Monday, May 24, 2010

Chapter Elleven Questions

1. Explain the triple constraint and its importance in project management


The triple constraint involves the scope, time and cost of a project and the tradeoffs between them. There will always be changes in the triple constraint throughout the project life cycle.


Image from:

http://dii.vermont.gov/sites/dii/files/triple_constraint.png


2. Describe the two primary diagrams most frequently used in project planning.


PERT chart: Shows the projects tasks and the relationships between them. There are two types: Dependency and Critical path.

Gantt chart: a bar chart that illustrates project tasks against a calender.


Exampe of a Gantt chart:


Image from:

http://www.gantt-chart.biz/gcImages/Gantt_Chart.gif

3. Identify the three primary areas a project manager must focus on managing to ensure success.


Managing People

Managing Communications

Managing Change



Image from:

http://158.132.178.85/norbert/images/ProjectManagement.jpg

4. Outline 2 reasons why projects fail and two reasons why projects succeed.


Fail:

Poor scope

Failure to align project with organizational objectives


Succeed:

Strong project management

Team members are working towards common goals



Image from:

http://ccsmallbusiness.files.wordpress.com/2009/11/teamwork.gif

Friday, May 14, 2010

Chapter Nine Questions

1. What is your understanding of CRM?

A CRM involves the managing of all aspects of the customer relationships within an organization with the intention of keeping good customers and increasing profits.


Image from: http://keremgomi.files.wordpress.com/2009/07/crm011.jpg



2. Compare operational and analytical customer relationship management.

  • Operational: transactional, records the types of things that directly happen with customer e.g. problems, calls, sales.
  • Analytical: long term issues, strategic type things, based around the concept of data- mining, looking for patterns and trends, across customer.

Analytical crm:


Image from:

http://www.yehtechnologies.com/crm.jpg




3.
Describe and differentiate the CRM technologies used by marketing departments and sales departments
  • Sales department: Automating the sales process, helping sales people coordinate their jobs, calendars, appointments, meeting, multimedia presentations, interested in contact management.
  • Marketing department: Campaign managing, cost, target market, return on investment, opportunity management including sales customers had, how likely it is that someone is going to make a purchase.


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


Day to day type stuff inlcuding list generators (the ability to provide info on specific aspects of the business), campaign management (planning, scheduling), help cross sell(sell a complementary product) and upsell (selling more stuff).



5. Describe business intelligence and its value to businesses.


Applications and technology that gather, provide access to and analyse data and information to support decision making. Businesses need business intelligence in order to keep up with the rapid changing needs and wants of their customers in order to stay competitive. Business intelligence can tell businesses about: who their best customers are, their most exceptional sales people, determine which activities are making and losing money etc.



Image from:

http://pbi2.com/assets/images/img_businessIntel.jpg



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


Businesses that were data rich before, didn't really know where they are, what their competitors were doing, cant make good decisions. Business intelligence can help make better decisions, make data available to all functional areas.




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

  • Increase profits
  • Better sales
  • Better deployment of resources
  • Single point of access
  • Up to the minute information
  • Business intelligence across all departments


Image from:

http://willscullypower.files.wordpress.com/2009/01/data-mining.jpg