Does country-of-origin brand personality generate retail customer lifetime value? A Big Data analytics approach

2018 ◽  
Vol 130 ◽  
pp. 177-187 ◽  
Author(s):  
Lan-Lung (Luke) Chiang ◽  
Chin-Sheng Yang
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Brand Personality ◽  
Country Of Origin ◽  
Big Data Analytics ◽  
Customer Lifetime Value ◽  
Customer Lifetime ◽  
Lifetime Value

Big Data In Banking: A Bird’s Eye View

GIS Business ◽  
2020 ◽  
Vol 14 (6) ◽  
pp. 1129-1139
Author(s):  
C. RADHA PRIYA ◽  
KANNIGA PRASHANTH
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Banking Industry ◽  
Big Data Analytics ◽  
Customer Segmentation ◽  
Customer Relationship ◽  
Risk Modeling ◽  
Customer Data ◽  
Customer Lifetime ◽  
Big Data Application

Banking industry is the backbone of any economy. It plays a very significant role in leading the country towards the growth path by improving the gross capital formation, which consecutively improves the GDP. Success of the banking industry depends on its ability to serve its customers efficiently and expeditiously. The functionality of the CRM (Customer Relationship Management) can be effectuated by felicitous use of customer data. Banks have voluminous data about their customers, which most of the banks failed to utilize in a well-timed manner. Banks can fortuitously satisfy their customers by offering much personalized and focused services by pursuing big data analytics and other hi-tech tools or applications. Big data analytics can be actuated in key areas like customer segmentation, offering customer lifetime value, fraud detection, risk modeling, etc. Preeminent banks in the industry are utilizing big data to leverage the accumulated customer data for improvising their services. Big data offers a promising scope of ventures to banks which consider it strategically. This article is attempts to present an overview of the big data application in the banking industry.

Antecedents to firm performance and competitiveness using the lens of big data analytics: a cross-cultural study

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abhishek Behl
Keyword(s):  
Big Data ◽  
Firm Performance ◽  
Competitive Advantage ◽  
Data Analytics ◽  
Positive Impact ◽  
Country Of Origin ◽  
Big Data Analytics ◽  
Content Type ◽  
Common Method Bias ◽  
India And China

PurposeThe study aims to understand how big data analytics capabilities of tech startups help them gain competitive advantage and improve their firm performance. The study is performed for two countries: India and China. A comparative analysis is also discussed in the study.Design/methodology/approachThe study collected responses from tech startups from both India and China. A total of 502 responses were collected with 269 from India and 233 from China. The results were analyzed using Warp PLS 6.0 after testing for common method bias, endogeneity and reliability of data. The study tested five primary hypotheses and also tested the effect of two control variables: country of origin of startup and age of the startup.FindingsWe found that big data analytics capabilities have a positive and significant impact on the firm performance and competitive advantage of tech startups. While organizational culture proved to have a positive impact as a moderator, innovation was found to have non-significant effect. The results also found to have non-significant effect of age of the firm while its country of origin does play an important role in defining its success.Originality/valueThe study offer key insights for the tech startups operating in two countries which are geographically neighbors but differ in the tech expertise from each other. Moreover, the study offers key insights on how does the origin of the country contributes significantly to explaining the success and competitiveness of the firm.

Brand personality in cultural tourism and sustainable development by using big data analytics

2022 ◽  
Vol 16 (1) ◽  
pp. 125
Author(s):  
Victor Chang ◽  
Xiaoqiong Li ◽  
Jingqi Zhang ◽  
Qianwen Xu ◽  
Raul Franco Valverde
Keyword(s):  
Sustainable Development ◽  
Big Data ◽  
Data Analytics ◽  
Brand Personality ◽  
Big Data Analytics ◽  
Cultural Tourism

scholarly journals PEMODELAN DAN PENERAPAN METODE RFM PADA ESTIMASI NILAI KONSUMEN (CUSTOMER LIFETIME VALUE) MENGGUNAKAN K-MEANS CLUSTERING MACHINE LEARNING

2021 ◽  
Vol 2020 (1) ◽  
pp. 1277-1285
Author(s):  
Bagaskoro Cahyo Laksono ◽  
Ika Yuni Wulansari
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Learning Model ◽  
Customer Lifetime Value ◽  
Customer Lifetime ◽  
Machine Learning Model ◽  
Lifetime Value

