Customer Behavior Analysis Research Articles

Research on the Impact of Customer Participation in the Innovation on Enterprise Innovation Performance Under the Background of MindCloud

The age of MindCloud gives more possibilities for companies to acquire the information of customer behaviour. This page analyzes the analyse the influence mechanism of customer participation on innovation performance, which can give the theoretical and managerial implication for companies. The results show that intelligent collection of customer behaviour and intelligent analysis of consumer behavior have significant impact on promoting company innovation performance. Meanwhile, the information from ordinary customer and special customer are all valuable for promoting company innovation performance.

Promoting fashion customer relationship management dimensions based on customer tendency to outfit matching: mining customer orientation and buying behaviour

The purpose of this study is to mining dimensions of customer relationship management (CRM) based on consumer tendency to outfit matching. Consumers are clustered into groups based on descriptive variables, consumer desire to outfit matching and customer relationship dimensions. According to the results of this research, female customers with the age 30 or younger, who have a bachelor degree and are single, are inspired to outfit matching and prefer customer involvement dimensions. Also, long-term partnership is the most significant dimensions of CRM for consumers who do not engage in outfit matching. Then, association rules were applied for extracting customer buying behaviour that influence customer tendency to outfit matching. These results can be useful for the fashion industry to apply more effective CRM systems and customise them with customer's preferences and behaviour analysis. Compared with traditional techniques, the data mining methods have great potential for investigating customers' preferences.

WITHDRAWN: Women’s e-commerce clothing sentiment analysis by probabilistic model LDA using R-SPARK

In today's fields of education and literature, electronic commerce (e-commerce) is a well-known subject. Excellent research on e-commerce focuses predominantly on developing economies. Customer behavior analysis is gaining more prominence as consumers move from visiting retail shops to shopping online from the e-commerce view. In this article, review is taken into account in the case of sentiment analysis to evaluate, implement and analyse the most effective techniques. In numerous research ventures, including in the area of text classification and information retrieval, probabilistic models currently play a major role. Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Indexing (PLSA) and Latent Dirichlet Allocation (LDA) represent probabilistic models. The LDA approach is used in this paper to define online feedback for sentiment analysis since the LDA approach is intended to address LSA and PLSA issues. Online shopping seems to become a central platform for shoppers with the development of e-commerce, and it is necessary to forecast the scale of e-commerce transactions. This paper is used to scan online data of women's apparel. By encouraging a more custom-made shopping experience, it will boost customer loyalty and sales, resulting in higher retention rates and viable improvement.

Customer behaviour analysis based on buying-data sparsity for multi-category products in pork industry: A hybrid approach

Understanding customer behaviour is crucial for business success. For achieving this goal, the Recency–Frequency–Monetary (RFM) model has been commonly recognised as an effective approach to analys...

INDUSTRIAL (B2B) MARKETING OF GLASS COMPANIES: MOBILE APPLICATIONS AS A SALES PROMOTION TOOL

The purpose of the paper is a qualitative analysis of mobile applications as a sales promotion tool in industrial (B2B) marketing of glass companies. Industrial marketing of the glass industry is poorly studied in terms of digital technology development and integration as well as mobile applications as tools for digital marketing communications. B2B companies’ digitalization is at the initial stage. Methodology. This research builds on the concepts of digital B2B marketing, the digitization capability of B2B companies, customer relationship building, and customer focus. The research methodology is based on the case study of ABC company and the content analysis of information available on the ABC’s website in the public domain. A content analysis was conducted of the patent for a method for determining the coated glass coating type using an application and a light source. Results demonstrate problems related to digital marketing concepts integration, customer focus, and customer relations formation. The results show an active digital strategy use for the company’s business growth through software development and integration of end product interactive visualization tools. As a result, additional business value is formed: 1) end customer company focus; 2) product knowledge development and B2B services personalization; 3) manufacturers transfer some sales and communication functions to their customers, which solves the problem of staff rotation in the sales subsystem; 4) the application becomes a tool for customer behaviour analysis. Mobile applications complement any traditional product promotion channels. The main domestic industrial market challenge is the poor rate of readiness for digital changes: applications become an effective marketing communication tool only when customers use them. Practical implications. Companies in different industries can use the results to understand real issues of applications integration in marketing strategy and communication. These problems include the digital unpreparedness of the Russian domestic market to accept new technological solutions. Value/originality. This research proves that mobile applications increase sales and is effective if company personnel, end users and B2B customers actively use them. Applications complement traditional B2B marketing channels.

