Thesis is focused on online content testing for the purpose of optimizing the performance of business and information channels in a global environment, i.e. where visitors come from different countries. This diversity causes different behavior of visitors, for example as the American perception of the content is entirely different from the Brazilian and French. Different perceptions and consumer behavior is caused by a different national culture in these countries. Therefore it is necessary or appropriate to test content for the purpose of optimizing on the local level. The simplest option is obviously to run the same test for each country separately. But that is extremely difficult in practice because of the duration of the test and human resources needed for test design, implementation, and evaluation. Therefore the aim of this thesis is to suggest modification of the general method used for testing the online content that will be sufficient for testing on the local level and will take cultural differences of each country into account, but yet also will be effective in terms of time and human resources consumption. Currently there isn't any publicly documented method which would cover this issue. The key of this modification is the segmentation of countries into groups based on similar national culture. Therefore the value of national culture has to be identified in some way and for this purpose it is possible to use model of the Dutch Professor Geert Hofstede, who identified six dimensions of national culture for each country and assigned them values. The benefit of this thesis is described modification of the testing method which is particularly suitable for companies operating on the global market or multiple markets simultaneously. This method, if it's used properly, is able to deliver growth of revenue while simultaneously reducing the consumption of human resources.

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.

In digital advertising, major search engines allow advertisers to set bid adjustments on their ad campaigns in order to capture the valuation differences that are a function of query dimensions. In this thesis, a model that uses bid adjustments is developed in order to increase the number of conversions and decrease the cost per conversion. A statistical model is used to select campaigns and dimensions that need bid adjustments along with several techniques to determine their values since they can be between -90% and 900%. In addition, an evaluation procedure is developed that uses campaign historical data in order to evaluate the calculation methods as well as to validate different approaches. We study the problem of interactions between different adjustments and a solution is formulated. Real-time experiments showed that our bid adjustments model improved the performance of online advertising campaigns with statistical significance. It increased the number of conversions by 9%, and decreased the cost per conversion by 10%. / I digital marknadsföring tillåter de dominerande sökmotorerna en annonsör att ändra sina bud med hjälp av så kallade budjusteringar baserat på olika dimensioner i sökförfrågan, i syfte att kompensera för olika värden de dimensionerna medför. I det här arbetet tas en modell fram för att sätta budjusteringar i syfte att öka mängden konverteringar och samtidigt minska kostnaden per konvertering. En statistisk modell används för att välja kampanjer och dimensioner som behöver justeringar och flera olika tekniker för att bestämma justeringens storlek, som kan spänna från -90% till 900%, undersöks. Utöver detta tas en evalueringsmetod fram som använder en kampanjs historiska data för att utvärdera de olika metoderna och validera olika tillvägagångssätt. Vi studerar interaktionsproblemet mellan olika dimensioners budjusteringar och en lösning formuleras. Realtidsexperiment visar att vår modell för budjusteringar förbättrade prestandan i marknadsföringskampanjerna med statistisk signifikans. Konverteringarna ökade med 9% och kostnaden per konvertering minskade med 10%.

In statistical methodology of analyzing data, two of the most commonly used techniques are classification and regression modeling. As scientific technology progresses rapidly, complex data often occurs and requires novel classification and regression modeling methodologies according to the data structure. In this dissertation, I mainly focus on developing a few approaches for analyzing the data with complex structures. Classification problems commonly occur in many areas such as biomedical, marketing, sociology and image recognition. Among various classification methods, linear classifiers have been widely used because of computational advantages, ease of implementation and interpretation compared with non-linear classifiers. Specifically, linear discriminant analysis (LDA) is one of the most important methods in the family of linear classifiers. For high dimensional data with number of variables p larger than the number of observations n occurs more frequently, it calls for advanced classification techniques. In Chapter 2, I proposed a novel sparse LDA method which generalizes LDA through a regularized approach for the two-class classification problem. The proposed method can obtain an accurate classification accuracy with attractive computation, which is suitable for high dimensional data with p>n. In Chapter 3, I deal with the classification when the data complexity lies in the non-random missing responses in the training data set. Appropriate classification method needs to be developed accordingly. Specifically, I considered the "reject inference problem'' for the application of fraud detection for online business. For online business, to prevent fraud transactions, suspicious transactions are rejected with unknown fraud status, yielding a training data with selective missing response. A two-stage modeling approach using logistic regression is proposed to enhance the efficiency and accuracy of fraud detection. Besides the classification problem, data from designed experiments in scientific areas often have complex structures. Many experiments are conducted with multiple variance sources. To increase the accuracy of the statistical modeling, the model need to be able to accommodate more than one error terms. In Chapter 4, I propose a variance component mixed model for a nano material experiment data to address the between group, within group and within subject variance components into a single model. To adjust possible systematic error introduced during the experiment, adjustment terms can be added. Specifically a group adaptive forward and backward selection (GFoBa) procedure is designed to select the significant adjustment terms. / Ph. D.

