Analyze Customer Data Research Articles

Advocacy Identifier Framework for Recognizing the Measurable Characters of Online Brand Advocate

The rise of digital technology has shifted marketing power from companies to customers, enabling them to express opinions, share experiences, and support brands on online platforms. This phenomenon, known as online brand advocacy, represents a powerful form of customer influence; however, the specific characteristics of advocacy behavior remain unclear, posing challenges for marketers aiming to identify and engage brand advocates effectively. This research seeks to establish a framework for recognizing measurable traits of online brand advocates, based on a systematic literature review identifying three key domains: expression, engagement, and expenditure. Expression reflects customer satisfaction through ratings and reviews; engagement is shown by actions such as sharing reviews and contributing to the online community; and expenditure highlights purchasing patterns, including purchase frequency and average order value. An expert panel validated this framework using the Content Validity Index (CVI) and Cronbach’s alpha, confirming both its relevance and reliability. This framework provides a valuable tool for businesses, enabling them to analyze customer data, segment brand advocates, and leverage their support in the data-driven marketing landscape.

AI-Driven Personalization in Financial Services: Enhancing Customer Experience and Operational Efficiency

This article explores how emerging technologies are influencing personalization in the financial services sector. Personalized services enhance customer experience and promote financial inclusion. By leveraging big data and AI, financial institutions can collect and analyze customer data to offer tailored services. AI-driven analytics provide real-time insights for financial modeling, risk management, and automation. The integration of machine learning structures in financial services to meet individual customer needs, ensuring product customization. Emerging technologies promise greater accuracy and personalization, driving better financial outcomes within the industry. This technological advancement is crucial for the modern financial landscape, enhancing performance and customer satisfaction.

Cyber Chatter

Abstract: Cyber Chatter, a web-based application built on the MERN stack, uses the power of GPT (Generative Pre-trained Transformer) technology to provide basic educational chat services and staff Building and ensure efficiency and they have been able to do it well. Cyber Chatter uses GPT-3.5, an advanced generic bid process, to provide accurate and meaningful answers to customer questions. With prior training in a large body of content knowledge, this demonstration demonstrates the incredible ability to develop and generate human-like emotions The application uses machine learning techniques to analyze customer data and continues to improve response quality over time. The use of Cyber Chatter has expanded to include Content Summarizer, Section Generator, Chat with Chatbot, Sci-fi image generator. Understudies can post questions, get answers, and engage in interesting conversations by using its natural user interface with GPT -a chatbot, Cyber Chatter. Cyber Chatter not only covers educational topics but also includes such features as content generators and JavaScript converters. Combining innovative features and an easy-to-use system, Cyber Chatter turns into a versatile punch where students can explore educational tools and interact with intelligent chatbots on a regular basis has enriched their learning experience

ВИКОРИСТАННЯ МАШИННОГО НАВЧАННЯ ДЛЯ ОБРОБКИ ДАНИХ НА МІКРОКОМП’ЮТЕРАХ

Machine learning is a data analysis method that automates the construction of an analytical model. It is a branch of artificial intelligence based on the idea that machines should be able to learn and adapt through experience. It is closely related to computational statistics, which makes predictions based on statistical data collected by a computer. It is sometimes confused with data mining, but that is more focused on analyzing intelligence data, while machine learning contains complex algorithms that are used mainly for prediction, where machine learning concentrates on predicting based on already known attributes obtained through training data, then data mining focuses more on finding unknown attributes in any data. attributes in any data. Machine learning applications may be associated with spam filtering, optical character recognition, and search engines. Machine learning uses data to determine which algorithm is best for generating results based on the quantity, quality, and nature of the data. This data is then used for intelligent analysis in various ways, such as recommendation systems such as similar products on eBay, personalized content on google plus pages, video ads on sites such as YouTube, and last but not least, suggestions from friends on Facebook. Machine learning and artificial intelligence can be used to solve business problems, namely: Automating repetitive tasks such as data entry, document processing and customer service. This frees up employees' time for more complex work. Optimization of business processes. ML/AI can analyze data to identify inefficiencies and optimize business processes, increasing company productivity. Service personalization. ML/AI helps analyze customer data to provide them with personalized offers, recommendations and support. Automation of answers to questions and anticipation of customer needs. Detection of fraudulent transactions and cyber security. Risk management.

