Customer Profiles Research Articles

Understanding the load profiles and electricity consumption patterns of PV mini-grid customers in rural off-grid east africa: A data-driven study

This paper analyzes the load profiles and electricity consumption patterns of different customer types electrified by off-grid solar photovoltaic (PV) mini-grids in two remote towns in Ethiopia using metered data collected over a 20-month period and a survey of 238 customers. Findings show that the load profiles of mini-grid customers differ significantly across locations, sectors, and time. The load curves at site one (Omorate) are interrupted and completely shed off for 13 h every day due to the demand consistently exceeding the generation. By contrast, the mini-grid at site two (Tum) generates enough electricity to meet the demand continuously. The average daily electricity consumption at Omorate, 1065 kW h, is more than 1.5 times the consumption at Tum, 640 kW h; despite the fact that the mini-grid at Omorate has a significantly lower installed capacity than the one at Tum. At both sites, the monthly consumption of productive users is more than three times that of households. At both sites, demand for electricity has significantly increased over time, but at varying rates. Regression analyses showed significant differences in the factors influencing electricity consumption between the two towns. Key policy implications of the study are discussed for informed planning of rural electrification through mini-grids.

Mathematical Modelling and Deep Learning: Innovations in E-Commerce Sentiment Analysis

This research explores e-commerce dynamics, focusing on the challenge of predicting customer churn using deep learning [65]. It integrates and analyses both textual and transactional data, including social media posts and customer feedback [59]. The approach uses an advanced deep learning model, involving data collection, pre-processing, and feature extraction [40]. Novel methods fuse data to create a detailed customer profile combining sentiment analysis with behavioural insights derived from transaction data [25]. The deep learning architecture is designed to analyse and predict customer sentiments and purchasing behaviours, informed by the latest research [65]. This study is significant as it provides an innovative solution for predicting customer churn in e-commerce, aiding sustainability [45]. It also enables targeted retention strategies and personalized customer engagement [59]. Additionally, it contributes insights to big data analytics and customer relationship management in e-commerce, showcasing deep learning's potential in transforming business practices and enhancing customer experience [40].

English

Customer profiling tools have been introduced in recent years to protect investors against the increasing complexity of financial instruments. Advisor experts have to combine measures of finance and behavioural finance studies in their evaluation. The need to integrate different competences and compare complex criteria naturally leads to the modelling of the portfolio selection problem as a group decision problem. The Analytic Network Process (ANP) is a useful technique that helps decision-makers evaluate the available alternatives and aggregate their professional knowledge. In this context, the Reaching Consensus Process (RCP) is crucial for the validity of choices due to the strong sensitivity of the ANP to the decision maker's judgement. However, the RCP may be affected by the behavioural characteristics of experts, the vulnerability of judgements and the uncertainty that characterises the decision-making context, leading to unbiased judgements that are not based on different expertise. Similarly, without the involvement of more experts, the process could be inefficient due to the sensitivity of the ANP decision-maker’s judgement. In order to maximise the aggregation of experts' knowledge, to minimise the influence of the individual in quantifying the relationships between criteria and alternatives, and to preserve the impartiality of evaluations, the present paper discusses RCP through Choquet’s discrete integral. This aggregation captures the non-linearity of preferences and includes the fuzziness of judgments. Thus, there is no need to reach a consensus when evaluating the criteria: each expert manages a single ANP whose synthesis are aggregated using Choquet’s discrete integral. In this sense, the aggregation defines an unbiased consensus which mitigates the sensitivity of the ANP to the preferences of the decision-maker. The methodology is applied to a case study in which several experts evaluate investment alternatives of an individual, simultaneously incorporating behavioural criteria and Markets in Financial Instruments Directive (MiFID) client profiling criteria.

