Customer-related Data Research Articles

A cloud infrastructure for enabling provision of the Material Testing as a Service (MTaaS)

This paper focuses on a ‘cloud infrastructure’ (MTaaS) that has been developed to support and coordinate integrated services and supply chain management. This will enable the seamless operation of a dynamic and adaptive system providing customer-centred material testing services. The cloud system is designed not only for decision making regarding the evaluation of the situations brought by industry and translating them into upscaling, characterisation and service workflows, but also for promoting existing products, improving facility usage and providing expertise consultancy. The development of MTaaS involves specifying the cloud infrastructure requirement, defining the back-end and front-end system architecture and modules, and outlining the operation fields of each module along with associated terminology, parameters and contents. This includes handling material testing service registration, verification, marketing, composition, preparation, offering, processing, monitoring as well as customer inquiries, monitoring and feedback. These efforts make MTaaS integrate product, service and customer-related information into a single platform, encompassing necessary operation related data, customer related data, inventory data and supply chain data.

A novel framework for investigating organizational adoption of AI-integrated CRM systems in the healthcare sector; using a hybrid fuzzy decision-making approach

CRMS (Customer Relationship Management system) is a brand-new technology that assists hospitals in managing their relationship with patients. Organizations’ analysis methods of huge customer-related data have been subjected to a massive change because of the Artificial Intelligence (AI) integrated CRMS. This integration enables healthcare institutes to quickly and precisely analyze the tremendous volume of patient-related data in order to make accurate, and automated decisions. On the contrary to prior surveys in the healthcare domain, which mainly focused on the individual acceptance of innovation, this study tries to consider both organizational and human-related factors to provide a better understanding of the CRMS adoption in hospitals. This paper will be the first of its kind to analyze and categorize parameters contributing to CRMS adoption in healthcare by introducing an integrated framework, including Human, Technology, Organization, Environment, and Cost (HTOEC) dimensions. The fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is applied to find the interdependence among adoption dimensions and variables. The Maximum Mean de-Entropy (MMDE) algorithm is exploited to choose the optimum threshold value in the DEMATEL method. The fuzzy Analytic Network Process (ANP) is deployed to prioritize adoption parameters. Based on the results, Environment and Human dimensions are the most crucial determinants of CRMS adoption in Iranian hospitals.

A DEA-based approach to customer value analysis

Firms have become increasingly customer-centric, implying that customers, rather than products, are treated as the most important asset of a firm. The switch to customer-centric strategies also implies that firms are collecting an enormous amount of customer-related data. The purpose of this paper is to propose a DEA-based methodology to determine the contribution of customer segments to firm value. We show the practical usefulness of our methodology through an application to Activity Based Costing (ABC) data collected from a large European telecom provider, which offers fixed telephone, mobile telephone, digital television and internet subscriptions. Our analysis reveals that the average cost reduction potential across all customer segments amounts to 1.26% of the total controllable costs, which represents approximately EUR 5 million when expressed in monetary terms. We also document substantial variation in the cost reduction potential across customer segments.

E-Banking Services and Performance of Cooperative Bank of Oromia

Electronic banking service is growing rapidly in the world, Cooperative bank of Oromia is putting its maximum exertion to travel with technology of E- banking services. E-banking has its own effect on financial performance of the bank. This research mainly investigates the effect of E- banking on the financial performance of the Cooperative Bank of Oromia by focusing on the bank’s performance and size. For primary data collection branch staffs and managers will be interviewed while secondary data will be collected from branch documents like report of daily transactions, profit of branch and customer related data will be reviewed. Keywords: Mobile Banking, Performance, Bank DOI: 10.7176/EJBM/14-17-04 Publication date: September 30 th 2022

Multi-Feature Extraction Method of Power Customer’s Portrait based on Knowledge Map and Label Extraction

In order to realize the visualization of power customer characteristics and better provide power services for power customers, a multi-feature extraction method of power customer’s portrait based on knowledge map and label extraction is studied. The power customer’s portrait construction model is designed, which uses the knowledge map construction link to collect the power customer related data from the power system official website and database, and clean and convert the data; In the multi-feature analysis section, natural language processing technology is used to analyze the characteristics of power customers through Chinese word segmentation, vocabulary weight determination and emotion calculation; Based on the feature analysis results, the portrait label is extracted to generate the power customer’s portrait. The power customer’s portrait is used to realize the application of power customer’s feature visualization, power customer recommendation, power customer evaluation and so on. The experimental results show that this method can effectively construct the knowledge map of power customers, accurately extract the characteristics of power customers, generate labels, and realize the visualization of power customer’s portraits.