A novel approach to optimizing customer profiles in relation to business metrics

<span lang="EN-US">Business is very closely related to customers. Each user owns the data, and the data is used to identify cross-selling opportunities for each customer. For example, the type of product or service purchased, the frequency of purchases, geographic location, and so on. By doing so, you can gain the ability to manage and analyze customer data, allowing you to create new opportunities in industries that were previously difficult to enter. The purpose of optimizing user profiles is to determine minimum or maximum business value and improve efficiency by determining user needs. In this study, multivariate adaptive regression spline (MARS) is a statistical model used to explain the relationship between the response variable and the predictor variable. Robust is used to find variable relationships to make predictions. To improve classification performance, the model is validated using a confusion matrix. The results show an accuracy value of 84.5%, with better time management (period management) reflected in the number of hours spent by merchants as well as discounts during that time period, which has a significant impact on any business. In addition, the distance between customers and merchants is also important, as customers prefer merchants who are closer to them to save time and transportation costs.</span>

Internal and external threats to enterprise security: essence and diagnostics

The paper studies the essence and diagnostics of internal and external threats to enterprise security. External threats to the enterprise are identified, in particular, unfavorable changes in the political and economic situation in the country, the use of unfair competition methods, unreliability of suppliers, partners and contractors, natural and technical emergencies, unauthorized access of competitors to confidential information of the enterprise, imperfection of the regulatory framework, industrial and economic espionage, changes in consumer tastes and preferences. The author identifies internal threats to the enterprise, in particular, low professional level of the top management of the enterprise, violation of labor discipline, outflow of qualified personnel, decrease in the professional qualification level of personnel, violation of the regime of commercial secrecy, low level of innovation activity of the enterprise, lack of an effective planning system at all levels, low technical and technological levels of production. The author analyzes what negative consequences can result from threats to the security of an enterprise from its administrative and managerial staff and personnel in general, suppliers and other contractors and consumers. The author identifies the tools whose implementation can preventively influence key business processes and minimize negative consequences in the event of threats. As for the tool that has effectively proven itself among enterprises in developed countries and is used to support the internal environment in terms of developing managerial and human resources, it is worth noting the corporate entrepreneurship assessment tool (CEAI). As for the tool that is actively used, including by domestic entrepreneurs, to collect and analyze customer data, which allows storing information about the history of interaction with customers and increasing their loyalty, it is worth mentioning a CRM system.

Трансформація комунікаційної політики для забезпечення резильєнтності підприємств роздрібної торгівлі

