Using Analytical CRM to drive Business

Develop a personal customer profile - when a website knows enough about a person's like and dislikes that it can fashion offers that are moe likely to appeal to that person (personalisation)

Data Mart - More specific view, a focus i.e. just the residential customers. Where as a Data Warehouse stores all the data as a whole.

These systems quickly aggregate, analyse, analyse,  and disseminate customer information throughout an organisation.

Analytical CRM information examples: Give customers more of what they want, value their time, Over-deliver, Contact frequently, Follow up.

Have a look at this video from Career Builder.

Current trends include: 

• Supplier Relationship Management (SRM).
• Partner Relationship Management (PRM).
• Employee Relationship Management (ERM).

The Ugly side of CRM:

Business 2.0 ranked you, the customer, as the number one person who mattered cost. The internet has given you a much louder, powerful and more visible voice.

Business Intelligence (BI)

Using information technology to lead to good business decisions.

Parallels between the challenges in business and challenges of war.

• Collecting information, Discerning patterns and meaning in the information, Responding to the resultant information.

Data Rich, Information Poor

Business face a data explosion as digital images, email inboxes, and broadband connections dude by 2010 (21% of the world connected by 2010) The amount of data generated doubles ever year.

BI enables business users to receive data for analysis that is: Reliable, Consistent, Understandable & Easily Manipulated. 

BI can answer tough customer questions. I.e. Why are sales below target? -> Because we sold less in the Western region. Why did we sell les in the west? -> Because sales of product X dropped.

Bi's Operation Value - The key is to shorten latencies. The time frame for opportunistic influences on customers, suppliers, and others is faster, more interactive, and better positioned. 

Data Mining Tools - Use a variety of techniques to find patterns and relationships in large volumes of information. Classification, Estimation, Affinity grouping, Clustering.

Data Mining - cluster analysis, assocation detection, statistical analysis.