Business Intelligence and Analytics for Business Development Report

This report delves into the application of Business Intelligence (BI) within the context of business development, specifically focusing on Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), and Knowledge Management Software (KMS). The analysis covers key areas such as customer analysis, emphasizing customer profiling, collaborative filtering, customer lifetime value, and customer loyalty. The report also examines revenue generation strategies, including the use of ERP systems for identifying customers, promoting products, upselling, cross-selling, and leveraging KMSs for market development and loyalty management. Furthermore, the report explores supply chain management, highlighting the role of ERP systems in supplier performance, just-in-time production, portfolio/demand/inventory analysis, and vendor management. The discussion extends to the use of KMSs in inventory control and distribution analysis. The report is based on research and includes a detailed reference list.

Running head: BUSINESS INTELLIGENCE 1
Business Intelligence
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BUSINESS INTELLIGENCE 2
Customer Analysis
In customer analysis, customer profiling involves the creation of the portrait customer by
use of the CRM software to design organization goods and services offered. It involves
personalization which is meeting the demands for the customers more efficient and effective
(Lee & Park, 2015). Collaborative filtering is also involved in this by making automatic
predictions regarding the customers’ interests by collecting their preferences for improvement.
Customer lifetime value is also taken into consideration by the prediction of the net profit
attributed through the entire future interactions with the customers (Sharma & Djiaw, 2011).
Customer loyalty is another key factor in keeping a positive emotional experience with the
customers for their satisfaction. The perceived positive value of the experience of the customers
includes the services and products. In involves have a physical and emotional attribute to retain
the customers by developing positive interactions (Olszak & Ziemba, 2017). It can be done by
satisfying their tests and preferences.
Revenue Generation
This involves the use of the ERP systems in the identification of the customers as well as
promotional products and services through mediums likely to reach the customers effectively.
Up selling is applied to encourage customers to purchase the products while cross-selling
involves the invitation of the customers to but the related products (Bose, 2018). KMSs support
market development in the identification of the new market for the currently produced products.
This can be achieved through loyalty management in business to meet the target right customers
and offering incentives (Chamoni, 2012). CRM helps in supporting the location analytics for the
blending of the business data together with the geographic data for the revealing of the
relationship that exists between location to people and events.

BUSINESS INTELLIGENCE 3
Supply Chain Management
ERP systems help in supplier performance which involves measure, analysis, and
management of suppliers’ performance to drive improvements by reducing the cost and risks
(Nunamaker Jr, 2015). Just in time aims at reducing the production systems by aligning the raw
materials from suppliers direct to the production systems. CRM software helps in
Portfolio/demand/inventory analysis by gaining all the possible insights into action performance
of the organizational production range to reduce the large volume of inventory (Forster, 2017).
Supplier and vendor management make use of the CRM software to enable the organization to
control all the possible costs and risks by driving better services for the improved value of its
vendors and suppliers (Khorasani, 2010). The process is associated with shipping in which items
are transported from one producer to the customer via different means. Through the use of
inventory control, KMSs help in minimizing the inventory of the company (Jafari, 2012). The
whole process involves the generation of the maximum level of the profits from the lowest
amount of the inventory without interfering with the customers’ demands. The process includes
the distribution analysis which is used as a tool for allowing one or more levels of distributors to
enter data and have a comparison of the same with the number of the statistics goodness statistics
(Watson & Wixom, 2017). The p-value is applied to test for the results especially in determining
the distribution representation of the provided data.

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References
Bose, R. (2018). Competitive intelligence process and tools for intelligence analysis. Industrial
management & data systems, 108(4), 510-528.
Chamoni, P. (2012). The impact of business intelligence tools on performance: a user satisfaction
paradox?. International Journal of Economic Sciences and Applied Research, 5(3), 7-32.
Forster, A. J. (2017, May). Business Process Monitoring and Alignment: An Approach Based on
the User Requirements Notation and Business Intelligence Tools. In WER (pp. 80-91).
Jafari, M. (2012). Evaluation model of business intelligence for enterprise systems using fuzzy
TOPSIS. Expert Systems with Applications, 39(3), 3764-3771.
Khorasani, R. (2010). Business intelligence tools for radiology: creating a prototype model using
open-source tools. Journal of digital imaging, 23(2), 133-141.
Lee, J. H., & Park, S. C. (2015). Intelligent profitable customers segmentation system based on
business intelligence tools. Expert systems with applications, 29(1), 145-152.
Nunamaker Jr, J. F. (2015). A visual framework for knowledge discovery on the Web: An
empirical study of business intelligence exploration. Journal of Management Information
Systems, 21(4), 57-84.
Olszak, C. M., & Ziemba, E. (2017). Approach to building and implementing business
intelligence systems. Interdisciplinary Journal of Information, Knowledge, and
Management, 2(1), 135-148.

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Sharma, R. S., & Djiaw, V. (2011). Realising the strategic impact of business intelligence
tools. Vine, 41(2), 113-131.
Watson, H. J., & Wixom, B. H. (2017). The current state of business
intelligence. Computer, 40(9), 96-99.