande om att antalet sökningar korrelerar med efterfrågan och vidare faktiska köp, beräknade studien att den genomsnittliga förväntade besparingen per biljett vid användning av modeller på de undersökta rutterna till 21% respektive 17%. Vidare undersökte studien hur en affärsmodell för prisjämförelse kan omformas för att inkorporera resultaten. Ramverkat som användes för detta var Business Model Canvas och mynnade ut i en rekommendation av implementering av en premiumtjänst genom vilken användare ges information biljett ska köpas eller ej vid en given sökning.

This Master's Thesis concerns the travel ticket sales at Prague Main Railway Station. Based on a detailed analysis of provided data, its aim is to put forward suggestions on how to optimize travel ticket sales. Through evaluating the volume of travel tickets sold within the given time period and the number of open cash-desks, along with a cost analysis of sales channels, this thesis reaches its final conclusions. Microsoft Excel was the main analysis tool for compiling graphs. A sound knowledge of the current legislation which is stated in the theoretical part is necessary to help draw the right conclusions. This Master's Thesis, then, chiefly proposes informed suggestions on current sale process optimization in the ČD Center of Prague Main Railway Station.

Tato práce popisuje realizaci webové aplikace, která umožňuje centrální administraci služeb pro sbor umělců, jako je například komorní orchestr. Do této administrace patří zejména správa obsahu webové prezentace, umělců a jejich smluv a honorářů, koncertů, skladeb a rezervací a objednávek online vstupenek. Aplikace integruje vestavný Konfigurátor a rezervační systém koncertních síní (KORES), vytvořený v rámci bakalářské práce a spravuje dále jím vytvořené objednávky.

The subject of the diploma thesis „Performance evaluation of a company“ is to analyze performance of a company providing travel services in global market. The theoretical part defines basic concepts and methods of performance evaluation. The analytical part is focused on analysis of the activities including comparasion of financial indicators with competitors in global martket and suggestions for improvments. The final part includes project, which should increase the overal performance of the company.

The master´s thesis is about creating of travel blog. In the beginning there is analysis of actual situation and creating of change. Right after will be created risk analysis to see and identify risk and in case to express and stand in front of risk regarding their risk seriousness. Blog asi t is will be created with help of tools of project management and it should be transformed to small business in selling of cheap flights through the business partner.

The theme of my thesis was published with the title of local government activities and a proposal of the methodological approach in dealing with the disruption of food supplies. The objective of my thesis was to outline the local government and also to focus on its activities. It also describes the basic principles of emergency planning as part of preparatory measures for dealing with crisis situations, including crisis situations with the disruption of food supplies with large-scale consequences. My thesis also contains a general overview of the economic measures for crisis situations, which includes regulatory measures. The regulatory measures are a central subject of my thesis research. The objective of my thesis was to create a proposal of the methodological approach in dealing with the disruption of food supplies. A presumptive hypothesis was subsequently formed for this objective. This thesis is classified as a theoretical type of qualifying work. This type of thesis is accompanied by a formative evaluation as a minimum. An essential basis for the work on this thesis was a relevant and rich source base. This platform was used for performing a comparison of published literary sources, current legislation, methodological proposals and procedures related to this issue. Based on analytical and critical approach, the data acquired was assessed and processed into a draft methodological guide. The draft methodological approach in dealing with the disruption of food supplies consists of two basic parts. The methodological guide is intended for local authorities that can use it in their planning documents for dealing with emergencies and crisis situations. The proposed methodology should help local governments in dealing with crisis situations with food supply disruptions and simplify orientation in the issue of economic measures for crisis situations, which include regulatory measures. It would be suitable to inform local authorities about the issue of economic measures for crisis situations, focusing on regulatory measures concerning the solutions to crisis situations with food supply disruptions with large-scale consequences, through training and professional seminars on the issue.

Thesis is focused on creation of an information system for a visitor center in Olomouc. This document describes whole lifecycle of that reservation system, including requirements specification, system design, its implementation and testing. Final system will be deployed in museum, where it will handle the control of turnstiles, ticket reservations and sales.

More and more aspects of today’s healthcare are becoming integrated with medical technology and dependent on medical IT systems, which consequently puts stricter re-quirements on the companies delivering these solutions. As a result, companies delivering medical technology solutions need to spend a lot of resources maintaining high-quality, responsive customer support. In this report, possible ways of increasing customer support efficiency using machine learning and NLP is examined at Sectra, a medical technology company. This is done through a qualitative case study, where empirical data collection methods are used to elicit requirements and find ways of adding decision support. Next, a prototype is built featuring a ticket recommendation system powered by GPT-3 and based on 65 000 available support tickets, which is integrated with the customer supports workflow. Lastly, this is evaluated by having six end users test the prototype for five weeks, followed by a qualitative evaluation consisting of interviews, and a quantitative measurement of the user-perceivedusability of the proposed prototype. The results show some support that machine learning can be used to create decision support in a customer support context, as six out of six test users believed that their long-term efficiency could improve using the prototype in terms of reducing the average ticket resolution time. However, one out of the six test users expressed some skepticism towards the relevance of the recommendations generated by the system, indicating that improvements to the model must be made. The study also indicates that the use of state-of-the-art NLP models for semantic textual similarity can possibly outperform keyword searches.

A customers propensity to buy a certain product is a widely researched field and is applied in multiple industries. In this thesis it is showed that using binary classification on data from Scandinavian Airlines can predict their customers propensity to book a flight within the next coming seven days. A comparison between logistic regression and support vector machine is presented and logistic regression with reduced number of variables is chosen as the final model, due to it’s simplicity and accuracy. The explanatory variables contains exclusively booking history, whilst customer demographics and search history is showed to be insignificant. / En kunds benägenhet att göra ett visst köp är ett allmänt undersökt område som applicerats i flera olika branscher. I den här studien visas det att statistiska binära klassificeringsmodeller kan användas för att prediktera Scandinavian Airlines kunders benägenhet att köpa en resa de kommande sju dagarna. En jämförelse är presenterad mellan logistisk regression och stödvektormaskin och logistisk regression med reducerat antal parametrar väljs som den slutgiltiga modellen tack vare sin enkelhet och träffsäkerhet. De förklarande variablerna är uteslutande bokningshistorik medan kundens demografi och sökdata visas vara insignifikant.