The Usage of Personal Data as Content in Integrated Marketing Communications

Personal user data has proven extremely valuable for firms in the digital age. The wealth of data available to firms has provided unprecedented access into the world of the consumer. Companies hoping to capitalize on their user's data have turned to several interesting outlets. This research addresses the repurposing of user data as content in marketing. By analyzing four cases of data presented as marketing communications across two companies, this research provides new insights into the public release of private user data for marketing purposes. Four cases of personal data used in marketing communications were chosen specifically for their time proximity, characteristics of the sending firms, and their disparate outcomes. These instances of marketing communications, two by Spotify and two by Netflix, were released during November and December of 2017 and each resulted in a diverse range of public opinion. An analysis of these cases was conducted using the comprehensive framework of integrated marketing communications (Tafesse & Kitchen, 2017). There is a significant difference in the perceptual outcomes of integrated marketing communication campaigns which display user data as content. This analysis provides insights into the characteristics of marketing communications and how their outcomes fit into broader marketing strategies. These case studies provide opportunities for marketers to improve their campaigns in line with their desired audience outcome. Patterns of scope, strategy, mode, and outcome do not suggest success or failure in the context of marketing communications, but rather a set of insights marketers should keep in mind when pursuing communication strategies which harness personal user data.No embargoAcademic Major: Marketin

Marketing relations and communication infrastructure development in the banking sector based on big data mining

Purpose: The article aims to study the methodological tools for applying the technologies of intellectual analysis of big data in the modern digital space, the further implementation of which can become the basis for the marketing relations concept implementation in the banking sector of the Russian Federation‘ economy. Structure/Methodology/Approach: For the marketing relations development in the banking sector in the digital economy, it seems necessary: firstly, to identify the opportunities and advantages of the big data mining in banking marketing; secondly, to identify the sources and methods of processing big data; thirdly, to study the examples of the big data mining successful use by Russian banks and to formulate the recommendations on the big data technologies implementation in the digital marketing banking strategy. Findings: The authors‘ analysis showed that big data technologies processing of open online and offline sources of information significantly increases the data amount available for intelligent analysis, as a result of which the interaction between the bank and the target client reaches a new level of partnership. Practical Implications: Conclusions and generalizations of the study can be applied in the practice of managing financial institutions. The results of the study can be used by bank management to form a digital marketing strategy for long-term communication. Originality/Value: The main contribution of this study is that the authors have identified the main directions of using big data in relationship marketing to generate additional profit, as well as the possibility of intellectual analysis of the client base, aimed at expanding the market share and retaining customers in the banking sector of the economy.peer-reviewe

Are black friday deals worth it? Mining twitter users' sentiment and behavior response

The Black Friday event has become a global opportunity for marketing and companies’ strategies aimed at increasing sales. The present study aims to understand consumer behavior through the analysis of user-generated content (UGC) on social media with respect to the Black Friday 2018 offers published by the 23 largest technology companies in Spain. To this end, we analyzed Twitter-based UGC about companies’ offers using a three-step data text mining process. First, a Latent Dirichlet Allocation Model (LDA) was used to divide the sample into topics related to Black Friday. In the next step, sentiment analysis (SA) using Python was carried out to determine the feelings towards the identified topics and offers published by the companies on Twitter. Thirdly and finally, a data-text mining process called textual analysis (TA) was performed to identify insights that could help companies to improve their promotion and marketing strategies as well as to better understand the customer behavior on social media. The results show that consumers had positive perceptions of such topics as exclusive promotions (EP) and smartphones (SM); by contrast, topics such as fraud (FA), insults and noise (IN), and customer support (CS) were negatively perceived by customers. Based on these results, we offer guidelines to practitioners to improve their social media communication. Our results also have theoretical implications that can promote further research in this area