Krisis Covid-19 berdampak pada revenue perusahaan, jika perusahaan tidak meningkatkan strategi pemasaran yang tepat terhadap konsumen, akan beresiko gulung tikar karena tidak memiliki target pasar yang jelas. Disamping itu, perusahaan dapat mengembangkan bisnisnya menggunakan big data untuk mendukung decision making. Big data dalam industry e-commerce yang mencakup ukuran dan kecepatan transaksi yang tinggi dapat digunakan untuk menganalisis perilaku konsumen bahkan memprediksi nilai konsumen. Pada zaman sekarang perusahaan mulai mengembangkan ketertarikan bisnis yang berorientasi konsumen daripada berorientasi produk. Salah satu cara yang dapat digunakan untuk menentukan nilai konsumen yaitu dengan menghitung Customer Lifetime Value (CLV). Dengan mengetahui CLV di level individu, akan berguna untuk membantu pengambil keputusan untuk mengembangkan segmentasi konsumen dan alokasi sumber daya. Penting dilakukan segmentasi atau pengelompokkan konsumen yang menggambarkan kelompok loyalitas konsumen. Oleh karena itu tujuan dalam penelitian ini adalah melakukan penghitungan CLV dan segmentasi konsumen dengan menggunakan metode analisis RFM dengan K-Means Clustering Machine Learning Model. Tahapan analisis diantaranya mendefinisikan RFM Segmentation Value yang merupakan clustering yang dibangun dari angka kumulatif yang berisi penjumlahan Recency, Frequency dan Monetary Level yang dimiliki masing-masing konsumen. Kombinasi nilai level yang tercipta berkisar antara 0,1,2,3,4,5,6 yang artinya semakin tinggi nilainya maka semakin berharga konsumen tersebut. Pada akhirnya, metode segmentasi konsumen yang di bangun penulis dapat digunakan untuk optimasi strategi perusahaan untuk mendapat profit yang maksimum. Metode ini dapat diterapkan pada berbagai kasus dan perusahaan lain. Hasil penelitian ini diharapkan dapat membantu perusahaan untuk bertahan di tengah krisis akibat Covid-19.

Examining the needs to adopt big data analytics in B2B organizations: development of propositions and model of needs

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jiwat Ram ◽  
Zeyang Zhang
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Classification Scheme ◽  
Current Knowledge ◽  
Theory Development ◽  
Big Data Analytics ◽  
Managerial Decision ◽  
Content Type ◽  
Customer Lifetime ◽  
Current Knowledge Base

Purpose Big data analytics (BDA) is becoming a strategic tool to harness data to achieve business efficiencies. While business-to-customer organizations have adopted BDA, its adoption in business-to-business (B2B) has been slow, raising concerns about the lack of understanding of the need to adopt BDA. Little knowledge exists on the subject and the purpose of this study is to examine BDA adoption needs among B2B organizations. Design/methodology/approach A systematic literature review (SLR) following the six-step SLR guidelines of Templier and Paré (2015) involved 1,051 articles, which were content analyzed. Findings The authors offer two-pronged findings. First, on the basis of the SLR, the authors develop a new four-category classification scheme of needs to adopt BDA and present a consolidated review of the current knowledge base along with these categories (i.e. innovation, operational efficiency, customer satisfaction and digital transformation). Second, underpinned by the theory of organizational motivation and literature evidence, the authors develop propositions and a corresponding model of BDA adoption needs. The authors show that BDA adoption among B2B organizations is driven by the need to augment customer lifetime value, champion the change, improve managerial decision cycle-time, tap into social media benefits and align with market transformation. Research limitations/implications The results facilitate theory development as the study creates a new classification scheme of needs and a model of needs to adopt BDA in large B2B organizations. Practical implications The findings will serve as a guideline framework for managers to examine their BDA adoption needs and strategize its adoption. Originality/value The study develops a new four-category classification scheme for understanding B2B organizations’ needs to adopt big data analytics. The study also develops a new model of needs which will serve as a stepping stone for the development of a theory of needs of technology adoption.

Big Data Analytics: The Next Big Thing

2019 ◽  
Vol 54 (5) ◽  
pp. 20
Author(s):  
Dheeraj Kumar Pradhan
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics
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