In today's dynamic marketplace, understanding consumer behavior has become increasingly complex, especially regarding high-involvement purchases. Nevertheless, it is just as important for both theorists and practitioners to understand. It is known that word-of-mouth (WOM) is one of the most influential factors in consumer behavior during the consumer decision journey (CDJ). Despite this, there is currently a lack of understanding regarding how consumers use WOM during the CDJ and whether different types of WOM are used in different ways. Researchers have asked for more research in the field for over a decade. The research gaps in the literature and the demand for an increased understanding within the literature laid the foundation for this study and the research question: How and why do consumers make use of different types of word-of-mouth during the consumer decision journey within high-involvement purchases? In addition to the research question, the study also aims to expand the understanding of why consumers use WOM during the CDJ. To further investigate the research question and purpose, we performed a qualitative study where we conducted semi-structured interviews with six consumers. The collected data was analyzed through an inductive content analysis which resulted in a data structure consisting of 18 first order concepts, seven second order themes, and four aggregate dimensions. The study's data structure formed the basis for the inductive bottom-up theorizing, which culminated in a theoretical model where consumer's use of WOM is integrated into the CDJ. The model visualizes how it can be assumed a typical consumer uses different types of WOM throughout the different phases of the CDJ, as well as the consumer's motives behind their use of WOM. The empirical findings emphasize that consumers seek different attributes in the type of WOM during various phases of the CDJ, whereupon they use different types of WOM based on various motives. We managed to identify six different motives for consumers' use of WOM, and these motives relate to the different phases in the CDJ. These findings, among others, are included in the model. The study's theoretical model contains several new unique findings that extend today's existing understanding of consumers' use of WOM which has resulted in theoretical, practical and societal contributions. The study contributes to closing the research gaps and enabling further research in the field of literature.

This paper aims to investigate whether or not it is possible to identify users who are about change provider of service with machine learning. It is believed that the Consumer Decision Journey is a better model than traditional funnel models when it comes to depicting the processes which consumers go through, leading up to a purchase. Analytical and operational Customer Relationship Management are presented as possible fields where such implementations can be useful. Based on previous studies, Random Forest and XGBoost were chosen as algorithms to be further evaluated because of its general high performance. The final results were produced by an iterative process which began with data processing followed by feature selection, training of model and testing the model. Literature review and unstructured and semi-structured interviews with the employer Growth Hackers Sthlm were also used as methods in a complementary fashion, with the purpose of gaining a wider perspective of the state-of-the-art of ML-implementations. The final results showed that Random Forest could identify the sought-after users (positive) while XGBoost was inferior to Random Forest in terms of distinguishing between positive and negative classes. An implementation of such model could support and benefit an organization’s customer acquisition operations. However, organizational prerequisites regarding the data infrastructure and the level of AI and machine learning integration in the organization’s culture are the most important ones and need to be considered before such implementations. / I det här arbetet undersöks huruvida det är möjligt att identifiera ett beteende bland användare som innebär att användaren snart ska byta tillhandahållare av tjänst med hjälp av maskininlärning. Målet är att kunna bidra till ett maskininlärningsverktyg i kundförvärvningssyfte, såsom analytical och operational Customer Relationship Management. Det sökta beteendet i rapporten utgår från modellen ”the Consumer Decision Journey”. I modellen beskrivs fyra faser där fas två innebär att konsumenten aktivt söker samt är mer mottaglig för information kring köpet. Genom tidigare studier och handledning av uppdragsgivare valdes algoritmerna RandomForest och XGBoost som huvudsakliga algoritmer som skulle testas. Resultaten producerades genom en iterativ process. Det första steget var att städa data. Därefter valdes parametrar och viktades. Sedan testades algoritmerna mot testdata och utvärderades. Detta gjordes i loopar tills förbättringar endast var marginella. De slutliga resultaten visade att framförallt Random Forest kunde identifiera ett beteende som innebär att en användare är i fas 2, medan XGBoost presterade sämre när det kom till att urskilja bland positiva och negativa användare. Dock fångade XGBoost fler positiva användare än vad Random Forest gjorde. I syfte att undersöka de organisatoriska förutsättningarna för att implementera maskininlärning och AI gjordes litteraturstudier och uppdragsgivaren intervjuades kontinuerligt. De viktigaste förutsättningarna fastställdes till två kategorier, datainfrastruktur och hur väl AI och maskininlärning är integrerat i organisationens kultur.

This study investigates the effects of the digital marketing channels Search engine optimization (SEO) and SEM, focusing on Google Ads, in the context of the consumer decision funnel in the Swedish market. This was done through a single-case study with a medium-sized digital marketing bureau in Sweden. Our research was summarized in a conceptual framework that aims to create a foundational understanding of best practices and strategies for companies to establish a robust and profitable online presence. The research shows that SEO and SEM has different main focuses within the digital marketing landscape where SEO has a more holistic approach and covers areas further up in the consumer funnel, and SEM operated more targeted with a high focus on measurable conversions. However, our research also finds how the respective channels both have the capacity to be used in all parts of the funnel. Concepts such as budget, strategy, timeline, and consumer behavior are key concepts within our research. The research highlights the importance of tailoring a digital marketing strategy to a company’s specific goals and circumstances. This research takes such aspects into consideration, and strategies depending on budget constraints and company size are explored separately. This paper thus provides marketers with a foundational understanding of how to approach digital marketing, and how to best tailor a digital marketing strategy to align with company goals. Effectively combining SEO and SEM creates a great opportunity for companies to establish a good online presence, which in today's society serves as a very effective media to reach out to consumers.

Title:  The effects of online third-party opinions toward Consumer Purchasing Decision on cosmetics products in Thai market Seminar date: June 4th , 2012 Course: Master thesis in International marketing, 15 credits Purpose: The overall purpose of this research is to explain the understanding of the effects of online third-party opinions toward consumer purchasing decision process on cosmetics products in Thai market. This includes the investigation of effective used of online third-party opinions with regards to source credibility and product involvement, specifically toward cosmetics products. Theoretical framework: The theoretical framework of this research is based on literature concerning online third party opinion, consumer decision journey (CDJ), product involvement and source credibility. Methodology: Both quantitative and qualitative method was applied. And thus a questionnaire with 147 respondents on Thai market and two interviews of expert marketers were conducted. The respondents for questionnaire are Thai women with specific age range and criteria. Conclusion: The online third-party opinion plays the important role in nowadays in the marketing perspective. And it does affect the consumer purchasing decision process both pre-purchase and post-purchase stage. However, it mostly affects in Consider and Evaluation Stage. With regards to the product involvement and source credibility applying to cosmetics products, these two variables affect to the use of online third-party opinion. Online third-party opinion works best with high involvement product. However, low involvement product also be considered as suitable to use with online third-party opinion. For source credibility, both online expert and consumer reviews perform better when using with high involvement product comparing with low involvement one.