Customer Relationship Management: Castle Bingo

Executive summary

A data management system is software that is used in retrieving, cataloging, and running data queries. The database manager can create, edit, and update customer data in the database. Therefore, the information can be edited, searched, added, or changed using all the data management systems. Several data management systems have been developed. They are important in information sharing; thus, most of them are equipped with open database connectivity that enhances inter-database information sharing. Some of the information that can be managed by the system include membership list and subscription list; bookkeeping and accounting records; scientific research data; customer information; inventory information, personal records, and library information (College of information and technology 2008).

Introduction

Customer relationship management is internet-enabled information access software that is used to manage customer relationships by entrepreneurs in an organized manner. It ensures that detailed customer information is accessed by all interested departments of the business. Some of the relevant information that can be accessed include reminding customers of product requirements and accessing the records of what a customer purchased earlier. All businesses are highly dependent on customers. Therefore, it is necessary to identify with customers so that their needs can be understood and met by the business in an effort to develop a good relationship. A small business can meet their customers daily and understand the needs of their customers due to the face to face interaction.

This is as opposed to large businesses like the supermarket, where there are long chains between the decision-makers and the customer care. In the past few years, customer relationship management has been critical to many companies as it helps to acknowledge and understand the customers. This will change the business from being less product-centric and focus on the customers (Trapp 2013). The development of this system will enable companies without technology to handle customers effectively and efficiently. In this case, this technology would integrate the other forms of communication such as emails, telephone, and internet conversations in writing. It has been realized that companies that use sophisticated technology would benefit more than the companies that use basic data collection methods. Analysis of customers’ past behavior and anticipation of the future trends are considered (College of information and technology 2008).

Findings

Thomas H Davenport and Jeanne G Harris have indicated that companies that have employed customer management systems have predictive analytics. This was exemplified in their book ‘competing on analytics.’ They also enjoy growth and positive performance results. Predictive analytics would enable the industry to identify the most profitable customers, as well as the least profitable ones. Different steps in the supply chain can be tested to identify potential problems and hold-ups. Proper analysis of the past pricing and historical sales would allow resetting favorable profitability for every transaction. The capital cost for the system management has been high in the past. However, Oracle has new low-cost software that would be affordable to many organizations. Indeed, some companies have been able to negotiate for lower costs.

Daryn Mason of Oracle implied that the system had enabled companies and small scale business enterprises to excel in the business world. However, the companies have faced challenges to fulfill the sudden rush of demand for products and services. Two years ago, Net store, UK-based information technology and services providing company, decided to develop the customer management system known as Siebel that is part of Oracle. Since then, Nestore has experienced growth and acquired two other companies. The company has plans to grow organically, and the sales team played an important role in the growth. This was through the acknowledgment of their customers and potential customers. Businesses that employ customer management systems have experienced growth. This has been witnessed in the sales department, as well as in the financial and commercial departments. Another important benefit of the system is that users only require minimal training place. (College of information and technology 2008).

Our company, Castle Bingo, has several clubs. Therefore, there is a need to capture and share data information to improve customer management and market growth. We are faced with the challenge of high volume data management. Several companies have achieved a competitive advantage over their competitors through the employment of big data analytics. New analytics are supported by data filters and forceful dominance of the data sources.

The big data and metrics

This is used to store, search and work on large quantities of data. Big data sets have various characteristics, including volume, velocity, and variety. However, the most important characteristic is the value of information recovery. Several companies have realized that information acquisition is a competitive advantage. Therefore, this is the time to implement big data as it has the capacity to collect large volumes of data. It processes both unstructured and structured data at high speed (Deloitte, 2012). There are different types of data sources, including operational sources, financial sources, constituency sources, and customer sources. Operational and financial sources contain objective metrics, while the customer and constituency sources contain attitudinal metrics. Operational data is used to analyze the quality of the business processes

. A good example is the use of the customer management system to track the call center interaction quality in terms of response time and call length. The system is also able to measure the financial quality of the company using the financial data obtained from the company’s financial reporting system. Customer data sources from surveys and online and social media are captured into the customer enterprise feedback system of large enterprise companies (Sun & Heller 2012). A company needs to realize what the big data implementation is addressing. This will ensure that one does not assume that big data alone would be enough to produce returns. The application of big data analytics should result in business returns. The company should not incur unnecessary costs as a result of a disjointedness. This is with regard to big data inputs and the preferable outcomes. This would lead to a waste of time, money, and effort. Many companies have adopted lean principles of big data to improve output quality and improve internal processes. These include the following:

Define business processes and customer objectives

Define the business goals and customer objectives, then build the business strategies on these goals and objectives. It is important to single out the business challenges and manage each with the objective of customer needs satisfaction. The point is how the potential customer would perceive the value of a service. For example, an expensive car can be given away at a price, but the chances of winning the prize may not motivate the customer.