Factors influencing millennials intention to use peer to peer accommodation

Our paper aimed to identify key factors affecting the use of peer to peer accommodation through the generation of millennials. The analysis of customer behaviour was based on a goal-oriented behavioural model (MGB) which was enlarged by the environmental responsibility and the impact of digital platforms. According to the findings of structural modelling, important influences on the desire to use a peer to peer (P2P) accommodation included the millennial generation’s attitudes towards social benefits and previous authentic experiences. The interesting finding is that environmental aspects alone were not a sufficient motivator to increase millennials’ desire to use a P2P accommodation.

Concept of Branding in General: An Analysis of Customers’ Perspective

When the word brand comes to mind, we, as a customer, can only think about those names which have created an impact in our mind. We do not keep those brand names in our mind which are not impactful. At the same time, it is a major concern for the brands to captivate the place in customers’ minds so that while making a purchase, customers can choose without having second thoughts. The concept of branding is very important in today’s globalized market, as there are lots of competitors in every field. It is the duty and responsibility of the top management of a company to maintain its brand image. But often they fail to do so due to lack of copy testing and customer behavior analysis. Furthermore, it is also important to know about customers’ perception on the concept ‘branding.’ This paper deals particularly with the perception of customers on the concept of branding. Researchers quantitatively analyzed the data collected from a survey to make conclusions which will help the managers to have an insight before making marketing strategies.

Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities

Big data analytics (BDA) in supply chain management (SCM) is receiving a growing attention. This is due to the fact that BDA has a wide range of applications in SCM, including customer behavior analysis, trend analysis, and demand prediction. In this survey, we investigate the predictive BDA applications in supply chain demand forecasting to propose a classification of these applications, identify the gaps, and provide insights for future research. We classify these algorithms and their applications in supply chain management into time-series forecasting, clustering, K-nearest-neighbors, neural networks, regression analysis, support vector machines, and support vector regression. This survey also points to the fact that the literature is particularly lacking on the applications of BDA for demand forecasting in the case of closed-loop supply chains (CLSCs) and accordingly highlights avenues for future research.

Using Big Data Analytics for Decision Making: Analyzing Customer Behavior using Association Rule Mining in a Gold, Silver, and Precious Metal Trading Company in Indonesia


 
 
 
 Indonesia is facing many challenges in the fourth industrial revolution (4IR) era. One of them is related to big data technologies and implementation that can be seen clearly from Indonesia Industry Readiness Index (INI) 4.0. Therefore, focusing on implementing big data analytics in a gold, silver, and precious metal trading company is the objective of this manuscript to support daily business operations. To be more specific, the aim is to discover meaningful patterns and ensure high quality of knowledge discovery from the big data available in a company in Indonesia. It is needed to support the Making Indonesia 4.0 as a roadmap to implement industrial digitalization in Indonesia. The methodology used for the big data implementation in this manuscript is the combination of the CRISP-DM framework and key steps for customer analytics. The result of this research is a list of recommendations that facilitate strategic planning based on evidence of measurable big data analytics to achieve the business goals of a company.
 
