The impact of skills and demographics on end-user developers’ use of support

There are many end-user developers but they are quite often left to their own devices when it comes to finding support for development tasks, particularly those who belong to small organisations. With less access to formal support sources we would expect them to turn to more informal as well as on-line sources. However, the use of on-line sources requires skill and confidence in using computers and the Internet. In this paper, we look at a group of small organisations and what impact the skill and demographic factors have on the use of different support sources among existing and potential end-user developers. The analysis was performed using the self-organizing map. It suggests that personal contacts form a default source for people and that increased skills leads to less reliance on these. Computer and Internet skill are the most important factors influencing support use, enabling some end-user developers to “self-help”

Mining textual contents of financial report

The message, stylistic focus, language and readability of financial reports are good indicators of the perspectives and developments of any company. These indicators can guide companies' decision makers to more efficient actions in the dynamic business environment. In this paper, we have studied the language and contents of quarterly financial reports using automated linguistic and text mining methods. We aim at comparing the results from linguistic analysis of quarterly reports by means of collocational networks and the results obtained from text mining analysis of quarterly report by means of the prototype matching. We perform the study on the quarterly reports from three leading companies in the telecommunications sector. Our results are somewhat controversial: some of the reports from the companies have as their closest matches the reports with similar collocational networks and some do not have.El mensaje, el enfoque estilístico, el idioma y facilidad para leer de los reportes financieros son buenos indicadores de las perspectivas y desarrollos de cualquier compañía. Estos indicadores pueden guiar la toma de decisiones de las compañías y dirigirlas hacia decisiones eficientes en el entorno dinámico de los negocios. En este artículo, hemos estudiado el lenguaje y contenidos de reportes financieros usando métodos lingüísticos automatizados. Nuestro objetivo es comparar los resultados del análisis lingüístico en función de las redes online de colocación y los resultados en función de los prototipos que encaje. Realizamos el estudio en los informes de tres empresas líderes en el sector de la telecomunicación. Nuestros resultados son de alguna forma controvertidos: algunos de los informes de las compañías tienen mayor coincidencia los informes con redes de colocación más similares, mientras que otros no

Strategic pricing possibilities of grocery retailers : an empirical study

The right pricing of products is one of the most important issues concerning the development of companies’ financial performance. Prices should be low enough to attract customers and at the same time high enough to cover all the emerged costs and expected profits. This research illustrates how self-organizing maps (SOM) can be used for pricing purposes. We show how changes in a company’s pricing policies would affect the company’s pricing position. The study illustrates clearly that companies have different possibilities to change their pricing positions. The SOM method is new and can be applied in many different ways through different pricing simulations.La tarifación correcta de los productos es una de los problemas más importantes, en relación con el desarrollo del desempeño financiero de las compañías. Los precios deberían ser lo suficientemente bajos para atraer a los clientes, y al mismo tiempo, lo suficientemente altos para cubrir todos los costes emergentes y los beneficios previstos. Esta investigación ilustra cómo los mapas auto-organizadores (SOM en inglés) pueden ser usados para fines de tarifación. Mostramos cómo los cambios en las políticas de tarifación en una empresa, pueden afectar a la posición de tarifación de la misma. El estudio muestra claramente que las empresas tienen diferentes posibilidades para cambiar dichas posiciones. El método SOM es nuevo y puede ser aplicado de muchas maneras mediante varias simulaciones de tarifación

Customer Portfolio Analysis Using the SOM

In order to compete for profitable customers, companies are looking to add value using Customer Relationship Management (CRM). One subset of CRM is customer segmentation, which is the process of dividing customers into groups based upon common features or needs. Segmentation methods can be used for customer portfolio analysis (CPA), the process of analyzing the profitability of customers. This study was made for a case organization, who wanted to identify their profitable and unprofitable customers, in order to gain knowledge on how to develop their marketing strategies. Data about the customers were gathered from the case organization’s own database. The Self-Organizing Map (SOM) was used to divide the customers into segments, which were then analyzed in light of product sales information

From Smart Meter Data to Pricing Intelligence -- Visual Data Mining towards Real-Time BI

The deployment of smart metering in the electricity industry has opened up the opportunity for real-time BI-enabled innovative business applications, such as demand response. Taking a holistic view of BI, this study introduced a visual data mining driven application in order to exemplify the potentials of real-time BI to the electricity businesses. The empirical findings indicate that such an application is capable of extracting actionable insights about customer’s electricity consumption patterns, which will lead to turn timely measured data into pricing intelligence. Based on the findings, we proposed a real-time BI framework, and discussed how it will facilitate the formulation of strategic initiatives for transforming the electricity utility towards sustainable growth. Our research is conducted by following the design science research paradigm. By addressing an emerging issue in the problem domain, it adds empirical knowledge to the BI research landscape

Customer Feedback Analysis using Collocations

Today’s ERP and CRM systems provide companies with nearly unlimited possibilities for collecting data concerning theircustomers. More and more of these data are more or less unstructured textual data. A good example of this type of data iscustomer feedback, which can potentially be used to improve customer satisfaction.However, merely getting an overview of what lies in an unstructured mass of text is an extremely challenging task. This isthe topic of the field of computational linguistics. Collocation analysis, one of the tools emerging from this field, is a tooldeveloped for this task in particular. In this paper, we use the collocation analysis to study a text corpora consisting of 64,806pieces of customer feedback collected through a case company’s online customer portal. Collocation analysis is shown to bea very useful tool for exploratory analysis of highly unstructured customer feedback

COMBINING VISUAL CUSTOMER SEGMENTATION AND RESPONSE MODELING

Customer Relationship Management (CRM) is a central part of Business Intelligence and sales campaigns are often used for improving customer relationships. This paper explores customer behavior during sales campaigns. We provide a visual, data-driven and efficient framework for customer segmentation and campaign-response modeling. First, the customers are grouped by purchasing behavior characteristics using a self-organizing map. To this behavioral segmentation model, we link segment migration patterns using feature plane representations. This enables visual monitoring of the customer base and tracking customer behavior before and during sales campaigns. In addition to the general segment migration patterns, this method provides the capability to drill down into each segment to visually explore the dynamics. The framework is applied to a department store chain with more than one million customers

Assessing the Feasibility of Self Organizing Maps for Data Mining Financial Information

Analyzing financial performance in today’s information-rich society can be a daunting task. With the evolution of the Internet, access to massive amounts of financial data, typically in the form of financial statements, is widespread. Managers and stakeholders are in need of a data-mining tool allowing them to quickly and accurately analyze this data. An emerging technique that may be suited for this application is the self-organizing map. The purpose of this study was to evaluate the performance of self-organizing maps for analyzing financial performance of international pulp and paper companies. For the study, financial data, in the form of seven financial ratios, was collected, using the Internet as the primary source of information. A total of 77 companies, and six regional averages, were included in the study. The time frame of the study was the period 1995-00. An example analysis was performed, and the results analyzed based on information contained in the annual reports. The results of the study indicate that self-organizing maps can be feasible tools for the financial analysis of large amounts of financial data