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El marketing experiencial es considerado un factor de gran valor para los consumidores. Es por ello que el sector restaurantes temáticos es un servicio que está en aumento en el Perú. Sin embargo, para los autores, la experiencia del cliente no es considerado uno de los factores relevantes. Debido a ello, que en la presente investigación se estudiará las dimensiones del marketing experiencial en relación a la satisfacción del consumidor en los restaurantes temáticos de la cultura americana por consumidores de 15 a 35 años de edad. Las variables que se han escogido son las siguientes: dimensiones del marketing, atención al cliente y satisfacción. Para conseguir comprobar las hipótesis que se plantean en el presente estudio se ha realizado una investigación mixta. Para el estudio cualitativo se realizaron dos focus y tres entrevistas a profundidad a expertos en el tema. Para el estudio cuantitativo, se realizó una encuesta aplicada a 250 personas. En adición a ello, el análisis que se realizó fue de manera correlacional entre la satisfacción final del cliente y cada tipo de marketing experiencial. En adición a ello, se realizó un análisis correlacional entre la atención al cliente y la satisfacción final, ya que es una variable que valora el cliente. Al procesar la información se obtuvo que, si existe una correlación entre la satisfacción final, y el marketing experiencial de “entretenimiento” con un nivel de correlación escasa o nula; y entre el marketing de experiencial de “estética” con un nivel de correlación moderado o fuerte. Asimismo, se obtuvo que existe una correlación modera o fuerte entre la “satisfacción con el personal” y la “satisfacción final”. / Experiential marketing is considered a factor of great value to consumers. That is why the theme restaurant sector is a growing service in Peru. However, for the authors, the customer experience is not considered one of the relevant factors. For this reason, this research will study the dimensions of experiential marketing in relation to consumer satisfaction in American culture's theme restaurants by consumers aged 15 to 35. The variables that have been chosen are the following: dimensions of marketing, customer service and satisfaction. To obtain evidence, the hypotheses that are presented in this study have conducted a mixed investigation. For the qualitative study two focus interviews and three in-depth interviews with experts in the subject are interviewed. For the quantitative study, a survey was applied to 250 people. In summary, the analysis that was performed was correlational between the final customer satisfaction and each type of experiential marketing. In summary, a correlational analysis was made between customer service and final satisfaction, since it is a variable that values ​​the customer. When analyzing the information, it was obtained that, if there is a correlation between the final satisfaction, and the experiential marketing of "entertainment" with a low level of correlation; and between experiential marketing of "aesthetics" with a moderate or strong level of correlation. Similarly, it is obtained that there is a moderate or strong correlation between "personal satisfaction" and "final satisfaction". / Trabajo de investigación

This master’s thesis provides solution for development of a customer support information system according to specific needs of a company. It contains process analysis and suggestions to improve processes, data and function model creation and evaluation of economic benefits after introduction of the system.

Background Daily, companies generate enormous amounts of customer support tickets which are grouped and placed in specialised queues, based on some characteristics, from where they are resolved by the customer support personnel (CSP) on a first-in-first-out basis. Given that these tickets require different levels of urgency, a logical next step to improving the effectiveness of the CSPs is to prioritise the tickets based on business policies. Among the several heuristics that can be used in prioritising tickets is sentiment polarity. Objectives This study investigates how machine learning methods and natural language techniques can be leveraged to automatically predict the sentiment polarity of customer support tickets using. Methods Using a formal experiment, the study examines how well Support Vector Machine (SVM), Naive Bayes (NB) and Logistic Regression (LR) based sentiment polarity prediction models built for the product and movie reviews, can be used to make sentiment predictions on email support tickets. Due to the limited size of annotated email support tickets, Valence Aware Dictionary and sEntiment Reasoner (VADER) and cluster ensemble - using k-means, affinity propagation and spectral clustering, is investigated for making sentiment polarity prediction. Results Compared to NB and LR, SVM performs better, scoring an average f1-score of .71 whereas NB scores least with a .62 f1-score. SVM, combined with the presence vector, outperformed the frequency and TF-IDF vectors with an f1-score of .73 while NB records an f1-score of .63. Given an average f1-score of .23, the models transferred from the movie and product reviews performed inadequately even when compared with a dummy classifier with an f1-score average of .55. Finally, the cluster ensemble method outperformed VADER with an f1-score of .61 and .53 respectively. Conclusions Given the results, SVM, combined with a presence vector of bigrams and trigrams is a candidate solution for extracting sentiments from email support tickets. Additionally, transferring sentiment models from the movie and product reviews domain to the email support tickets is not possible. Finally, given that there exists a limited dataset for conducting sentiment analysis studies in the Swedish and the customer support context, a cluster ensemble is recommended as a sample selection method for generating annotated data.

