Customer stratification theory and value evaluation—analysis based on improved RFM model

2020 ◽  
pp. 1-13
Author(s):  
Yi Zong ◽  
Hao Xing
Keyword(s):  
Customer Value ◽  
Comprehensive Evaluation ◽  
Clustering Method ◽  
Value Evaluation ◽  
For Profit ◽  
Stratification Theory ◽  
Operating Profit ◽  
Rfm Model ◽  
Stratification Method ◽  
The Impact

Scientific customer stratification method can help enterprises identify valuable customers, thus effectively improving the operating profit of enterprises. However, current customer stratification methods have not considered the impact of cost to service (CTS) on customer value (such as the RFM model). In this paper, K-mean clustering method is adopted to classify customers into four categories, namely 1) the most valuable customers, 2) valuable customers, 3) general customers and 4) customers with low contribution. By adding a new evaluation dimension of CTS, the original RFM model is improved. In this way, the RFMC model is built and can provide more comprehensive evaluation on customer value. Finally, the results show that the addition of CTS index significantly changes the clustering results of the original RFM model and the overall consideration of consumption amount and CTS truly reflect the customer value. Thus, the improved RFMC model optimizes the results of customer stratification and it can effectively sort out the valuable customers for enterprises. Enterprises will be more dedicated to serving the valuable customers so as to maximize profits and reduce service costs of customers with lower value to make up for profit losses.

Competing for Bachelor’s Degrees: Are Community Colleges Cutting Into the Market Share of 4-Year Institutions?

2020 ◽  
pp. 000283122094630
Author(s):  
Dennis A. Kramer ◽  
Justin C. Ortagus ◽  
Jacqueline Donovan
Keyword(s):  
Community Colleges ◽  
Market Share ◽  
Comprehensive Evaluation ◽  
Bachelor's Degree ◽  
Degree Programs ◽  
For Profit ◽  
Degree Production ◽  
Bachelor's Degrees ◽  
Negative Effect ◽  
The Impact

To address local workforce needs and expand access to affordable bachelor’s degrees, some states allow community colleges to offer bachelor’s degree programs. Despite concerns that community college baccalaureate (CCB) programs will duplicate efforts and cut into the market share of nearby 4-year institutions, extant literature has yet to examine the impact of CCB adoption on bachelor’s degree program enrollment and bachelor’s degree production at 4-year institutions. Using program-level data, our findings show that local CCB degree programs have a negative effect on overall bachelor’s degree enrollment and bachelor’s degree production at 4-year institutions, but this effect is concentrated primarily within for-profit 4-year institutions. This study represents the first comprehensive evaluation of the impact of CCB degree programs on neighboring 4-year institutions.

Cloud computing demand elasticity algorithm based on ant colony algorithm

2020 ◽  
Vol 13 ◽  
Author(s):  
Chunyu Liu ◽  
Fengrui Mu ◽  
Weilong Zhang
Keyword(s):  
Ant Colony Algorithm ◽  
Theory Of Elasticity ◽  
Ant Colony ◽  
Demand Elasticity ◽  
Network Efficiency ◽  
Elasticity Of Demand ◽  
Operating Profit ◽  
Network Load ◽  
Network Utilization ◽  
The Impact

Background: In recent era of technology, the traditional Ant Colony Algorithm (ACO) is insufficient in solving the problem of network congestion and load balance, and network utilization. Methods: This paper proposes an improved ant colony algorithm, which considers the price factor based on the theory of elasticity of demand. The price factor is denominated in the impact on the network load which means indirect control of network load, congestion or auxiliary solution to calculate the idle resources caused by the low network utilization and reduced profits. Results: Experimental results show that the improved algorithm can balance the overall network load, extend the life of path by nearly 3 hours, greatly reduce the risk of network paralysis, and increase the profit of the manufacturer by 300 million Yuan. Conclusion: Furthermore, results shows that the improved method has a great application value in improving the network efficiency, balancing network load, prolonging network life and increasing network operating profit.

scholarly journals On Present Value Evaluation under the Impact of Behavioural Factors Using Oriented Fuzzy Numbers

