Database Marketing Research Articles

Exploring Human-Computer Interaction in Database Marketing of E-commerce Stores through AI-Powered CAD

Exploring Human-Computer Interaction in Database Marketing of E-commerce Stores through AI-Powered CAD

Analisis Media Sosial dalam Peningkatan Pemasaran Global

Social media has an increasingly important role in global marketing strategies in today's digital era. This research aims to analyze the impact of social media, especially e-commerce, in improving global marketing. Using literature study and online research methods, this research explores the role of social media in increasing global market access, building global brand awareness, influencing consumer behavior, supporting data-based marketing strategies, and achieving higher marketing efficiency. The results show that social media is not only a promotional tool, but also a key player in global digital business transformation, providing opportunities for companies to grow and compete in an increasingly digitally connected global market.

Penerapan Customer Relationship Management sebagai strategi pemasaran berbasis data di PT.Gragaz Berkah Sejahtera

Implementing Customer Relationship Management (CRM) as a data-based marketing strategy has become the main focus for many companies in optimizing relationships with customers. This research explores this through a case study at PT. Gragaz Berkah Sejahtera, a pharmaceutical retail company or pharmacy business that adopts this approach. This research aims to identify how PT. Gragaz Berkah Sejahtera integrates technology and Customer Relationship Management (CRM) theory to improve interactions with customers and how to optimize data-based marketing and sales processes. The research method used is a qualitative descriptive analysis method with the object studied being PT. Gragaz Berkah Sejahtera which involves collecting data, implementing CRM technology, and evaluating its impact on customer loyalty, retention, and increasing sales. While the results of this research highlight the company's success in implementing data-based marketing strategies in an effort to improve marketing and sales, as well as increase customer satisfaction, however these results have not achieved maximum success because there are obstacles in implementing the concept of customer relationship management such as system failures. data-based information technology applications. So that efforts to implement CRM programs are disrupted and have an impact on sales which have not achieved significant growth through this data-based marketing strategy. This research can provide important information and insights for other companies wishing to adopt the same approach in an effort to improve relationships with customers and achieve optimal and sustainable marketing and sales.

Stage by stage E- Ecommerce market database analysis by using machine learning models

In the recent era, advertising strategies are far more sophisticated than those of their predecessors. In marketing, business contacts are essential for online transactions. For that, communication needs to develop a database; this database marketing is also one of the best techniques to enhance the business and analyze the market strategies. Businesses may improve consumer experiences, streamline supply chains, and generate more income by analyzing E-Commerce market datasets using machine learning models. In the ever-changing and fiercely competitive world of e-commerce, the multi-stage strategy guarantees a thorough and efficient use of machine learning. Analyzing the database can help to understand the user's or industry's current requirements. Machine Learning models are developed to support the marketing sector. This machine learning model can efficiently operate or analyze e-commerce in different stages, i.e., systematic setup, status analysis, and model development with the implementation process. Using these models, it is possible to analyze the marketing database and create new marketing strategies for distributing marketing objects, the percentage of marketing channels, and the composition of marketing approaches based on the analysis of the marketing database. It underpins marketing theory, data collection, processing, and positive and negative control samples. It is suggested that e-commerce primarily adopt the database marketing method of the model prediction. This is done by substituting the predicted sample into the model for testing. The issue of unequal marketing item distribution may be resolved by machine learning algorithms on the one hand, and prospective customer loss can be efficiently avoided on the other. Also, a proposal for an application approach that enhances the effectiveness of existing database marketing techniques and supports model prediction is made.

Analisis Penggunaan Media Sosial Sebagai Alat Komunikasi Pemasaran dalam Meningkatkan Brand Awareness

