The potential impact of Big Data on customer loyalty in the retail sector

2020 ◽  
Vol 1 (1) ◽  
pp. 51-58
Author(s):  
Yuanchen Meng ◽  
Jiangfeng Cao
Keyword(s):  
Big Data ◽  
Customer Loyalty ◽  
Retail Sector ◽  
Potential Impact

scholarly journals STUDY AND ANALYSIS OF CONSUMER BUYING BEHAVIOR IN RETAIL MARKET

2020 ◽  
Vol 3 (11) ◽  
pp. 1-6
Author(s):  
Manish Dubey ◽  
Siddharth Saini ◽  
Srishti Umekar
Keyword(s):  
Customer Loyalty ◽  
Sales Force ◽  
Sales Promotion ◽  
Retail Sector ◽  
Audience Participation ◽  
Brand Name ◽  
Buying Behavior ◽  
Sales Promotions ◽  
The Impact ◽  
Consumer Buying Behavior

The aim of this study are determining the impact of the most used tools of sales promotion in retail sector such as coupons, sample, price discount and buy one get one free on consumer buying behavior from two aspects are brand switching and customer loyalty. Consumer promotions should stimulate purchases, sustain brand-name recognition, and gain audience participation. Themes are underlying messages. Media should be selected. In this way include direct mail, newspapers, magazines, television, the personal sales force, and group meetings. The duration of a sales promotion is set. The feasibility of shared sales promotions is weighed.

scholarly journals Big Data & Analytics as a sustainable Customer Loyalty Instrument in Banking and Finance

2019 ◽  
Vol 3 (4) ◽  
pp. 74-88 ◽  
Author(s):  
C. Giebe ◽  
L. Hammerström ◽  
D. Zwerenz
Keyword(s):  
Big Data ◽  
Customer Loyalty ◽  
Banking Sector ◽  
Big Data Analytics ◽  
Strong Relationship ◽  
Business Coaching ◽  
Banking Services ◽  
Banking And Finance ◽  
Customer Information ◽  
Corporate Social

The performance of the banking sector depends on the ability of a range of banking products to meet customer needs in a timely and complete manner. Due to the specific features of the banking sector, technological capabilities to accumulate a massive pool of customer information about banking services, the German banking sector has more capacity than other industries to launch and sell banking services that will be in high demand among users. The author points out that innovative methods and solutions were developed on the basis of mathematical and statistical models. It is stated that a progressive tool for providing customer-oriented services and products, in the banking sector, is currently defined as “Big Data & Analytics”. The main purpose of the study is to identify the peculiarities of the use in the banking practice of the analytical tool “Big Data & Analytics” and the functional ability of this tool to ensure stable customer loyalty in the course of using banking services. The study empirically confirmed (based on a survey conducted in the fall of 2019) and theoretically proved that there is a strong relationship between the use of the Big Data & Analytics tool and the provision of key principles of customer loyalty in the following areas of the banking sector: advice to clients by banking employees systems must be objective and comprehensive, be individualized and be provided in a timely and comprehensive manner. Emphasis is placed on the need for further research on the effectiveness of internal and external business coaching, which is particularly relevant in the context of a total digital transformation of all spheres of society and entrepreneurship. Keywords: big data and analytics, corporate social responsibility, customer loyalty tool, business ethics.

scholarly journals Statistical Modeling and Simulation of Online Shopping Customer Loyalty Based on Machine Learning and Big Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jui-Chan Huang ◽  
Po-Chang Ko ◽  
Cher-Min Fong ◽  
Sn-Man Lai ◽  
Hsin-Hung Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Credit Risk ◽  
Customer Loyalty ◽  
Online Shopping ◽  
Big Data Analysis ◽  
Web Page ◽  
The Web

With the increase in the number of online shopping users, customer loyalty is directly related to product sales. This research mainly explores the statistical modeling and simulation of online shopping customer loyalty based on machine learning and big data analysis. This research mainly uses machine learning clustering algorithm to simulate customer loyalty. Call the k-means interactive mining algorithm based on the Hash structure to perform data mining on the multidimensional hierarchical tree of corporate credit risk, continuously adjust the support thresholds for different levels of data mining according to specific requirements and select effective association rules until satisfactory results are obtained. After conducting credit risk assessment and early warning modeling for the enterprise, the initial preselected model is obtained. The information to be collected is first obtained by the web crawler from the target website to the temporary web page database, where it will go through a series of preprocessing steps such as completion, deduplication, analysis, and extraction to ensure that the crawled web page is correctly analyzed, to avoid incorrect data due to network errors during the crawling process. The correctly parsed data will be stored for the next step of data cleaning or data analysis. For writing a Java program to parse HTML documents, first set the subject keyword and URL and parse the HTML from the obtained file or string by analyzing the structure of the website. Secondly, use the CSS selector to find the web page list information, retrieve the data, and store it in Elements. In the overall fit test of the model, the root mean square error approximation (RMSEA) value is 0.053, between 0.05 and 0.08. The results show that the model designed in this study achieves a relatively good fitting effect and strengthens customers’ perception of shopping websites, and relationship trust plays a greater role in maintaining customer loyalty.