This work's topic is conversion rate optimization (CRO) -- activity, that leads to better business and website results, higher company income, through detailed knowledge of website visitors' behaviour and psychology and consecutive website adjustments. Conversion optimization's goal isn't getting more visitors to the website, it only focuses on unlocking maximum commercial potential of them -- orders, leads or other goals. The main purpose of this work is to summarize this newly developing topic complexly, as one of the first works in Czech, and demonstrating conversion rate optimization potential on a practical example. The work consists of following parts: - What is conversion rate optimization -- which disciplines and areas it comprises, the optimization process, pros and cons of optimization and redesign and more basic information on conversion rate optimization. - Some of conversion rate methods: * Multivariate and A/B testing -- what it is, how it is done, technological solutions, best practices. * Web analytics -- what it comprises, what functions it takes, what are the technological solutions and their pros and cons, web analytics implementation. * User testing -- how to do it properly. * Personas and user scenarios -- what they are and how to use them. - Case study about conversion optimization of the website Kentico.com -- utilization of described methods on a practical example including the results of optimization.

This thesis deals with conversion rate optimization and evaluation of its results. Thesis introduces and compares conversion rate optimization options -- web analytics, heatmaps, user testing and A/B testing. The practical part consists conversion rate optimization of Faculty of Management website and quantification its results.

A/B testing refers to the statistical procedure of experimental design and analysis to compare two treatments, A and B, applied to different testing subjects. It is widely used by technology companies such as Facebook, LinkedIn, and Netflix, to compare different algorithms, web-designs, and other online products and services. The subjects participating in these online A/B testing experiments are users who are connected in different scales of social networks. Two connected subjects are similar in terms of their social behaviors, education and financial background, and other demographic aspects. Hence, it is only natural to assume that their reactions to online products and services are related to their network adjacency. In this research, we propose to use the conditional autoregressive model (CAR) to present the network structure and include the network effects in the estimation and inference of the treatment effect. The following statistical designs are presented: D-optimal design for network A/B testing, a re-randomization experimental design approach for network A/B testing and covariate-assisted Bayesian sequential design for network A/B testing. The effectiveness of the proposed methods are shown through numerical results with synthetic networks and real social networks.