Research on Early Warning of Customer Churn Based on Mutual Information and Integrated Learning—Taking Ctrip as an Example

With the increasing competition pressure among tourism e-commerce platforms, how to reduce customer churn to the greatest extent is of great significance to tourism e-commerce platforms. Based on this, this paper takes Ctrip's hotel customer-related data as an example, and first uses a supervised feature selection method based on mutual information to select features that have an important impact on customer churn. Then, by using the cross-validation method, combined with evaluation indicators such as accuracy rate, F1-sorce, AUC, etc., select the best model set from Logistic regression, Support Vector Machine, Decision Tree, Random Forest, GBDT, XGBoost, and LightGBM. A subset of the optimal models, and then the optimal model fusion is performed. The empirical results show that the multi-model fusion has higher accuracy and stability. In addition, based on the model fusion results, this paper obtains the importance ranking of customer personal characteristics. Finally, this paper puts forward relevant suggestions on how to accurately manage Ctrip and reduce the customer churn rate.

Marketing Strategy Based on CRM (Customer Relationship Management) at PT. Great Food Prosperity in Tangerang City

This study aims to produce a marketing model design based on CRM (Customer Relationship Management), in establishing long-term relationships with retailers and in increasing customer loyalty. The case study was conducted at PT Jaya Pangan Makmur which is engaged in the coffee business with the Harum Sweet brand. The implementation method with literature study, secondary and primary data was carried out to produce a model. The results of the activity show the marketing strategy design model based on CRM (Customer Relationship Management), in establishing long-term relationships with retailers, in introducing the existence of sweet fragrant coffee to the community. It is necessary to use information and communication technology to develop the functions of CRM which include strategic, operational and analytical functions. Customer-centered strategy that aims to win and retain customers, plays a strategic role in attracting and retaining its customers. Operations focuses on automating customer-facing processes such as sales, marketing, and customer service. Analytical CRM transforms customer-related data into customer-based data that is decrypted in the customer database program. It is important for companies to have customer data based, in order to maintain continuity of customers and ultimately reduce costs. The implementation of the Customer Relationship Management (CRM) program will help companies strengthen relationships with customers which can reduce costs and increase productivity and profitability in the business.

Web-based Information Management Systems Feature Usage Case Study on European Telecom Sector

Modern service providers use web-based information management systems (IMS’s) to enhance their relationship with customers. This helps them better manage customer-related data and information. Also, the use of search engines, user management features, chat-bots or forums can help customers manage their own data, like consumption or invoices. So, the use of web-based information management systems may be the ideal solution for a win-win relationship between a service provider and a customer. The study of the features embedded in websites may reveal some insights regarding the way they are used and how they will look like in the future.

App Launch for Customer Processes of a Real Estate Company

A paperless office is not only environmentally friendly but also advantageous in many aspects. The digitalisation of documents and corresponding processes saves time, reduces mistakes and therefore realizes cost savings for companies. This time can be invested in better customer management. Real estate companies work with thousands of rented apartments and as many customers and thus have to maintain and manage a lot of customer related data. IT has already created manifold remedies with Enterprise-Resource-Planning (ERP) and Customer- Relationship-Management (CRM) systems and models, but there is still plenty of development potential to be exploited by integrating mobile solutions into the IT architecture. The main challenge thereby is not the programming of an app but the seamless integration into customer processes and the creation of customer acceptance and thus value. Therefore, it is investigated how mobile applications can better control customer processes and whether such an implementation is profitable. For this purpose, typical work and customer processes of real estate companies must be analysed, understood and transferred to appropriate mobile solutions. This paper examines the details which need to be considered in such projects.

Does Using Accounting Data in Performance Evaluations Spoil Team Creativity? The Role of Leadership Behavior

ABSTRACTOrganizations frequently use teams for creative problem-solving. Evaluating the performance of creative teams based on customer-related accounting data, such as client satisfaction measures, revenues, or profits, clarifies which kind of creativity is expected from the team and potentially makes the creative process more effective. However, the use of such accounting data in performance evaluations may amplify the tensions between the artistic merit and the commercial success of creative ideas and, thus, create conflicts that undermine team creativity. We argue that it depends on the team leader's leadership style whether the negative or positive effects of using customer-related data in performance evaluations on team creativity will prevail. If the team leader shows a leadership style that helps team members internalize the values and standards underlying the employed performance measures, then negative conflict inside the team is avoided and the positive effects of using customer-related data in performance evaluations can be realized.