El presente trabajo muestra los pasos para mejorar la calidad de la atención brindada en uno de los principales clientes en un Contact Center de la ciudad de Lima, Perú. El documento desarrolla en su marco teórico la evolución de los servicios, el concepto de tercerización y la normativa legal en el Perú, el negocio de los Contact Center, y las metodologías y herramientas para la mejora de procesos. La investigación continúa, analizando la situación del sector y de la empresa donde se desarrolla la investigación, para luego presentar la problemática, delimitar el ámbito de estudio y conocer el impacto económico que genera el problema. Con la problemática definida y la evaluación de varias metodologías que ayudan en la mejora de procesos, se decide implementar un PDCA para mitigar el problema. Para ello, se eligen diversas acciones en función de las causas raíz encontradas, que van a generar un mayor impacto de acuerdo al presupuesto y tiempo que dispone la empresa. Antes de desplegar las acciones a toda la operación, se implementa un piloto de prueba para validar el impacto del grupo de acciones seleccionadas. Terminado el piloto y con las mediciones realizadas durante el mismo, se hace la comparación con el proceso anterior donde se evidencian, las diferencias a nivel proceso. Finalmente, se muestra el beneficio económico de la implementación de las acciones a toda la operación, se hacen las sugerencias para la continuidad del proyecto y se alerta sobre otros posibles desvíos que no son parte de la investigación. / This work shows the steps to improve the quality of the attention provided in one of the main clients in a Contact Center in Lima, Peru. In the first chapter, the document develops the evolution of services, the concept of outsourcing and Peruvian´s regulations, the business of the Contact Centers, and methodologies and tools for process improvement. In the second chapter, the research continues, analyzing the situation of the industry and the company where the research is carried out. After that, the problem is presented, the scope of study defined and the economic impact generated by the problema is calculated. With the problem defined and the evaluation of several methodologies that help in the improvement of processes, it is decided to implement a PDCA to reduced the problem. In order to achive that, some actions are chosen based on the root causes founded, which will generate a positive impact according to the cost and time that the company has. Before deploying the actions to the entire operation, a test pilot is implemented to prove the impact of the group of chosen actions. Once the pilot is finished and with the measurements made during the pilot, the comparison is made between the previous process ans the new one, where the differences at the process level are evidenced. Finally, the economic benefit of the implementation of the actions to the entire operation is shown, suggestions are made for the continuity of the project and alerts about other possible deviations that are not part of the investigation. / Trabajo de Suficiencia Profesional