Since its inception, Marketing has always needed more clearly defined incrementality, i.e., a measurement of advertisement effectiveness. Nowadays, Marketing is an evergrowing business; within it, Digital Marketing is taking the spotlight. Digital Marketing brings multiple benefits, such as a global reach and a lower cost associated with customer communication. However, more importantly, customer interaction and engagement can be clearly tracked, which can help measure Marketing impact. Nowadays, this problem is tackled in two ways, A/B testing and attribution models. Even though statistically solid and proven, A/B testing, a form of hypothesis testing, faces implementation issues and other practical aspects, leading to only sometimes being used in real-world applications. On the other hand, Attribution models are not comparable, thus not quantifiable, and good attribution models are hard to develop, leaving companies relying on third-party providers. In short, this paper suggests that the impact of each marketing campaign can be measured in a two-step process: (1) Training a model to predict a customer's conversion, given their previous advertisement interactions; (2) Applying explainable machine learning methods to said model to infer the importance of each advertisement interaction in a user journey. The main methods used are permutation feature importance and Shapley values. The dataset is designed such that each type of advertisement interaction is a model's feature; thus, an importance value can be calculated for each interaction. On top of that, a local method - counterfactual explanations - and a possible implementation of a hyper-personal application are discussed. The proposed solution is shown to provide more accurate attributions than most common attribution models, with the possibility of augmenting the accuracy by changing the underlying model. It is also suggested that it could benefit significantly from more data on customer demographics, generating insights into how campaigns affect different customer segments. / Marknadsföring har sedan dess begynnelse alltid behövt en tydligare definition av inkrementalitet, det vill säga, mätningen av annonsens effektivitet. Marknadsföring är numera en ständigt växande verksamhet och inom den är det den digitala marknadsföringen som står i fokus. Digital marknadsföring ger flera fördelar t.ex. global räckvidd och lägre kostnader för kundkommunikation. Viktigare är dock att kundernas interaktion och engagemang kan spåras tydligt, detta bidrar i sig till att mäta marknadsföringens effektivitet. Det här problemet hanteras på två sätt: AB-testning och tilldelningsmodeller. Även om AB-testning är statistiskt sett både gedigen och beprövad leder oftast problem med genomförandet och andra praktiska aspekter till att det endast ibland används i korrekta tillämpningar. Å andra sidan är tillskrivningsmodeller inte jämförbara - de saknar mätbarhet - och det är svårt att utveckla bra tillskrivningsmodeller vilket gör att företagen förlitar sig på tredjepartsleverantörer. I korthet föreslår denna artikel att effekten av varje marknadsföringskampanj kan mätas i en tvåstegsprocess. (1) Träning av en modell för att förutsäga en kunds konvertering baserad på deras tidigare annonsinteraktioner. (2) Tillämpning av difinierade maskininlärningsmetoder på nämnda modeller för att härleda betydelsen av varje annonsinteraktion i en användares resa. De viktigaste metoderna som användes var permutation feature importance och Shapley-värden. Datamängden utformad så att varje typ av annonsinteraktion blir en modells funktion; på så sätt kan ett betydelsevärde beräknas för varje interaktion. Dessutom diskuteras en lokal metod - kontrafaktiska förklaringar - och ett möjligt genomförande av en hyperpersonlig applikation. Den föreslagna lösningen visade sig ge mer exakta tillskrivningar än de flesta vanliga tillskrivningsmodeller, med möjlighet att öka noggrannheten genom att ändra den underliggande modellen. Det föreslås också att den skulle kunna dra stor nytta av mer data om kundernas demografi, vilket skulle generera insikter om hur kampanjer påverkar olika kundsegment.

For an e-commerce business to grow, there are many ways one could try to improve the business in order to gain greater reach and increase sales. One of the main goals of such businesses is to convert as many visitors as possible into customers. Even though many e-commerce businesses already have web analytics tools installed, e-merchants find difficulty in identifying where to start optimizing, what data to extract from analysis reports, and how to make use of such data in order to produce a successful design that will increase the conversion rate. The purpose of this thesis is to (without spending resources on marketing-related factors) guide companies to find a low cost and efficient way to increase the conversion rate by creating well-thought-through designs based on analytic data, qualitative research, and human-computer interaction principles. Google Analytics, a web analytics tool, was used in identifying high-valued pages to optimize and to identify demographics/target groups, while qualitative e-commerce related research was used to shape design-proposal hypotheses. This, along with two A/B tests conducted using Optimizely, is the basis for the guidelines and conclusions. The results of both A/B tests showed an increase in conversions with designs highlighting: evidence of a secure shopping environment, incentives that will attract visitors to buy, and by removing auxiliary navigation elements at the check-out page. The evaluation of the results and its statistical significance was done using both Optimizely’s statistical engine and null hypothesis testing. The increases in conversions were not statistically significant per Optimizely; however, they were significant using traditional statistics. In conclusion, using metrics such as high exit-rates combined with many page views and high revenue-generating pages will allow e-merchants to identify where to start their optimization process. Furthermore, to know what valuable data needs to be extracted, one should seek the data that needs to be inserted into HCI concepts, such as personas and scenarios. This, along with qualitative research allows designers to create well-thought out design-proposals that will potentially lead to an increased conversion rate. / För att få en e-handelsbutik att växa finns det många arbetsområden man kan försöka förbättra för att nå ut till fler samt öka försäljning. Ett av huvudmålen för dessa butiker är att konvertera så många besökare till kunder som möjligt på sin hemsida. Även om många e-handelsbutiker redan har webbanalytiska redskap till sitt förfogande, har många tjänsteleverantörer svårigheter med att fastställa var på hemsidan det skall optimeras, vilken data som ska hämtas från analysrapporter, och hur man använder sig av dessa data för att skapa en lyckad design som kommer öka konverteringsgraden. Syftet med avhandlingen är att, utan marknadsföringsrelaterade investeringar, vägleda företag till billiga och effektiva sätt att öka konverteringsgraden. Detta ska uppfyllas genom att skapa väl genomtänkta designer grundade på analytisk data, kvalitativ forskning, samt människa-datorinteraktions principer. Webbanalysverktyget Google Analytics användes för att identifiera högt värderade sidor att optimera och demografier/målgrupper medan kvalitativ e-handels-relaterad forskning användes för att forma hypoteser kring designförslagen. Detta, tillsammans med två A/B tester som genomfördes med hjälp av Optimizely, är grunden till riktlinjerna och slutsatserna. Resultaten från båda testerna visade en ökning i konverteringar med designer som framhäver; övertygande eller bevis för en säker handelsmiljö, incitament som kommer locka besökare att handla, och genom att ta bort extra navigeringselement vid kassasidan. Utvärdering av resultaten och dess statistiska signifikans gjordes med Optimizelys statistiska motor såväl som egen nollhypotes prövning. Ökningarna av konverteringar var inte statistiskt signifikanta enligt kalkyl från Optimizely, men lyckades nå signifikans enligt traditionell statistik. Sammanfattningsvis, med hjälp av mätvärden så som höga utgångsfrekvenser i kombination med högt antal sidvisningar samt höga intäktsgenererande sidor, kan tjänsteleverantörer nu identifiera var man kan påbörja optimeringsprocessen. För att veta vilken värdefull data man bör extrahera skall man ta reda på vilken data som behövs för att stoppa in i Människa–datorinteraktion (MDI) koncept, som personas och scenarier. Detta, tillsammans med kvalitativ forskning, tillåter webbdesigners att skapa väl genomtänkta designförslag som förhoppningsvis leder till en ökad konverteringsgrad.