Customer process management

PurposeThe proliferation of customer-related data provides companies with numerous service opportunities to create customer value. The purpose of this study is to develop a framework to use this data to provide services.Design/methodology/approachThis study conducted four action research projects on the use of customer-related data for service design with industry and government. Based on these projects, a practical framework was designed, applied, and validated, and was further refined by analyzing relevant service cases and incorporating the service and operations management literature.FindingsThe proposed customer process management (CPM) framework suggests steps a service provider can take when providing information to its customers to improve their processes and create more value-in-use by using data related to their processes. The applicability of this framework is illustrated using real examples from the action research projects and relevant literature.Originality/value“Using data to advance service” is a critical and timely research topic in the service literature. This study develops an original, specific framework for a company’s use of customer-related data to advance its services and create customer value. Moreover, the four projects with industry and government are early CPM case studies with real data.

APLIKASI AKUNTANSI BERBASIS WEB PADA UKM

Adiraka Group is a small and medium enterprises (SMEs) located in Bogor that engaged in furniture business. Adiraka Group currently still uses manual systems on Financial Records, which can lead to errors by the finance department in recording process. Adiraka Group often faces problem’s such a difficult to obtain customer related data because are not recorded in detail. The purpose of this final paper is to design and implement accounting. Interviews, observation, documentation of internship programs, literature studies, and internet sources are methods used in the application making process. Design and Implementation of accounting applications using PHP Hypertext Preprocessor and MySQL through several stages consist of menu structure design, interface design, and application implementation. The designed data will be processed using PHP Hypertext Preprocessor into table, queries, and report. The owner and finance departments can make decisions faster and easier using this application.

Analytics Driven Data Model in Digital Services

Data models are generally applied to construct consolidated abstraction of various rich and different domains of data. In this paper, we focus on the digital services domain in particularly customer related data model and its structure in helping to shape the analytics capabilities. The traditional Entity Relationship Diagram (ERD) is used as the cornerstone of the strategy and further elaboration is made through abstraction to encompass areas in the digital services. A data model is developed to cover both static aspects (customers’ profile) and dynamic aspects (customers’ behaviour). The foundation of the customer aspect is constructed in classes that represent different types of customer touch points represented as digital footprint which analogize physical activities. The customer dynamic aspects of digital service are modeled with a group of classes where priority is embodied in different associations involving creation and termination of the identified interaction. The suggested data model can be deployed in development of frameworks for customer related applications and enhancement of analytics capabilities. Keywords: Data model, Analytics, Digital services

Using customer-related data to enhance e-grocery home delivery

PurposeThe development of e-grocery allows people to purchase food online and benefit from home delivery service. Nevertheless, a high rate of failed deliveries due to the customer’s absence causes significant loss of logistics efficiency, especially for perishable food. The purpose of this paper is to propose an innovative approach to use customer-related data to optimize e-grocery home delivery. The approach estimates the absence probability of a customer by mining electricity consumption data, in order to improve the success rate of delivery and optimize transportation.Design/methodology/approachThe methodological approach consists of two stages: a data mining stage that estimates absence probabilities, and an optimization stage to optimize transportation.FindingsComputational experiments reveal that the proposed approach could reduce the total travel distance by 3-20 percent, and theoretically increase the success rate of first-round delivery approximately by18-26 percent.Research limitations/implicationsThe proposed approach combines two attractive research streams on data mining and transportation planning to provide a solution for e-commerce logistics.Practical implicationsThis study gives an insight to e-grocery retailers and carriers on how to use customer-related data to improve home delivery effectiveness and efficiency.Social implicationsThe proposed approach can be used to reduce environmental footprint generated by freight distribution in a city, and to improve customers’ experience on online shopping.Originality/valueBeing an experimental study, this work demonstrates the effectiveness of data-driven innovative solutions to e-grocery home delivery problem. The paper also provides a methodological approach to this line of research.

Dynamic communities in evolving customer networks: an analysis using landmark and sliding windows

The widespread availability of Customer Relationship Management applications in modern organizations, allows companies to collect and store vast amounts of high-detailed customer-related data. Making sense of these data using appropriate methods can yield insights into customers’ behaviour and preferences. The extracted knowledge can then be explored for marketing purposes. Social Network Analysis techniques can play a key role in business analytics. By modelling the implicit relationships among customers as a social network, it is possible to understand how patterns in these relationships translate into competitive advantages for the company. Additionally, the incorporation of the temporal dimension in such analysis can help detect market trends and changes in customers’ preferences. In this paper, we introduce a methodology to examine the dynamics of customer communities, which relies on two different time window models: a landmark and a sliding window. Landmark windows keep all the historical data and treat all nodes and links equally, even if they only appear at the early stages of the network life. Such approach is appropriate for the long-term analysis of networks, but may fail to provide a realistic picture of the current evolution. On the other hand, sliding windows focus on the most recent past thus allowing to capture current events. The application of the proposed methodology on a real-world customer network suggests that both window models provide complementary information. Nevertheless, the sliding window model is able to capture better the recent changes of the network.