Previous studies highlight the major technological advances in artificial intelligence (AI) and the benefits of introducing artificial intelligence systems in the form of chatbots in, among other things, customer support. They also describe the impact of chatbots on organizations. Artificial intelligence technology provides computers with the ability to display human-like traits, such as reasoning, creativity, learning and planning. The advantages of artificial intelligence-based chatbots can be, for example, improved processes in customer support, reduced costs and less burden on employees regarding monotonous tasks. It is important beforehand, and also during the course of the project, to have a well-functioning operation from an organizational and technical perspective. If there are problems within the business that are not addressed, this can have consequences. It is therefore not only the implementation of the chatbot that should be focused on in order to succeed with the change, but also organizational and technical parts of the business that will very likely be affected by the implementation. About 87% of artificial intelligence system implementation projects fail. This is because several factors relating to the project or to the product implemented have not been taken into account. Therefore, this study investigates the research question: What critical success factors need to be considered when implementing chatbots in customer support? The purpose of this research question is to identify which critical success factors (CSFs) are most necessary for decision makers to be aware of. This is to have better conditions to succeed with the implementation and use of artificial intelligence-based chatbots in customer support. To answer this question, a qualitative study has been carried out in the form of a case study with interviews as a data collection method. Respondents relevant to the research area have been interviewed. In conducting this study, thematic analysis has been used as a data analysis method to identify, analyze and report patterns in the collected data. These have then been used to answer our question. The result generated 3 themes, 15 categories and 45 codes. The categories are divided according to critical success factors from previous studies identified using the codes extracted from the interviews. These critical success factors are in turn divided into three different themes that represent the different characteristic features among the factors, these are organizational, technical and resource factors. The critical success factors that are included in the theme organizational factors are: Lack of benefits visibility, Change management, Organizational culture, Organizational structure, Resistance and Ambiguous strategic vision. In the technical factors: Ethics issues, Insuf icient quantity of data, Integration complexity, Low data quality, Data governance issues, Security and confidentiality and Scalable and flexible system. The theme resource factors include: Selection of vendors and High cost of AI. The conclusion of this study suggests that there are a variety of critical success factors of organizational, technical, and resource-related nature that are important to consider when implementing chatbots in customer support. Those considered to be of the highest priority are the technical factors. Next in line are the organizational factors Organizational culture, Organizational structure and Resistance, followed by the rest of the factors in the same theme. The factors considered to be of the lowest priority are the ones included in the theme resource factors. There are also critical success factors from previous studies, that this study builds upon, that are considered not important enough to take into account. The critical success factors generated in this investigation are discussed regarding how the data extracted has a connection to a certain critical success factor. In the discussion, critical success factors generated are compared with the critical success factors from previous studies. Since this is a case study containing a limited number of respondents, there is of course a likelihood that there are more critical success factors affecting customer support chatbot implementation projects. However, the critical success factors that this study resulted in are considered to be of higher priority and can therefore have the greatest impact on such a project.

More and more aspects of today’s healthcare are becoming integrated with medical technology and dependent on medical IT systems, which consequently puts stricter re-quirements on the companies delivering these solutions. As a result, companies delivering medical technology solutions need to spend a lot of resources maintaining high-quality, responsive customer support. In this report, possible ways of increasing customer support efficiency using machine learning and NLP is examined at Sectra, a medical technology company. This is done through a qualitative case study, where empirical data collection methods are used to elicit requirements and find ways of adding decision support. Next, a prototype is built featuring a ticket recommendation system powered by GPT-3 and based on 65 000 available support tickets, which is integrated with the customer supports workflow. Lastly, this is evaluated by having six end users test the prototype for five weeks, followed by a qualitative evaluation consisting of interviews, and a quantitative measurement of the user-perceivedusability of the proposed prototype. The results show some support that machine learning can be used to create decision support in a customer support context, as six out of six test users believed that their long-term efficiency could improve using the prototype in terms of reducing the average ticket resolution time. However, one out of the six test users expressed some skepticism towards the relevance of the recommendations generated by the system, indicating that improvements to the model must be made. The study also indicates that the use of state-of-the-art NLP models for semantic textual similarity can possibly outperform keyword searches.

This diploma thesis deals with measuring and analyzing customer satisfaction in a chosen company, whose main field is providing customer support services to other companies. On the basis of the theoretical background of the findings and analysis, the work includes proposals to improve the overall level of customer satisfaction.

Fortnox takes in many errands via their support chat. Some of the questions can be hard to interpret, making it difficult to know where to delegate the question further. It would be beneficial if the process was automated to answer the questions instead of need to put in time to analyze the questions to be able to delegate them. So, the main task is to find an unsupervised model that can take questions and put them into topics. A literature review over NLP and clustering was needed to find the most suitable models and techniques for the problem. Then implementing the models and techniques and evaluating them using support chat questions received by Fortnox. The unsupervised models tested in this thesis were LDA and K-means. The resulting models after training are analyzed, and some of the clusters are given a label. The authors of the thesis give clusters a label after analyzing them by looking at the most relevant words for the cluster. Three different sets of labels are analyzed and tested. The models are evaluated using five different score metrics: Silhouette, AdjustedRand Index, Recall, Precision, and F1 score. K-means scores the best when looking at the score metrics and have an F1 score of 0.417. But can not handle very small documents. LDA does not perform very well and got i F1 score of 0.137 and is not able to categorize documents together.