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 468
Author(s):  
Krzysztof Piasecki ◽  
Anna Łyczkowska-Hanćkowiak
Keyword(s):  
Fuzzy Numbers ◽  
Asset Price ◽  
Financial Knowledge ◽  
Present Value ◽  
Market Knowledge ◽  
Value Evaluation ◽  
Starting Point ◽  
Behavioural Factors ◽  
Objective Financial Knowledge ◽  
The Impact

In general, the present value (PV) concept is ambiguous. Therefore, behavioural factors may influence on the PV evaluation. The main aim of our paper is to propose some method of soft computing PV evaluated under the impact of behavioural factors. The starting point for our discussion is the notion of the Behavioural PV (BPV) defined as an imprecisely real-valued function of distinguished variables which can be evaluated using objective financial knowledge or subjective behavioural premises. In our paper, a BPV is supplemented with a forecast of the asset price closest to changes. Such BPV is called the oriented BPV (O-BPV). We propose to evaluate an O-BPV by oriented fuzzy numbers which are more useful for portfolio analysis than fuzzy numbers. This fact determines the significance of the research described in this article. O-BPV may be applied as input signal for systems supporting invest-making. We consider here six cases of O-BPV: overvalued asset with the prediction of a rise in its price, overvalued asset with the prediction of a fall in its price, undervalued asset with the prediction of a rise in its price, undervalued asset with the prediction of a fall in its price, fully valued asset with the prediction of a rise in its rice and fully valued asset with the prediction of a fall in its rice. All our considerations are illustrated by numerical examples. Presented examples show the way in which we transform superposition of objective market knowledge and subjective investment opinion into simple return rate.

scholarly journals Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yahya Albalawi ◽  
Jim Buckley ◽  
Nikola S. Nikolov
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Comprehensive Evaluation ◽  
Classification Problem ◽  
Data Sets ◽  
Word Embeddings ◽  
Data Set ◽  
Lower Accuracy ◽  
Health Related ◽  
The Impact

AbstractThis paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

The Impact of Public Policy on Support Services for Indigenous Families with Children with Special Education Needs

2017 ◽  
Vol 47 (2) ◽  
pp. 198-205 ◽  
Author(s):  
Karen Trimmer ◽  
Roselyn Dixon
Keyword(s):  
National Policy ◽  
Disability Services ◽  
For Profit ◽  
The Past ◽  
Not For Profit ◽  
Special Education Needs ◽  
Provision Of Services ◽  
Education Needs ◽  
Families With Children ◽  
The Impact

In Australia and Europe, government agencies and not-for-profit organisations (NFPOs) have had long involvement in the funding and provision of community disability services. Significant change has occurred in Australia over the past two decades in the way government funds are expended, with marketplace mechanisms increasingly being used. As a consequence of economic and governance imperatives, funding of services via NFPOs has changed significantly with a move away from the provision of grants to the contracting of these organisations for the provision of services. In 2013, a new national policy, the National Disability Insurance Scheme (NDIS), was introduced that has impacts for the provision of disability services for children and their families. In particular, Indigenous families are likely to experience barriers in accessing services. This paper reviews the impact of international changes in policy and associated funding models and considers the impacts and research implications of Australia's initial experience of implementation of the NDIS.

Improving Customer Value Index and Consumption Forecasts using a Weighted RFM Model and Machine Learning Algorithms

2022 ◽  
Vol 30 (3) ◽  
pp. 0-0
Keyword(s):  
Customer Value ◽  
Marketing Strategies ◽  
Machine Learning Algorithms ◽  
Entropy Weight ◽  
Rfm Model ◽  
Consumption Data ◽  
Entropy Weight Method ◽  
Customer Profiling ◽  
Weight Method ◽  
Rmf Model

Collecting and mining customer consumption data are crucial to assess customer value and predict customer consumption behaviors. This paper proposes a new procedure, based on an improved Random Forest Model by: adding a new indicator, joining the RFMS-based method to a K-means algorithm with the Entropy Weight Method applied in computing the customer value index, classifying customers to different categories, and then constructing a consumption forecasting model whose RMSE is the smallest in all kinds of data mining models. The results show that identifying customers by this improved RMF model and customer value index facilitates customer profiling, and forecasting customer consumption enables the development of more precise marketing strategies.

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