With growing information availability recommendation systems help users navigate and filter the many options. The home rental market has been pointed out as one of the unexplored areas for recommendations system. This project examines the effects of incorporating historical data for probabilistic weighting and matching algorithms for increased recommendation diversity for a tenant to landlord recommendation system. This was done by implementing two new recommendation systems. The first uses probabilistic weighting to measure the similarity between tenants and landlord homes. The second combines this probabilistic weighting with a variant of the Deferred Acceptance algorithm to enhance recommendation diversity. These two recommendation systems were A/B tested together with the existing tenant recommendation system on the Qasa platform, a digital end-to-end rental apartments marketplace in Sweden. With the objective of having the recommendation system increase landlord engagement a good recommendation was defined as one where the landlord choose to contact the tenant. After the A/B test period, the three recommendation variants were evaluated on Coverage@N, Gini-Index@K, Precision@K and Recall@K. The result revealed that the use of the Deferred Acceptance algorithm did increase the recommendation diversity, but it led to reduced precision in the top recommendations compared to the first new implementation that only used probabilistic weighting. However, the incorporation of historical data for the probabilistic weighting for similarity in booth new recommendation systems showed higher precision and number of contacted tenants compared to the existing tenant recommendation model on the Qasa platform. / Med växande informationstillgänglighet hjälper rekommendationssystem användarna att navigera och filtrera bland många alternativ. Hyresmarknaden har pekats ut som ett av de outforskade områdena för rekommendationssystem. Detta projekt undersöker effekterna av att inkorporera historiska data för statistiska vikter och matchningsalgoritmer för ökad rekommendations mångfald i ett rekommendationssystem från hyresgäster till hyresvärdar. Detta gjordes genom att implementera två nya rekommendationssystem. Det första använder statistiska vikter för att mäta likheten mellan hyresgäster och hyresvärdars bostäder. Det andra kombinerar dessa statistiska vikter med en variant av deferred acceptance algorithm algoritmen för att förbättra rekommendations mångfaldet. Dessa två rekommendationssystem A/B testades tillsammans med det befintliga rekommendationssystemet av hyresgäster på Qasa-plattformen, en digital marknadsplats för andrahandsuthyrning av lägenheter i Sverige. Med målet att rekommendationssystemet skulle öka hyresvärdens engagemang definierades en bra rekommendation som en där hyresvärden valde att kontakta hyresgästen. Efter A/B-testperioden utvärderades de tre rekommendationsvarianterna baserat på Coverage@N, Gini-Index@K, Precision@K och Recall@K. Resultatet visade att användningen av algoritmen för uppskjuten acceptans ökade mångfaldet i ett rekommendationssystem, men det ledde till minskad precision i de första rekommendationerna jämfört med den första nya implementationen som endast använde statistiska vikter. Däremot visade inkorporeringen av historiska data för statistiska vikter vid uträkning av likhet, något som gjordes i båda nya rekommendationssystem, högre precision och fler antal kontaktade hyresgäster jämfört med den befintliga modellen för hyresgästrekommendationer på Qasa-plattformen.