Development of the Tourism Industry in a Region: Digital Decision-Making Tools Based on Geoanalytical Data

Relevance and objectives of the research. Consumer research, development of customer profiles of target segments is an important activity of any organization, including the tourism business and tourism administrations. Knowing the target audience allows you to create a value-oriented client-oriented offer of tourist products, plan tourist flows and develop territories. The purpose of this study is to demonstrate how analytical work with digital data on the behavior of tourists and the construction of digital tourist profiles based on the obtained data can be used for the most effective management of the tourism industry in the Russian Federation, as well as the creation of customer-oriented regional tourism products.Materials and methods. The methodological base of the study was made up of general scientific methods, methods of analysis of secondary and primary data. The following research methods were used in the work: the method of analysis and synthesis, comparison and generalization of information, the method of induction and deduction, secondary and primary marketing research, digital analytical data. The information base of the study was the materials of the Federal State Statistics Service, Penzastat, the Ministry of Culture and Tourism of the Penza Region, documents defining the strategic development of the tourism industry in the Russian Federation, the results of scientific and marketing research of the tourism market, data from the GeoEffect analytical digital platform of the MTS software, authors’ own research.Results. Based on the data of the GeoEffect digital platform, an analysis was made of the structure of tourist flows in the Penza region, digital profiles of tourists were described, the most popular visiting areas were identified, on the basis of which recommendations were developed for a roadmap for tourism development in the Penza region.Conclusions . Digital data analytics and building digital profiles of tourists allows the tourism business to take into account consumer behavior in their activities, and tourism administrations to focus the resources of the region on objects and locations that are attractive from the point of view of consumers, forming a value proposition for them.

Innovation, a necessity for organizations to get competitive advantage and business perennity

Innovation is extremely important for organizations to prosper in the current market, but achieving success using innovation may not be such a simple task, as each organization needs a different form of innovation given its specificity and the objective it intends to achieve. One way to identify the best way to innovate for an organization is through the Value Proposition Canvas, because through it it will be possible to draw the profile of customers, understanding their pain points, in order to satisfy their needs. Thus, organizations can innovate through the aspects of products and services, value proposition, processes, marketing, organizational, among others. However, this is just the beginning of the innovative process, as innovation must be understood and incorporated by everyone in the organization, a very important factor for its operation. In this way, it is necessary to install the innovation mentality within the organization, stimulating creativity, allowing failures to obtain improvements, and promoting a collaborative environment. Using flexibility, training knowledge, and recognizing the accomplishments of employees will be essential to create a culture of innovation, thus providing competitive advantage and business continuity. This paper aims to highlight the need for innovation in organizations, identifying in which areas innovation can occur to obtain competitive advantage and business continuity, how to install an innovation mindset within organizations, noting which resources and practices are used to create this culture of innovation, and understanding its concepts and importance.

'Selling the Third Sector' - Critical Processes to Create Successful Marketing Initiatives for Charities and Other Nonprofit Organisations

Purpose: The purpose of this research is to explore the marketing strategies and practices employed by charity and community organisations to engage with stakeholders, attract and retain funding, and raise their organisational profile. The study aims to provide an holistic view of marketing activities, identify unique selling points, define products or services, and target populations, compile customer profiles, and develop marketing initiatives tailored to individual requirements. Methods: The research employed a mixed-methods approach, involving interviews with practitioners and a review of key articles from principal journals. Data was extracted using inductive methods, and recommendations were formulated using a systems approach to assist practitioners in creating successful marketing initiatives. Findings: The study identified key themes and practices in marketing strategies for charity and community organisations. These included the importance of identifying unique selling points, defining products or services and target populations, compiling customer profiles, and developing tailored marketing initiatives. The findings also highlighted the need for diversification of funding sources, evaluation of potential activities using organisational principles, and the use of key performance indicators to define standards of delivery. Originality: This research contributes to the existing literature by providing a comprehensive framework for marketing strategies in charity and community organisations. Unlike previous studies that have focused on specific aspects of marketing, this study offers a holistic view of marketing activities, including the identification of unique selling points, customer profiling, and tailored marketing initiatives. The use of a systems approach in formulating recommendations adds to the originality of the study. Keywords: charity marketing, community marketing, unique selling point, customer profiles, key performance indicators, funding diversification

