Description
This is a presentation describes about competition on the basis of analytics. It gives various examples of analytical competition and its types.
STAGE 5 ANALYTICAL COMPETITORS
p. 35 What makes an analytical competitor? STAGE 4 ANALYTICAL COMPANIES
The Five Stages of Analytical Competition
STAGE 3 ANALYTICAL ASPIRATIONS
STAGE 2 LOCALIZED ANALYTICS
STAGE 1 ANALYTICALLY IMPAIRED
ANALYTIC COMPETITORS ARE FOUND IN A VARIETY OF INDUSTRIES
Consumer Products Anheuser-Bush E & J Gallo Winery Mars Proctor & Gamble Industrial Products CEMEX John Deere & Company Telecommunications Financial Services Barclays Bank Capital One Royal Bank of Canada Progressive Casualty Insurance WellPoint
Pharamceuticals
AstroZeneca Solvay Vertex Pharmaceuticals, Inc Transportation FedEx Schneider National United Parcel Service
Sprint O2 Bouygues Telecom
ANALYTIC COMPETITORS ARE FOUND IN A VARIETY OF INDUSTRIES
Hospitality and Entertainment Oakland A’s Boston Red Sox Harrah’ Entertainment Marriott International New England Patriots Retail Amazon.com JCPenny Tesco Wal-Mart eCommerce Google Netflix, Inc. Yahoo!
STAGE 1
Analytically Impaired
Localized Analytics
STAGE 2 Functional management builds analytics momentum and executives’ interest through application of basic analytics
An organization has some data and management interest in analytics
Top management support: full-steam-ahead path
Analytical Aspirations Terminal Stage: some companies’ analytics efforts never receive management support and stall here as a result
STAGE 3
Executives commit to analytics by aligning resources and setting a timetable to build a broad analytic capability
Road map to becoming an analytical competitor
Analytical Companies STAGE 4
Enterprise-wide analytics capability under development; top executives view analytic capability as a corporate priority
Analytical Competitors STAGE 5
p. 109 A Roadmap to Enhanced Analytics Capability
Organizations routinely reaping benefits of its enterprise-wide analytics capability and focusing on continuous analytics renewal
ANALYTICAL TECHNOLOGIES
SPREADSHEETS:
pp. 168-171 Architecture of Business Intelligence
Excel is most popular; data arrayed in rows, columns, sheets
ONLINE ANALYTICAL PROCESSORS (OLAP):
Business Objects and Cognos are among the leading vendors. Data can be arrayed in data cubes that can have more than three dimensions. Much easier to do spreadsheet like analysis then with RDBMS.
STATISTICAL OR QUANTITATIVE ALGORITHMS
SAS; SPSS; Dedicated Statistical packages
RULE ENGINES These are like queries and can be used to take a policy decision. Example: If the applicant for a motorcycle insurance policy is male and under 25, and does not either own his own home oe have a graduate degree, do not issue a policy.” DATA MINING TOOLS Range of techniques; straightforward arithmetic, aritificial intelligence, statistics, decision trees, neural networks, and Bayesian network theory. TEXT MINING
Identify emerging trends in near real time. Spiders, or data crawlers which identify and count words and phrases on Web sites, are a simple example of text mining.
SIMULATION MODELS Build valid mathematical model of the system processes using equations or arrival and departure patterns. Then try re-engineering experiments on the model.
EMERGING ANALYTICAL TECHNOLOGIES (in USA)
TEXT CATEGORIZATION GENETIC ALGORITHMS EXPERT SYSTEMS AUDIO AND VIDEO MINING SWARM INTELLIGENCE INFORMATION EXTRACTION
COMPETING ON ANALYTICS
Blockbuster Video fine of $40 prompts software businessman Reed Hastings to start Netflix. He had just sold off his existing company for a large sum of money. Netflix mails customers the DVD of their choice for viewing and they can return the DVD when they like. This service is provided for an annual membership fee. They have an analytical engine that monitors customer preferences and ratings.