scholarly journals UPSELLING STRATEGY: A REVIEW

2021 ◽  
pp. 157-168
Author(s):  
Mehal Pandya ◽  
Abhigna Dholakia
Keyword(s):  
Customer Loyalty ◽  
Service Sector ◽  
Consumer Behaviour ◽  
Practical Implication ◽  
Retail Sector ◽  
Customer Lifetime ◽  
Comprehensive Search ◽  
The Relationship ◽  
A Company ◽  
Comprehensive Search Strategy

The purpose of this study is to systematically review the existing literature on Upselling and its major impacts on consumer behaviours. In addition, this study seeks to shed light on trends and dynamics in Service sector and Retail sector, so as to find the effectiveness of Upselling strategy. A systematic literature review of published peer-reviewed articles on Upselling was conducted. A comprehensive search strategy was applied on different databases, including Google Scholar, JSTOR, ScienceDirect, Pro quest, Emerald, Academia and the likes and the renowned articles were then selected from leading journals published between 1995 and 2020.The research finding suggest that a company may get the profitability at times but not all the time using the upselling strategy. To get the profitability firm should focus on Customer loyalty and satisfaction of the consumer for that the results show that there is a need to replace the Upselling strategy with Up-buying strategy in studies focused on consumer behaviour analysis. The studies included in this review are extensively based on peer-reviewed 40 articles published in high-ranked marketing journals and articles within a span of 30 days which may be perceived as a limitation in the present study. The relationship between Upselling and the Customer satisfaction in many occasion prove to be negative hence there is a requirement of change in strategy so as to shift focus from salesforce centric strategy to customer centric strategy, of which the companies will have a considerable practical implication aiming to increase profit and simultaneously increase in Customer Lifetime value along with Customer Loyalty. The authors hereby expect the current review to significantly find the effectiveness of Upselling strategy in consumer behaviour. This review provides a strong contribution to Upselling literature by recommending the need to find the effectiveness of Upselling strategy in Services and Retail sector. No exclusive paper is available which evaluates effectiveness of UpSelling strategy which can be further studied for strategic purpose.

scholarly journals Healthcare data scientist qualifications, skills, and job focus: a content analysis of job postings

2019 ◽  
Vol 26 (5) ◽  
pp. 383-391 ◽  
Author(s):  
Melanie A Meyer
Keyword(s):  
Big Data ◽  
Content Analysis ◽  
Performance Improvement ◽  
Care Quality ◽  
Healthcare Organizations ◽  
Job Level ◽  
Healthcare Data ◽  
Data Scientist ◽  
Potential Impact ◽  
Job Postings

Abstract Objective Growth in big data and its potential impact on the healthcare industry have driven the need for more data scientists. In health care, big data can be used to improve care quality, increase efficiency, lower costs, and drive innovation. Given the importance of data scientists to U.S. healthcare organizations, I examine the qualifications and skills these organizations require for data scientist positions and the specific focus of their work. Materials and Methods A content analysis of U.S. healthcare data scientist job postings was conducted using an inductive approach to capture and categorize core information about each posting and a deductive approach to evaluate skills required. Profiles were generated for 4 job focus areas. Results There is a spectrum of healthcare data scientist positions that varies based on hiring organization type, job level, and job focus area. The focus of these positions ranged from performance improvement to innovation and product development with some positions more broadly defined to address organizational-specific needs. Based on the job posting sample, the primary skills these organizations required were statistics, R, machine learning, storytelling, and Python. Conclusions These results may be useful to organizations as they deepen our understanding of the qualifications and skills required for data scientist positions and may aid organizations in identifying skills and knowledge areas that have been overlooked in position postings.

scholarly journals Impact of Sales Promotion, Advertising and Direct Marketing on Sale of Cosmetic Products

2021 ◽  
Vol 2 (1) ◽  
pp. 20-34
Author(s):  
Zawar Khan
Keyword(s):  
Customer Loyalty ◽  
Direct Marketing ◽  
Sales Promotion ◽  
Retail Sector ◽  
Cosmetic Products ◽  
Buying Behavior ◽  
Sales Promotions ◽  
Retail Markets ◽  
The Impact ◽  
Consumer Buying Behavior

Retail markets have been one of the most rapid-growing markets in the world for the last decade; to stay competitive, retailers use effective sales promotions tools and that become a vital technique for marketers to stimulate consumer buying behavior towards purchasing any product. This study aims to determine the impact of the most used tools of sales promotion in the retail sector such as coupons, sample, price discount and buy one get one free on consumer buying behavior from two aspects: brand switching and customer loyalty. This study based on a literature review, conceptual framework and hypothesis which open the door for future researchers to expand more in this field.

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