The determinants of the quality of Sales-Marketing Interface in a Multinational Customer Brand Focused Company: The Latin American Branches

Customer evolution and changes in consumers, determine the fact that the quality of the interface between marketing and sales may represent a true competitive advantage for the firm. Building on multidimensional theoretical and empirical models developed in Europe and on social network analysis, the organizational interface between the marketing and sales departments of a multinational high-growth company with operations in Argentina, Uruguay and Paraguay is studied. Both, attitudinal and social network measures of information exchange are used to make operational the nature and quality of the interface and its impact on performance. Results show the existence of a positive relationship of formalization, joint planning, teamwork, trust and information transfer on interface quality, as well as a positive relationship between interface quality and business performance. We conclude that efficient design and organizational management of the exchange network are essential for the successful performance of consumer goods companies that seek to develop distinctive capabilities to adapt to markets that experience vertiginous changes

Some insights for a relationship marketing model integrating SERVQUAL and customer loyalty in dental clinics

The demand of new services, the emergence of new business models, insufficient innovation, underestimation of customer loyalty and reluctance to adopt new management are evidence of the deficiencies and the lack of research about the relations between patients and dental clinics. In this article we propose the structure of a model of Relationship Marketing (RM) in the dental clinic that integrates information from SERVQUAL, Customer Loyalty (CL) and activities of RM and combines the vision of dentist and patient. The first pilot study on dentists showed that: they recognize the value of maintaining better patients however they don't perform RM actions to retain them. They have databases of patients but not sophisticated enough as compared to RM tools. They perceive that the patients value "Assurance" and "Empathy" (two dimensions of service quality). Finally, they indicate that a loyal patient not necessarily pays more by the service. The proposed model will be validated using Fuzzy Logic simulation and the ultimate goal of this research line is contributing a new definition of CL.

Customer training, oriented toward the customer in the spanish affiliate

On that date , the Spanish affiliate offered for the 1st. time to its customers courses oriented toward the user, not the product, within the area of programming, in the subarea of application programming

Linguistic linked data for sentiment analysis

In this paper we describe the specification of amodel for the semantically interoperable representation of language resources for sentiment analysis. The model integrates "lemon", an RDF-based model for the specification of ontology-lexica (Buitelaar et al. 2009), which is used increasinglyfor the representation of language resources asLinked Data, with Marl, an RDF-based model for the representation of sentiment annotations (West-erski et al., 2011; Sánchez-Rada et al., 2013)

Customer loyalty plan at Residencia Gómez-Pardo, Universidad Politécnica de Madrid

Accommodation is a first need and one of the most important decisions that university students have to decide taking into account their limited budget. The satisfaction grade of these students is the relevant aspect for the administrators and managers of the university residences, because it allows assuring the viability and sustainability of this kind of accommodation. In a situation of decline in rate of retention of students into the residence, coupled with an environment of economic crisis. Hence, of disposable income reduction, it seems essential to get to know what factors affect the motivation to remain into the university residence more than others when it comes to the final choice. The offer?s increase of different kind of accommodation is another variable to be considered when taking the decision related to the management of this kind of accommodation. Thus, there is the need to know which are the key factors and to obtain information about these variables in order to go deep into the relevance grade with the aim to pursue the strategic objectives, that will allow to improve the relationship with the customer and to respond to his accommodation? needs. This article researches the motivation elements that lead the students to remain in a university residence or to abandon it in exchange or a different accommodation, as per example shared flats or individual apartments. This research work intends to be useful for the university residence?s managers in order to increase its incomes, to raise the satisfaction degree among its residents and to obtain better end results in the management of these properties. The fieldwork conducted in the Residencia Universitaria Gómez Pardo (RUGP), Universidad Politécnica de Madrid (UPM), for four semesters, which means students from 27 different grades (undergraduates) and 81 surveys finished, shows the following conclusions. Not only the relation with the residence?s personnel but also the quality and quantity of the feeding and the availability and quality of the internet service, constitute key factors when it comes to make the decision of remaining or of abandoning the residence when the semester comes to its end.

