Week 10 Questions

Customer Relationship Management and Business Intellegence


1. What is your understanding of CRM?

Customer relationship management (CRM) involves managing all aspects of a customer's relationship with an organisation to increase customer loyalty and retention as well as an organisation's profitability. As organisations begin to migrate from the traditional product-focused organisation toward customer- driven organisations, they are recognising their customers as experts, not just revenue generators. Organisations are quickly realising that without customers they simply would not exist, and it is critical they do everything they can to ensure their customer's satisfaction. CRM is one of the most valuable assets a company can acquire.

Customer Relationship Management (CRM)

2. Compare operational and analytical customer relationship management.

Operational CRM supports traditional transactional processing for day-to-day front-office operations or systems that deal directly with customers. Analytical CRM supports back-office operations and strategic analysis and includes all systems that do not deal directly with customers. The primary difference between operational CRM and analytical CRM is the ddirect interaction between the organisation and its customers.

3. Describe and differentiate the CRM technologies used by marketing departments and sales departments.

Marketing CRM technologies include:

• List generator - compile customers information from a variety of sources and segment the information for different marketing campaigns. Information sources include website visits, website questionnaires, online and offline surveys, flyers, toll-free numbers, current customer lists etc. List generators provide the marketing department with a solid understanding of the type of customer it needs to target for marketing campaigns.
• Campaign management - systems that guide users through marketing campaigns, peforming such tasks as campaign definition, planning, scheduling, segmentation and success analysis.
• Cross-selling and up-selling - cross selling is selling additional products and services to a customer. Up selling is increasing the value of the sale. CRM systems offer marketing departments all kinds of information about their customers and their products, which can help them identify cross-selling and up-selling marketing campaigns.

Sales CRM technologies include:

• Sales management CRM systems - automate each phase of the sales process, helping individual sales representative co-ordinate and organise all of their accounts. These systems can even provide an analysis of the sales cycle and calculate how each individual sales representative is performing during the sales process.
• Contact management CRM systems - maintains customer information and identifies prospective customers for future sales. Contact management systems include such features as maintaining organisations charts, detail customer notes, and supplemental sales information.
• Opportunity management CRM systems - target sales opportunities by finding new customers or companies for future sales. Opportunity management systems determine potential customers and competitors and define selling efforts, including budgets and schedules.

4. How could a sales department use operational CRM technologies?

Sales departments were the first to begin developing CRM systems. Sales departments has two primary reasons to track customer sales information electronically. First, sales representatives were struggling with the overwhelming amount of customer account information they were required to maintain and track. Second, companies were struggling with the issue that much of their vital customer and sales information remained in the heads of their sales representatives. One of the first CRM components built to help address these issues was the sales force automation component.

5. Describe business intelligence and its value to businesses.

Business Intellegence (BI) refers to applications and technologies that are used to gather, provide access to and analyse data and information to support decision-making efforts.
To improve the quality of business decisions, managers can provide existing staff with BI systems and tools that can assist them in making better, more informed decisions. The result creates an gile intelligent enterprise.

6. Explain the problem associated with business intellegence. Describe the solution to this business problem.

Data rich, information poor
As businesses increase their reliance on enterprise systems such as CRM, they are rapidly accumulating cast amounts of data. Every interaction between departments or with the outside world, historical information of past transactions, as well as external market information, is entered into information systems for future use and access.
The amount of data being generated is doubling every year and some think it will soon begin to double every month. Data are a strategic asset for a business; if the asset is not used, the business is wasting resources.
As a result, information has to be requested from different departments or IT, who must dedicate staff to pull together various reports. The challenge is to transform datainto useful information.

The solution - BUSINESS INTELLIGENCE
As stated in the previous question, to improve the quality of business decisions, managers can provide existing staff with BI systems and tools that can assist them in making better informed decisions.

7. What are two possible outcomes a company could get from using data mining?

Data Mining is the process of analysing data to extract information not offered by the raw data alone. Data mining and also begin at a summary information level and progress through increasing levels to detail (drilling down) or the reverse (drilling up). Two possible outcomes a company could get from using data mining are:

1. Cluster Analysis - a technique used to divide an information set into mutually exclusive groups such that the members of each group are as close together as possible to one another and the different groups are as far apart as possible. Data mining tools that 'understand' human language are finding unexpected applications in medicine.
2. Association detection - reveals the degree to which variables are related and the nature and frequency of these relationships in the information. One of the most common forms of association detection analysis is market based analysis, which analyses such items as websites and checkout scanner information to detect customers' buying behaviour and predict future behaviour.