 
 

Promoting Fashion Customer Relationship Management Dimensions Based On Customer Tendency to Outfit Matching: Mining Customer Orientation and Buying Behavior

The purpose of this study is to mining dimensions of customer relationship management (CRM) based on consumer tendency to outfit matching. Consumers are clustered into groups based on descriptive variables, consumer desire to outfit matching and customer relationship dimensions. According to the results of this research, female customers with the age 30 or younger, who have a bachelor degree and are single, are inspired to outfit matching and prefer customer involvement dimensions. Also, long-term partnership is the most significant dimensions of CRM for consumers who do not engage in outfit matching. Then, association rules were applied for extracting customer buying behaviour that influence customer tendency to outfit matching. These results can be useful for the fashion industry to apply more effective CRM systems and customise them with customer's preferences and behaviour analysis. Compared with traditional techniques, the data mining methods have great potential for investigating customers' preferences.

Using Technology and Customer Behaviour Characteristics to Improve Hotel Sales Performance

Booking window is one of the critical characteristics of customer behaviour that can influence hotel sales performance. Previous studies were focused mainly on the importance of booking window reporting in revenue management with lack of evaluation. This paper focuses on the evaluation of revenue management activities by analysis of customer behaviour with a focus on the use of modern technologies (Booking Engine, Channel Manager). Results show that the selected hotel is not following basic revenue management principles, which can be a reason for the year-to-year decrease in direct online sales and overall poor performance.

Need of Business Analytics and Prediction Modeling in Retail Marketing in Indian Context

In the digital era, Indian Retail market growing fast than ever and creating stiff competitive business environment. So, analyzing customer behavior, buying pattern and the ability to customize the products to meet demand targeted customer in time has become more important. Therefore, analyzing, diagnosing and channelizing customer data for the benefits of customer as well as the business growth is important to survive in industry for long run. International retail players are already using effective customer analytics systems or big data analytics software’s for each and every stage of the retail process starting from market study of current trends, customer purchasing behavior, sales and demand forecasting through predictive analytics, product optimization, offers and promotions and many more. After all, identifying the targeted customers interested in a specific product line by the analysis of purchase history, customer demographics and finding out effective medium to reach them through Omni-channel marketing strategies is the core of customer analytics. This paper is an outcome of a descriptive research on the retail industry in India and the application of customer analytics to shape the retail industry’s business strategy.

Customer Behavior Analysis and Revenue Prediction System

In the era of e-commerce there are many organizations that have implemented customer behaviour analytics for their growth in business. It is a crucial challenge for the organizations in the e-commerce world to study and analyse the behaviour of the online buyers. The success of every organization is within the satisfaction of the customers they have and to gain new customers as well, and this is done by targeting the potential customers that can generate revenue to the organizations. RFM analysis is used to indicate recently buying customers, frequently buying customers, and huge spending customers. It is one of the best methods to segment organization’s revenue generating customers around other customers. Also 80/20 rule is implemented which focuses on the 20 percent of the customers that generate 80 percent of the revenue for the organization. The model is developed using Light GBM (Gradient Boosting Method) which is a machine learning algorithm.

RETRACTED ARTICLE: Customer behavior analysis using Naive Bayes with bagging homogeneous feature selection approach

The significant success of an organization greatly depends upon the consumers and their relationship with the organization. The knowledge of consumer behavioral and a excellent understanding of consumer expectations is important for the development of strategic management decisions in support of improving the business value. CRM is intensively applied in the analysis of consumer behavior patterns with the use of Machine Learning (ML) Techniques. Naive Bayes (NB) one of the ML supervised classification models is used to analyze customer behavior predictions. In some domain, the NB performance degrades which involves the existence of redundant, noisy and irrelevant attributes in the dataset, which is a violation of underlying assumption made by naive Bayes. Different enhancements have been suggested to enhance the primary assumption of the NB classifier-independence assumption between the attributes of given class label. In this research, we suggest a simple, straight forward and efficient approach called BHFS (Bagging Homogeneous Feature Selection) which is based upon Ensemble data perturbation feature selection methods. The BHFS method is applied to eliminate the correlated, irrelevant attributes in the dataset and selecting a stable feature subset for improving performance prediction of the NB model. The advantage of the BHFS method requires less running time and selects the best relevant attributes for the evaluation of naive Bayes. The Experimental outcomes demonstrate that the BHFS-naive Bayes model makes better predictions compared to the standard NB. The running time complexity is also less with BHFS-NB since the naive Bayes is constructed using selected features obtained from BHFS.