This bachelor thesis examines the potential advantages of implementing artificial intelligence (AI) services in the context of customer support. With the rise of chatbots utilizing Natural Language Processing (NLP), AI has recently become a widely debated topic. The authors aim to investigate the comparative costs of using AI-powered chatbots versus human-powered alternatives for customer support, focusing on analyzing the financial impacts of chatbot incorporation and the quality of the responses. This research seeks to offer insights to businesses considering using chatbots as a tool for customer support. The objective is to assist these businesses in making informed decisions concerning AI adoption and associated costs. The bachelor’s thesis is a case study that employs a qualitative research method using an analytical- and abductive approach. The thesis addresses the question: ’Can commercially available AI services reduce costs within the media analysis industry?’ The results demonstrate that cost savings can be achieved by reducing time-consuming tasks from manual labour. However, using AI services is not a simple solution and expected positive results are not always guaranteed. Numerous issues need be highlighted if the intend is to have customers using and having direct access to the AI chatbot. Incorrect responses from a chatbot can create problems for customers and companies. An important question is how to handle the incorrect responses sent to the customer. Who bears the ultimate responsibility when wrong actions are recommended by automation and carried out by a customer? On the other hand, these issues become less relevant if the chatbot is used by the Customer Support team as a complement to reduce time spent per task. The findings of the thesis indicate that while the prototype can complement the Customer Support team, it is insufficient to handle all customer support responsibilities due to its 59.85% accuracy score and limited capability to effectively address follow-up inquiries. In conclusion, while the findings support the potential cost savings achievable through automation with AI, it is crucial to further refine and enhance the capabilities of the prototype to better meet the requirements of comprehensive customer support. / Denna kandidatrapport undersöker de potentiella kostnadsfördelarna med att integrera Artificiell intelligens (AI) inom kundservice, med särskilt fokus på kommersiellt tillgängliga AI-tjänster. Användningen av chattbotar som använder naturlig språkbearbetning (NLP) har ökat markant under de senaste åren, och AI är ett väldebatterat ämne både inom politiken och i media. Författarna avser att jämföra manuella kostnader med automatiserade kostnader. Fokus ligger på att analysera de finansiella konsekvenserna av en automatisering samt mäta kvaliteten på de svar som kan produceras med hjälp av AI. Studien genomförs som en kvalitativ fallstudie. Författarna avser att tillhandahålla ett underlag till företag som överväger att använda chattbotar som ett verktyg för kundtjänst och avser att studien ska kunna ligga till grund för beslut om införande av AI i företagens verksamheter. Rapporten svarar på frågan: "Kan kommersiellt tillgängliga AI-tjänster minska kostnaderna inom medieanalysbranschen?" Resultaten visar att kostnadsbesparingar kan uppnås genom tidsbesparing av manuellt arbete. Det finns dock flera viktiga frågor som behöver betonas om avsikten är att kunderna ska använda och ha direkt tillgång till AI-chattboten. Felaktiga svar från en chattbot kan skapa problem för både kunder och företag. En viktig fråga är hur man ska hantera de felaktiga svaren som skickas till kunden. Vem bär det yttersta ansvaret när felaktiga åtgärder rekommenderas av chattbotten och genomförs av kunder? Å andra sidan blir dessa frågor mindre relevanta om chattboten används av kundservice teamet som ett komplement för att minska tidsåtgången per uppgift. Prototypen kan komplettera kundservice men är otillräcklig för att hantera alla kundservicefrågor. Validering gav en träffsäkerhet på 59.85%. Det innebär att prototypen ger rätt svar på 59.85% av frågorna. Slutsatsen är att medan resultaten stöder de potentiella kostnadsbesparingar som kan uppnås genom automatisering med AI, är det viktigt att ytterligare förbättra och förstärka prototypens förmågor för att bättre kunna möta kraven på omfattande kundsupport.