scholarly journals Business Intelligence Applications; sustaining Retail Businesses in Rivers State, Nigeria

2021 ◽  
Vol 04 (03) ◽  
Author(s):  
Patrick N Nwinyokpugi ◽  
Keyword(s):  
Data Mining ◽  
Performance Management ◽  
Data Warehouse ◽  
Data Visualization ◽  
Business Intelligence ◽  
Business Sustainability ◽  
Retail Business ◽  
Retail Outlets ◽  
Rivers State ◽  
Intelligence Application

The contemporary retail outlets are no more run by manual practices given the electronic nature of company to customers (C-C); customers to customers (C-C) transactions globally. The application of business intelligence has given an edge to retail outlets operation. This study therefore, strives to examine the relationship between business intelligence application and retail business sustainability in Rivers State, Nigeria. The study used descriptive research technique through the adoption of cross- sectional survey design. Nine judgmentally sampled retail outlets especially large scale malls & superstores were studied in Port Harcourt base on their inherent electronic driven operations. Using structured closed ended questionnaire, 45 census senior managers of these sampled retail outlets were studied. Data gathered were analysed using the Pearson Product Moment Correlation Coefficient (PPMCC) statistics and presented with the aid of SPSS version 20.0 for easy interpretation. The results of analysed data showed that, the dimensions of business intelligence application which included but not limited to customers’ performance management, data warehouse, data mining and advanced data visualization significantly correlated positively with the measures of retail business sustainability being profitability and customers’ patronage. The finding also showed a high moderating effect of organizational culture on business intelligence application and retail business sustainability in Rivers State, Nigeria. Relying on the empirical findings, the study concluded that business intelligence application has positive significant relationship with retail business sustainability. It is therefore recommended that, the dimensions of business intelligence: customers’ performance management, data warehouse, data mining & advance data visualization identified in this study should be utilized as it enhances retail business operational sustainability.

Predicting retail business success using urban social data mining

2020 ◽  
Vol 12 (3) ◽  
pp. 263-277 ◽  
Author(s):  
Georgios Papadimitriou ◽  
Andreas Komninos ◽  
John Garofalakis
Keyword(s):  
Data Mining ◽  
Business Success ◽  
Social Data ◽  
Retail Business ◽  
Social Data Mining

Business Intelligence Applications in Retail Business: OLAP, Data Mining & Reporting Services

2010 ◽  
Vol 09 (02) ◽  
pp. 171-181 ◽  
Author(s):  
Ipek Deveci Kocakoç ◽  
Sabri Erdem
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Business Environment ◽  
Sql Server ◽  
Daily Growth ◽  
Data Mining Algorithms ◽  
Retail Business ◽  
Information Technology Investments ◽  
Crucial Information ◽  
Mining Algorithms

As a result of today's competitive business environment, companies have been trying to improve the utilization of funds effectively in their budgets for information technology investments. These companies retrieve more information with the same set of resources by means of business intelligence methods. According to Rubin (Chabrow, 2004) IT budgets are not simply declining or levelling off, rather, companies are shifting from a pure cost-cut mode to a model that emphasises agility and efficiency. Tremendous daily growth of the company data requires more funds and investment for establishing the technologies and infrastructure necessary for gathering fast and crucial information that supports the decision making process. This necessity gave birth to various business intelligence methods, which mainly aim to process mass amount of collected data from their existing application, and represent it in a way with which companies can apply to their daily competitive decisions. This application primarily concerns the implementation of business intelligence for a retail business company. The aim is to implement built-in business intelligence solutions of the Microsoft SQL Server that holds the commercial information of the company for the past three years. The customer company has already been using Microsoft products. The key items used for analyzing data are sales, momentary inventory and logistics information. The application can be grouped in five main areas: Building the data warehouse, constructing OLAP cubes, applying data mining algorithms on OLAP cubes, representing the results in reports with reporting services, and implementation.

scholarly journals Reduce Time Process Using Apriori Algorithm On K-Wayjoin Based To Find Retail Business Data Relationship Pattern

2019 ◽  
Vol 1 (1) ◽  
pp. 12-22
Author(s):  
Joko Aryanto ◽  
Yuli Asriningtyas
Keyword(s):  
Data Mining ◽  
Query Language ◽  
Market Competition ◽  
Query Languages ◽  
Research Approach ◽  
Apriori Algorithm ◽  
Time Process ◽  
Business People ◽  
Retail Business ◽  
Short Time

The process of running a trading business, businesses must always update information on market competition that occurs. Running a trading business is not just opening a business place and waiting for consumers to shop. Consumers will lightly come to shopping places to shop for various reasons. From cheap prices, attractive arrangements, large parking lots, to the ease of finding items to buy. From these problems, business people will compete on how to easily attract customers to their place of business. One way to solve these problems is by structuring merchandise with the aim of customers to easily get the items they are looking for. One way to solve these problems is by structuring merchandise with the aim of customers to easily get the items they are looking for. Arrangements that are made do not originate from arranging the location of goods according to taste but are carried out on the basis of trends or trends in goods purchased by consumers when shopping. This process is often referred to as the "Data Mining" process is one of the effective methods for finding consumers' preferences to choose the items they buy. This process can be completed using the Apriori algorithm. The principle used by the Apriori algorithm is that if an itemset appears frequently, then all subset of itemset must also appear frequently. This results in repeated checking and will require a short time. Problems that require a short time, a method is proposed, namely by developing to be able to reduce the travel time of the process. The length of time that occurs in the process of calculating the value of support and confidance and the repetition process to find the value. The method used is to manipulate the use of query languages with a k-way join research approach so that the optimal query language arrangement can be obtained. The results obtained in this study are that execution times are relatively faster, with the results of the same association rules as those produced by the Priori method without any development or modification.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining
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