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.

User retention is a key factor for Software as a Service (SaaS) companies to ensure long-term growth and profitability. One area which can have a lasting impact on a digital product’s user retention is its user onboarding experience, that is, the methods and elements that guide new users to become familiar with the product and activate them to become fully registered users. Within the area of user onboarding, multiple authors discuss “best practice” design patterns which are stated to positively influence the user retention of new users. However, none of the sources reviewed showcase any statistically significant proof of this claim. Thus, the objective of this study was to: Design and implement a set of commonly applied design patterns within a digital product’s user onboarding experience and evaluate their effects on user retention Through A/B testing on the SaaS product GetAccept, the following two design patterns were evaluated: Reduce friction – reducing the number of barriers and steps for a new user when first using a digital product; and Monitor progress – monitoring and clearly showcasing the progress of a new user’s journey when first using a digital product. The retention metric used to evaluate the two design patterns was first week user retention, defined as the share of customers who after signing up, sign in again at least once within one week. This was tested by randomly assigning new users into different groups: groups that did receive changes related to the design patterns, and one group did not receive any changes. By then comparing the first week user retention data between the groups using Fisher’s exact test, the conclusion could be drawn that with statistical significance, both of the evaluated design patterns positively influenced user retention for GetAccept. Furthermore, due to the generalizable nature of GetAccept’s product and the aspects evaluated, this conclusion should also be applicable to other companies and digital products with similar characteristics, and the method used to evaluate the impact of implementing the design patterns should be applicable for evaluating other design patterns and/or changes in digital products. However, as the method used for data collection in the study could not ensure full validity of it, the study could and should be repeated with the same design patterns on another digital product and set of users in order to strengthen the reliability of the conclusions drawn.