DAEDALUS at SemEval-2014 Task 9: Comparing approaches for sentiment analysis in twitter

This paper describes our participation at SemEval- 2014 sentiment analysis task, in both contextual and message polarity classification. Our idea was to com- pare two different techniques for sentiment analysis. First, a machine learning classifier specifically built for the task using the provided training corpus. On the other hand, a lexicon-based approach using natural language processing techniques, developed for a ge- neric sentiment analysis task with no adaptation to the provided training corpus. Results, though far from the best runs, prove that the generic model is more robust as it achieves a more balanced evaluation for message polarity along the different test sets.

Customer Relationship Management-PeopleSoft CRM

1 RESUMEN 1.1 Resumen (español) El intercambio y comercio tanto de bienes como servicios se remonta a tiempos inmemoriales dentro de la historia de la humanidad. Desde sus inicios tempranos con el intercambio o trueque de productos en el Neolítico hasta nuestra época híper globalizada, en la que existen clientes potenciales en el otro extremo del mundo, podemos decir que se ha recorrido un largo camino. Con el paso del tiempo y la evolución de la sociedad y la tecnología, así como la evolución empresarial, se ha visto necesario la implementación de estrategias para lograr la fidelización y satisfacción de los clientes. De esta forma entendimos que ya no valía simplemente con vender un producto a un cliente, si no que si queríamos establecer una relación continúa con el mismo, debíamos lograr su satisfacción y por tanto su fidelización. Como forma de extender la relación más allá de una simple venta, las empresas modernas empezaron a implementar diversas estrategias. De esta forma aparecieron los primeros centros de atención al cliente, las primeras aplicaciones hechas a medida para dar soporte a los clientes y por fin los sistemas CRM tal y como los concebimos hoy día. El presente proyecto fin de carrera da una explicación de dichos sistemas indicando cuáles son sus objetos fundamentales y cómo implementan la estrategia CRM y profundiza en uno de los sistemas CRM más utilizados: PeopleSoft CRM, dando una explicación detallada de dicho sistemas así como de los conceptos y lenguaje de programación de dicho sistema CRM. 1.2 SUMMARY (ENGLISH) The exchange and trade of goods as well and services goes back to ancient times in the history of mankind. Since its early beginning with the bartering of products in the Neolithic to our globalized hyper era, in which there are potential customers on the other side of the world, we can say that it has come a long way. After a certain length of time, the society and technology evolution, and also the enterprise development, has been necessary to implement strategies to achieve customer loyalty and satisfaction. We understood in this way that it no longer simply worth to sell a product to a customer, otherwise if we wanted to establish a relationship continues with the same, we should ensure their satisfaction and thus their loyalty. As a way to extend the relationship beyond a simple sale, modern enterprises began to implement several strategies. Therefore appeared the first customer service centers, the first applications tailored to support customers and finally the CRM systems as we know it today. This final project gives an explanation of such systems by indicating what the core objects are and how to implement the CRM strategy, deeping into one of the most widely used CRM systems: PeopleSoft CRM, and also giving a detailed explanation of this system and its programming language.

Towards a unified sentiment lexicon based on graphics processing units

This paper presents an approach to create what we have called a Unified Sentiment Lexicon (USL). This approach aims at aligning, unifying, and expanding the set of sentiment lexicons which are available on the web in order to increase their robustness of coverage. One problem related to the task of the automatic unification of different scores of sentiment lexicons is that there are multiple lexical entries for which the classification of positive, negative, or neutral {P, Z, N} depends on the unit of measurement used in the annotation methodology of the source sentiment lexicon. Our USL approach computes the unified strength of polarity of each lexical entry based on the Pearson correlation coefficient which measures how correlated lexical entries are with a value between 1 and -1, where 1 indicates that the lexical entries are perfectly correlated, 0 indicates no correlation, and -1 means they are perfectly inversely correlated and so is the UnifiedMetrics procedure for CPU and GPU, respectively. Another problem is the high processing time required for computing all the lexical entries in the unification task. Thus, the USL approach computes a subset of lexical entries in each of the 1344 GPU cores and uses parallel processing in order to unify 155802 lexical entries. The results of the analysis conducted using the USL approach show that the USL has 95.430 lexical entries, out of which there are 35.201 considered to be positive, 22.029 negative, and 38.200 neutral. Finally, the runtime was 10 minutes for 95.430 lexical entries; this allows a reduction of the time computing for the UnifiedMetrics by 3 times.