The communication policy of a trade enterprise always depends on the general goals of the enterprise, but it is impossible to consider the communications of a trade enterprise as a permanent component. Communication policy is a moving thing, it is constantly influenced by internal and external factors, which gives impetus to the company’s marketers to constantly adjust it to modern market requirements. The aim of the article is to determine the resilience of marketing strategies of trade enterprises in relation to communication and advertising activities under the influence of macro factors. We include the following macro factors: the COVID-19 pandemic and the full-scale military aggression of the russian federation from 24.02.2022 onward. We analyzed the relevant literature and found that the impact of general macro factors on the marketing strategy of trade enterprises is mostly incomprehensible and unpredictable, which requires new approaches to marketing activities. In particular, modern open sources and scientific literature do not sufficiently examine the methods and technologies of retailers in the communication strategy under the influence of stress and crisis. The study of contemporary behavior in the communication policy of trade enterprises in the market, including the growth of online commerce, has led to a shift in emphasis to digital advertising, which promotes sales via the Internet. Advertisers are actively working to promote products and services that can be purchased online. The pandemic has created many uncertainties, and advertisers have proven to be more flexible and ready to adapt quickly to changes in economic and social conditions. Particular attention should be paid to the advertising activities of trade enterprises during the wartime, that can become a significant challenge and require a careful approach. The warime creates its own unique challenges for businesses and marketers, and it is important to consider ethical and social aspects in advertising campaigns. We have summarized the aspects of the influence of external factors on the advertising activities of trade enterprises during the wartime. Since most retail sales still take place offline, trade marketers are reviewing the in-store experience, trying to combine the reach of physical retail with the involvement of digital channels, actively implementing in-store media communication. An effective in-store retail media strategy isn’t as simple as adding advertising around the store. This strategy requires technology to collect and analyze customer data, including where the customer went to in the store (particular departments), how much time they spent and what they did in there. It is necessary to thoroughly work out a map of buyer behavior. In the course of the study, the key components of advertising technologies in stores were identified, which should be considered in order to collect high-quality marketing information on consumer behavior, and subsequently – to convey advertising information to them at a specific point of sale. It is concluded that in the future, it will be extremely important for stores to apply a seamless omnichannel customer experience at the point of sale. Numerous studies demonstrate the positive impact of technology in retail on the customer journey to the store, as well as the customer journey in the store, from product selection to payment for purchase. Further development of communication strategies of retail trade enterprises envisages the use of modern technologies, the involvement of artificial intelligence and the improvement of customer service methods in trading floors.

Opportunities and challenges of artificial intelligence in banking and financial services

AI is a crucial element of the banking and financial industries, providing affordable and reliable banking services. The market for AI in the banking sector is expected to grow at a CAGR of 32.6% from 2021 to 2030, reaching $64.03 billion by 2030. Banks use AI technologies to automate their operational processes, improve customer support, and mitigate potential risks, ultimately increasing efficiency and productivity. AI can help banks identify fraudulent activities and prevent them from occurring. It can also analyze customer data and provide personalized services to customers. Additionally, AI can help banks make better decisions by analyzing large amounts of data. By using AI, banks can reduce costs and increase profits while providing better services to their customers.

Retail Business Strategy In Increasing Sales Through Innovation And Customer Experience

In retail, strategies in increasing sales through innovation and customer experience can be a critical factor in business success. In this case, artificial intelligence can be used to analyze customer data and market trends to develop innovations and customer experiences that can increase sales. To achieve this goal, it is necessary to implement an effective data management strategy and the use of appropriate technology to collect and analyze customer data in real-time. Thus, retail businesses can understand customer behavior, and design innovations that suit their needs. In addition, artificial intelligence can help retail businesses in the development of effective and relevant marketing strategies, leading to increased customer traction and experience. Through data analysis, retail businesses can identify market trends and customer needs, and develop innovative solutions that can improve customer experience. In addition, artificial intelligence can help retail businesses in developing effective and relevant marketing strategies, leading to increased customer attraction and experience.

Розвиток малого підприємництва в територіальних громадах України в післявоєнний період

The article deals with the ways of small business development in Ukrainian territorial communities in the post-war period in Ukraine. The aim of this article is to give recommendations and ideas on small business development in Ukrainian territorial communities in the post-war period. In order to achieve the research goal, the following tasks were used: the scientific literature analysis on the research topic, the secondary data analysis from open Internet resources, and providing ideas for small business development in Ukraine’s territorial communities in the post-war period. The following ideas for small business development in Ukraine in the post-war period are proposed: the relationship between small businesses in territorial communities and e-government in improving their services; using artificial intelligence in order to collect and analyze customer data to make informed decisions about the target audience and its preferences; a hub creation for individual entrepreneurs, where they can exchange their ideas; on the basis of the Politech-Consult CTC to create courses for aspiring individual entrepreneurs from territorial communities, where they will be taught all the aspects of opening and running their small business.

Enterprise marketing strategy using big data mining technology combined with XGBoost model in the new economic era.

