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ردیف | عنوان | نوع |
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1 |
Brave New World? On AI and the Management of Customer Relationships
دنیای جدید شجاع؟ در مورد هوش مصنوعی و مدیریت روابط مشتری-2020 In light of the emerging discourse on AI systems effect on society, whose perception swings widely between utopian and dystopian, we
conduct herein a critical analysis of how artificial intelligence (AI) affects the essential nature of customer relationship management (CRM). To do
so, we survey the AI capabilities that will transform CRM into AI-CRM and examine how the transformation will influence customer acquisition,
development, and retention. We highlight in particular how AI-CRMs improving ability to predict customer lifetime value will generate an
inexorable rise in implementing adapted treatment of customers, leading to greater customer prioritization and service discrimination in markets.
We further consider the consequences for firms and the challenges to regulators. Keywords: Artificial intelligence | Customer relationships | Customer lifetime value | Customer Prioritization | CRM | Regulation |
مقاله انگلیسی |
2 |
Calculating tourists customer equity and maximizing the hotels ROI
محاسبه خرید گردشگران و به حداکثر رساندن ROI هتل-2018 This study is the first attempt to calculate customer equity (CE) and to project the marketing return on investment (ROI) by using risk simulation in the context of tourism and hospitality. Based on the results from focus groups and an online survey of tourists, the study identified the five CE drivers, the CE-based market segments, and demonstrated the calculation of CE related to tourists CE segments and hotel type. The marketing effort responsiveness for each hotel profile was measured by using the three input variables for calculating customer lifetime value (CLV) and CE. Findings include that the tourists five CE segments have different financial impact of CE drivers according to the hotel type and high-end hotels have the largest success regarding significant CE drivers in terms of ROI and CE. Theoretical and managerial implications were suggested regarding the application of the CE and its measurement.
keywords: Customer equity| Customer equity drivers| Customer equity-based segmentation| Customer lifetime value| @ Risk simulation| Tourists hotel selection |
مقاله انگلیسی |
3 |
Does country-of-origin brand personality generate retail customer lifetime value? A Big Data analytics approach
آیا شخصیت نام تجاری کشور مبدا باعث افزایش طول عمر مشتریان خرده فروشی می شود؟ رویکرد تجزیه و تحلیل داده های بزرگ-2018 Many retail firms have witnessed the erosion of customer loyalty with the rise of e-commerce and its resulting
benefits to consumers, including increased choices, lower prices, and ease of brand switching. Retailers have
long collected data to learn about customer purchasing habits; however, many currently do not use data-mining
analytics to increase marketing effectiveness by predicting future buying patterns and potential customer life
time value, particularly to important segments such as loyal and potential repeat customers. Data mining can
efficiently analyze large amounts of business data (“Big Data”) in an effort to forecast consumer needs and
increase the lifetime value of customers (CLV). Previous studies on these topics primarily focus on conceptual
assumptions and generally do not present empirically valid models.
The present study sought to fill the research gap by using Big Data analytics to analyze approximately 44,000
point-of-sale transaction records for 26,000 customers of a Taiwanese retail store to understand how consumer
personality traits relate to the country-of-origin (COO) traits (brand personality) of beer brands, and to predict
potential customer lifetime value (CLV). The findings revealed that consumers tend to purchase and co-purchase
brands with traits similar to their own personality traits (i.e., Japan—peacefulness, Belgium—openness,
Ireland—excitement, etc.). Significantly, customers with the group of personality traits associated with
“peacefulness” and “openness” were the most profitable customers among the five analyzed clusters (CLV
value = 0.3149, 0.2635). The study provides valuable new insights into COO brand personality and consumer
personality traits with co-purchase behaviors via data mining techniques, and highlights the value of extending
CLV in developing useful marketing strategies.
Keywords: Big Data analytics ، Customer-driven marketing strategy ، Country-of-origin ، Brand personality ، Customer lifetime value ، Retail industry |
مقاله انگلیسی |
4 |
Does country-of-origin brand personality generate retail customer lifetime value? A Big Data analytics approach
آیا شخصیت نام تجاری کشور مبدا باعث افزایش طول عمر مشتریان خرده فروشی می شود؟ رویکرد تجزیه و تحلیل داده های بزرگ-2017 Many retail firms have witnessed the erosion of customer loyalty with the rise of e-commerce and its resulting
benefits to consumers, including increased choices, lower prices, and ease of brand switching. Retailers have
long collected data to learn about customer purchasing habits; however, many currently do not use data-mining
analytics to increase marketing effectiveness by predicting future buying patterns and potential customer life
time value, particularly to important segments such as loyal and potential repeat customers. Data mining can
efficiently analyze large amounts of business data (“Big Data”) in an effort to forecast consumer needs and
increase the lifetime value of customers (CLV). Previous studies on these topics primarily focus on conceptual
assumptions and generally do not present empirically valid models.
The present study sought to fill the research gap by using Big Data analytics to analyze approximately 44,000
point-of-sale transaction records for 26,000 customers of a Taiwanese retail store to understand how consumer
personality traits relate to the country-of-origin (COO) traits (brand personality) of beer brands, and to predict
potential customer lifetime value (CLV). The findings revealed that consumers tend to purchase and co-purchase
brands with traits similar to their own personality traits (i.e., Japan—peacefulness, Belgium—openness,
Ireland—excitement, etc.). Significantly, customers with the group of personality traits associated with
“peacefulness” and “openness” were the most profitable customers among the five analyzed clusters (CLV
value = 0.3149, 0.2635). The study provides valuable new insights into COO brand personality and consumer
personality traits with co-purchase behaviors via data mining techniques, and highlights the value of extending
CLV in developing useful marketing strategies.
Keywords: Big Data analytics | Customer-driven marketing strategy | Country-of-origin | Brand personality | Customer lifetime value | Retail industry |
مقاله انگلیسی |