Effectiveness, Efficiency, and Ethics of Marketing Analytics

Abstract The concept of big data has influenced the marketing field in numerous ways. By having access to more information about their consumers than ever before, marketers are presented with a unique opportunity to make the marketing process more streamlined and effective than ever; however, this also creates a challenge in understanding how this targeted advertising affects the brand’s perception by consumers. This study looks at the concepts of data marketing and re-targeted ads from three aspects. First, are marketers being as effective as possible to ensure they are sending the right advertisement, to the right customer, at the right time? Second, are marketers being as efficient as possible when choosing the correct platform to reach their target customers? Third, are companies remembering the ethical components of collecting this information on consumers, and ensuring they understand when consumers feel specialized advertising becomes an invasion of their privacy? To answer these questions, I first performed secondary research in the form of a literature review. From surveying the scope of the subject, I then performed primary research by conducting in-depth interviews and a survey. The results show that there are two distinct type of consumers: one group who is accepting of these re-targeted advertisements and welcoming of the specialized marketing, and a second group who is skeptical of this form of marketing and concerned over privacy issues. Marketers must be aware of these two distinct types of consumers and ensure they are choosing their advertising methods carefully to ensure an efficient utilization of resources and to make sure they are not presenting a detriment to their brand for the consumers who do not want catered advertisements

The Effectiveness of Internet Advertising on Consumer Behaviour

Advertising is a communication medium where companies made to know the consumers about the product or it is a medium where companies tries to increase the sales and branding the product and many other definitions proposed by various researches, as days past on advertising medium was classified into 2 modes 1. Online advertising and 2. Offline advertising. In this paper, internet advertising mode was explained. The objective populace becomes the publicizing companies and their customers. The research applied a defined testing strategy to pick 60 exam respondents every day.  Content research turned into utilized to dissect subjective facts simultaneously as the quantitative facts changed into broke down utilizing clean measurements utilizing SPSS. Relapse and Correlation examination changed into applied to reveal the connections among the elements. The statistics were brought via rates, implies, fashionable deviations and frequencies. The research found that web promoting turned into a hit on attain and making of mindfulness because of diverse use, and set up that its dependability as a publicizing media was low contrasted with TV. Web publicizing has huge courting with the consumers' purchase desire and along those lines is a critical determinant in impacting purchaser behaviour

Social media and sentiment in bioenergy consultation

Purpose: The push to widen participation in public consultation suggests social media as an additional mechanism through which to engage the public. Bioenergy companies need to build their capacity to communicate in these new media and to monitor the attitudes of the public and opposition organisations towards energy development projects. Design/methodology/approach: This short paper outlines the planning issues bioenergy developments face and the main methods of communication used in the public consultation process in the UK. The potential role of social media in communication with stakeholders is identified. The capacity of sentiment analysis to mine opinions from social media is summarised, and illustrated using a sample of tweets containing the term ‘bioenergy’ Findings: Social media have the potential to improve information flows between stakeholders and developers. Sentiment analysis is a viable methodology, which bioenergy companies should be using to measure public opinion in the consultation process. Preliminary analysis shows promising results. Research limitations/implications: Analysis is preliminary and based on a small dataset. It is intended only to illustrate the potential of sentiment analysis and not to draw general conclusions about the bioenergy sector. Originality/value: Opinion mining, though established in marketing and political analysis, is not yet systematically applied as a planning consultation tool. This is a missed opportunity

Fashion Conversation Data on Instagram

The fashion industry is establishing its presence on a number of visual-centric social media like Instagram. This creates an interesting clash as fashion brands that have traditionally practiced highly creative and editorialized image marketing now have to engage with people on the platform that epitomizes impromptu, realtime conversation. What kinds of fashion images do brands and individuals share and what are the types of visual features that attract likes and comments? In this research, we take both quantitative and qualitative approaches to answer these questions. We analyze visual features of fashion posts first via manual tagging and then via training on convolutional neural networks. The classified images were examined across four types of fashion brands: mega couture, small couture, designers, and high street. We find that while product-only images make up the majority of fashion conversation in terms of volume, body snaps and face images that portray fashion items more naturally tend to receive a larger number of likes and comments by the audience. Our findings bring insights into building an automated tool for classifying or generating influential fashion information. We make our novel dataset of {24,752} labeled images on fashion conversations, containing visual and textual cues, available for the research community.Comment: 10 pages, 6 figures, This paper will be presented at ICWSM'1

