Zugl.: Münster (Westfalen), Universiẗat, Diss., 2009.

This doctoral dissertation thesis focuses on current turnover management following principles of Revenue management in the field of hospitality in the Czech Republic. Final part of the thesis offers also prognosis of future development. The goal is to introduce Revenue management as an economic and management approach to direction of accommodation establishments, which maximize turnover. It uses optimum recovery ratio of accommodation capacity in combination with maximum average rate and price per available room, leading to optimization of economic results (when costs adequately controlled). It is a very complex theme which surpasses the capacity of this thesis. Therefore, the author chose a few segments with important influence on key economic indicators of accommodation establishments and where the initial optimum strategic settings are very important. These segments were examined both theoretically and practically. The author defines key terms using Czech and foreign literature, her own training materials and structured interviews with revenue/hotel managers. Several questionnaires, foreign case studies and personal experience of revenue management techniques implementation in accommodation establishments in the Czech Republic were used in the period from 2013 till 2016 as supporting materials for the practical part of the thesis. Results show that Revenue management has developed and requirements for the third generation of revenue managers are on the same level as for the top management. Czech accommodation establishments have, in comparison with western world or international chains, still room for improvement. The situation improves also in regions thanks to strong enlightenment and necessary reaction to strong competition and difficult economic conditions. Research shows that the best practice combines adequate modern technologies with well-trained and experienced human resources. Results point out room for further development of research in the field of Revenue management techniques usage, not only for hospitality services, but also for the field of tourism generally.

Advances in machine learning, together with an abundance of available data has lead to an explosion in personalized offerings and being able to predict what consumers want, and need without them having to ask for it. During the last decade, it has become a multi billion dollar industry, and a capability upon many of the leading tech companies rely on in their business model. Indeed, in today's business world, it is not only a capability for competitive advantage, but in many cases a matter of survival. This thesis aims to create a machine learning model able to predict customers interested in an upselling opportunity of changing their payment method after completing a purchase with the Swedish payment solutions company, Klarna Bank. Hence, the overall aim is to personalize the customer experience on the confirmation page. Two gradient boosting methods and one deep learning method were trained, evaluated and compared for this task. A logistic regression model was also trained and used as a baseline model. The results showed that all models performed better than the baseline model, with the gradient boosting methods showing the best performance. All of the models were also able to outperform the current solution with no personalization, with the best model reducing the amount of false positives by 50%. / Tillgång till stora datamängder har tillsammans med framsteg inom maskininlärning resulterat i en explotionsartad ökning i personifierade erbjudanden och möjligheter att förutspå kunders behov. Det har under det senaste decenniet utvecklats till en multimiljardindustri och en förmåga som många av de ledande techbolagen i världen förlitar sig på i sina verksamheter. I många fall är det till och med en förutsättning för att överleva i dagens industrilandskap. Det här examensarbetet ämnar att skapa en maskininlärningsmodell som är kapabel till att förutspå kunders intresse för att "uppgradera" sin betalmetod efter ett slutfört köp med den svenska betallösningsföretaget Klarna Bank. Konceptet att erbjuda en kund att uppgradera en redan vald produkt eller tjänst är på engelska känt som upselling. Det övergripande syftet för detta projekt är därför att skapa en personifierad kundupplevelse på Klarnas bekräftelsesida. Följaktligen implementerades och utvärderades två så kallade gradient boosting - metoder samt en djupinlärningsmetod. Vidare implementerades även en logistisk regressionsmodell som basmodell för att jämföra de övriga modeller med. Resultaten visar hur alla modeller överträffade den tillämpade basmodellen, där gradient boosting-metoderna påvisade bättre resultat än djupinlärningsmetoden. Därtill visar alla modeller en förbättring i jämförelse med dagens lösning på Klarnas bekräftelssesida, utan personifiering, där den bästa modellen förbättrade utfallet med 50%.