Chapter 9 - Customer Relationship Management & Business Intelligence

What is your understanding of CRM?

Customer Relationship Management (CRM), is a business strategy used by a company in order to reduce costs and increase profits through solidifying customer relationship by customer satisfaction, loyalty and retention.

Compare operational and analytical customer relationship management.

Operational CRM provides support to "front office" business processes, including sales, marketing and service. Each interaction with a customer is generally added to a customer's contact history, and staff can retrieve information on customers from the database as necessary.

One of the main benefits of this contact history is that customers can interact with different people or different contact “channels” in a company over time without having to repeat the history of their interaction each time.

Consequently, many call centers use some kind of CRM software to support their call centre agents.

Analytical CRM analyses customer data for a variety of purposes including:

1. design and execution of targeted marketing campaigns to optimise marketing effectiveness
2. design and execution of specific customer campaigns, including customer acquisition, cross-selling, up-selling, retention
3. analysis of customer behaviour to aid product and service decision making (e.g. pricing, new product development etc.)
4. management decisions, e.g. financial forecasting and customer profitability analysis
5. prediction of the probability of customer defection (churn).

Link: CRM

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

There are three types of CRM technologies used by marketing departments. They are:

1. List Generator: Complies customer information from a variety of sources and segment the information for different marketing campaigns.
2. Campaign management systems: Guide users through marketing campaigns performing such tasks as campaign definition, planning, scheduling, segmentation and success analysis.
3. Cross-selling & Up-selling: Is selling additional products or services to a customer. Up-selling is increasing the value of the sale.

 Sales department also uses three CRM technologies. They are:

1. Sales management: Automate each phase of the sales process, helping individual sales representatives co-ordinate and organise all of their accounts.
2. Contact management: Maintains customer contact information and identifies prospective customers for future sales.
3. Opportunity management: Target sales opportunity by finding new customers or companies for future sales.

How could a sales department use operational CRM technologies?

Sales department uses operational CRM technologies, to help plan customer meetings, provide an analysis of sale cycle, maintaining organisational charts, determine potential customer and competitors.

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Describe business intelligence and its value to businesses.

Business intelligence (BI) refers to applications and technologies that are used to gather, provide access to analyse data and information to support decision-making efforts. An example is if a company is looking to buy new machinery, they can take statistical data of previous revenue to project what may happen next year in order to make sure that their costs are covered. It is an extremely valuable resource. 

BI can also help calculate the best sales people of the company, the demographics of customers and which customers buy more than others.

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

Business Intelligence may not always be the most reliable form of calculating for the future, for example there could be the launch of a new competitive business which will effect the sales of your business, or a sudden turn in the economy could also effect revenue.

What are the two possible outcomes a company would get from using data mining?

Data mining is finding patterns in statistical data, this could show a business the downfalls and up's of certain activities e.g. like what time of he year a product is bought the most. This can allow for businesses to prepare for certain times of the year. Data mining can also allow a company to understand the frequency of products that are bought.