Social media has now become something that is embedded in people's lives. In this digital era, social media users have increased a lot, so to gain effectiveness in marketing communications, Vebcake chooses to use social media. Through this strategy, Vebcake is able to increase brand awareness of its business. Marketing communications itself plays an important role in increasing brand awareness. Vebcake is a business operating in the bakery sector in Tulungagung that is well known to the public. This can be seen through Vebcake's social media, which has tens of thousands of followers and sales turnover which increases every year. Based on previous research, Vebcake already has good brand awareness. Consumers have a good perception of the Vebcake Brand and say that the products from the Vebcake Brand are delicious and high quality at an affordable price. This research aims to describe the implementation of marketing communications via social media carried out by Vebcake to increase brand awareness. This research uses a qualitative approach with a descriptive type. The results of this research found that Vebcake implements a marketing communication mix regularly so that the public can be aware of the existence of the Vebcake Brand. The marketing communication mix carried out is in the form of advertising, sales promotion, public relations and publicity, online marketing and social media, mobile marketing, direct and database marketing, and personal selling. The obstacles that Vebcake faced while carrying out this marketing communication were the problem of miscommunication and the communication device (smartphone) which was damaged.
 Keywords: brand awareness; marketing communications; social media

Influence of Digital Marketing on Purchase Decision A Qualitative Study on Pakistani Customers

Previous studies rarely focused on exploring the factors that influence Pakistani customers towards purchase decisions through digital marketing. This research will explore the factors that influence customer purchase decision in the Pakistan consumer market through digital marketing with the help of thematic analysis. This research will significantly drive implications for marketers over customer purchase decision and also helps the policymakers to identify the key factors for their specific products. The explored factors will also indicate customers’ behavioural approach of purchasing through digital marketing platforms. A qualitative research strategy has been embraced to lead this research exploration. Semi-structured interviews were conducted and thematic analysis was applied as a tool for this research after transcribing the interview data through NVivo QSR version 10. The themes are comprised of factors obtained during data analysis. The topics comprise Customer impulsivity is increased by digital marketing, price affects customers' buying decisions, the main variables influencing a customer's buying intention are social, psychological, personal, and cultural. Even though social impact influences decision-making in digital marketing, few studies have considered social influence in decision support systems since, until recently, social interaction data was not properly collected in digital marketing databases. Despite this, the clientele for digital marketing is gradually shifting from a transactional to a relationship-based culture.

Analisis Penggunaan Media Sosial Sebagai Alat Komunikasi Pemasaran dalam Meningkatkan Brand Awareness: Studi Kasus pada UKM Vebcake Tulungagung

Social media has now become something that is embedded in people's lives. In this digital era, social media users have increased a lot, so to gain effectiveness in marketing communications, Vebcake chooses to use social media. Through this strategy, Vebcake is able to increase brand awareness of its business. Marketing communications itself plays an important role in increasing brand awareness. Vebcake is a business operating in the bakery sector in Tulungagung that is well known to the public. This can be seen through Vebcake's social media, which has tens of thousands of followers and sales turnover which increases every year. Based on previous research, Vebcake already has good brand awareness. Consumers have a good perception of the Vebcake Brand and say that the products from the Vebcake Brand are delicious and high quality at an affordable price. This research aims to describe the implementation of marketing communications via social media carried out by Vebcake to increase brand awareness. This research uses a qualitative approach with a descriptive type. The results of this research found that Vebcake implements a marketing communication mix regularly so that the public can be aware of the existence of the Vebcake Brand. The marketing communication mix carried out is in the form of advertising, sales promotion, public relations and publicity, online marketing and social media, mobile marketing, direct and database marketing, and personal selling. The obstacles that Vebcake faced while carrying out this marketing communication were the problem of miscommunication and the communication device (smartphone) which was damaged.

The Impact of Data-Based Marketing Activities On Consumer Purchase Intention

Hedef kitlede istedikleri yönde davranış değişikliği yapmayı, onlarda kendi ürün ve hizmetlerine yönelik satın alma niyeti oluşturmayı amaçlayan işletmeler, gözetim pratiklerine başvurmakta ve elde ettikleri tüketici verilerinin analizlerini yaparak pazarlama stratejilerini oluşturmaktadır. Fakat, tüketici verilerinin analiz edilerek pazarlama stratejisi geliştirilmesine yönelik tüketici tepkisi bilinmemektedir. Çalışma, veri tabanlı pazarlama faaliyetlerine yönelik tüketici davranışını ortaya koyarak, pazarlama iletişimi stratejilerinin doğru yapılmasına katkı sunması açısından önemlidir. Bu çalışmanın amacı; elektronik gözetime dayalı veri tabanlı pazarlama faaliyetlerinin tüketicide satın alma niyeti yaratmada etkisi olup olmadığını ortaya koymaktır. Çalışmanın evrenini, Türkiye’de yaşayan ve internetten alışveriş yapan 16-74 yaş aralığında yer alan kişiler oluşturmaktadır. Çalışmada nicel araştırma yöntemi ve online anket tekniği kullanılmıştır. Tesadüfi olmayan örnekleme yöntemlerinden biri olan kolayda örnekleme yöntemi kullanılmıştır. Verilerin analizinde SPSS 24.0 paket programından yararlanılmıştır. T-Testi ve Anova testleri uygulanmış ve insanların veri tabanlı pazarlama uygulamalarına yönelik güveninin düşük olduğu tespit edilmiştir. Araştırma bulgularına göre, veri tabanlı pazarlama faaliyetleri, hedef kitlenin satın alma niyetini olumsuz yönde etkilemektedir.

