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"Meningkatnya penggunaan e-commerce telah mempengaruhi perkembangan industri fashion online pada saat ini dan meningkatkan kompetisi di antara para pemain. Oleh karena itu, setiap pelaku bisnis perlu menyiapkan strategi pemasaran terbaik untuk dapat bersaing dan menarik konsumen,seperti melakukan segmentasi pelanggan untuk dapat memahami karakteristik pelanggan dan melakukan strategi pemasaran secara efektif kepada pelanggan yang tepat. Hal tersebut yang juga ingin dilakukan The Blouse yang merupakan toko fashion online yang menjual pakaian melalui e-commerce. Penelitian ini dilakukan untuk mencapai tujuan tersebut, yang dilakukan dengan menilai pelanggan berdasarkan model RFM. Setelah itu, dapat dilakukan analisis klaster untuk melakukan klasterisasi nilai RFM dari setiap pelanggan menggunakan metode klasterisasi k-means dan CLARA. Setelah klasterisasi terbaik ditentukan melalui analisis siluet dan Dunn index, maka akan diketahui klaster-klaster pelanggan yang dimiliki oleh The Blouse. Hasil dari klasterisasi tersebut kemudian dapat dianalisis untuk mengetahi bagaimana profil pelanggan dari setiap klaster yang terbentuk. Dengan begitu, dapat disimpulkan bagaimana segmentasi dan profil pelanggan The Blouse sebagai dasar untuk memberikan saran terbaik untuk permasalahan yang dihadapi perusahaan.

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The increasing use of e-commerce has influenced the development of the online fashion industry at this time and increased competition among players. Therefore, every business person needs to prepare the best marketing strategy to be able to compete and attract consumers, such as segmenting customers to be able to understand customer characteristics and carry out effective marketing strategies for the right customers. This is what The Blouse, which is an online fashion store that sells clothes through e-commerce, also wants to do. This research was conducted to achieve this goal, which was carried out by assessing customers based on the RFM model. After that, cluster analysis can be performed to cluster the RFM values of each customer using the k-means and CLARA clustering methods. After the best clustering is determined through silhouette analysis and Dunn index, it will be known the customer clusters owned by The Blouse. The results of the clustering can then be analyzed to find out how the customer profile of each cluster is formed. That way, it can be concluded how the segmentation and customer profile of The Blouse is the basis for providing the best advice for the problems faced by the company"

"Akhirnya kita tiba di era kecerdasan buatan di mana teknologi telah dilatih untuk meniru kecerdasan manusia. Machine Learning adalah salah satu terobosan dalam kecerdasan buatan yang menyajikan banyak potensi untuk menghadirkan keunggulan kompetitif dengan kemampuannya untuk mengoptimalkan analisis data secara otomatis. Industri perbankan komersial selalu menjadi pengadopsi awal berbagai kecerdasan buatan. Namun, potensi Machine Learning di perbankan komersial masih belum tergali. Lama setelah krisis keuangan yang hebat, industri perbankan komersial telah menjadi lebih besar dan lebih kompetitif, dengan banyak pengganggu yang mengubah lingkungan persaingan di industri tersebut. Industri perbankan kini memasuki era transformasi digital berikutnya, di mana persaingan semakin ditentukan oleh teknologi. Di bank komersial, layanan pelanggan adalah area krusial di mana semua titik kontak langsung dengan pelanggan terjadi. Bank harus terus mencari cara baru dalam meningkatkan kemampuannya dalam memberikan layanan berkualitas tinggi yang memenuhi bahkan melebihi harapan nasabah. Untuk mencapai hal tersebut diperlukan pengetahuan yang intensif tentang pelanggan. Makalah ini bertujuan untuk membahas potensi machine learning dalam meningkatkan berbagai aktivitas customer service di bank umum. Secara khusus, Machine Laerning meningkatkan pengembangan layanan yang dipersonalisasi, pencegahan penipuan, dan bantuan pelanggan virtual yang sangat penting untuk kelangsungan hidup bank komersial saat ini.

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We have finally arrived in the age of artificial intelligence where technologies have been trained to imitate human intelligence. Machine learning is one of the breakthroughs in artificial intelligence that serve a lot of potential to bring competitive advantage with its ability to automatically optimize data analyses. Commercial banking industry has always been the early adopter of various artificial intelligence. Yet, the potentials of machine learning in commercial banking are still unexplored. As banking industry is now entering the next era of digital transformation, the competition is increasingly defined by technology. Technological transformation has changed the competitive environment in banking industry, and influenced consumer behaviour. Machine learning offers new ways in which banks could overcome these challenges. In commercial banking, customer service is the crucial area where all direct touch-points with customers take place. Banks must continuously find new ways in improving its ability to deliver high quality service that meets and even exceeds customer expectation. The objective of this paper is to discuss the potential of machine learning in improving various customer service activities in a commercial bank. This paper is particularly relevance for managers in banking industry as it provides comprehensive discussion about the business implications of machine learning. The research question that I aim to answer in this paper is about: How Does Machine Learning Help Commercial Banks to Sustain Competitive Advantage in Customer Service?"

"This book brings together contributions from researchers and practitioners in a celebration of achievements with the intention of adding to the wider understanding of how service innovation develops. Each case presents a brief description of the context in which the innovation occurred, the opportunity that led to the innovation and an overview of the innovation itself, also addressing how success was measured, what success has been achieved to date and providing links to further information. The book is organized around five major themes, each reflecting recognized sources of service innovation. Business model innovation, new ways of creating, delivering or capturing economic, social, environmental and other types of value. The organization in its environment, an organization engaging beyond its own boundaries, with public private partnerships, sourcing knowledge externally, innovation networks, and open or distributed innovation. Innovation management within an organization, an organization actively encouraging innovation within its own boundaries using project teams, internal governance of innovation, and methods or tools that stimulate innovation. Process innovation, changes in service design and delivery processes, such as consumer led innovation or consumers as part of the innovation process, service operations management, and educational processes. Technology innovation, the use of technology, including ICT enabled innovation, ICTs that are themselves innovative and support the delivery of new services, new ICT services, new ways of delivering services associated with ICT products, and technology other than ICT. The final part of the book is given to four extended cases allowing for a more in-depth treatment of innovation within a complex service system. The extended cases also illustrate two important and growing trends, firstly the need for, and benefits of, a more customer centric approach to service innovation and secondly the need for better understanding of public services and the role of public-private partnerships in identifying and achieving innovation. "

"Spells out surefire strategies for success in an entertaining, and practical way. This book reveals inside secrets of successful customer service initiatives, from Internet startups to venerable brands, and shows how companies of every stripe can turn casual customers into fervent supporters who can spread the word far and wide-online and off."

"Ever-expanding technologies are raising the bar, as customers increasingly expect fast, sophisticated solutions and results in their interactions with the library. Drawing on their system of improved customer service available through technology, the authors show how automating traditional library services can decrease staff workloads while improving speed and access for customers. Real-life lessons and visual examples from libraries who have implemented these systems provide a customizable model for your library to achieve the same goals, from offering virtual library cards, to program registration for your patrons online."