DOMINANT IDEAS OF FINANCIAL TECHNOLOGIES IN DIGITAL BANKING

The purpose of the research is the definition of the dominant ideas of financial technologies in digital banking. The methods of theoretical generalization, qualitative, quantitative and correlation analysis, causality tests, description and explanation are used, which made it possible to establish the relationship between the volume of investments in financial technologies and the performance of the banking system, identify the areas of application of financial technologies in the activities of the bank, determine the dominant ideas of financial technologies in digital banking and to uncover the factors and prospects of intensifying the use of financial technologies in digital banking in Ukraine. Results of the research are to substantiate the impact of artificial intelligence, biometrics, cloud services, big data, blockchain and open banking services on digital banking. Due to financial technologies in digital banking, it is possible to generate and store large amounts of data, simultaneously analyze and apply the results of their analysis, provide personalized banking services, perform the functions of central storage of information about the client of financial and non-financial nature, which facilitates the effective investment and credit decision-making, as well as improving the level of information security of banking operations. Practical implications. Financial services markets are transformed by the impact of financial technologies. Development of financial technology instruments by non-banking institutions necessitates the identification of opportunities for their use in banks. The set of financial technologies used by banks forms the digital banking system, the development level of which is the main competitive advantage of the bank in the business environment. Digital banking is characterized by the continuity and security of banking services, which provide the consumer with the ability to receive them online anywhere around the clock, personalization of banking services, digital authentication of users and digitization of banking transactions with the replacement of paperwork. The use of financial technologies in digital banking enables to automate customer segmentation processes, reduce costs on payment transactions, optimize accounting, financial and tax accounting, improve customer service and expand your customer base while maximizing revenue in certain business segments. Value/originality. The basic spheres of the use of financial technologies in digital banking, as well as the factors and prospects of intensifying the use of their instruments in Ukraine are revealed. The main areas of use of financial technologies in digital banking are customer behavior analysis, transaction monitoring, customer identification and segmentation, fraud management, banking services personification, risk assessment and regulatory compliance, customer response analysis, process automation, financial advice, investment decision-making, trade facilitation, syndicated loan services, and P2P transfers. The prospects for developing financial technology tools in digital banking include strengthening the interaction between regulators, banks and financial technology companies, the increased use of biometrics, the development of neo-banking and open banking services.

Customer Behavior Analysis using Weighted Selective Naive Bayesian

The predominant success of each enterprise relies upon the efficient analysis of customer behavior and a deep understanding of customers' needs. Performing better customer analysis provides an effective analysis of potential customers, better decision-making, improved business processes, the measure of customers churn and enhance customer retention. Efficient customer analysis can be performed using the Naive Bayes (NB), which is a simple classifier used for predicting customer behavior. However, the performance prediction of Naive Bayes is strongly affected in some real-time datasets which involve the presence of correlated attributes and this creates a breach of the assumption made by the Naive Bayes model on the dataset. To enhance the performance prediction of the NB and to eliminate correlated variables, this research suggests a simple WSNB (weighted Selective Naive Bayesian) method that uses the C4.5 DT for selecting the attributes with high information values. Then the selected weighted attributes are further used with the Naive Bayes for improving performance prediction. The Experimental approach is tested with a bank client dataset, which indicates that WSNB makes better predictions than the standard Naive Bayes. Also, WSNB reduces the running time of the classifier by eliminating the correlated attributes, which in effect minimize the size of learning and testing data.