Implementing Technology Invention For Micro, Small, And Medium-Sized Enterprises’ Human Resource Improvement: A Conceptual Model

Micro, Small, and Medium-Sized Enterprises (MSMEs) in Indonesia are now commercial organizations that can employ many people. There is a need for an efficient information system since there is a dearth of knowledge about marketing goals, despite the fact that many different MSME products have the potential to become high-quality exports. The more people there are who utilize the internet, the more probable it is that businesses will benefit from the increased competition. Therefore, the information system must provide MSMEs with access to digital marketing databases. To address this challenge, this piece presents a theoretical framework for applying technological innovation to the enhancement of human resources in micro, small, and medium-sized enterprises (MSME). Given the current shortcomings of HR improvement procedures in Indonesian MSMEs, this paper offers a comprehensive evaluation as a template for development in the field. In this article, we compare the current HR improvement practice in Indonesian MSMEs to national and international practices reported in the literatures reviewed and conclude that, in accordance with the national marketing information system of MSME owners, implementing technology invention has the potential to support HR improvement in Indonesian MSMEs.

Research on China’s tourism marketing model in the data perspective: based on ecological perspective

Abstract With the continuous improvement of technology and residents’ living standards. Should actively adapt to the opportunities and challenges brought by the Internet to the tourism marketing model, For the sustainable and rapid development of tourism on the wings of takeoff. In this paper, based on a brief explanation of the basic theory of database marketing, to achieve sustainable development of tourism marketing. Adopted the development of ecotourism boutique routes, Implementing multi-faceted marketing channels and means. Improvement of ecotourism infrastructure and service levels and other development measures. Create a tourism marketing model that responds to the trend of the times. Innovative marketing with big data technology can greatly stimulate the tourist crowd. Two times more efficient with dual cores. Get 56 times the execution efficiency with 64 cores. Get the efficiency of linear growth. The most direct way to increase the share of tourism companies in the consumer market. Maximize the value of marketing economic inputs and outputs. The results show that the improved PSO algorithm is more effective in database stability and dispersion. This makes it important for tourism marketing.

Improvement of CRM through Database Marketing

Abstract: CRM marketing and database marketing are the most recent marketing concepts; however they serve different purposes. Each are distinct in their own fields, with benefits and disadvantages. Database marketing is classic (1970), but CRM was created in 1990 and cannot function without the assistance of a database. CRM marketing services include sales force automation, marketing automation, contact center automation, workflow automation, lead management, and human resource management among others. Database marketing is a sort of direct marketing in which a company's client databases are utilized to create customized email lists for marketing campaigns, a practice known as customer segmentation. A customer data platform (CDP) can assist in the consolidation of customer data from many sources such as CRM systems, ERP systems, e-commerce systems, and legacy systems into a single golden record of the customer. The key advantages of this marketing include the ability to identify and segment client groups, the ability to provide appropriate content and customize the consumer experience, increased marketing efficiency, and effective customer loyalty programmed. This research paper will explain the distinction between these two marketing concepts and how one is dependent on the other and how database helps CRM to run

Abstract No. 289 Modern Search Analytics: What Are Patients Asking ecan Uterine Artery Embolization?