Relevant data set identification

The relevant output would be obtained only if the data identified measure for the outcome. Publishers and technology companies are paid to provide data. Therefore, due to this cost, data should only be purchased if the intended outcome will impact the business positively.

Design analysis

The processes and steps of data collection, storage, analysis, and visualization should be integrated fully into the business processes and services.

Analytic and outcome measure implementation

The analytics implemented and data collected should measure the business processes performance and services.

The pull don’t pull principle

Emphasize analytic processes that are customer-demand oriented to ensure the processes are relevant to the value derived by both the customer and business.

Customer feedback integration in the business processes

The low and poor quality data and unnecessary analytics should be eliminated from the business process. This should be replaced with more insightful analytics to ensure a and perfectly refined outcome. The problem with big data is the application of data. Thus, only useful insights should be extracted to avoid unnecessary costs (Hayes 2012).

Database management systems have got several advantages. One of the biggest advantages is that information is made available to all potential users. The system is designed in a way that minimizes data redundancy as the information in it only appears once. The data stored in the system remain accurate, consistent, and of integrity since changes can only be made from one point. Data consistency enhances data management when many programmers are involved. The system is user friendly as access and manipulation of data is easy since the reliance on a specialist is minimized. It is beneficial to access information from one source of storage even though the system faces security challenges. The security risks can be avoided if access is only limited to authorized individuals by the use of passwords (College of information and technology 2008).

Benefits of big data metrics

Financial fraud detection, prevention, and remediation

Criminals tend to defraud companies through several strategies. Therefore, the use of big data volumes enables a company to discover any suspicious event that indicates fraud. Several companies have become victims of fraud due to the inability to refine their fraud prediction models. With big data and a more sophisticated IT team, a company can improve on the fraud detection models.

Execution of high-value campaigns

This is possible due to improved model execution capability campaigns. The campaigns are intended to market company services to increase market base and popularity.

High-performance analytics

High-performance analytics makes a difference in terms of fraud and risk prevention.

Improved delinquent collection

Just like prepaid phone services, the big data with high-performance analytics have improved delinquent collection processes to increase collection. Bingo would allow customers to access prepaid services (Spakes 2012).

Ethical and Legal Issues

Confidentiality: There should be informed consent to allow data sharing to ensure that customer information is not leaked out to other parties outside the business. This is made possible through the restriction to access. The sensitivity of the data should be evaluated before it is put on share point so that only relevant customer information is stored. The company should have a confidentiality review among the employees to ensure that the customer details are confidential. Otherwise, penalties will be charged to the culprits. The institutions that access sensitive customer information like financial information should have a binding code of conduct that should be signed by all employees in charge. This is meant to ensure that all employees comply with customer confidentiality guidelines. The company should investigate what the national laws say about protecting their customer information. This will ensure that such laws are integrated into the company’s guidelines.

Recommendations

  • The development of a data management system requires proper planning to ensure that the effort applied produces the company’s desired results. The plan should include:
  • The description of the project. There should be a detailed education and understanding of the project research, as well as the organization and staff involved. This will ensure that the project objectives and goals are clarified.
  • The staff involved should understand the data collection methods to be employed and the format of the data.
  • Security of the acquired information should be assured. This is related to the short term storage system and local backups and ensures that important information is not lost or misused.
  • Ethical and legal matters in terms of access policies and provisions should be employed to protect customer information.
  • Long term data preservation should be put in place, which includes archives for future reference.
  • The assigned data managers should have clearly set responsibilities and a data management checklist to guide the plan.

Conclusion

Social media has played a role in ensuring that companies and organizations understand their customers and their perception of the brands and services. Data mining from the network systems cannot help an organization to achieve the desired goal. The application of the big data principles can help an organization or a company to improve customer relationships. For this reason, firms have the mandate to develop their own data management systems. This will enable them to obtain credible feedbacks and handle customers in a manner that would improve the good customer relationship.

Reference List

College of information and technology 2008, Database fundamental, Web.

Deloitte, 2012, Big data, Time for a Lean process of financial audit, Web.

Hayes, B 2012, Big Data has Big Implications for Customer Experience Management, Web.

Spakes, G 2012, Providing software solutions since 1976: Four ways big data can benefit your business, Web.

Sun, H & Heller, P 2012, Oracle Information: An Architect’s Guide to Big Data, Web.

Trapp, R 2013, How Customer Relationship Management systems can be of benefit to your business, Web.