McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved. 4-1 BUSINESS DRIVEN TECHNOLOGY Chapter Four: Measuring the Success of Strategic Initiatives

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved. 4-2 LEARNING OUTCOMES 4.1 Compare efficiency IT metrics and effectiveness IT metrics 4.2 List and describe five common types of efficiency IT metrics 4.3 List and describe four types of effectiveness IT metrics 4.4 Explain customer metrics and their importance to an organization

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved. 4-3 CHAPTER FOUR OVERVIEW Efficiency and effectiveness IT metrics are two ways to measure the success of IT strategic initiatives – Efficiency IT metrics – measure the performance of the IT system itself including throughput, speed, availability, etc. – Effectiveness IT metrics – measure the impact IT has on business processes and activities including customer satisfaction, conversion rates, sell-through increases, etc.

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved. 4-4 BENCHMARKING – BASELINING METRICS Benchmarks – baseline values the system seeks to attain Benchmarking – a process of continuously measuring system results, comparing those results to optimal system performance (benchmark values), and identifying steps and procedures to improve system performance

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved. 4-5 BENCHMARKING – BASELINING METRICS E-government benchmarks for IT efficiency and effectiveness

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved. 4-6 THE INTERRELATIONSHIPS OF EFFICIENCY AND EFFECTIVENESS IT METRICS Efficiency IT metrics focus on technology and include: – Throughput – amount of information that can travel through a system at any point in time – Speed – amount of time to perform a transaction – Availability – number of hours a system is available – Accuracy – extent to which a system generates correct results – Web traffic – includes number of pageviews, number of unique visitors, and time spent on a Web page – Response time – time to respond to user interactions

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved. 4-7 THE INTERRELATIONSHIPS OF EFFICIENCY AND EFFECTIVENESS IT METRICS Effectiveness IT metrics focus on an organization’s goals, strategies, and objectives and include: – Usability – the ease with which people perform transactions and/or find information – Customer satisfaction – such as the percentage of existing customers retained – Conversion rates – number of customers an organization “touches” for the first time and convinces to purchase products or services – Financial – such as return on investment, cost-benefit analysis, etc.

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved. 4-8 THE INTERRELATIONSHIPS OF EFFICIENCY AND EFFECTIVENESS IT METRICS Security is an issue for any organization offering products or services over the Internet It is inefficient for an organization to implement Internet security, since it slows down processing time. However, to be effective it must implement Internet security – Secure Internet connections must offer encryption and Secure Sockets Layers (SSL denoted by the lock symbol in the lower right corner of a browser)

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved. 4-9 THE INTERRELATIONSHIPS OF EFFICIENCY AND EFFECTIVENESS IT METRICS The interrelationships between efficiency and effectiveness

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved DETERMINING IT EFFICIENCY AND EFFECTIVENESS Customer metrics – assess the management of customer relationships by the organization and include: – Market share – Customer acquisition – Customer satisfaction – Customer profitability

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved Web Traffic Analysis Most companies measure the traffic on a Web site as the primary determinant of the Web site’s success However, a large amount of Web site traffic does not necessarily equate to large sales Many organizations with high Web site traffic have low sales volumes

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved Web Traffic Analysis Web site traffic analysis can include: – Cookie – a small file deposited on a hard drive by a Web site containing information about customers and their Web activities – Click-through – a count of the number of people who visit one site and click on an advertisement that takes them to the site of the advertiser – Banner ad – a small ad on one Web site that advertises the products and services of another business, usually another dot-com business – Interactivity – visitor interactions with the target ad

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved Behavioral Metrics Click-stream data tracks the exact pattern of a consumer’s navigation through a Web site Click-stream data can reveal: – Number of pageviews – Pattern of Web sites visited – Length of stay on a Web site – Date and time visited – Number of customers with shopping carts – Number of abandoned shopping carts

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved Behavioral Metrics Visitor Web site metrics

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved Behavioral Metrics Exposure, visit, and hit Web site metrics

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved OPENING CASE STUDY QUESTIONS How Levi’s Got Its Jeans into Wal-Mart 1.Formulate a strategy for how Levi’s can use efficiency metrics to improve its business 2.Formulate a strategy for how Levi’s can use effectiveness metrics to improve its business

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved CHAPTER FOUR CASE How Do You Value Friendster? Friendster specializes in social networking Friendster received over $13 million in VC capital Google recently offered to buy Friendster for $30 million A venture capital company recently valued Friendster at $53 million Friendster has yet to generate any revenue

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc. All rights reserved CHAPTER FOUR CASE QUESTIONS 1.How could you use efficiency metrics to help place a value on Friendster? 2.How could you use effectiveness metrics to help place a value on Friendster? 3.Explain how a venture capital company can value Friendster at $53 million when the company has yet to generate any revenue 4.Explain why Google would be interested in buying Friendster for $30 million when the company has yet to generate any revenue