The technological development in the new economic era has brought challenges to enterprises. Enterprises need to use massive and effective consumption information to provide customers with high-quality customized services. Big data technology has strong mining ability. The relevant theories of computer data mining technology are summarized to optimize the marketing strategy of enterprises. The application of data mining in precision marketing services is analyzed. Extreme Gradient Boosting (XGBoost) has shown strong advantages in machine learning algorithms. In order to help enterprises to analyze customer data quickly and accurately, the characteristics of XGBoost feedback are used to reverse the main factors that can affect customer activation cards, and effective analysis is carried out for these factors. The data obtained from the analysis points out the direction of effective marketing for potential customers to be activated. Finally, the performance of XGBoost is compared with the other three methods. The characteristics that affect the top 7 prediction results are tested for differences. The results show that: (1) the accuracy and recall rate of the proposed model are higher than other algorithms, and the performance is the best. (2) The significance p values of the features included in the test are all less than 0.001. The data shows that there is a very significant difference between the proposed features and the results of activation or not. The contributions of this paper are mainly reflected in two aspects. 1. Four precision marketing strategies based on big data mining are designed to provide scientific support for enterprise decision-making. 2. The improvement of the connection rate and stickiness between enterprises and customers has played a huge driving role in overall customer marketing.

AI-driven ensemble three machine learning to enhance digital marketing strategies in the food delivery business

PurposeThis study focuses on the use of ensemble machine learning (ML) in digital marketing for the food delivery business. MethodologyArtificial intelligence (AI) techniques are used to analyze customer data, identify customer preferences, and predict customer behavior to provide AI-based recommendations. The ensemble method combines the outputs of decision trees, naïve Bayes, and nearest neighbor algorithms to generate a single prediction. FindingsThe accuracy matrix plots for both the decision tree and nearest neighbor algorithms yielded perfect predictions, with an accuracy of 100.000% and 0.000 error, respectively. Meanwhile, the naïve Bayes algorithm had an overall accuracy matrix of 97.175%, with a 0.028 error, indicating successful identification of the correct labels across all classes with a high level of accuracy. OriginalityThe majority voting method with a probability success rate greater than 90% can potentially integrate models into this process while utilizing less than half the randomized data, blended with customer experience data, thus reducing customer irritation. The driven ensemble of three ML algorithms is shown to successfully improve digital marketing strategies in the food delivery business by decreasing time and costs.

Takeout Platform Uses Big Data for Price Discrimination—Meituan Takeout Platform Case Study

In the era of big data, more and more take-out platforms use algorithms to analyze customer data. These algorithms will enable users to retrieve items that are more suitable for them, and also accelerate the speed of their receipt of goods, so that users can get a better shopping experience. However, the takeaway platform uses these data to implement price discrimination through algorithms, and treats different users differently, thus damaging the rights and interests of consumers. The price discrimination based on big data algorithm has the characteristics of concealment and complexity. Therefore, there are certain difficulties in the governance of this behavior, which requires the joint efforts of the national legal department, take-out platform and consumers.

ANALYSIS AND VISUALIZATION OF MARKET SEGEMENTATION IN BANKING SECTOR USING KMEANS MACHINE LEARNING ALGORITHM

Segmentation is a way of assigning each dataset to a segment called cluster. It is widely applied in different area of human endeavor such as banking sector, health sector, retail, media etc. Many organizations are faced with problems of ineffective customer care services and intelligent management decisions because of inability to effectively analyze customer data that will give insight to the nature of customers to help in effective customer services and intelligent management decision. Kmeans algorithm is the widely used algorithm for market segmentation, normally the k value of Kmeans algorithm are randomly picked. Picking the optimal k value is usually a challenge in application of Kmeans algorithm and this usually affects the performance of Kmeans algorithm. This work applies elbow method to obtain the optimal k value that was applied to analyze dataset from banking sector (in this case United Bank of Africa) for better insight, business management and marketing strategy. The customer cluster created was evaluated using visual plots and cluster centers. The optimal k value of six (6) was obtained using the elbow function. The dataset was thus segmented based on the optimal k value of 6 obtained. The clustering results obtained showed high intra cluster similarity (data within a cluster are similar) and low inter cluster similarity (data from different clusters are dissimilar). The result also showed that customers in cluster 3 and 4 has similar marketing needs and can be served together

