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"In today’s fast growing digital world, the web, mobile, social networks and other digital platforms are producing enormous amounts of data that hold intelligence and valuable information. Correctly used it has the power to create sustainable value in different forms for businesses. The commonly used term for this data is Big Data, which includes structured, unstructured and hybrid structured data. However, Big Data is of limited value unless insightful information can be extracted from the sources of data.The solution is Big Data analytics, and how managers and executives can capture value from this vast resource of information and insights. This book develops a simple framework and a non-technical approach to help the reader understand, digest and analyze data, and produce meaningful analytics to make informed decisions. It will support value creation within businesses, from customer care to product innovation, from sales and marketing to operational performance.The authors provide multiple case studies on global industries and business units, chapter summaries and discussion questions for the reader to consider and explore. Big Data for Managers also presents small cases and challenges for the reader to work on – making this a thorough and practical guide for students and managers."

"There's more than one way to sell online, and for savvy sellers who want to build on their eBay success or are dissatisfied with eBay, this book offers powerful strategies for targeting not just more buyers, but the right buyers. By taking advantage of specialty sites, other auction sites, and online storefronts, sellers can dramatically increase their profits, and "Selling Beyond eBay" shows them how, step by step."

"ABSTRAKPenelitian ini menjelaskan mengenai penerapan segmentasi konsumen berdasarkan CLV agar dapat menghasilkan profil konsumen bagi perusahan untuk memberikan perlakuan yang tepat bagi masing ndash; masing konsumen. Dalam memproses data yang akan digunakan, penelitian ini mengunakan segmentasi RFM sebagai langkah awal untuk mengidentifiksi data dan dilanjutkan dengan klasterisasi menggunakan K-Means agar mendapatkan gambaran data konsumen yang lebih jelas. Hasil dari penelitian ini menunjukan adanya lima jenis profile konsumen yang berbeda berdasarkan perhitungan RFM dan K-Means. Setiap kelompok memiliki karakteristik yang berbeda yang dapat digunakan oleh perusahaan untuk membentuk strategi dalam memberikan pendekatan kepada konsumen. Terdapat juga perbandingan antara kelompok konsumen yang akan dihasilkan apabila perusahaan menggunakan segmentasi konsumen menggunakan CLV dengan saat tidak menggunakan CLV. Dengan memberikan perlakuan yang tepat bagi konsumen yang dapat memberikan keuntungan bagi perusahaan dimasa yang akan datang, maka perusahaan dapat membentuk strategi yang lebih efektif dan tepat sasaran.

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ABSTRACTThis research focus on how to make a consumer segmentation based on CLV in order to create a customer profile for the company to provide appropriate treatment for each consumer. In order to process the data, this research uses RFM segmentation as the first step to identify the data and continued with K Means clustering to get a better interpretation of consumer data. The results of this research show five different types of consumer profiles based on RFM and K Means calculations. Each cluster has a different characteristic that can be used for a company to define a better strategy in order to approach their customer. There is also a comparison between the consumer groups if the company uses consumer segmentation using CLV or when not using CLV. By providing the right treatment for profitable customer, the company can form an effective and targeted strategy in the future. "

"ABSTRAK<>br>Tesis ini akan membahas tentang strategi penjualan General Sales Agency, yang ditunjuk oleh penerbangan di daerah tertentu, berdasarkan data penjualan penerbangan. Akan tetapi, beberapa penerbangan menggunakan mata uang yang.berbeda tergantung lokasi agensi itu berada. Beberapa Negara menetapkan penggunaan mata uang lokal mereka ketika melakukan transaksi. Oleh karena itu, untuk menganalisa perubahan mata uang, analisa model regresi dinamis diterapkan. Di samping itu, tesis ini juga menggambarkan kecenderungan dari perilaku agen dengan menggunakan analisis RFM dengan metode kuartil. Metode ini digunakan karena implementasinya yang mudah dan membagi agen dengan jumlah yang sama. Setelah ditetapkan, korelasi digunakan untuk menganalisa kecenderungan penjualan terhadap agen-agen tersebut. Dengan menemukan kecenderungan dan tingkat signifikan dari klasifikasi RFM, perumusan untuk strategi berdasarkan data penjualan dapat ditentukan. Dalam hal ini, proses dalam Big Data Analytics digunakan untuk mencari nilai yang tersembunyi. Hasil dari tesis ini adalah penjualan akibat kebijakan pemerintah secara statistic tidak berpengaruh terhadap penjualan, klasifikasi RFM, dan terdapat korelasi negatif antara resensi dan frekuensi dan resensi dan monetary, dan korelasi positif antara frekuensi dan monetary.

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ABSTRACT<>br>This thesis gives explanation regarding sales strategy of General Sales Agency based on sales data, appointed by airlines rsquo principle to sell the tickets in Indonesia. Some principles are using different currencies, depends on the operating countries where General Sales Agency resides. Dynamic regression model is used to analyze any changing due to external environment such as policy regulation. The tendencies of classification based on RFM analysis with quartile method to cluster agents using correlation were also examined. RFM analysis of quartile method is used because it is easy to implement and divide the agents equally. After that, correlations are used to examine the tendencies of sales data towards RFM classifications. Finding general tendencies and level of significance of RFM classifications would help to formulate strategy based on available data given by the company. Big Data analytics is used to get the hidden value from the data. The results indicate no statistically significant changes of sales due to policy regulation, RFM classifications are created, statistically significant negative correlations between recency and frequency, and between recency and monetary, and statistically significant positive correlation between frequency and monetary."