Patients considering elective medical procedures such as uterine artery embolization (UAE) often turn to search engines. For each search term, Google distills frequently asked questions and links users to websites with answers. The purpose of this study is to characterize this data for UAE, a condition with a relatively younger (and perhaps more technologically literate) patient population, which may improve patient counseling and awareness. SearchResponse.io is a commercially available marketing database that has scraped the “People Also Ask” section of Google for questions, links, and popularity. For UAE, the database was queried for “UFE,” “uterine artery embolization,” and “uterine fibroid embolization.” Using established methods in the literature, questions were categorized using Rothwell’s classification (Fact, Policy, Value), and further divided into subcategories (Fact: Specific Activities & Restrictions, Recovery, Technical Details, Cost, Condition Details; Policy: Indications, Management of Risks & Complications; Value: Pain, Evaluation of Symptoms, Evaluation of Intervention). Websites were classified into five categories (Commercial, Academic, Medical Practice, Government, and Social Media). JAMA Benchmark scores were determined for all websites linked to assess credibility. Two reviewers performed the classification; a third reviewer resolved discrepancies. 170 questions were extracted from 87 unique links at 54 unique domains. The most popular subcategories were recovery (45, 26.4%), evaluation of intervention (34, 20%), technical details (34, 20%), cost (19, 11.1%), specific activities and restrictions (15, 8.8%), pain (12, 7.1%), indications (6, 3.5%), condition details (4, 2.4%), evaluation of symptoms (1, 0.6%). The most common website types were academic (72, 42.4%), medical practice (45, 26.5%), government (33, 19.4%), commercial (15, 8.8%), social media (5, 2.9%). JAMA scores (0-4) were highest for government websites (mean score = 3.64), followed by commercial (2.33), academic (2.31), social media (2.0), and medical practice (1.89). Patients searching for UAE online were most interested in UAE recovery, value of UAE, and technical details. This may reflect the major appeal of UAE’s minimally invasive nature. While > 68% of websites linked to academic centers and medical practices, their associated JAMA benchmark scores were among the lowest. Accurate patient-facing information of UAE resources must be developed to empower patients to choose their optimal medical treatment.

A novel evolution model to investigate the collaborative innovation mechanism of green intelligent building materials enterprises for construction 5.0

<abstract> <p>Green intelligent building materials is an effective way for building materials industry to reduce carbon. However, a small amount of research and development (R&D, unstable R&D investment and imperfect collaborative innovation mode hinder the development of green intelligent building materials industry. However, few scholars study the development mechanism of green intelligent building materials industry from the perspective of industrial chain considering the above obstacles. In this study, the game models under market mechanism and government regulation were constructed to analyze the income distribution mechanism for the development mechanism of green intelligent building materials industry. Finally, the questionnaire method was used to discuss the game strategy of collaborative innovation behavior among agents. The results are as follows. In the game strategy selection of collaborative innovation behavior among green intelligent building materials, factors such as database marketing maturity, information flow and technology volume generated by collaborative innovation, technical benefit coefficient, social benefit coefficient and profit and loss barrier factors are conducive to the collaborative innovation behavior of green intelligent building materials. When the market mechanism fails, the incentive effect of cost subsidy adopted by the government is more efficient and fast, and the driving force of achievement reward is more lasting. The combination of the two incentives is the best. Moderate supervision and punishment lower than the free rider income can not ensure fair competition among green intelligent building materials enterprises. The punishment above the threshold can effectively restrain the negative impact of free rider income and prospect profit and loss. This study not only theoretically expands the development theory of digital industry from the perspective of industrial chain by considering the maturity factor of database, but also provides policy guidance for the development of green intelligent building materials industry in practice.</p> </abstract>

Roles of Customer Databases and Database Marketing in Customer Relationship Management

Development in information technology helps companies to build customer databases, perform database marketing, and do relationship management. The study discusses the various aspects of customer databases and database marketing and their roles in relationship management. It discusses the ways in which a customer database can be used to achieve best results. The study discusses the various issues of database marketing, ways of overcoming the issues, internal changes in an organization which should be made for implementing database marketing, and emphasizes that database marketing is not the solution to every issue. Organizations should analyze all these issues during CRM implementation. It also provides a direction for achieving the goals in relationship management. Organizations should be aware of and should consider all these aspects of customer databases and database marketing to develop customer relationships, to do CRM implementation, to achieve business excellence, and to succeed in the long run.