A social CRM analytic framework for improving customer retention, acquisition, and conversion

• Proposes a social CRM analytic framework for improving customer retention, acquisition, and conversion. • The framework uses topic modeling, sentiment analysis, and Fuzzy-Kano to extract knowledge from social media data. • Identifies customers’ classes and categories using Random Forest and K -means. • Optimization techniques are used to improve the decision-making process. Social Customer Relationship Management (social CRM) has become one of the central points for many companies seeking to improve their customer experience. It comprises a set of processes that allows decision-makers to analyze customer data, in order to launch an efficient, customer-centric, and cost-effective marketing strategy. Nonetheless, the inclusion of social media data in CRM introduces new challenges, as it requires advanced analytical approaches to extract actionable insight from such a huge amount of data. Thus, in this paper, we propose a social CRM analytic framework, which includes various analytical approaches, aiming at improving customer retention, acquisition, and conversion. This framework has been tested on various datasets and extensively evaluated based on several performance metrics. The obtained results suggest that the proposed framework can effectively extract relevant information and support decision-making processes. From an academics perspective, the study contributes to an understanding of customers’ experiences throughout their engagement on social media and focuses on long-term relationships with customers. From a managerial perspective, companies should leverage the insight generated through every customer engagement on social media to drive effective marketing strategies.

ІДЕНТИФІКАЦІЇ ПОТЕНЦІЙНОГО СПОЖИВАЧА ПРОДУКЦІЇ РИНКУ ЕЛЕКТРОННОЇ КОМЕРЦІЇ МЕТОДОМ ГРАДІЄНТНОГО БУСТІНГУ

E-commerce is an integral part of the developed economy in the country. Small and large businesses can sell their products or services online, meeting the needs of consumers anywhere and anytime. The development of e-commerce is impossible without the knowledge of consumer behavior. Data has become one of the world's most valuable resources due to the rapid digital transformation of global industries. Collecting customer data has become a top priority for businesses. As more and more advanced technologies are developed to collect and analyze customer data, more companies are able to contextualize, retrieve and monetize information from them. Knowing why people buy, companies can grow more effectively in e-commerce and do so strategically, knowing what next steps to take. From consumer behavior to predictive analytics, companies regularly collect, store and analyze large amounts of quantitative and qualitative data about their consumer base on a daily basis. E-commerce services are one of the areas where new data is collected every day. Therefore, it seems necessary to use data analysis methods in this area. User interactions with e-commerce platforms are complex patterns of behavior that, if analyzed, can enable businesses to understand consumer needs. Consumer buying behavior is influenced by many factors, and different consumer demands lead to large differences in consumer buying behavior. To predict consumer buying behavior it is necessary to determine the hidden characteristics of data in the array of information left by users on the e-commerce platform, and then to determine the desire of future users to buy on the e-commerce platform. The article considers an example of application of the gradient boosting algorithm for consumer identification in the e-commerce market.

Consequential factors of Big Data's Analytics Capability: how firms use data in the competitive scenario

PurposeThe article's objective is to analyze the consequent factors of Big Data Analytics Capability for firms in the competitive scenario, using different analytical models.Design/methodology/approachThe research had a quantitative approach, using a survey of data from firms located in the state of São Paulo – Brazil. Structural Equation Modeling (SEM) was used to validate the model.FindingsThe results reveal that all hypotheses were accepted. Business value was the construct that had the most explanatory power in the model. It is necessary to invest more in analytical tools, as well as people trained in the analysis of these models, in addition to a change of mindset, which will dictate the bias of the firm's strategic decision-making. The Big Data analysis is evident from firms' growing investments, particularly those that operate in complex and fast-paced environments.Practical implicationsThe proposed theoretical model makes it possible to verify firms' analytical structure and whether they are better positioned to analyze customer data and information in real-time, generate insights and implement solutions to maintain and improve their market position.Originality/valueThe contribution of this article is to present a proposal to expand the research models in the literature that analyzed the direct and indirect relationship between “Big Data Analytics Capability” and “Product Innovation Performance”.