The unified sentiment lexicon using GPUs

This approach aims at aligning, unifying and expanding the set of sentiment lexicons which are available on the web in order to increase their robustness of coverage. A sentiment lexicon is a critical and essential resource for tagging subjective corpora on the web or elsewhere. In many situations, the multilingual property of the sentiment lexicon is important because the writer is using two languages alternately in the same text, message or post. Our USL approach computes the unified strength of polarity of each lexical entry based on the Pearson correlation coefficient which measures how correlated lexical entries are with a value between 1 and -1, where 1 indicates that the lexical entries are perfectly correlated, 0 indicates no correlation, and -1 means they are perfectly inversely correlated and the UnifiedMetrics procedure for CPU and GPU, respectively.

A linked data approach to sentiment and emotion analysis of twitter in the financial domain

Sentiment analysis has recently gained popularity in the financial domain thanks to its capability to predict the stock market based on the wisdom of the crowds. Nevertheless, current sentiment indicators are still silos that cannot be combined to get better insight about the mood of different communities. In this article we propose a Linked Data approach for modelling sentiment and emotions about financial entities. We aim at integrating sentiment information from different communities or providers, and complements existing initiatives such as FIBO. The ap- proach has been validated in the semantic annotation of tweets of several stocks in the Spanish stock market, including its sentiment information.

Generating Linked-Data based Domain-Specific Sentiment Lexicons from Legacy Language and Semantic Resources

We present a methodology for legacy language resource adaptation that generates domain-specific sentiment lexicons organized around domain entities described with lexical information and sentiment words described in the context of these entities. We explain the steps of the methodology and we give a working example of our initial results. The resulting lexicons are modelled as Linked Data resources by use of established formats for Linguistic Linked Data (lemon, NIF) and for linked sentiment expressions (Marl), thereby contributing and linking to existing Language Resources in the Linguistic Linked Open Data cloud.

EUROSENTIMENT: Linked Data Sentiment Analysis

Sentiment and Emotion Analysis strongly depend on quality language resources, especially sentiment dictionaries. These resources are usually scattered, heterogeneous and limited to specific domains of appli- cation by simple algorithms. The EUROSENTIMENT project addresses these issues by 1) developing a common language resource representation model for sentiment analysis, and APIs for sentiment analysis services based on established Linked Data formats (lemon, Marl, NIF and ONYX) 2) by creating a Language Resource Pool (a.k.a. LRP) that makes avail- able to the community existing scattered language resources and services for sentiment analysis in an interoperable way. In this paper we describe the available language resources and services in the LRP and some sam- ple applications that can be developed on top of the EUROSENTIMENT LRP.

Linked-data based domain-specific sentiment lexicons

In this paper we present a dataset componsed of domain-specific sentiment lexicons in six languages for two domains. We used existing collections of reviews from Trip Advisor, Amazon, the Stanford Network Analysis Project and the OpinRank Review Dataset. We use an RDF model based on the lemon and Marl formats to represent the lexicons. We describe the methodology that we applied to generate the domain-specific lexicons and we provide access information to our datasets.

Development of a Dashboard for Sentiment Analysis of Football in Twitter based on Web Components and D3.js

This thesis is the result of a project whose objective has been to develop and deploy a dashboard for sentiment analysis of football in Twitter based on web components and D3.js. To do so, a visualisation server has been developed in order to present the data obtained from Twitter and analysed with Senpy. This visualisation server has been developed with Polymer web components and D3.js. Data mining has been done with a pipeline between Twitter, Senpy and ElasticSearch. Luigi have been used in this process because helps building complex pipelines of batch jobs, so it has analysed all tweets and stored them in ElasticSearch. To continue, D3.js has been used to create interactive widgets that make data easily accessible, this widgets will allow the user to interact with them and �filter the most interesting data for him. Polymer web components have been used to make this dashboard according to Google's material design and be able to show dynamic data in widgets. As a result, this project will allow an extensive analysis of the social network, pointing out the influence of players and teams and the emotions and sentiments that emerge in a lapse of time.