Utveckling av lojalitetsprogram - individanpassade erbjudanden : Marknadsföring genom CRM-strategier

Den ökade konkurrenssituationen mellan företag är ett resultat på att handeln har globaliserats. Konsumenter är idag mer medvetna om vad företag har att erbjuda, och det är lätt att välja alternativa företag om ett specifikt företag inte lever upp till konsumentens förväntningar. Detta ställer krav på att företag ständigt måste arbeta med och utveckla sina strategier för att skapa konkurrensfördelar. Idag arbetar många företag med lojalitetsprogram som är ett typexempel på en CRM-strategi. CRM-strategier och lojalitetsprogram syftar till att göra konsumenten mer lojal, för att möjliggöra att konsumenten skall välja att göra återupprepande och mer frekventa köp. Lojalitetsprogrammen måste dock uppdateras och förändras, för att ständigt kunna vara konkurrenskraftiga. Företag arbetar därmed för att skapa ett mervärde, det vill säga erbjuda en servicelösning som är utöver vad konsumenten förväntar sig att få från företag. Kundlojalitet kan utvecklas genom att företag skapar långsiktiga relationer med konsumenterna, och en relation mellan parterna kan utvecklas genom att företag lyssnar på konsumenterna, för att få reda på dess behov och önskemål. Forskning indikerar att individanpassade erbjudanden kan bidra till att konsumenterna än mer tenderar att bli lojala, detta eftersom individanpassade erbjudanden är erbjudanden som baseras på konsumentens unika köpbeteende, vilket möjliggör för träffsäkerhet och relevans. Syftet med denna studie är att mäta konsumenters efterfrågan på individanpassade erbjudanden, för att undersöka om konsumenterna faktiskt efterfrågar och kommer ta till sig dessa erbjudanden. Frågeställningarna är; på vilket sätt kan efterfrågan på individanpassade erbjudanden bidra till ett företags konkurrensfördelar samt på vilket sätt skiljer sig efterfrågan på individanpassade erbjudanden beroende på butikernas geografiska placering. Studien är baserad på en kvantitativ surveyundersökning riktad till konsumenter, med en kvalitativ semi-strukturerad intervju för att erhålla viktig information vid utformandet av frågeformuläret. Resultatet för studien visade att konsumenter värdesätter att få individanpassade erbjudanden, och att dessa erbjudanden kan bidra till ett företags konkurrensfördelar eftersom erbjudandena möjliggör att servicekvaliteten kan öka vilket innebär att dessa erbjudanden kan upplevas ha ett mervärde. I denna studie återfanns dock inte några skillnader på efterfrågan av individanpassade erbjudanden beroende på butikernas geografiska placering.

Targeting the undruggable: immunotherapy meets personalized oncology in the genomic era.

Owing to recent advances in genomic technologies, personalized oncology is poised to fundamentally alter cancer therapy. In this paradigm, the mutational and transcriptional profiles of tumors are assessed, and personalized treatments are designed based on the specific molecular abnormalities relevant to each patient's cancer. To date, such approaches have yielded impressive clinical responses in some patients. However, a major limitation of this strategy has also been revealed: the vast majority of tumor mutations are not targetable by current pharmacological approaches. Immunotherapy offers a promising alternative to exploit tumor mutations as targets for clinical intervention. Mutated proteins can give rise to novel antigens (called neoantigens) that are recognized with high specificity by patient T cells. Indeed, neoantigen-specific T cells have been shown to underlie clinical responses to many standard treatments and immunotherapeutic interventions. Moreover, studies in mouse models targeting neoantigens, and early results from clinical trials, have established proof of concept for personalized immunotherapies targeting next-generation sequencing identified neoantigens. Here, we review basic immunological principles related to T-cell recognition of neoantigens, and we examine recent studies that use genomic data to design personalized immunotherapies. We discuss the opportunities and challenges that lie ahead on the road to improving patient outcomes by incorporating immunotherapy into the paradigm of personalized oncology.

Narrowing the gap of personalized medicine in emerging countries: the case of multiple endocrine neoplasias in Brazil

The finished version of the human genome sequence was completed in 2003, and this event initiated a revolution in medical practice, which is usually referred to as the age of genomic or personalized medicine. Genomic medicine aims to be predictive, personalized, preventive, and also participative (4Ps). It offers a new approach to several pathological conditions, although its impact so far has been more evident in mendelian diseases. This article briefly reviews the potential advantages of this approach, and also some issues that may arise in the attempt to apply the accumulated knowledge from genomic medicine to clinical practice in emerging countries. The advantages of applying genomic medicine into clinical practice are obvious, enabling prediction, prevention, and early diagnosis and treatment of several genetic disorders. However, there are also some issues, such as those related to: (a) the need for approval of a law equivalent to the Genetic Information Nondiscrimination Act, which was approved in 2008 in the USA; (b) the need for private and public funding for genetics and genomics; (c) the need for development of innovative healthcare systems that may substantially cut costs (e.g. costs of periodic medical followup); (d) the need for new graduate and postgraduate curricula in which genomic medicine is emphasized; and (e) the need to adequately inform the population and possible consumers of genetic testing, with reference to the basic aspects of genomic medicine.

