customer profiling ethics
149776162
Profiling the international academic ghost writers who are providing low cost essays and assignments for the contract cheating industry
Purpose Students have direct access to academic ghost writers who are able to provide for their assessment needs without the student needing to do any of the work. These ghost writers are helping to fuel the international industry of contract cheating, raising ethical dilemmas, but not much is known about the writers, their business or how they operate. This paper aims to explore how the ghost writers market their services and operate, based on observable information. Design/methodology/approach This paper reviews data from providers actively offering contract cheating services available to the public on Fiverr.com, a low cost micro outsourcing site. The search term write essay is used to identify providers, finding 103 Gigs from 96 unique providers. Visible information, such as provider marketing, advertised services, pricing information and customer reviews, is analysed. Findings The results demonstrate that bespoke essays are readily available to students at a low cost. The majority of providers operate from Kenya. Revenue calculations indicate a price point of US$31.73 per 1,000 words, below the cost of traditional essay mills, but show that these 96 providers have generated around US$270,000 of essay writing business between them. Originality/value This study affords a look into a complex and established industry whose inner workings are normally kept private and for which little published information currently exists. The research adds to what is known about the extent, location and operation of the contract cheating industry.
J. Inf. Commun. Ethics Soc.
Thomas Lancaster
104292752
Cognitive computing for customer profiling: meta classification for gender prediction
Analyzing data from micro blogs is an increasingly interesting option for enterprises to learn about customer sentiments, public opinion, or unsatisfied needs. A better understanding of the underlying customer profiles (considering e.g. gender or age) can substantially enhance the economic value of the customer intimacy provided by this type of analytics. In a design science approach, we draw on information processing theory and meta machine learning to propose an extendable, cognitive classifier that, for profiling purposes, integrates and combines various isolated base classifiers. We evaluate its feasibility and the performance via a technical experiment, its suitability in a real use case, and its utility via an expert workshop. Thus, we augment the body of knowledge by a cognitive method that enables the integration of existing, as well as emerging customer profiling classifiers for an improved overall prediction performance. Specifically, we contribute a concrete classifier to predict the gender of German speaking Twitter users. We enable enterprises to reap information from micro blog data to develop customer intimacy and to tailor individual offerings for smarter services.
Electron. Mark.
Robin Hirt Niklas Kuhl Gerhard Satzger
201687636
Customer Profiling in Complex Analytical Environments Using Swarm Intelligence Algorithms
Customer profiling is always an interesting task from perspective of business. It became even bigger challenge in situation of complex analytical environment. Complex analytical environment can be caused by multiple modality of output variable as well as from big data environment, which cause data complexity in way of data quantity. As an illustration of presented concept particle swarm optimization algorithm will be used as a tool, which will find profiles from developed predictive model of neural network. Presented methodology has practical value for decision support in business, where information about customer profiles which prefers to buy some product or group products are valuable information for campaign planning and customer portfolio management.
128034828
EVALUASI VALUE PROPOSITION DITINJAU DARI CUSTOMER PROFILING, VALUE MAPPING DAN FIT PADA PERUSAHAAN CAHAYA PUTRA BERJAYA
Industri otomotif memiliki perkembangan yang pesat dan memberikan dampak yang cukup besar bagi perekonomian di Indonesia. Peluang tersebut dimanfaatkan oleh Cahaya Putra Berjaya untuk membangun perusahaan yang bergerak di bidang otomotif dan mengeluarkan produk sabun cuci mobil tanpa air (Glaive Waterless Car Wash) Glaive Waterless Car Wash sendiri memiliki value proposition yang dapat bermanfaat bagi konsumen, yaitu kepraktisan dalam mencuci mobil karena dapat mencuci mobil dimana saja dan kapan saja, selain itu tidak harus repot menggunakan air dan memiliki harga yang terjangkau. Tujuan dari penelitian ini adalah untuk mengevaluasi value proposition pada produk Glaive Waterless Car Wash, hasil dari evaluasi ini akan digunakan perusahaan Cahaya Putra Berjaya untuk dapat memperbaiki kelemahan dan meningkatkan kekuatan yang telah ada. Cara melakukan evaluasi pada penelitian ini menggunakan metode dari Alexander Osterwalder yaitu customer profiling, value mapping dan fit. Metode penelitian ini menggunakan metode kualitatif dengan melakukan wawancara secara langsung kepada enam narasumber secara semi terstruktur. Hasil dari penelitian ini adalah perusahaan Cahaya Putra Berjaya selama ini hanya sebatas problem solution fit saja, dimana hanya bisa menjawab kebutuhan konsumen saja. Perusahaan Cahaya Putra Berjaya mendapatkan banyak masukan dan kritik dari beberapa sumber informan dan perusahaan sejenis yang dapat digunakan untuk bisa mengembangkan perusahaan Cahaya Putra Berjaya menjadi lebih baik lagi.
Elfrad Brian Gunawan Fransisca Desiana Pranatasari
88479549
A Customer Profiling' Machine Learning Approach, for In store Sales in Fast Fashion
In the last few decades, fast fashion retailers competitiveness is highly increasing, to get the market shares. With few historical data, the data analysis is a real challenge in this field. From the other hand, the customer service focus is a must, since the expectations of consumers are extremely selective. In this context, data science and machine learning tools are the latest trend in big data analysis, with short calculation time, that attract leader from many sectors to test their abilities in problem solving. This paper is a contribution to a machine learning based procedure, for customer profiling in fast fashion retail. It helps to build linking rules, of customers and their choices. Results will be a support in customer assistance, for increasing the in store sales basket size.
