The large potential of big data and how it has brought value into various industries have been established in research. Since big data has such large potential if handled and analyzed in the right way, revealing information to support decision making in an organization, this thesis is conducted as a case study at an automotive manufacturer with access to large amounts of customer usage data of their vehicles. The reason for performing an analysis of this kind of data is based on the cornerstones of Total Quality Management with the end objective of increasing customer satisfaction of the concerned products or services. The case study includes a data analysis exploring how and if patterns about what affects fuel consumption can be revealed from aggregated customer usage data of trucks linked to truck applications. Based on the case study, conclusions are drawn about how a company can use this type of analysis as well as how to handle the data in order to turn it into business value. The data analysis reveals properties describing truck usage using Factor Analysis and Principal Component Analysis. Especially one property is concluded to be important as it appears in the result of both techniques. Based on these properties the trucks are clustered using k-means and Hierarchical Clustering which shows groups of trucks where the importance of the properties varies. Due to the homogeneity and complexity of the chosen data, the clusters of trucks cannot be linked to truck applications. This would require data that is more easily interpretable. Finally, the importance for fuel consumption in the clusters is explored using model estimation. A comparison of Principal Component Regression (PCR) and the two regularization techniques Lasso and Elastic Net is made. PCR results in poor models difficult to evaluate. The two regularization techniques however outperform PCR, both giving a higher and very similar explained variance. The three techniques do not show obvious similarities in the models and no conclusions can therefore be drawn concerning what is important for fuel consumption. During the data analysis many problems with the data are discovered, which are linked to managerial and technical issues of big data. This leads to for example that some of the parameters interesting for the analysis cannot be used and this is likely to have an impact on the inability to get unanimous results in the model estimations. It is also concluded that the data was not originally intended for this type of analysis of large populations, but rather for testing and engineering purposes. Nevertheless, this type of data still contains valuable information and can be used if managed in the right way. From the case study it can be concluded that in order to use the data for more advanced analysis a big-data plan is needed at a strategic level in the organization. The plan summarizes the suggested solution for the managerial issues of the big data for the organization. This plan describes how to handle the data, how the analytic models revealing the information should be designed and the tools and organizational capabilities needed to support the people using the information.

Title: No thanks to unnecessary advertising! -A study on targeted marketing via Big Data in a consumer perspective Authors: Ricky Carlsson and Alexander Vilhelmsson Supervisor: Anders Parment Key words: Targeted marketing, Big Data, Customer segmentation, Buying process, Integrity concern, Customer relationship management, Marketing communication, Strategic management, Big Data management, Online Behavioural Targeting Introduction: In a world that is globalizing and where digital development is advancing, companies have had to adapt. In recent times with the increasingly more digital world, technology has become an increasingly more relevant factor, not least in marketing. A digital method that has emerged is Big Data, whichmakes it possible forcompanies tocollect large amounts of information about consumers. By analysing the information extracted from Big Data, it is easier to find and understand consumers' needs and what motivates their buying process. It is important that companies analyse the information correctly so that they do not run the risk of creating negative effects from targeted marketing via Big Data. Purpose: To investigate Swedish consumers' attitudestowards targeted marketing via Big Data and to find out how companies that sell goods and services to consumers can improve their use of Big Data in targeted marketing from a consumer perspective. Method: The study is a cross-sectional study of a qualitative and quantitative nature. The qualitative empirical data consists of 11 semi-structured interviews with students in Sweden. The quantitative empirical data consists of 203 survey answers collected from consumers around Sweden. The study is based on an abductive approach and has a hermeneutic approach. Conclusion: The result of the study shows that there are both opportunities and challenges for companies when using Big Data in targeted marketing. Targeted marketing with the help of Big Data that is performed correctly should only have a positive impact on the targeted marketing and something that creates value for both the consumers and the companies, but this is not the case today. The population of the study perceives that marketing often does not match their needs; this shows that companies must become better at analysing the data. If the data extracted from Big Data is analysed in a better way, the segmentation of consumers will also be better. / Titel: Nej tack till onödig reklam! - En studie om riktad marknadsföring via Big Data i ett konsumentperspektiv. Författare: Ricky Carlsson och Alexander Vilhelmsson Handledare: Anders Parment Bakgrund: I en värld som globaliseras och där den digitala utvecklingen går framåt har företag varit tvungna att anpassa sig. På senare tid i takt med den ständigt mer digitaliserade världen har teknologi blivit en alltmer relevant faktor, inte minst inom marknadsföring. En digital metod som har vuxit fram är Big Data genom vilken företag har möjlighet att samla in stora mängder information om konsumenter. Genom att analysera informationen som utvinns från Big Data går det att lättare finna och förstå konsumenters behov och vad som motiverar deras köpprocess. Det är viktigt att företag analyserar informationen på rätt sätt för att inte löpa risken att skapa negativa effekter av den riktade marknadsföringen via Big Data. Syfte: Att undersöka svenska konsumenters attityder till riktad marknadsföring via Big Data samt ta reda på hur företag som säljer varor eller tjänster till konsumenter kan förbättra användningen av Big Data inom riktad marknadsföring utifrån ett konsumentperspektiv. Metod: Studien är en tvärsnittsstudie av kvalitativ och kvantitativ karaktär. Den kvalitativa empirin består av 11 semi-strukturerade intervjuer med studenter i Sverige. Den kvantitativa empirin består av 203 insamlade enkätsvar från konsumenter runt om i Sverige. Studien grundas i en abduktiv ansats och har ett hermeneutiskt synsätt. Slutsatser: Resultatet i studien visar på att det finns möjligheter och utmaningar för företag vid användning av Big Data inom riktad marknadsföring. En riktad marknadsföring medhjälp av Big Data som utförs på rätt sätt borde enbart ha en positiv påverkan på den riktade marknadsföringen och något som skapar värde för konsumenter och företag, men så är inte fallet idag. Då studiens population uppfattar att den riktade marknadsföringen ofta inte matchar deras behov bör företag bli bättre på att analysera data. Om data som utvinns från Big Data analyseras på ett bättre sätt kommer även segmenteringen av konsumenter att bli bättre.