Research on big data marketing method based on statistical machine learning algorithmei

In the innovation and development of human science and technology, from the simple relationship between buying and selling to today's huge and complex online trading, not only reflects the main content of the progress of The Times, but also indirectly proves the diversified characteristics of marketing methods and technological means. In the marketing market at home and abroad, database marketing, with high efficiency and low cost, has been paid more attention to by the marketing staff, and gradually become the focus of the marketing market. As database marketing belongs to the foreknowledge and ferryman in the increasingly competitive market environment, it is more inclined to analyze simple data before application in practice. Therefore, in contrast, the database with machine learning algorithm as the core, marketing is the main content of marketing research and discussion. Based on the analysis of big data marketing methods and application effects in recent years, this paper mainly discusses the main content of statistical machine learning algorithms, and takes a Tmall flagship store as the research target to systematically understand the database marketing content with machine learning algorithms as the core. The final results show that the flagship store is mainly based on the database marketing method of model prediction, and the effective measures of how to improve the marketing effect with the help of model prediction are put forward.

RISE FOR HEALTH: Rationale and protocol for a prospective cohort study of bladder health in women.

The spectrum of bladder health and the factors that promote bladder health and prevent lower urinary tract symptoms (LUTS) among women are not well understood. This manuscript describes the rationale, aims, study design, sampling strategy, and data collection for the RISE FOR HEALTH (RISE) study, a novel study of bladder health in women conducted by the Prevention of Lower Urinary Tract Symptom (PLUS) Research Consortium. RISE is a population-based, multicenter, prospective longitudinal cohort study of community-dwelling, English- and Spanish-speaking adult women based in the United States. Its goal is to inform the distribution of bladder health and the individual factors (biologic, behavioral, and psychosocial) and multilevel factors (interpersonal, institutional, community, and societal) that promote bladder health and/or prevent LUTS in women across the life course. Key study development activities included the: (1) development of a conceptual framework and philosophy to guide subsequent activities, (2) creation of a study design and sampling strategy, prioritizing diversity, equity, and inclusion, and (3) selection and development of data collection components. Community members and cross-cultural experts shaped and ensured the appropriateness of all study procedures and materials. RISE participants will be selected by simple random sampling of individuals identified by a marketing database who reside in the 50 counties surrounding nine PLUS clinical research centers. Participants will complete self-administered surveys at baseline (mailed paper or electronic) to capture bladder health and LUTS, knowledge about bladder health, and factors hypothesized to promote bladder health and prevent LUTS. A subset of participants will complete an in-person assessment to augment data with objective measures including urogenital microbiome specimens. Initial longitudinal follow-up is planned at 1 year. Findings from RISE will begin to build the necessary evidence base to support much-needed, new bladder health promotion and LUTS prevention interventions in women.

The Roles of Customer Databases and Database Marketing in Marketing Intelligence

Customer databases and database marketing play important roles in marketing intelligence and in building and developing customer relationships for organizations. Development of customer databases and performing database marketing are made possible mainly by the advancements in information technology. The study discusses various aspects of customer databases and database marketing and their roles in marketing intelligence and in building customer relationships. It discusses the ways in which companies can use customer databases effectively to achieve the best results. The study discusses the issues of database marketing, ways of overcoming them, internal changes in an organization required for the implementation of database marketing, and emphasizes that maintenance of customer databases and performing database marketing are not the solutions to every problem in building customer relationships. It will help practitioners and researchers in finding a direction for generating customer insights, achieving the goals in relationship management, and in achieving business excellence.

Research on E-Commerce Database Marketing Based on Machine Learning Algorithm.

