Preparing for New Competition in the Retail Industry

2010 ◽  
pp. 245-266 ◽  
Author(s):  
Goran Klepac
Keyword(s):  
Data Mining ◽  
Business Case ◽  
Business Environment ◽  
Indicator System ◽  
Data Mining Techniques ◽  
Business Cases ◽  
Market Opportunities ◽  
Marketing Activities ◽  
Mining Methods ◽  
Using Data

A business case presents a retail company facing new competitors and consequently preparing a customer retention strategy. The business environment in which the company was operating prior to the arrival of new competitors can be described as a stable market. Bearing in mind the plans and marketing activities of a competitor retail chain and making use of the data mining methods a system is being devised for the purpose of preventing or at least buffering the churn trend. Development of an early warning indicator system based on data mining methods is also being described as a support to the management in early detection of both market opportunities and threats. Research in data mining could also be concentrated on applying existing data mining techniques to find the best solution regarding practical business problems in the public or private sector. Knowledge regarding how some business cases were solved using data mining techniques could contribute in a better understanding of the nature or data mining nature and help solve specific business issues.

Churn Case Studies

2014 ◽  
pp. 237-289
Keyword(s):  
Business Case ◽  
Business Environment ◽  
Indicator System ◽  
Retention Strategy ◽  
Business Cases ◽  
Market Opportunities ◽  
Mining Methods ◽  
Almost All ◽  
Retail Company

This case study chapter brings two business cases in the domain of churn, both unique in many ways, combining almost all the topics covered inside book. The first business case presents a retail company facing new competitors and consequently preparing a customer-retention strategy. The case introduces the business environment in which the company was operating prior to the arrival of new competitors while the model is being devised for the purpose of preventing or at least buffering the churn trend as a reaction to the new competition. Development of an early warning indicator system based on data mining methods is also described as a support to the management in the early detection of both market opportunities and threats. The second business case describes the situation in a telecommunication company in the domain of churn prediction and churn mitigation. The churn project was divided into a few stages and is fully described in the chapter. The case explains how the company can decrease the churn rate and gives directions for better understanding of customer needs and behaviors.

scholarly journals Customers Grouping Using Data Mining Techniques in the Food Distribution Industry (A Case Study)

2020 ◽  
Keyword(s):  
Data Mining ◽  
Customer Segmentation ◽  
Food Distribution ◽  
Data Mining Techniques ◽  
Customer Lifetime ◽  
Rfm Model ◽  
Marketing Activities ◽  
Using Data ◽  
Production Company

Significant data development has required organizations to use a tool to understand the relationships between data and make various appropriate decisions based on the information obtained. Customer segmentation and analysis of their behavior in the manufacturing and distribution industries according to the purposefulness of marketing activities and effective communication and with customers has a particular importance. Customer segmentation using data mining techniques is mainly based on the variables of recency purchase (R), frequency of purchase (F) and monetary value of purchase (M) in RFM model. In this article, using the mentioned variables, twelve customer groups related to the BTB (business to business) of a food production company, are grouped. The grouping in this study is evaluated based on the K-means algorithm and the Davies-Bouldin index. As a result, customer grouping is divided into three groups and, finally the CLV (customer lifetime value) of each cluster is calculated, and appropriate marketing strategies for each cluster have been proposed.

scholarly journals Mining on Social Media Data: To Determine the Personality of Unrevealed Person

2019 ◽  
Vol 8 (4) ◽  
pp. 8574-8577
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Information Mining ◽  
Social Media Data ◽  
Data Mining Techniques ◽  
Network Discovery ◽  
The Social ◽  
Mining Methods ◽  
Using Data ◽  
Media Data

The unavoidable utilization of online networking like Facebook is giving exceptional measures of social information. Information mining methods have been broadly used to separate learning from such information. The character of the person is predicted whether he is good or not by using data mining techniques from user self-made data. Mining methods are being broadly using to separate learning from such information, main examples for them are network discovery and slant investigation. Notwithstanding, there is still a lot of room to investigate as far as the occasion information (i.e., occasions with timestamps, for example, posting an inquiry, altering an article in Wikipedia, and remarking on a tweet. These occasions react users' personal conduct standards and working forms in the social media websites.

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

Recognizing Job Apathy Patterns of Iraqi Higher Education Employees Using Data Mining Techniques

2019 ◽  
Vol 54 (4) ◽  
Author(s):  
Mustafa S. Abd ◽  
Suhad Faisal Behadili
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Psychiatric Patients ◽  
Human Life ◽  
A Priori ◽  
Psychological Research ◽  
Attribute Selection ◽  
Specific Class ◽  
Data Mining Techniques ◽  
Using Data

Psychological research centers help indirectly contact professionals from the fields of human life, job environment, family life, and psychological infrastructure for psychiatric patients. This research aims to detect job apathy patterns from the behavior of employee groups in the University of Baghdad and the Iraqi Ministry of Higher Education and Scientific Research. This investigation presents an approach using data mining techniques to acquire new knowledge and differs from statistical studies in terms of supporting the researchers’ evolving needs. These techniques manipulate redundant or irrelevant attributes to discover interesting patterns. The principal issue identifies several important and affective questions taken from a questionnaire, and the psychiatric researchers recommend these questions. Useless questions are pruned using the attribute selection method. Moreover, pieces of information gained through these questions are measured according to a specific class and ranked accordingly. Association and a priori algorithms are used to detect the most influential and interrelated questions in the questionnaire. Consequently, the decisive parameters that may lead to job apathy are determined.

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