EMOTIONS THAT INFLUENCE PURCHASE DECISIONS AND THEIR ELECTRONIC PROCESSING

Recent studies have shown that most of our purchasing choices and decisions are theresult of a careful analysis of the advantages and disadvantages and of affective and emotionalaspects. Psychological literature recognizes that the emotional conditions are always present andinfluence every stage of decision-making in purchasing process. Consumers establish with companybrands an overall emotional relationship and express, also with web technologies, reviews andsuggestions on product/service. In our department we have developed an original algorithm ofsentiment analysis to extract emotions from online customer opinions. With this algorithm we haveobtained good results to polarize this opinions in order to reach strategic marketing goals.emotions, emotional marketing, emotional brand, emotions measurement, sentiment analysis.

The effect of company responses to social media negative word of mouth: A text mining approach

Word-of-mouth (WOM) is emerging in importance for brand reputation and understanding of consumer behavior. Motivations to engage in WOM has been largely studied in marketing literature. How companies respond to WOM online was accounted in marketing literature to deliver distinguishing managerial response strategies to brands. This research project focuses on identifying which response strategy is the most crucial to make customers satisfied after a negative WOM. Text mining and sentiment analysis were used in order to draw conclusions from actual online consumer behavior. Negative WOM (NWOM) was extracted from different brand pages on Facebook, as well as the responses from the companies to these NWOM and the reaction from the NWOM’s writer to the brand’s response. A literature-based framework using Davidow's Facilitation, Apology and Attentiveness, and Benoit's Corrective Action was tested on the data. Further moderation analysis was conducted to test effects of NWOM’s polarity and industry on the relationship between the responses and satisfaction. Results reveal that Facilitation is important to response satisfaction. Whenever brands re-directed original NWOM writers to formal complaint means, their satisfaction increased. This was especially true for hospitality and e-commerce industries. Reversely, for hospitality and e-commerce industries, Apology had a negative impact on response satisfaction. Results yielded that Attentiveness decreased response satisfaction when polarity was a moderator. Managers should provide effective means for consumers to voice their disappointment and not rely on apologies alone. Future research should tackle more in depth the intricacies of languages and the distinction of complainers and brand haters on response strategies.O word-of-mouth (WOM) está a crescer em importância no ramo da reputação da marca e a compreensão do comportamento do consumidor. As motivações para engajar em WOM tem sido amplamente estudado na literatura de marketing. A forma como as empresas respondem ao WOM online foi contabilizado na literatura para fornecer às marcas estratégias de resposta diferenciadas. Este projeto concentra-se em identificar qual a estratégia mais crucial para satisfazer os clientes. O método escolhido foi o text mining e sentiment analysis devido à necessidade na literatura de obter respostas sobre comportamentos reais de consumidores. Extraímos WOM negativo (NWOM) de diferentes páginas de marcas no Facebook, as suas respostas e a reação dos escritores do NWOM a essas respostas. Um modelo da literatura utilizando Facilitation, Apology, Attentiveness de Davidow e Corrective Action de Benoit, foi construído. Análises de moderação foram realizadas para testar os efeitos da polaridade e da indústria da NWOM na relação entre os tipos de respostas e a satisfação. Os resultados revelam que Facilitation é importante para a satisfação. Quando as marcas redirecionavam os escritores da NWOM para meios formais de reclamação, a sua satisfação aumentava. Revela-se verdade para as indústrias de hospitalidade e e-commerce. Adicionalmente, Apology teve impacto negativo na satisfação. Attentiveness diminui a satisfação quando a polaridade é moderador. Os gestores devem construir melhores meios de reclamações e não contar somente nas suas desculpas. Futuros investigadores devem abordar a complexidade das línguas e a distinção entre escritores de reclamações e aversão à marca nas estratégias de resposta