WHAT ARE ANALYTICS?
Access to data and reporting: reports of problems, root cause analysis, prediction of future, worst case scenario, best case scenario, that is analytics.
WHY COMPETE ON ANALYTICS?
Capital One issues credit cards based on a shrewd analysis of customer credit profiles.
Harrah Entertainment is known for customer loyalty and service.
Practical organizations selling cement have used analytics. However some sectors are more amenable to analytics than others. THIS BOOK BY DAVENPORT AND HARRIS Part I deals with examples and key attributes of analytical organizations. Part II deals with how you can use analytics with suppliers and customers.
THE ORIGINS OF ANALYTIC COMPETITION
ERP: Enterprise Resource Planning Systems POS: Point of Sale systems Web-sites Decision Support Systems (DSS) in the 1960s. Operations Research: Some say this term has come into usage since World War II. Others maintain that this field has been current only since the advent of high speed computing. SAS/SPSS
OLAP
Blink by Malcolm Gladwell is a great book about the intuitive side of business capability.
Amazon.com : Jeff Bezos
Proctor and Gamble acquired Gilette after using much analytics. RFID (Radio Frequency ID) on every palette and carton. Fair Isaac Corporation Business Objects Cognos Many Americans Sports team managers swear by statistics and analytics
COMPETING ON ANALYTICS
WHAT MAKES AN ANALYTICAL COMPETITOR Four Characteristics 1. Analytics supported a strategic capability 2. The embrace was enterprise wide and not limited to one or two employees. 3. Senior management was committed to the use of analytics. 4. The company made a significant investment based on analytics.
COMPETING ON ANALYTICS
The authors conducted a survey of 371 firms about their analytics capabilities. 10% responded in the affirmative that analytical capability is a key element of strategy. The companies that demonstrate these four companies I Netflix, Harrah’s entertainment, Marriott International, Wal-Mart, Pro sports are all known for applying analytics to distinct capability. FICO score (from Fair Isaac Corporation) is used to Consumer Finance to evaluate the risk of lending to a particular borrower.
Progressive Insurance and Capital One analyse and sift the factors of FICO scores to identify less risky customers out of the poor scoring consumers.
COMPETING ON ANALYTICS
II Enterprise Level Approach BICC Business Intelligence Competency Center: “Cross-functional team with a permanent formal organizationanl structure. It is owned and staffed by the company and has defined tasks, roles, responsibilities and processes for supporting and promoting the effective use of business intelligence across the organisation. This approach seems reasonable when confronted with the ideal of an enterprise wide embrace of analytics. CEO of Scheider National ( a large trucking and logistics company) is a Ph.D in O.R. His BICC unit works on internals and also customers and suppliers.
COMPETING ON ANALYTICS III Senior Management Commitment
Low ranked champions of analytics can influence company attitude to analytics but nothing works as well as endorsement from senior management. IV Betting on analytics Companies have invested on “yield management” and other methods to gain and save millions of dollars.
doc_593552995.ppt
This is a presentation describes about competition on the basis of analytics. It gives various examples of analytical competition and its types.
STAGE 5 ANALYTICAL COMPETITORS
p. 35 What makes an analytical competitor? STAGE 4 ANALYTICAL COMPANIES
The Five Stages of Analytical Competition
STAGE 3 ANALYTICAL ASPIRATIONS
STAGE 2 LOCALIZED ANALYTICS
STAGE 1 ANALYTICALLY IMPAIRED
ANALYTIC COMPETITORS ARE FOUND IN A VARIETY OF INDUSTRIES
Consumer Products Anheuser-Bush E & J Gallo Winery Mars Proctor & Gamble Industrial Products CEMEX John Deere & Company Telecommunications Financial Services Barclays Bank Capital One Royal Bank of Canada Progressive Casualty Insurance WellPoint
Pharamceuticals
AstroZeneca Solvay Vertex Pharmaceuticals, Inc Transportation FedEx Schneider National United Parcel Service
Sprint O2 Bouygues Telecom
ANALYTIC COMPETITORS ARE FOUND IN A VARIETY OF INDUSTRIES
Hospitality and Entertainment Oakland A’s Boston Red Sox Harrah’ Entertainment Marriott International New England Patriots Retail Amazon.com JCPenny Tesco Wal-Mart eCommerce Google Netflix, Inc. Yahoo!