Role of digital maturity and customer profiling: potentials for lead generation - a study in oil and gas industry

Role of digital maturity and customer profiling: potentials for lead generation - a study in oil and gas industry

Personalized Electricity Tariff Recommendation Method for Residential Customers Lacking Historical Metering Data Incorporating Customer Profiles and Behavioral Changes

Personalized Electricity Tariff Recommendation Method for Residential Customers Lacking Historical Metering Data Incorporating Customer Profiles and Behavioral Changes

Remote Identification of Users as a Management and Marketing Tool

Introduction. Despite the large number of publications dedicated to the problems of remote identification in remote service delivery and remote work, most of these studies focus on the technical aspects, while its role as a managerial and marketing tool remains underexplored. The aim of this article is to identify the marketing and managerial functions of remote identification. Methods. The authors rely on works published in peer-reviewed Russian and foreign publications. Results. The article shows that remote identification allows for the creation of customer profiles, which in turn enables the company to create personalized offers for its clients and manage their behavior. Furthermore, remote identification reduces the company's expenses on paying permanent employees and creating job positions, as it creates opportunities for remote employment. The advantages and disadvantages of remote identification as a marketing and management tool are revealed. Based on the classification of models, it is shown that remote identification can be active and passive, as well as direct and indirect. Conclusions. Promising directions for research include the development of seamless remote identification models to enhance the quality of customer service, as well as the establishment of a legal framework for conducting remote identification.

Customer profiling, segmentation, and sales prediction using AI in direct marketing

In the current business environment, where the customer is the primary focus, effective communication between marketing and senior management is vital for success. Effective customer profiling is a cornerstone of strategic decision-making for digital start-ups seeking sustainable growth and customer satisfaction. This research investigates the clustering of customers based on recency, frequency, and monetary (RFM) analysis and employs validation metrics to derive optimal clusters. The K-means clustering algorithm, coupled with the Elbow method, Silhouette coefficient, and Gap Statistics method, facilitates the identification of distinct customer segments. The study unveils three primary clusters with unique characteristics: new customers (Cluster A), best customers (Cluster B), and intermittent customers (Cluster C). For platform-based Edutech start-ups, Cluster A underscores the importance of tailored learning content and support, Cluster B emphasizes personalized incentives, and Cluster C suggests re-engagement strategies. By understanding and addressing the diverse needs of these clusters, digital start-ups can forge enduring connections, optimize customer engagement, and fuel sustainable business growth.

GREEN PACKAGING AND BRANDING IMPACT ON GREEN PURCHASE INTENTION WITH GREEN BRAND IMAGE AS A MEDIATOR: STUDY AT LOVE BEAUTY AND PLANET OF GEN Z IN WEST JAVA

In the present era, there has been a widespread global adoption of environmental consciousness, leading to a swift transformation in people's actions and attitudes. Observing this occurrence, companies are starting to adjust and incorporate environmental considerations into account by creating and producing environmentally sustainable products or services. One of the ways of contributing is by providing sustainable packaging options for their products.This article aims to examine the influence of green packaging and branding on the intention of Generation Z consumers in West Java to purchase Love Beauty and Planet items that are environmentally friendly. A digital survey was undertaken to gather data from 255 individuals. Subsequently, the questionnaire data was analyzed using Structural Equation Modelling (SEM) employing the Partial Least Squares (PLS) technique through SmartPLS 4. In the end, this study found that Gen Z in West Java sees green packaging and branding as a significant direct force towards green purchase invention. This study also found that green brand image is an important factor in driving green purchase intention. However, this study concentrates on a particular brand and customer profile. Therefore, future research can investigate different brands or specialized markets as the primary subject.

