Content marketing performance measurement system in the context of customer acquisition

This thesis examines how content marketing is used in B2B customer acquisition and how content marketing performance measurement system is built and utilized in this context. Literature related to performance measurement, branding and buyer behavior is examined in the theoretical part in order to identify the elements influence on content marketing performance measurement design and usage. Qualitative case study is chosen in order to gain deep understanding of the phenomenon studied. The case company is a Finnish software vendor, which operates in B2B markets and has practiced content marketing for approximately two years. The in-depth interviews were conducted with three employees from marketing department. According to findings content marketing performance measurement system’s infrastructure is based on target market’s decision making processes, company’s own customer acquisition process, marketing automation tool and analytics solutions. The main roles of content marketing performance measurement system are measuring performance, strategy management and learning and improvement. Content marketing objectives in the context of customer acquisition are enhancing brand awareness, influencing brand attitude and lead generation. Both non-financial and financial outcomes are assessed by single phase specific metrics, phase specific overall KPIs and ratings related to lead’s involvement.

Utilizing analytic modelling process in industrial retail context

Nykypäivän monimutkaisessa ja epävakaassa liiketoimintaympäristössä yritykset, jotka kykenevät muuttamaan tuottamansa operatiivisen datan tietovarastoiksi, voivat saavuttaa merkittävää kilpailuetua. Ennustavan analytiikan hyödyntäminen tulevien trendien ennakointiin mahdollistaa yritysten tunnistavan avaintekijöitä, joiden avulla he pystyvät erottumaan kilpailijoistaan. Ennustavan analytiikan hyödyntäminen osana päätöksentekoprosessia mahdollistaa ketterämmän, reaaliaikaisen päätöksenteon. Tämän diplomityön tarkoituksena on koota teoreettinen viitekehys analytiikan mallintamisesta liike-elämän loppukäyttäjän näkökulmasta ja hyödyntää tätä mallinnusprosessia diplomityön tapaustutkimuksen yritykseen. Teoreettista mallia hyödynnettiin asiakkuuksien mallintamisessa sekä tunnistamalla ennakoivia tekijöitä myynnin ennustamiseen. Työ suoritettiin suomalaiseen teollisten suodattimien tukkukauppaan, jolla on liiketoimintaa Suomessa, Venäjällä ja Balteissa. Tämä tutkimus on määrällinen tapaustutkimus, jossa tärkeimpänä tiedonkeruumenetelmänä käytettiin tapausyrityksen transaktiodataa. Data työhön saatiin yrityksen toiminnanohjausjärjestelmästä.

Impact of Word-of-Mouth on consumer decision-making: An information processing perspective in the context of a high-involvement service

This doctoral study conducts an empirical analysis of the impact of Word-of-Mouth (WOM) on marketing-relevant outcomes such as attitudes and consumer choice, during a high-involvement and complex service decision. Due to its importance to decisionmaking, WOM has attracted interest from academia and practitioners for decades. Consumers are known to discuss products and services with one another. These discussions help consumers to form an evaluative opinion, as WOM reduces perceived risk, simplifies complexity, and increases the confidence of consumers in decisionmaking. These discussions are also highly impactful as WOM is a trustworthy source of information, since it is independent from the company or brand. In responding to the calls for more research on what happens after WOM information is received, and how it affects marketing-relevant outcomes, this dissertation extends prior WOM literature by investigating how consumers process information in a highinvolvement service domain, in particular higher-education. Further, the dissertation studies how the form of WOM influences consumer choice. The research contributes to WOM and services marketing literature by developing and empirically testing a framework for information processing and studying the long-term effects of WOM. The results of the dissertation are presented in five research publications. The publications are based on longitudinal data. The research leads to the development of a proposed theoretical framework for the processing of WOM, based on theories from social psychology. The framework is specifically focused on service decisions, as it takes into account evaluation difficulty through the complex nature of choice criteria associated with service purchase decisions. Further, other gaps in current WOM literature are taken into account by, for example, examining how the source of WOM and service values affects the processing mechanism. The research also provides implications for managers aiming to trigger favorable WOM through marketing efforts, such as advertising and testimonials. The results provide suggestions on how to design these marketing efforts by taking into account the mechanism through which information is processed, or the form of social influence.

