scholarly journals Research on Forecast Model and Application of Customer Loyalty under the Background of Big Data

2014 ◽  
Vol 9 (10) ◽  
pp. 209-222
Author(s):  
Yihua Zhang ◽  
Yuan Wang ◽  
Chunfang He ◽  
TingTing Yang
Keyword(s):  
Big Data ◽  
Customer Loyalty ◽  
Forecast Model

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.

Research on Electricity Sales Forecast Model Based on Big Data

2021 ◽  
pp. 316-328
Author(s):  
Kai Li ◽  
Jian Li ◽  
Siming Chen ◽  
Jun Tang ◽  
Jiao Wu ◽  
...  
Keyword(s):  
Big Data ◽  
Forecast Model ◽  
Sales Forecast ◽  
Model Based

scholarly journals Predictive analytics using big data for increased customer loyalty: Syriatel Telecom Company case study

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Wissam Nazeer Wassouf ◽  
Ramez Alkhatib ◽  
Kamal Salloum ◽  
Shadi Balloul
Keyword(s):  
Big Data ◽  
Customer Loyalty ◽  
Predictive Analytics

scholarly journals Big Data Analytics: A Spotify Case Study

2021 ◽  
Vol 9 (10) ◽  
pp. 1823-1829
Author(s):  
Suraj Ingle
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Customer Satisfaction ◽  
Customer Loyalty ◽  
Data Analytics ◽  
Exploratory Data Analysis ◽  
Big Data Analytics ◽  
Exploratory Data ◽  
Insight Into

Abstract: By developing products that are in line with consumer needs, anticipating their profitability and manufacturing them, Big Data has opened up a lot of possibilities for building customer loyalty and commercial business by proactively engaging and comprehensively streamlining offers across all customer touch points. The use of big data to determine the best, most efficient ways to engage and interact with their customers will be discussed in this paper. An insight into how Spotify intends to provide music lovers additional ways to find their favourite songs, interact with artists, and improve Spotify recommendations has been provided. Keywords: Big Data, Data Analytics, Customer Satisfaction, Exploratory Data Analysis

scholarly journals ETLAnow: A Model for Forecasting with Big Data

2016 ◽  
Author(s):  
Joonas Tuhkuri
Keyword(s):  
Big Data ◽  
Economic Crisis ◽  
Unemployment Rate ◽  
Forecast Model ◽  
Real People ◽  
Search Data ◽  
The Moment ◽  
Google Search ◽  
Economic Forecasts ◽  
The Eu

In this paper we document the ETLAnow project. ETLAnow is a model for forecasting with big data. At the moment, it predicts the unemployment rate in the EU-28 countries using Google search data. The model is publicly available at the ETLAnow’s website, http://www.etlanow.eu. The forecast model is based on the idea that volumes of Google searches could be associated with the current and future level of an economic index. And these data are available earlier than official statistics. The motivation for our approach is that big data could help produce more accurate economic forecasts. Those forecasts would inform better policy and decisions, and help real people—especially during an economic crisis.

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