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International Journal of Business Forecasting and Marketing Intelligence最新文献

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Machine learning based classification and segmentation techniques for CRM: a customer analytics 基于机器学习的客户关系管理分类和细分技术:客户分析
International Journal of Business Forecasting and Marketing Intelligence Pub Date : 2020-09-18 DOI: 10.1504/ijbfmi.2020.10031824
Narendra Singh, Pushpa Singh, Krishnavir Singh, Akansha Singh
{"title":"Machine learning based classification and segmentation techniques for CRM: a customer analytics","authors":"Narendra Singh, Pushpa Singh, Krishnavir Singh, Akansha Singh","doi":"10.1504/ijbfmi.2020.10031824","DOIUrl":"https://doi.org/10.1504/ijbfmi.2020.10031824","url":null,"abstract":"Machine learning and data mining help companies to build a tool that can make and take actions based on customer knowledge and information. Customer information is the base of maintaining long term relationship with customers and also known as customer relationship and management (CRM). Classification and segmentation of customer data set is utilised to maintain efficient relation with customers and subsequently increase the profitability and productivity. In this paper, author proposed customer segmentation based on demographic properties like gender, age and spending score and analysed the data set for interesting fact. The derived attribute data set is investigated for classification. Classification is used to categorise each customer into a number of classes, i.e., 'gold', 'silver', 'elite' and 'occasional'. Comparison of different classification algorithm is simulated by WEKA tool. Multi-layer perceptron (MLP) is found as the best classification algorithm with an accuracy of 98.33% compared to Naive Bayes, regression and J48.","PeriodicalId":253351,"journal":{"name":"International Journal of Business Forecasting and Marketing Intelligence","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115768963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
Neuromarketing: A change in marketing tools and techniques 神经营销:营销工具和技术的变化
International Journal of Business Forecasting and Marketing Intelligence Pub Date : 2019-12-05 DOI: 10.1504/ijbfmi.2020.10023522
Sadhna Shukla
{"title":"Neuromarketing: A change in marketing tools and techniques","authors":"Sadhna Shukla","doi":"10.1504/ijbfmi.2020.10023522","DOIUrl":"https://doi.org/10.1504/ijbfmi.2020.10023522","url":null,"abstract":"The much-hyped field in cognitive sciences, neuromarketing, is still in its nascent stage which examines the brain responses to various marketing stimuli in order to comprehend the physiological genesis of consumer's choice. This study provides an integrative outline of the sophisticated tools and technologies used by neuromarketers for gaining hidden insight into consumer behaviour. Conventional marketing research approaches provided biased, intermediary solutions in revealing the true opinion of consumers. This paper also strives to present a snapshot of merits and demerits of traditional marketing and advance neuromarketing research approaches. However, earlier studies on neuromarketing have explored how different methodologies of neuromarketing are used to reveal the mysteries of consumer decision-making. Attempt has been made to integrate all the methodologies of market research to effectively probe and synthesise customer's underlying emotions with the help of triune brain model. Thereby, concluding with ethical issues and dilemma faced by the neuromarketing companies in today's world.","PeriodicalId":253351,"journal":{"name":"International Journal of Business Forecasting and Marketing Intelligence","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121787195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
An interpretative structural modelling approach to Brand experience for consumer durables brands 耐用品品牌品牌体验的解释性结构建模方法
International Journal of Business Forecasting and Marketing Intelligence Pub Date : 2019-12-05 DOI: 10.1504/ijbfmi.2020.10024053
Mukesh Govind Kharat, Rekha D. Chikhalkar, M. Jha, M. G. Kharat
{"title":"An interpretative structural modelling approach to Brand experience for consumer durables brands","authors":"Mukesh Govind Kharat, Rekha D. Chikhalkar, M. Jha, M. G. Kharat","doi":"10.1504/ijbfmi.2020.10024053","DOIUrl":"https://doi.org/10.1504/ijbfmi.2020.10024053","url":null,"abstract":"The present study aims to recognise the facilitator and outcome variables of brand experience and investigate interlinks to develop a relationship among them. The variables were identified from the germane literature and brainstorming sessions with the academic and industry experts. Interpretative structural modelling (ISM) is applied to understand and model the relationship between the variables and their influence over each other. Further, the driving and dependence power is calculated to evaluate their influence. Results show that brand-related stimuli, appearance and marketing initiatives variables require a great deal of consideration due to their strategic significance. The results can be referred to as devise a marketing plan for offering a memorable and pleasant brand experience. The present study has significant implications to provide superior brand experience and strategic positioning. The study can be useful in managing and allocating the resources by understanding the strength and weakness of the studied variables.","PeriodicalId":253351,"journal":{"name":"International Journal of Business Forecasting and Marketing Intelligence","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127169609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Acceptance of Mobile payments and UPI technology- Indian context 接受移动支付和UPI技术-印度背景