A multi-attribute data mining model for rule extraction and service operations benchmarking

PurposeCustomer differences and similarities play a crucial role in service operations, and service industries need to develop various strategies for different customer types. This study aims to understand the behavioral pattern of customers in the banking industry by proposing a hybrid data mining approach with rule extraction and service operation benchmarking.Design/methodology/approachThe authors analyze customer data to identify the best customers using a modified recency, frequency and monetary (RFM) model and K-means clustering. The number of clusters is determined with a two-step K-means quality analysis based on the Silhouette, Davies–Bouldin and Calinski–Harabasz indices and the evaluation based on distance from average solution (EDAS). The best–worst method (BWM) and the total area based on orthogonal vectors (TAOV) are used next to sort the clusters. Finally, the associative rules and the Apriori algorithm are used to derive the customers' behavior patterns.FindingsAs a result of implementing the proposed approach in the financial service industry, customers were segmented and ranked into six clusters by analyzing 20,000 records. Furthermore, frequent customer financial behavior patterns were recognized based on demographic characteristics and financial transactions of customers. Thus, customer types were classified as highly loyal, loyal, high-interacting, low-interacting and missing customers. Eventually, appropriate strategies for interacting with each customer type were proposed.Originality/valueThe authors propose a novel hybrid multi-attribute data mining approach for rule extraction and the service operations benchmarking approach by combining data mining tools with a multilayer decision-making approach. The proposed hybrid approach has been implemented in a large-scale problem in the financial services industry.

Implementing Online Analytical Processing in Hotel Customer Relationship Management

Online Analytical Processing (OLAP) is increasingly being used by applying technology concepts that use a multidimensional view of grouping data to provide quick access to strategic information for analysis purposes. In the tourism industry, especially hospitality, this is very useful, especially in processing hotel operational data. Although OLAP technology has been widely applied in the hotel industry in reporting business sales, marketing, and reporting management analysis, there is still little research that discusses customer activity analysis. This study develops an OLAP-based analytical CRM system to analyze customer data and classify it into two main segments: geographically and demographically. Transactional data for hotels that have existed for the last three years are converted into a data warehouse, including the ETL process from the original database to a star schema database. It has a fact table and dimensions. Furthermore, OLAP cube operations are performed and generate customer reports. Testing in this study was carried out by calculating the total data’s access speed and accuracy from 100 to 5000 customers, and the estimated value was 45.50 to 80 milliseconds in 30 experiments. For further research, it can be developed by integrating data from the hotel transactional system so that the analytical process can run in real-time.

Penerapan Metode Klasifikasi Decision Tree dan Algoritma C4.5 dalam Memprediksi Kriteria Nasabah Kredit Mega Auto Finance

One of the tasks in the field of marketing finance is to analyze customer data to find out which customers have the potential to do credit again. The method used to analyze customer data is by classifying all customers who have completed their credit installments into marketing targets, so this method causes high operational marketing costs. Therefore this research was conducted to help solve the above problems by designing a data mining application that serves to predict the criteria of credit customers with the potential to lend (credit) to Mega Auto Finance. The Mega Auto finance Fund Section located in Kotim Regency is a place chosen by researchers as a case study, assuming the Mega Auto finance Fund Section has experienced the same problems as described above. Data mining techniques that are applied to the application built is a classification while the classification method used is the Decision Tree (decision tree). While the algorithm used as a decision tree forming algorithm is the C4.5 Algorithm. The data processed in this study is the installment data of Mega Auto finance loan customers in July 2018 in Microsoft Excel format. The results of this study are an application that can facilitate the Mega Auto finance Funds Section in obtaining credit marketing targets in the future