Customer lifetime value (CLV) is a forecasted expectation of the future value of a customer to the firm. There are two customer behavioral components of CLV that represent a particular modeling challenge: 1) how many transactions we expect from a customer in the future, and 2) how likely it is the customer remains active. Existing CLV models like the Pareto/NBD are valuable managerial tools because they are able to provide forward-looking estimates of transaction patterns and customer churn when the event of a customer leaving is unobservable, which is typical for most noncontractual goods and services. The CLV model literature has for the most part maintained its original assumption that the number of customer transactions follows a stable transaction process. Yet there are many categories of noncontractual goods and services where the stable transaction rate assumption is violated, particularly seasonal purchase patterns. CLV model estimates are further biased when there is an excess of customers with no repeat transactions. To address these modeling challenges, within this thesis I develop a generalized CLV modeling framework that combines three elements necessary to reduce bias in model estimates: 1) the incorporation of time-varying covariates to model data with transaction rates that change over time, 2) a zero-inflated model specification for customers with no repeat transactions, and 3) generalizes to different transaction process distributions to better fit diverse customer transaction patterns. This CLV modeling framework provides firms better estimates of the future activity of their customers, a critical CRM application.

The problem addressed in this thesis is sound, scalable, demand-driven null-dereference verification for Java programs via over-approximated weakest preconditions analysis. The base version of this analysis having been described in a previous publication, in this thesis we focus primarily on describing two major optimizations that we have incorporated that allow for longer program paths to be traversed more efficiently, hence increasing the precision of the approach. The first optimization is to bypass certain expensive-to-analyze constructs, such as virtual calls with too many possible targets, by directly transferring dataflow facts from points after the construct to points before along def-use edges of a certain kind. The second optimization is to use manually constructed summaries of Java container class methods, rather than analyze the code of these methods directly. We evaluate our approach using 10 real world Java programs, as well as several micro benchmarks. We demonstrate that our optimizations result in a 45% reduction in false positives over the base version on the real programs, without significant impact on running time.

The thesis investigates the view and knowledge among large versus small and medium sized enterprises (SME) regarding the implementation of the Corporate Sustainability Reporting Directive (CSRD). To enable a broad collection of data the study carries out semi-structured interviews and surveys on companies and stakeholders within the Swedish energy sector, as well as a literature review. The aim of the thesis is to identify the expected strengths, weaknesses, opportunities and threats and the knowledge base within the Swedish energy sector. Furthermore, the aim is to analyze potential consequences of implementation and to suggest potential mitigations where possible. The results indicate that large companies and SMEs to a great extent identify the same challenges and possibilities. However, large companies on average have a higher knowledge level than SMEs. The potential reasons behind this include differences in reporting time frame, level of engagement among sustainability responsible roles, lack of specific ESRS and difficulty in applying the directive. The greatest challenges relate to information dissemination in general and internally, information and data collection along the value chain, implementation for SMEs, increasing the knowledge level among companies and the structure of the directive. The potential solutions include different types of systemic structuring, collaborative efforts and extended responsibilities for stakeholders and authorities. Overall, the directive will entail positive changes and implement CSR as a statutory aspect, however, the directive still requires improvement to fully reach its’ desired potential. / I avhandlingen undersöks synen på och kunskapen om implementeringen av Corporate Sustainability Reporting Directive (CSRD) bland stora kontra små och medelstora företag (SME) i den svenska energisektorn. För att möjliggöra en bred insamling av data grundas studien på semistrukturerade intervjuer och enkäter hos företag, intervjuer med intressenter och en litteraturstudie. Syftet med avhandlingen är att identifiera förväntade styrkor, svagheter, möjligheter och hot samt kartlägga kunskapsbasen inom den svenska energisektorn. Vidare är syftet att analysera potentiella konsekvenser av implementeringen och föreslå potentiella åtgärder där det är möjligt. Resultaten visar att företag oavsett storlek i stor utsträckning identifierar samma utmaningar och möjligheter. Inom stora företagen finns dock i genomsnitt en högre kunskapsnivå än hos de små och medelstora företagen. De potentiella orsakerna till detta är bland annat skillnader i tidsramar för rapportering, graden av engagemang hos hållbarhetsansvariga, brist på specifika ESRS och svårigheter att tillämpa direktivet. De största utmaningarna gäller informationsspridning externt och internt, information och datainsamling inom värdekedjan, genomförande för små och medelstora företag, ökad kunskapsnivå bland företag och direktivets struktur. De potentiella lösningarna inkluderar olika typer av systemstrukturering, insatser och ansvarskrav för berörda parter och myndigheter. På det hela taget kommer direktivet att medföra positiva förändringar och göra företagens hållbarhetsansvar till en lagstadgad aspekt, men direktivet behöver fortfarande förbättras för att fullt ut nå sin önskade potential.