From simple commercial relations to complex online transactions at this stage, it not only highlights the progress of science and technology, but also indirectly explains the diversified evolution of marketing methods and means. In marketing, database marketing has been favored by more marketers with its low cost and high efficiency and has become the “rookie” in marketing in recent years. However, as a kind of prediction and ferry, database marketing tends to be applied after simple data analysis in unpredictable market and in practice. In contrast, database marketing combined with machine learning algorithms has always been a depression in the marketing field. Therefore, this paper takes e-commerce as the research object and carries out database marketing research based on machine learning algorithm from four stages: theoretical preparation, status analysis, model construction, and results application. Firstly, the connotation, advantages, and specific operation procedures of database marketing are discussed. At the same time, four excellent machine learning algorithms including logistic regression, random forest, support vector machine, and gradient boosted decision tree (GBDT) are selected to explain the basic principles and algorithm introduction, respectively, laying a theoretical foundation for the model training chapter. Secondly, it analyzes the current situation of e-commerce from the distribution of marketing objects, the proportion of marketing channels, and the composition of marketing methods and finds new marketing ideas based on the main problems existing at the present stage of database marketing using machine learning algorithm. Thirdly, on the premise of marketing ideas, data acquisition, data processing, and positive and negative sample setting. At the same time, four machine learning algorithms are used to combine features from the perspectives of consumers, stores, and the relationship between consumers and stores. Finally, by substituting the predicted sample into the model for testing, the crowd whose predicted score is between 80 and 99 is selected to be put into the market as the model predicted crowd, and it is proposed that e-commerce should mainly adopt the database marketing method of model prediction. On the one hand, machine learning algorithm can solve the problem of uneven distribution of marketing objects, and on the other hand, it can effectively prevent the loss of potential consumers. In addition, the application strategy of optimizing other database marketing methods and assisting model prediction to improve marketing effect is also put forward.

Developing a New Customer Journey Path: Case Study on a Fixed Broadband ISP Company

The need for internet connectivity use has grown to the point that it can be considered a primary need that cannot be separated from the support of many needs and sectors of life. This momentum has attracted InterHome as one of the providers of fixed broadband Internet Service Providers (ISP) to meet these needs. As a result, the value of the engagement rate, which increased significantly in 2021 compared to the previous year but this thing is inversely proportional to the InterHome brand’s sentiment, which fluctuated every month in 2021 and, on average, rarely reached the point where the brand sentiment became an illustration of the context behind the interaction. In this study, using external and internal analysis in the analysis of the business environment, it is obtained an overview of the significant competitive industry contained in the SWOT analysis. As a follow-up to the problems faced by InterHome, the author proposes a customer journey using the 5 A’s Customer Journey Path guidelines from Marketing 5.0 by Philip Kotler, Hermawan Kartajaya, and Iwan Setiawan to obtain a portrait of the existing and expected conditions by InterHome customers. While the company has made various attempts to date, there is still a lack of investigation into what customers need and desire on each journey. Therefore, this study aims to increase customer retention in line with increasing positive brand sentiments and reach the target using a customer journey approach. The combination of quantitative and qualitative is used in this study, where quantitative data brings a breadth of research, and qualitative data allows depth. As a result of the study, the proposed customer journey can be described at the 5 A’s stages, providing a clear view of the improvement initiatives that could be a solution for each stage and helping companies align around common goals. The detailed mapping of the proposed customer journey is divided into six key points, including common customer activities that explain what InterHome consumers typically do when they decide to subscribe to InterHome. Furthermore, customer needs and desires represent what customers will seek to be captured by the brand at every stage of its journey. The point where the consumer interacts with the brand is described as the touchpoint in the customer journey. Business goals and Key Performance Indicators (KPIs) are measurable values ​​that will measure the company’s performance and success in achieving business goals and in line with the implementation of data-based marketing.

A Case Study on Building a Cross-Selling Model through Machine Learning in the Insurance Industry

Cross-selling, has become widespread in recent years and has increased in importance, is a strategy of selling interrelated products or services to the customer by analyzing the general buying trend. In this study, firstly, its usage in data-based marketing and insurance is explained. As known, possibilities are very important in the insurance industry. For example, premiums to be determined in the next year in life insurance are based on the number of deaths (mortality) in the past years among certain age groups. Accordingly, the probability of customers with private pension contracts to obtain life insurance will be estimated. While making this estimation, besides the personal information of the customers, their behavior in the past periods of 1-3-6 months and the various traces they left on the system will be used. Machine learning, decision trees, and Cross Sales have been studied in detail. Customer data of an insurance company in Turkey is used in the implementation of the project. Then, it was examined whether a product can be purchased based on the past behavior of individual customers with the Chaid, C5.0 and Crt algorithms used in decision trees. Finally, it will analyzed that this study does not contribute to company sales, and new generation sales techniques will be used instead of traditional sales methods.