CHAPTER 9: Customer Relationship Management & Business Intelligence

What is your understanding of CRM?


CRM (Customer relationship management) involves managing all aspects of a customer’s relationship with an organisation to increase customer loyalty and retention and an organisation's profitability.

CRM allows an organisation to gain insights into customers shopping and buying behaviours. It also helps companies make their interactions with customers seem friendlier through individualism 

Other benefits of CRM are said to:

• Provide better customer service
• Improve call centre efficiency 
• Cross-sell products more effectively 
• Helps sales staff close deals faster
• Simplifies Marketing and Sales processes
• Discovers new customers
• Increases customer revenues 

Compare operational and analytical customer relationship management.

Operational CRM	Analytical CRM
Supports traditional transactional processing for day-to-day front-office operations or systems that deal directly with the customers	Supports back-office operations and strategic analysis and includes all systems that do not deal directly with the customers

The primary difference between operational CRM and analytical CRM is the direct interaction between the organisation and its customers.

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

CRM example video

Marketing	Sales	Customer Service
List Generator: Compiles customer information from a variety of sources and segment the information for different marketing campaigns	Sales Management: Automate each phase of the sales process, helping individual sales representatives co-ordinate and organize all of their accounts. Includes calendars to help plan customer meetings, alarms for important tasks etc	Contact centre: Where customer service representatives answer customer inquiries and respond to problems through a number of different customer touch points
Campaign Management: Guides users through marketing campaigns	Contact Management: Maintains customer contact information and identifies prospective customers for future sales. Includes maintaining organizational charts, detailed customer notes, and supplemental sales information.	Web-based self-service: Allow customers to use the web to find answers to their questions or solutions to their problems
Cross Selling: Selling additional products or services And Up Selling: Increasing the value of the sale	Opportunity Management: Target sales opportunities by finding new customers or companies for future sales. Determine potential customers and competitors and define selling efforts, including budgets and schedules.	Call scripting: Access organizational databases that track similar issues or questions and automatically generate the details for the CSR who can then relay them to the customer

Indian Call Centre- click here for video 

How could a sales department use operational CRM technologies?

Sales departments had 2 primary reasons for tracking customer sales information electronically

• Sales representatives were struggling with the overwhelming amount of customer account information they were required to maintain and track 
• Companies were struggling with the issue that much of their vital customer and sales information remained in the heads of their sales representatives. 

Sales force automation is a system that automatically tracks all of the steps in the sales process, focusing on increasing customer satisfaction, building customer relationships and improving product sales by tracking all sales information. 

Describe business intelligence and its value to businesses

BI refers to applications and technologies that are used to gather, provide access to and analyse data and information to support decision-making efforts. 

BI includes simple MS Excel Pivot tables to highly sophisticated software that fetches data from the different front and back office systems. 

MS Excel Pivot Table

Explain the problem associated with business intelligence. Describe the solution to this business problem

The Problem: Data Rich: Information Poor.
Companies have a lot of data, however they are not able to benefit from levering this information and turning it into useful data for analytical and strategic decision making.

The Solution: Business Intelligence. 
In every organisation, employees make hundreds of decisions each day. They can range from whether to give customer X a discount, whether to start producing part Y, whether to launch another direct mail campaign, whether to order additional materials, etc. 


These decisions are sometimes based on facts, but mostly based on experience, accumulated knowledge, and rule of thumb. That poses a problem because experience, knowledge, and rule of thumb can take years to develop.
Some employees never acquire them. Those who do may still fall prey to decision traps or biases in judgment. 


Improving the quality of business decisions has a direct impact on costs and revenue. For instance, giving a customer a discount may or may not help the bottom line, depending on the profitability of the client over the duration of the relationship. 


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 agile intelligent enterprise

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 covered by the raw data alone. It is the application of statistical techniques to find patterns and relationships among data and to classify and predict.

• Can begin at a summary information level (coarse granularity) and Progress through increasing levels of detail (drilling down)
• or the other way around (drilling up)

Two possible outcomes of Data Mining:


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. 
It is frequently used to segment customer information for customer relationship management systems to help organisations identify customers with similar behavioural traits, such as cluster of best customers or one-time customers. 

Statistical Analysis:
Performs such functions as information correlations, distributions, calculations, variance analysis, just to name a few.