Use of airborne laser scanner data in demanding forest conditions

Most of the applications of airborne laser scanner data to forestry require that the point cloud be normalized, i.e., each point represents height from the ground instead of elevation. To normalize the point cloud, a digital terrain model (DTM), which is derived from the ground returns in the point cloud, is employed. Unfortunately, extracting accurate DTMs from airborne laser scanner data is a challenging task, especially in tropical forests where the canopy is normally very thick (partially closed), leading to a situation in which only a limited number of laser pulses reach the ground. Therefore, robust algorithms for extracting accurate DTMs in low-ground-point-densitysituations are needed in order to realize the full potential of airborne laser scanner data to forestry. The objective of this thesis is to develop algorithms for processing airborne laser scanner data in order to: (1) extract DTMs in demanding forest conditions (complex terrain and low number of ground points) for applications in forestry; (2) estimate canopy base height (CBH) for forest fire behavior modeling; and (3) assess the robustness of LiDAR-based high-resolution biomass estimation models against different field plot designs. Here, the aim is to find out if field plot data gathered by professional foresters can be combined with field plot data gathered by professionally trained community foresters and used in LiDAR-based high-resolution biomass estimation modeling without affecting prediction performance. The question of interest in this case is whether or not the local forest communities can achieve the level technical proficiency required for accurate forest monitoring. The algorithms for extracting DTMs from LiDAR point clouds presented in this thesis address the challenges of extracting DTMs in low-ground-point situations and in complex terrain while the algorithm for CBH estimation addresses the challenge of variations in the distribution of points in the LiDAR point cloud caused by things like variations in tree species and season of data acquisition. These algorithms are adaptive (with respect to point cloud characteristics) and exhibit a high degree of tolerance to variations in the density and distribution of points in the LiDAR point cloud. Results of comparison with existing DTM extraction algorithms showed that DTM extraction algorithms proposed in this thesis performed better with respect to accuracy of estimating tree heights from airborne laser scanner data. On the other hand, the proposed DTM extraction algorithms, being mostly based on trend surface interpolation, can not retain small artifacts in the terrain (e.g., bumps, small hills and depressions). Therefore, the DTMs generated by these algorithms are only suitable for forestry applications where the primary objective is to estimate tree heights from normalized airborne laser scanner data. On the other hand, the algorithm for estimating CBH proposed in this thesis is based on the idea of moving voxel in which gaps (openings in the canopy) which act as fuel breaks are located and their height is estimated. Test results showed a slight improvement in CBH estimation accuracy over existing CBH estimation methods which are based on height percentiles in the airborne laser scanner data. However, being based on the idea of moving voxel, this algorithm has one main advantage over existing CBH estimation methods in the context of forest fire modeling: it has great potential in providing information about vertical fuel continuity. This information can be used to create vertical fuel continuity maps which can provide more realistic information on the risk of crown fires compared to CBH.

Power and Performance Management Strategies on NoC-Based Multicore Platforms, Using DVFS, Adaptive Algorithm and Task Parallelism

This work presents synopsis of efficient strategies used in power managements for achieving the most economical power and energy consumption in multicore systems, FPGA and NoC Platforms. In this work, a practical approach was taken, in an effort to validate the significance of the proposed Adaptive Power Management Algorithm (APMA), proposed for system developed, for this thesis project. This system comprise arithmetic and logic unit, up and down counters, adder, state machine and multiplexer. The essence of carrying this project firstly, is to develop a system that will be used for this power management project. Secondly, to perform area and power synopsis of the system on these various scalable technology platforms, UMC 90nm nanotechnology 1.2v, UMC 90nm nanotechnology 1.32v and UMC 0.18 μmNanotechnology 1.80v, in order to examine the difference in area and power consumption of the system on the platforms. Thirdly, to explore various strategies that can be used to reducing system’s power consumption and to propose an adaptive power management algorithm that can be used to reduce the power consumption of the system. The strategies introduced in this work comprise Dynamic Voltage Frequency Scaling (DVFS) and task parallelism. After the system development, it was run on FPGA board, basically NoC Platforms and on these various technology platforms UMC 90nm nanotechnology1.2v, UMC 90nm nanotechnology 1.32v and UMC180 nm nanotechnology 1.80v, the system synthesis was successfully accomplished, the simulated result analysis shows that the system meets all functional requirements, the power consumption and the area utilization were recorded and analyzed in chapter 7 of this work. This work extensively reviewed various strategies for managing power consumption which were quantitative research works by many researchers and companies, it's a mixture of study analysis and experimented lab works, it condensed and presents the whole basic concepts of power management strategy from quality technical papers.

Building governance in the international financial system: Context and challenges

ABSTRACTThe international financial system has undergone deep changes since the 1970s and its stability cannot be reached in spite of actor's interests or the existence of countless coordination fora. Analyzing the system's incentive structure, one can note that its stability depends on the control of imbalances, which are not always harmful for States, creating, thus, a disturbing component in the quest for international financial management. Furthermore, non-state actors have acquired a disproportional share of power following financial globalization, escaping the control of States and of the international community.

Digital Content Marketing in the Business-to-Business Context. A Case Study on Marketing Communications Agencies.