International Journal of Business Forecasting and Marketing Intelligence Pub Date : 2019-12-05 DOI: 10.1504/ijbfmi.2020.10025239
Sruthy S. Pillai, G. Sandhya, G. Rejikumar
{"title":"Acceptance of Mobile payments and UPI technology- Indian context","authors":"Sruthy S. Pillai, G. Sandhya, G. Rejikumar","doi":"10.1504/ijbfmi.2020.10025239","DOIUrl":"https://doi.org/10.1504/ijbfmi.2020.10025239","url":null,"abstract":"The trend shows that more people prefer using the non-cash modes when cash shortage was at its crest even for small transaction. The context paved way for the researchers to examine consumer. We deployed the technology acceptance model (TAM) as the base theoretical framework to investigate into the subject matter. The premise for adopting the technology acceptance model is to measure and analyse the behaviour and attitude of customers towards adopting of mobile payment and UPI technology for transactions. The results suggested that there is a significant positive influence of simplicity and interoperability on the mobile payment adoption, timely contacts and security proved to have a negative influence on the dependent variable. People are more concerned about the security aspects as they believe that their monetary details are not safe while doing online transactions.","PeriodicalId":253351,"journal":{"name":"International Journal of Business Forecasting and Marketing Intelligence","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116717999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Fashion self-concept and Fashion involvement: A mediation model 时尚自我概念与时尚参与:一个中介模型
International Journal of Business Forecasting and Marketing Intelligence Pub Date : 2019-12-05 DOI: 10.1504/ijbfmi.2019.10025238
Rajan Saxena, Kiran Desai
{"title":"Fashion self-concept and Fashion involvement: A mediation model","authors":"Rajan Saxena, Kiran Desai","doi":"10.1504/ijbfmi.2019.10025238","DOIUrl":"https://doi.org/10.1504/ijbfmi.2019.10025238","url":null,"abstract":"Single item fashion self-concept measures are useful in forecasting clothing expenditure. Actual fashion self-concept explains more variation than ideal fashion self-concept. Self-concept measures were mediated by the fashion involvement index in explaining their relationship with clothing expenditures. The short five item fashion involvement index has echoed its utility in fashion research in Indian context as well. A self-reported pen and paper survey amongst 159 respondents was conducted in a mega-city of India. The model was assessed for mediation relationships using Amos.","PeriodicalId":253351,"journal":{"name":"International Journal of Business Forecasting and Marketing Intelligence","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123944139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Predicting Indian Basket Crude Prices through Machine Learning Models-A Comparative Approach 通过机器学习模型预测印度一篮子原油价格——一种比较方法
International Journal of Business Forecasting and Marketing Intelligence Pub Date : 2019-12-05 DOI: 10.1504/ijbfmi.2019.10024188
P. Mitra, Charu Banga
{"title":"Predicting Indian Basket Crude Prices through Machine Learning Models-A Comparative Approach","authors":"P. Mitra, Charu Banga","doi":"10.1504/ijbfmi.2019.10024188","DOIUrl":"https://doi.org/10.1504/ijbfmi.2019.10024188","url":null,"abstract":"Forecasting crude price can bring some stability in the decision making process for the firms dealing with it. Crude oil is a very volatile commodity so only linear time series modelling is not sufficient to predict its price. A nonlinear model like an artificial neural network is a better choice. The paper tries to test the prediction accuracy of a conventional neural network model and deep learning model using monthly data of Indian basket price of crude oil for 18 years. A simple MLP neural network model and a deep learning model of long short-term memory are used in the present study to find accuracies in predicting the crude price. The paper finds that a simple MLP model can provide better forecasting accuracy compared to a complicated LSTM model.","PeriodicalId":253351,"journal":{"name":"International Journal of Business Forecasting and Marketing Intelligence","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116803010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Critical Success Factors for Digital Platforms in B2B and B2C Environments: An Explicative Multiple Case Study Approach B2B和B2C环境中数字平台的关键成功因素:一种解释性的多案例研究方法
International Journal of Business Forecasting and Marketing Intelligence Pub Date : 2019-12-05 DOI: 10.1504/ijbfmi.2020.10023986
Anika Berens, Stefan Kolb, H. Haase
{"title":"Critical Success Factors for Digital Platforms in B2B and B2C Environments: An Explicative Multiple Case Study Approach","authors":"Anika Berens, Stefan Kolb, H. Haase","doi":"10.1504/ijbfmi.2020.10023986","DOIUrl":"https://doi.org/10.1504/ijbfmi.2020.10023986","url":null,"abstract":"Digital platforms belong to the fastest growing businesses in today's world. This paper explores critical success factors for the establishment of such platforms, focusing on both the B2B and B2C context. For this purpose, it uses a novel and confirmative qualitative approach, conducting explicative case studies of six globally leading digital platforms. Although all research theses investigated (business model's frontend and backend, agility, team diversity, ecosystem partnerships, visionary leadership and growth engines) were relevant for the analysed platforms, pivoting and branding strategy resulted as outstanding success factors. From a theoretical viewpoint, the paper contributes to enhancing the knowledge about the underlying drivers of digital platform businesses and offers options for future research. In practical terms, the outcomes contribute to effective platform creation and management. In particular, factors such as first-mover strategies, technology and design, frictionless entering, transparent information and ease of use lead to certain competitive advantages of digital platforms.","PeriodicalId":253351,"journal":{"name":"International Journal of Business Forecasting and Marketing Intelligence","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129072230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Comparing third-party logistics network and fourth-party logistics network using social network analysis approach 运用社会网络分析方法对第三方物流网络与第四方物流网络进行比较