Psychographic and demographic segmentation and customer profiling in mobile fintech services

PurposeThis research aims to comprehend the factors that impact the emerging inclination of consumers toward mobile finance technology (fintech) services over banking institutions. The study focuses on users' demographics and psychographics to delineate their unique segments and profiles.Design/methodology/approachThe study proposes a segmentation and profiling framework that includes variance analysis, two-step cluster analysis and pairwise statistical tests. This framework is applied to a dataset of customers using a range of mobile fintech services, specifically robo-investment, peer-to-peer (P2P) payments, robo-advisory and digital savings. The analysis creates distinct customer profile clusters, which are later validated using pairwise statistical tests based on segmentation output.FindingsEmpirical results reveal that P2P payment service users exhibit a higher frequency of usage, proficiency and intention to continue using the service compared to users of robo-investment or digital savings platforms. In contrast, individuals utilizing robo-advisory services are identified to have a significantly greater familiarity and intention to sustain engagement with the service compared to digital savings users.Practical implicationsThe findings provide financial institutions, especially traditional banks with actionable insights into their customer base. This information enables them to identify specific customer needs and preferences, thereby allowing them to tailor products and services accordingly. Ultimately, this understanding may strategically position traditional banks to maintain competitiveness amidst the increasing prominence of fintech enterprises.Originality/valueThis research provides an in-depth examination of customer segments and profiles within the mobile fintech services sphere, thus giving a nuanced understanding of customer behavior and preferences and generating practical recommendations for banks and other financial institutions. This study thereby sets the stage for further research and paves the way for developing personalized products and services in the evolving fintech landscape.

Network-based exploratory data analysis and explainable three-stage deep clustering for financial customer profiling

Effective customer segmentation and communication of these findings to non-experts is a pressing task in the financial services sector, with the potential for widespread applications. This study employs a three-stage dimension reduction and clustering technique to segment a large, high-dimensional dataset, emphasizing explainability and intuitive visualization. We present the high-dimensional data and feature set using novel network-based visualization methods and identify the multi-stage process's optimal configuration. The approach segments 14,837 potential customers, each with 163 categorical and 143 numerical features. The first stage of the dimension reduction process employs deep neural network-based autoencoders. The second and third stage uses a non-neural network-based dimension reduction algorithm and clustering algorithm contingent on clustering performance. Subsequently, game theory-inspired Shapley values are computed for each feature to enhance explainability. The optimal approach involves an autoencoder, isometric mapping to three dimensions, and K-means clustering. Lastly, we derive investment portfolios for each segment to demonstrate an expert system application in financial investment advisory to underscore the importance of explainable segmentations.

Above 20 GHz high frequency operation of mid-infrared doughnut-shaped microcavity quantum cascade lasers

Microresonator-based high-speed single-mode quantum cascade lasers are ideal candidates for on-chip optical data interconnection and high sensitivity gas sensing in the mid-infrared spectral range. In this paper, we propose a high frequency operation of single-mode doughnut-shaped microcavity quantum cascade laser at ∼4.6 µm. By leveraging compact micro-ring resonators and integrating with grounded coplanar waveguide transmission lines, we have greatly reduced the parasitics originating from both the device and wire bonding. In addition, a selective heat dissipation scheme was introduced to improve the thermal characteristics of the device by semi-insulating InP infill regrowth. The highest continuous wave operating temperature of the device reaches 288 K. A maximum −3 dB bandwidth of 11 GHz and a cut-off frequency exceeding 20 GHz in a microwave rectification technique are obtained. Benefiting from the notch at the short axis of the microcavity resonator, a highly customized far-field profile with an in-plane beam divergence angle of 2.4° is achieved.