Naila Fares Maria Lebbar Najiba Sbihi
56442129
Customer profiling using CEP architecture in a Big Data context
Today Big Data tools are not just a phenomenon of the massive information collection; they are also the best way to approach a customer target. These technologies allow the profiling of the customers of an organization thanks to the histories of purchases, the products that they consult; the data that they share through the social networks. They also make it possible to anticipate the purchase of actions via behavioral analysis. Therefore, the combination of the power of CRM and the performance of BIG DATA tools brings a great added value for customers profile analysis, especially if it is about events triggered in real time. It is in this context that the present work is positioned. Our goal is to intercept events (customer behaviors) and analyze them in real time. We will use the Complex Events Process (CEP) architecture that perfectly meets this need. In order to successfully implement our CEP architecture, we will use the ontology approach.
Zakaria Elyusufi Yasyn Elyusufi M Aitkbir
198325255
Adaptive Customer Profiling for Telecom Churn Prediction Using Computation Intelligence
Nowadays, the telecom industries are going through a big problem that is customer churn. Recently, the market for mobile telecom industry has to change very promptly and there is a ferocious competition between them. Most of the telecom companies always concentrate on to obtain a new customer, but they do not pay too much attention to their existing customer. That's why the company tries to find out that customers those have tendency to switch over in future. The information picked up from telecom industry to find out the logic of churning and try to solve those problems. The company targets those customers with a special program. The aim of this paper is to predict the customer churn for telecom industries using machine learning techniques namely Logistic Regression, Naive Bayes and Decision Trees. In telecom industries, the principal objective of churning is to accurately calculate the customer survival and customer risk capabilities to gather the entire information of churn over the client residency. This paper summarizes the technique of predicting the churn so have a wide understanding of the customer churn. So that the telecom industries are aware in advance the big hazard customer and rectify their services to repeal the decision of churn. Customer profiling for predicting the customer who have churned in advance are also analyzed.
CICBA
Swarup Kumar Das Soumen Kundu Subrata Majee Chandrika Sarkar Manju Pathania Biswas
169404525
CUSTOMER PROFILING AND SEGMENTATION IN RETAIL BANKS USING DATA MINING TECHNIQUES
The objective of achieving profitability is one of the main targets of any banking sector for longer sustainable existence. The customer satisfaction index determines the longevity in relation of customer bank and thereby provides the idea of devising new policies and strategies for healthy connection of customers with the bank. Offering the services and products to the customer based on his choice and needs requires understanding the customer. The customer data available in the bank can provide the deep insights to the bank for designing the customized service and products. Deriving useful information from customer data using data mining techniques is of paramount importance in these days. Leveraging existing granular customer data can help banks gain deep actionable customer insights useful to understand the customers and to reveal and unlock opportunities for increasing profitability .customer segmentation and profiling are vital in achieving two main objectives of CRM(Customer Relationship Management)i.e. customer retention and customer development. The main aims of customer profiling and segmentation include expanding customer base, design of tailor made products, micro targeting of sales, aligning right channels for right products, increasing effectiveness of cross selling and up selling, enhanced customer experience by focused customer relationship, prioritizing relationship with high value customers, effectively managing cost with low value customers based on the profiling and segmentation of customers. In this paper we are using data mining techniques i.e. Naive Bayes classification algorithm for customer profiling and BIRCH clustering algorithm for customer segmentation
Mohd Mubasher Hassan
201069307
Improve Profiling Bank Customer's Behavior Using Machine Learning
In the banking industry, credit card evolution is a noticeable occurrence. Each banking system includes a huge dataset for customer's transactions of their credit cards. Therefore, banks would be in need of customer profiling. Profiling bank customer's cognize the issuer's decisions about whom to give banking facilities and what a credit limit to provide. It also helps the issuers get a better understanding of their potential and current customers. In previous research, Customer profiling mainly depends on transaction data or demographic data, but in this research, we merge both data in order to get a more accurate result and minimize the risk. By finding the best technique, it leads to improvement in accuracy and helps banks to get higher profitability by customer satisfaction through a focus on the valuable customer (companies) which consider as the main engine in the bank's profitability. This study aims at using k mean, improved k mean, fuzzy c means and neural networks. The used dataset is labeled and creating a ynew label as a target for neural network classification is the main aspect of this study, which helps to reduce the clustering execution time and get the best accuracy results. Finally, by comparing the accuracy ratio it shows that the neural network yis the best clustering technique.
IEEE Access
Emad Abd Elaziz Dawood Essamedean Elfakhrany Fahima A Maghraby
52815650
Profiling ethics orientation through play
ABSTRACT Research studies and recruitment processes often rely on psychometric instruments to profile respondents with regards to their ethical orientation. Completing such questionnaires can be tedious and is prone to self presentation bias. Noting how video games often expose players to complex plots, filled with dilemmas and morally dubious options, the opportunity emerges to evaluate player's moral orientation by analysing their in game behaviour. In order to explore the feasibility of such an approach, we examine how users' moral judgment correlates with choices they make in non linear narratives, frequently present in video games. An interactive narrative presenting several moral dilemmas was created. An initial user study (N 80) revealed only weak correlations between the users' choices and their ethical inclinations in all ethical scales. However, by training a genetic algorithm on this data set to quantify the influence of each branch on recognising moral inclination we found a strong positive correlation between choice behaviour and self reported ethical inclinations on a second independent group of participants (N 20) The contribution of this work is to demonstrate how genetic algorithms can be applied in interactive stories to profile users' ethical stance.
Behav. Inf. Technol.
Carlos Pereira Santos Vassilis-Javed Khan Panos P Markopoulos