Mouse Movement Pattern Based Analysis of Customer Behavior (CBA-MMP) Using Cloud Data Analytics

In the vast field of E-Commerce and E-Business, the appropriate way of customer analysis is very much important to make a business more successful. In such modes of online business, the behavior of customers is analyzed through various processes of data analytics to effectively satisfy them will improved services. The behaviors of the customers can be well analyzed through their mouse movement patterns in a very exact manner. With that concern, this paper contributes to developing a model customer behavior analysis based on the mouse movement pattern. This helps in deriving or mining information and aids to predict customer activities in the E-Market. Typically, for the behavioral analysis, one of the effective data mining algorithms called decision tree algorithm and a classification technique called Multi-layer Neural Network techniques have been incorporated. Through these techniques, customer behaviors are exactly analyzed and determined. For experimentation and evaluation, some benchmark datasets are used and the results show that the proposed model produces better analysis than existing works.

Anomalous Telecom Customer Behavior Detection and Clustering Analysis Based on ISP’s Operating Data

Mobile networks and smart phones have become ubiquitous in our daily life. Large amount of customer related telecom data from various sources are generated every day, from which diversified behavior patterns can be revealed, including some anomalous behaviors that are vicious. It becomes increasingly important to achieve both efficient and effective customer behavior analysis based on the telecom big data. In this paper, the Multi-faceted Telecom Customer Behavior Analysis (MTCBA) framework for anomalous telecom customer behavior detection and clustering analysis is proposed. In this framework, we further design the hierarchical Locality Sensitive Hashing-Local Outlier Factor (hierarchical LSH-LOF) scheme for suspicious customer detection, and the Autoencoders with Factorization Machines (FM-AE) structure for dimension reduction to achieve more efficient clustering. Hierarchical LSH-LOF is an improved algorithm of LOF, in which we design a hierarchical LSH process that selects the approximate k nearest neighbors from coarse to fine by gradually narrowing down the scope. Experiments show its superiority over KD-tree w.r.t searching speed. FM-AE exploits Factorization Machines for learning second order feature interactions, which we prove to be useful by designing comparative experiments with five dimension reduction algorithms. With the proposed MTCBA framework, efficient and effective telecom customer behavior analysis including anomalous customer behavior detection and clustering analysis is performed on the real world telecom operating data provided by one of the major Internet service providers (ISPs) in China. Meanwhile, interpretable clustering results of six clusters are obtained to provide valuable information for the precision marketing of telecom operators, criminal combating, and social credit system construction.

Joint Probabilistic People Detection in Overlapping Depth Images

Privacy-preserving high-quality people detection is a vital computer vision task for various indoor scenarios, e.g. people counting, customer behavior analysis, ambient assisted living or smart homes. In this work a novel approach for people detection in multiple overlapping depth images is proposed. We present a probabilistic framework utilizing a generative scene model to jointly exploit the multi-view image evidence, allowing us to detect people from arbitrary viewpoints. Our approach makes use of mean-field variational inference to not only estimate the maximum a posteriori (MAP) state but to also approximate the posterior probability distribution of people present in the scene. Evaluation shows state-of-the-art results on a novel data set for indoor people detection and tracking in depth images from the top-view with high perspective distortions. Furthermore it can be demonstrated that our approach (compared to the the mono-view setup) successfully exploits the multi-view image evidence and robustly converges in only a few iterations.

A Comparative Study using Feature Selection to Predict the Behaviour of Bank Customers

Though banks hold an abundance of data on their customers in general, it is not unusual for them to track the actions of the creditors regularly to improve the services they offer to them and understand why a lot of them choose to exit and shift to other banks. Analyzing customer behavior can be highly beneficial to the banks as they can reach out to their customers on a personal level and develop a business model that will improve the pricing structure, communication, advertising, and benefits for their customers and themselves. Features like the amount a customer credits every month, his salary per annum, the gender of the customer, etc. are used to classify them using machine learning algorithms like K Neighbors Classifier and Random Forest Classifier. On classifying the customers, banks can get an idea of who will be continuing with them and who will be leaving them in the near future. Our study determines to remove the features that are independent but are not influential to determine the status of the customers in the future without the loss of accuracy and to improve the model to see if this will also increase the accuracy of the results.