Exploring the socio-economic dimensions of agri-input customer profiles: A case study in farming communities

Exploring the socio-economic dimensions of agri-input customer profiles: A case study in farming communities

Exploring consumer segments defined by affective responses to naturalness in logo design

PurposeFocusing on small- and medium-sized enterprises (SMEs), which are characterized by resource restrictions, this paper aims to explore consumer segment profiles by considering demographic, personality and creativity traits to determine whether consumers with different profiles exhibit distinct affective reactions to different logo design types (organic, cultural and abstract).Design/methodology/approachThis exploratory study incorporates recent methodological developments, such as the novel response style correction method, to account for response style effects in evaluations of affect toward logo design. In separate analyses, respondents are segmented according to response style–corrected logo affect and personality and creativity items. The segmentation analysis relies on reduced k-means, a joint dimension and cluster analysis method, which accounts for dependencies between items while maximizing between-cluster variability. A total of 866 respondents from the Iberian Peninsula (Portugal: n = 543; Spain: n = 323) participated.FindingsBased on a study using unknown logos (proxy for lower levels of budget communication, characteristics of SMEs), results reveal that there are three segments of consumers based on their affective response toward logo design: logo design insensitives, cultural logo dislikers and organic logo lovers. These segments are associated with different personality traits, creativity and biological sex (although biological sex is not a discriminant variable).Research limitations/implicationsThe decision not to control logos by color, to increase external validity, could limit the study’s internal validity if this aspect interacts with relevant study variables. Nevertheless, the empirical evidence can be used to further test associations between consumer profiles and responses to logo design.Practical implicationsFindings highlight the relevance of considering complex profile segments, combining demographics, psychographics and creativity to predict affective consumer responses to brand logo design. This research provides guidelines for SMEs when choosing or modifying their logo design to appeal to different consumer segments.Originality/valueThis study provides managers of SMEs (less present nowadays in empirical studies) with evidence suggesting that complex customer profiles help to understand differences in affective responses to natural logo designs. Furthermore, it relies on the use of a novel methodological development that improves the accuracy of the exploratory study developed.

Boutique Hotel Service Digitalization: A Business Owner Study

The COVID-19 pandemic has generated negative, economic impacts on the tourism and leisure sector in Thailand, especially small boutique hotels. These hotels have had to develop more efficient and innovative approaches to meet new normal expectations, for example, contactless service. Digital technologies, such as Machine Learning and Artificial Intelligence, can open new possibilities and opportunities for hotels to digitize their customers’ services. A review of the literature indicated that data important to the management of hotel products and services include Customer Segmentation, Customer Profiling, Menu Engineering, Productivity Indexing, Customer Associations, Forecasting, Energy Consumption, and Room Rates. These characteristics can be examined by machine learning. This study used a mixed qualitative and quantitative research method. The data were gathered by interviewing two boutique hotel owners in Bangkok and collecting the hotels’ data, including online travel booking agents and direct booking logs, for the period April 2016 – September 2021. The analysis was conducted using the booking data from the two hotels: 3946 records from Hotel A and 3948 from Hotel B. In this research, k-means clustering was used to segment hotel guests. Two-class logistic regression and a two-class boosted decision tree were used to predict the prospective customer, while linear regression and decision forest regression were used to forecast the market demand. The findings reveal a model of hotel business owners’ requirements to innovate new service solutions, such as the contactless software solution, that guests can employ for check-in, check-out, order services, and talk to the hotel through the mobile application. This would help hotel owners to manage costs, employees, and customers. The solution also means that hotel managers would no longer need to be involved in the manual implementation of revenue management tasks. This data analytics approach can effectively sift through the signals detected from market variables, discover patterns and anomalies, make predictions for guest arrivals, and calculate optimum prices in real-time, as the market changes.