Synchronous learner support for music sequencing software

In the field of music technology there is a distinct focus on networking between spatially disparate locales to improve teaching and learning through real-time communication. This article proposes a new delivery model for learner support based on a review of technical and learning services, pilot research using remote desktops to teach music-sequencing software, and recent education research regarding professional development. A 24/7 delivery model using remote desktops, mobile devices and shared calendars offers a flexible real-time addition to the learner support services already on offer. Treating every user of the service as a potential expert, the model aims to deliver universal support situated in a personalized context, which will serve the technical and education requirements of teachers and learners.

The self-Googling phenomenon : investigating the performance of personalized information resources

This paper investigates self–Googling through the monitoring of search engine activities of users and adds to the few quantitative studies on this topic already in existence. We explore this phenomenon by answering the following questions: To what extent is the self–Googling visible in the usage of search engines; is any significant difference measurable between queries related to self–Googling and generic search queries; to what extent do self–Googling search requests match the selected personalised Web pages? To address these questions we explore the theory of narcissism in order to help define self–Googling and present the results from a 14–month online experiment using Google search engine usage data.

Personalized recommender systems integrating social tags and item taxonomy

The social tags in web 2.0 are becoming another important information source to profile users' interests and preferences to make personalized recommendations. To solve the problem of low information sharing caused by the free-style vocabulary of tags and the long tails of the distribution of tags and items, this paper proposes an approach to integrate the social tags given by users and the item taxonomy with standard vocabulary and hierarchical structure provided by experts to make personalized recommendations. The experimental results show that the proposed approach can effectively improve the information sharing and recommendation accuracy.

User profiling based on folksonomy information in Web 2.0 for personalized recommender systems

Information overload has become a serious issue for web users. Personalisation can provide effective solutions to overcome this problem. Recommender systems are one popular personalisation tool to help users deal with this issue. As the base of personalisation, the accuracy and efficiency of web user profiling affects the performances of recommender systems and other personalisation systems greatly. In Web 2.0, the emerging user information provides new possible solutions to profile users. Folksonomy or tag information is a kind of typical Web 2.0 information. Folksonomy implies the users‘ topic interests and opinion information. It becomes another source of important user information to profile users and to make recommendations. However, since tags are arbitrary words given by users, folksonomy contains a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise makes it difficult to profile users accurately or to make quality recommendations. This thesis investigates the distinctive features and multiple relationships of folksonomy and explores novel approaches to solve the tag quality problem and profile users accurately. Harvesting the wisdom of crowds and experts, three new user profiling approaches are proposed: folksonomy based user profiling approach, taxonomy based user profiling approach, hybrid user profiling approach based on folksonomy and taxonomy. The proposed user profiling approaches are applied to recommender systems to improve their performances. Based on the generated user profiles, the user and item based collaborative filtering approaches, combined with the content filtering methods, are proposed to make recommendations. The proposed new user profiling and recommendation approaches have been evaluated through extensive experiments. The effectiveness evaluation experiments were conducted on two real world datasets collected from Amazon.com and CiteULike websites. The experimental results demonstrate that the proposed user profiling and recommendation approaches outperform those related state-of-the-art approaches. In addition, this thesis proposes a parallel, scalable user profiling implementation approach based on advanced cloud computing techniques such as Hadoop, MapReduce and Cascading. The scalability evaluation experiments were conducted on a large scaled dataset collected from Del.icio.us website. This thesis contributes to effectively use the wisdom of crowds and expert to help users solve information overload issues through providing more accurate, effective and efficient user profiling and recommendation approaches. It also contributes to better usages of taxonomy information given by experts and folksonomy information contributed by users in Web 2.0.

Personalized recommender system based on item taxonomy and folksonomy

Item folksonomy or tag information is popularly available on the web now. However, since tags are arbitrary words given by users, they contain a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise brings difficulties to improve the accuracy of item recommendations. In this paper, we propose to combine item taxonomy and folksonomy to reduce the noise of tags and make personalized item recommendations. The experiments conducted on the dataset collected from Amazon.com demonstrated the effectiveness of the proposed approaches. The results suggested that the recommendation accuracy can be further improved if we consider the viewpoints and the vocabularies of both experts and users.