Marketing has changed because of digitalization. Marketing is moving towards digital channels and more companies are transitioning from “pushing” advertising messages to “pull” marketing, that attracts audience with the content that interests and benefits the audience. This kind of marketing is called content marketing or “inbound” marketing. This study focuses on how marketing communications agencies utilize digital content marketing and what are the best practices with the selected digital content marketing channels. In this study, those channels include blogs, Facebook, Twitter, and LinkedIn. The qualitative research method was utilized in order to examine the phenomenon of digital content marketing in-depth. The chosen data collecting method was semi-structured interviewing. A total of seven marketing communications agencies, who currently utilize digital content marketing, were selected as case companies and interviewed. All the case companies are from the marketing communications industry because that industry can be assumed to be well adapted to digital content marketing techniques. There is a research gap about digital content marketing in the B2B context, which increases the novelty value of this research. The study examines what is digital content marketing, why B2B companies use digital content marketing, and how should digital content marketing be conducted through blogs and social media. The informants perceived digital marketing to be a fundamental part of their all marketing. They conduct digital content marketing for the following reasons: to increase sales, to improve their brand image and to demonstrate their own skills. Concrete results of digital content marketing for the case companies include sales leads, new clients, better brand image, and that recruiting is easier. The most important success factors with blogs and social media are the following: 1) Audience-centric thinking. All content planning should start from figuring out which themes interests the target audience. Social media channel choices should be based on where the target audience can be reached. 2) Companies should not talk only about themselves. Instead, content is made about themes that interests the target audience. On social media channels, only a fragment of all shared content is about the company. Rather, most of the shared content is industry-specific content that helps the potential client.

Understanding round-tripping phenomenon in Russian context – case Cyprus

Russian FDI has a few peculiarities. One of them is round-tripping. Round-tripping is defined as transfer of funds abroad, usually to offshore financial centers (OFCs), and then bringing all or some of the investment back as foreign investment. Russian context was chosen for this study because the share of round-tripping investments from country’s total FDI is extensive. However, it needs to be addressed that this is not just a Russian phenomenon. Round-tripping is used by many developed and developing countries, and most of the countries have their own designated destinations for this kind of capital, much like Cyprus is the main destination for Russian capital. It is important to study this phenomenon further, since it falsifies FDI statistics and can lead to poor decisions on state level. Theoretical part of the study tries to determine weather traditional FDI and internationalization theories fit to explain the Russian round-tripping phenomenon. Traditional FDI and internationalization theories are first introduced in general terms, and then further examined in Russian context. In traditional endogenic FDI theories, when the capital is formed in one country it goes abroad to find better profits. At a first glance, this seemed not to be the case in round-tripping. However, during the study it became rather clear that with few adjustments and changes in perspective, traditional theories could be used to explain round-tripping phenomenon. For example, OLI paradigm can be further developed into OLIH paradigm with ‘H’ representing the important home country institutions. Transaction based view and resource seeking theories were also seen well equipped to explain round-tripping with a change in perspective. The latter part of the study focused on holistic understanding of Russian –Cyprian investment relationship. Study aims to shed light into the determinants and consequences of this phenomenon for both countries involved. The two share historical, cultural and political ties, but most importantly common financial interests. Russian companies seek security and financial knowledge to maneuver their assets and Cyprian economy largely is dependent on their disproportionally large financial sector. Consequences for Cyprian economy were seen in current economic crisis, when the need for their financial services diminished. Russian government on the other hand is losing vast amounts of tax money due to this phenomenon. A rather extreme view was also introduced in this study. Round-tripping phenomenon and OFCs are an important reason why corruption exists, since if one does not have a way to make ill-gained money legitimate why try to ill-gain the money at the first place. The most important finding of the study is that round-tripping companies are in a better competitive position than genuine and purely domestic investor due to their institutional knowledge.

Detection of Epilepsy with a Commercial EEG Headband

Epilepsy is a chronic brain disorder, characterized by reoccurring seizures. Automatic sei-zure detector, incorporated into a mobile closed-loop system, can improve the quality of life for the people with epilepsy. Commercial EEG headbands, such as Emotiv Epoc, have a potential to be used as the data acquisition devices for such a system. In order to estimate that potential, epileptic EEG signals from the commercial devices were emulated in this work based on the EEG data from a clinical dataset. The emulated characteristics include the referencing scheme, the set of electrodes used, the sampling rate, the sample resolution and the noise level. Performance of the existing algorithm for detection of epileptic seizures, developed in the context of clinical data, has been evaluated on the emulated commercial data. The results show, that after the transformation of the data towards the characteristics of Emotiv Epoc, the detection capabilities of the algorithm are mostly preserved. The ranges of acceptable changes in the signal parameters are also estimated.