Customer Profiling System with Residual Network-Based Face Recognition

Customer profiling is an essential aspect of customer relationship management. Knowing who your customers are, what they need, and how to reach them is crucial in creating an effective marketing strategy. However, it can be challenging for some sellers to identify and track their loyal customers. This is where a customer profiling system can be invaluable. Such a system uses data analysis and deep learning techniques to track customer behaviour and identify preferences. One approach to customer profiling is through face recognition technology. Facial recognition is an effective method for identifying people, and it can be used to track customer attendance and identify regular customers. Therefore, this work presented the development of a customer profiling system using a deep learning technique to detect customer faces in real time. Experimental results showed that the system obtained 90% accuracy in detecting customers' faces. This work conducted a user acceptance test (UAT) to evaluate the system's effectiveness. The results indicated that the system provides many benefits and advantages to customers and sellers, including improved customer loyalty and satisfaction.

Error theory study on EKF-based SOC and effective error estimation strategy for Li-ion batteries

Accurate state-of-charge (SOC) estimation is crucial for ensuring the safe and reliable operation of battery management systems (BMS). Among the various algorithms used for SOC estimation in real-vehicle BMS, the extended Kalman filter (EKF) algorithm holds significance due to its adherence to optimal estimation principles and its property of insensitivity to initial values. By studying the relationship between error sources and SOC estimation errors, it becomes possible to develop targeted measures for enhancing the accuracy of SOC estimation based on the EKF. From a probabilistic perspective, this paper derives theoretical equations that establish the connection between SOC estimation errors and various error sources, including measured voltage, measured current, open circuit voltage curve, capacity, ohmic internal resistance, and polarization resistance. Furthermore, the paper analyzes the relationship among multiple error sources in generating SOC estimation errors. Building upon the outcomes of this theoretical analysis, a joint SOC estimation method that combines the EKF with Ampere-hour counting (AHC) is employed to identify errors. In scenarios where simultaneous faults occur in the current and voltage sensors, they are identified based on the slope and Euclidean distance of the two SOC trajectories, respectively. Subsequently, sensor faults correction and SOC compensation are implemented by leveraging simplified equations involving capacity and SOC increments and measured voltage and SOC estimation errors. In addition to addressing sensor faults, the paper also considers battery model parameter errors. By incorporating customized current pulse profile and theoretical equations relating to error sources and SOC estimation errors, a comprehensive error estimation of model parameters is achieved, capable of handling single and multiple errors. The derived simplification bridges the gap between error sources and SOC estimation errors, offering a novel approach for parameter sensitivity analysis and a theoretical foundation for quantifying these errors. The experimental results demonstrate the effectiveness and rapidity of the proposed method for identifying and correcting sensor faults and model parameter errors.

Improving the Data Management: ETL Implementation on Data Warehouse at Indonesian Vehicle Insurance Industry

Unknowingly, risk is an essential part of an individual's life. Every day individuals have the potential to be threatened by possibilities that can produce something that has fatal social, human, and financial consequences. Insurance can help individuals to relieve the financial burden caused by unwanted things by transferring individual losses to insurance companies. This transfer of losses will distinguish individuals from possible bankruptcy and financial security. Automotive insurance is a liability for loss or damage to motorized vehicles. This time we will take the example of an insurance company specializing in automotive insurance, namely Top Gear Insurance (TGI). This company uses customer data storage using an ordinary manual database that does not yet use a data warehouse system. This ordinary manual database makes it difficult for TGI to retrieve data for reprocessing and makes data inaccessible from anywhere, asynchronous, concise, and inefficient. There is a solution to the TGI problem of creating a data warehouse with a star schema approach for storing and processing data. The data warehouse is likely to make the data within the company more accessible, efficient, simple, and understandable so that TGI can develop its business through data analysis from the data it already has. Datawarehouse has many business advantages, such as increasing Business Intelligence, data quality and consistency, saving time, and supporting historical data analysis and queries. The data warehouse consists of datamart, OLTP, OLAP, and Star Schema. Using Mondrian as a visualization showed that TGI can get information about customer data, policies, and claims easily, quickly, and concisely. That can also help TGI create customer profiles and targeted marketing and company evaluation based on the visualization provided.