International Journal of Business Forecasting and Marketing Intelligence Pub Date : 2019-08-02 DOI: 10.1504/IJBFMI.2019.10023033
Sumit Sakhuja, V. Jain, Shilpa Jain
{"title":"Comparing third-party logistics network and fourth-party logistics network using social network analysis approach","authors":"Sumit Sakhuja, V. Jain, Shilpa Jain","doi":"10.1504/IJBFMI.2019.10023033","DOIUrl":"https://doi.org/10.1504/IJBFMI.2019.10023033","url":null,"abstract":"Logistics services are keys to the success of almost all the manufacturing firms. Logistics companies have great responsibility to minimise the supply chain risks. Third-party logistics provider has limited role in the supply chain, whereas fourth-party logistics provider performs series of operations in the supply chain. In this study, the role of logistics service providers has been quantified and the structure of 3PL and 4PL network is compared using social network analysis approach. A case study on logistics network has been discussed in this paper which is analysed with the help of social network analysis approach. The role of individual players in 3PL and 4PL has been described based on the outcomes of social network analysis of the networks. The work is an attempt to understand the role of key players in the network and thus can aid in making important decisions while collaborating with partners and making negotiations.","PeriodicalId":253351,"journal":{"name":"International Journal of Business Forecasting and Marketing Intelligence","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124805469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
VANFIS: virtual adaptive neuro-fuzzy inference system for modeling and forecasting stock data 用于股票数据建模和预测的虚拟自适应神经模糊推理系统
International Journal of Business Forecasting and Marketing Intelligence Pub Date : 2019-08-02 DOI: 10.1504/IJBFMI.2019.10022518
S. Nayak, B. Misra
{"title":"VANFIS: virtual adaptive neuro-fuzzy inference system for modeling and forecasting stock data","authors":"S. Nayak, B. Misra","doi":"10.1504/IJBFMI.2019.10022518","DOIUrl":"https://doi.org/10.1504/IJBFMI.2019.10022518","url":null,"abstract":"Random fluctuations take place in stock data. At fluctuation points, it is difficult to predict the next data points from the previous data. Close enough data points are more informative to the forecasting process but not available adequately. This article presents deterministic and stochastic methods for exploration of virtual data positions from actual data and incorporated to original dataset. An adaptive neuro-fuzzy inference system (ANFIS) is exposed to such virtual data positions and works in a virtual environment. The model is termed as VANFIS, i.e., virtual ANFIS and employed to infer future stock indices of real stock markets. The performance of VANFIS methods are validated using 15 years data from ten stock markets and using five metrics. Also, relative worth of proposed methods are carried out over ANFIS. Experimental results show the significant improvement in prediction accuracy when proposed methods adopted.","PeriodicalId":253351,"journal":{"name":"International Journal of Business Forecasting and Marketing Intelligence","volume":"203 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131735448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Cross selling of investment products and services: a case study of leading financial services organisation 投资产品和服务的交叉销售:领先金融服务机构的案例研究
International Journal of Business Forecasting and Marketing Intelligence Pub Date : 2019-08-02 DOI: 10.1504/IJBFMI.2019.10023043
S. Dutta, Sujoy Bhattacharya
{"title":"Cross selling of investment products and services: a case study of leading financial services organisation","authors":"S. Dutta, Sujoy Bhattacharya","doi":"10.1504/IJBFMI.2019.10023043","DOIUrl":"https://doi.org/10.1504/IJBFMI.2019.10023043","url":null,"abstract":"We have utilised 360-degree view of the customer information from multiple aspects of financial transaction, product purchase history and account details and provide insights to marketing teams in identifying the potential customers who can be targeted for cross sell of different financial investment products. Advanced analytics algorithms and statistical techniques like deep learning, neural network and logistic regression were applied on the transformed data to predict likelihood of cross sell response different financial product. The analysis of customer data helps to identify the potential customers who can be targeted for cross sell of different investment products and services. The result indicates deep leaning approach provide outstanding performance compare to neural network and logistic regression.","PeriodicalId":253351,"journal":{"name":"International Journal of Business Forecasting and Marketing Intelligence","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116596696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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