Connecting users and items with weighted tags for personalized item recommendations

Social tags are an important information source in Web 2.0. They can be used to describe users’ topic preferences as well as the content of items to make personalized recommendations. However, since tags are arbitrary words given by users, they contain a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise brings difficulties to improve the accuracy of item recommendations. To eliminate the noise of tags, in this paper we propose to use the multiple relationships among users, items and tags to find the semantic meaning of each tag for each user individually. With the proposed approach, the relevant tags of each item and the tag preferences of each user are determined. In addition, the user and item-based collaborative filtering combined with the content filtering approach are explored. The effectiveness of the proposed approaches is demonstrated in the experiments conducted on real world datasets collected from Amazon.com and citeULike website.

Mining users’ opinions based on item folksonomy and taxonomy for personalized recommender systems

Item folksonomy or tag information is a kind of typical and prevalent web 2.0 information. Item folksonmy contains rich opinion information of users on item classifications and descriptions. It can be used as another important information source to conduct opinion mining. On the other hand, each item is associated with taxonomy information that reflects the viewpoints of experts. In this paper, we propose to mine for users’ opinions on items based on item taxonomy developed by experts and folksonomy contributed by users. In addition, we explore how to make personalized item recommendations based on users’ opinions. The experiments conducted on real word datasets collected from Amazon.com and CiteULike demonstrated the effectiveness of the proposed approaches.

A Personalized Ontology Model for Web Information Gathering

As a model for knowledge description and formalization, ontologies are widely used to represent user profiles in personalized web information gathering. However, when representing user profiles, many models have utilized only knowledge from either a global knowledge base or a user local information. In this paper, a personalized ontology model is proposed for knowledge representation and reasoning over user profiles. This model learns ontological user profiles from both a world knowledge base and user local instance repositories. The ontology model is evaluated by comparing it against benchmark models in web information gathering. The results show that this ontology model is successful.

Integrating collaborative filtering and search-based techniques for personalized online product recommendation

The existing Collaborative Filtering (CF) technique that has been widely applied by e-commerce sites requires a large amount of ratings data to make meaningful recommendations. It is not directly applicable for recommending products that are not frequently purchased by users, such as cars and houses, as it is difficult to collect rating data for such products from the users. Many of the e-commerce sites for infrequently purchased products are still using basic search-based techniques whereby the products that match with the attributes given in the target user's query are retrieved and recommended to the user. However, search-based recommenders cannot provide personalized recommendations. For different users, the recommendations will be the same if they provide the same query regardless of any difference in their online navigation behaviour. This paper proposes to integrate collaborative filtering and search-based techniques to provide personalized recommendations for infrequently purchased products. Two different techniques are proposed, namely CFRRobin and CFAg Query. Instead of using the target user's query to search for products as normal search based systems do, the CFRRobin technique uses the products in which the target user's neighbours have shown interest as queries to retrieve relevant products, and then recommends to the target user a list of products by merging and ranking the returned products using the Round Robin method. The CFAg Query technique uses the products that the user's neighbours have shown interest in to derive an aggregated query, which is then used to retrieve products to recommend to the target user. Experiments conducted on a real e-commerce dataset show that both the proposed techniques CFRRobin and CFAg Query perform better than the standard Collaborative Filtering (CF) and the Basic Search (BS) approaches, which are widely applied by the current e-commerce applications. The CFRRobin and CFAg Query approaches also outperform the e- isting query expansion (QE) technique that was proposed for recommending infrequently purchased products.

Mining a data reasoning model for personalized text classification

It is a big challenge to acquire correct user profiles for personalized text classification since users may be unsure in providing their interests. Traditional approaches to user profiling adopt machine learning (ML) to automatically discover classification knowledge from explicit user feedback in describing personal interests. However, the accuracy of ML-based methods cannot be significantly improved in many cases due to the term independence assumption and uncertainties associated with them. This paper presents a novel relevance feedback approach for personalized text classification. It basically applies data mining to discover knowledge from relevant and non-relevant text and constraints specific knowledge by reasoning rules to eliminate some conflicting information. We also developed a Dempster-Shafer (DS) approach as the means to utilise the specific knowledge to build high-quality data models for classification. The experimental results conducted on Reuters Corpus Volume 1 and TREC topics support that the proposed technique achieves encouraging performance in comparing with the state-of-the-art relevance feedback models.