Knygos.lt nariams
Knygos.lt nariams
- Išsiųsime per 12–18 d.d.
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- Autorius: Jeeva Jose
- Leidėjas: Anchor Academic Publishing
- ISBN-10: 3960671040
- ISBN-13: 9783960671046
- Formatas: 14.8 x 21 x 0.4 cm, minkšti viršeliai
- Kalba: Anglų
Customer Payment Trend Analysis based on Clustering for Predicting the Financial Risk of Business Organizations (el. knyga) (skaityta knyga) | knygos.lt
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Aprašymas
With the opening of the Indian economy, many multinational corporations are shifting their manufacturing base to India. This includes setting up green field projects or acquiring established business firms of India. The region of this business unit is expanding globally. The variety and size of the customer base is expanding and the business risk related to bad debts is increasing. Close monitoring and analysis of payment trends helps to predict customer behavior and predict the chances of customer financial strength. The present manufacturing companies generate and store tremendous amount of data. The amount of data is so huge that manual analysis of the data is difficult. This creates a great demand for data mining to extract useful information buried within these data sets. One of the major concerns that affect companies' investments and profitability is bad debts; this can be reduced by identifying past customer behavior and reaching the suitable payment terms. The Clustering and Prediction module was implemented in WEKA - a free open source software written in Java. This study model can be extended to the development of a general purpose software package to predict payment trends of customers in any organisation.
- Autorius: Jeeva Jose
- Leidėjas: Anchor Academic Publishing
- ISBN-10: 3960671040
- ISBN-13: 9783960671046
- Formatas: 14.8 x 21 x 0.4 cm, minkšti viršeliai
- Kalba: Anglų
With the opening of the Indian economy, many multinational corporations are shifting their manufacturing base to India. This includes setting up green field projects or acquiring established business firms of India. The region of this business unit is expanding globally. The variety and size of the customer base is expanding and the business risk related to bad debts is increasing. Close monitoring and analysis of payment trends helps to predict customer behavior and predict the chances of customer financial strength. The present manufacturing companies generate and store tremendous amount of data. The amount of data is so huge that manual analysis of the data is difficult. This creates a great demand for data mining to extract useful information buried within these data sets. One of the major concerns that affect companies' investments and profitability is bad debts; this can be reduced by identifying past customer behavior and reaching the suitable payment terms. The Clustering and Prediction module was implemented in WEKA - a free open source software written in Java. This study model can be extended to the development of a general purpose software package to predict payment trends of customers in any organisation.
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