Customers Behavior Modeling by Semi-Supervised Learning in Customer Relationship Management

Leveraging the power of increasing amounts of data to analyze customer base for attracting and retaining the most valuable customers is a major problem facing companies in this information age. Data mining technologies extract hidden information and knowledge from large data stored in databases or data warehouses, thereby supporting the corporate decision making process. CRM uses data mining (one of the elements of CRM) techniques to interact with customers. This study investigates the use of a technique, semi-supervised learning, for the management and analysis of customer-related data warehouse and information. The idea of semi-supervised learning is to learn not only from the labeled training data, but to exploit also the structural information in additionally available unlabeled data. The proposed semi-supervised method is a model by means of a feed-forward neural network trained by a back propagation algorithm (multi-layer perceptron) in order to predict the category of an unknown customer (potential customers). In addition, this technique can be used with Rapid Miner tools for both labeled and unlabeled data.

Application of data mining techniques in the financial sector for profitable customer relationship management

The paper presents the benefits of applying data mining (DM) techniques in customer relationship management (CRM) of the financial sectors like banking, forecasting stock market, currency exchange rate and bank bankruptcies. The competition in the financial sector of the business is growing and the firms find it very difficult to sustain the ever-changing behaviour of the customer. To sustain in the competitive world, firms are taking the advantage of the CRM, the new emerging concept in the business world. DM is helping the firms to achieve profitable and efficient CRM by providing them with advance techniques to analyse the already existing data in the databases of the firms using the complex modelling algorithms The paper demonstrates the ability of the data mining to automate the process of searching the mountain of customer's related data to find patterns that are good predictors of the behaviours of the customer which help achieve successful CRM. The paper gives an idea of how data mining capabilities can provide the increased customer retention and minimises the risk involved in the financial sectors to achieve competitive advantage and concludes by providing the limitations and opportunities in this field.

Integrating analytical Customer Relationship Management with Customer Relationship Management in organisations

This paper aims to examine how Customer Relationship Management (CRM) systems are in place in firms in practice with a focus on the customer needs and requirements, i.e., how analytical CRM (aCRM) systems are used to support existing CRM systems and how such a system can be integrated with CRM systems. Customer Relationship Management (CRM) has always been widely regarded for developing and retaining customers through their increased satisfaction and loyalty. This paper also discusses how customer information is used by powerful analytical CRM where CRM systems are already in place in organisations. To support the arguments made, the paper presents the analytical CRM framework. Analytical Customer touch points and interesting patterns of analytical customer related data are presented. A clear difference between CRM and aCRM is presented. The application of analytical CRM has been very recent and its visibility is substantially low, and the provision of these systems is limited to a few leading firms in the world. The paper explains the value of analytical CRM systems inform of customer touch points.

A framework for analytical CRM: a data mining perspective

The authors in this paper present customer relationship management (CRM) analytics and its framework from data mining (DM) perspective. The paper also presents the benefits to any enterprise that will reap the same by incorporating the framework. As all of us are aware, DM is the extraction of previously unknown and hidden information from the databases. Its tools can answer customer-related questions that were too time-consuming to resolve earlier and even in some case not possible to answer. They provide the hidden patterns, predictive information that experts may miss. Analytical CRM helps in better understanding of the customers by evaluating customer-related data using the tools like DM. Greenberg (2004) defined analytical CRM as the capture, storage, extraction, processing, interpretation and reporting of customer data to a user. The paper also justifies the framework with an empirical example.

Techniques, process and enterprise application for e-CRM using data mining

The paper presents the techniques, process and application of electronic CRM (e-CRM) using data mining (DM) techniques. The increase in the competition in every sector of the business has forced the business organisation to become a brand of universal acceptance. This is possible only when the accessibility of the organisation product is to every common people. The e-CRM has provided the means for the business organisation to achieve this. The e-CRM with the application of the DM tools can help better analyse the customer data which will enchase the profitability of the organisation and also helps to improve the relationship of the customers with the firms. The paper demonstrates the ability of the DM to automate the process of searching the mountain of customer's related data to find patterns that are good predictors of behaviours of the customers which help achieve successful e-CRM in the web domain. The paper gives an idea of how DM capabilities can increase the profitability of the firms by effective e-CRM.