STAGE 1
Analytically Impaired
Localized Analytics
STAGE 2 Functional management builds analytics momentum and executives’ interest through application of basic analytics
An organization has some data and management interest in analytics
Top management support: full-steam-ahead path
Analytical Aspirations Terminal Stage: some companies’ analytics efforts never receive management support and stall here as a result
STAGE 3
Executives commit to analytics by aligning resources and setting a timetable to build a broad analytic capability
Road map to becoming an analytical competitor
Analytical Companies STAGE 4
Enterprise-wide analytics capability under development; top executives view analytic capability as a corporate priority
Analytical Competitors STAGE 5
p. 109 A Roadmap to Enhanced Analytics Capability
Organizations routinely reaping benefits of its enterprise-wide analytics capability and focusing on continuous analytics renewal
ANALYTICAL TECHNOLOGIES
SPREADSHEETS:
pp. 168-171 Architecture of Business Intelligence
Excel is most popular; data arrayed in rows, columns, sheets
ONLINE ANALYTICAL PROCESSORS (OLAP):
Business Objects and Cognos are among the leading vendors. Data can be arrayed in data cubes that can have more than three dimensions. Much easier to do spreadsheet like analysis then with RDBMS.
STATISTICAL OR QUANTITATIVE ALGORITHMS
SAS; SPSS; Dedicated Statistical packages
RULE ENGINES These are like queries and can be used to take a policy decision. Example: If the applicant for a motorcycle insurance policy is male and under 25, and does not either own his own home oe have a graduate degree, do not issue a policy.” DATA MINING TOOLS Range of techniques; straightforward arithmetic, aritificial intelligence, statistics, decision trees, neural networks, and Bayesian network theory. TEXT MINING
Identify emerging trends in near real time. Spiders, or data crawlers which identify and count words and phrases on Web sites, are a simple example of text mining.
SIMULATION MODELS Build valid mathematical model of the system processes using equations or arrival and departure patterns. Then try re-engineering experiments on the model.
EMERGING ANALYTICAL TECHNOLOGIES (in USA)
TEXT CATEGORIZATION GENETIC ALGORITHMS EXPERT SYSTEMS AUDIO AND VIDEO MINING SWARM INTELLIGENCE INFORMATION EXTRACTION
COMPETING ON ANALYTICS
Blockbuster Video fine of $40 prompts software businessman Reed Hastings to start Netflix. He had just sold off his existing company for a large sum of money. Netflix mails customers the DVD of their choice for viewing and they can return the DVD when they like. This service is provided for an annual membership fee. They have an analytical engine that monitors customer preferences and ratings.
WHAT ARE ANALYTICS?
Access to data and reporting: reports of problems, root cause analysis, prediction of future, worst case scenario, best case scenario, that is analytics.
WHY COMPETE ON ANALYTICS?
Capital One issues credit cards based on a shrewd analysis of customer credit profiles.
Harrah Entertainment is known for customer loyalty and service.
Practical organizations selling cement have used analytics. However some sectors are more amenable to analytics than others. THIS BOOK BY DAVENPORT AND HARRIS Part I deals with examples and key attributes of analytical organizations. Part II deals with how you can use analytics with suppliers and customers.
THE ORIGINS OF ANALYTIC COMPETITION
ERP: Enterprise Resource Planning Systems POS: Point of Sale systems Web-sites Decision Support Systems (DSS) in the 1960s. Operations Research: Some say this term has come into usage since World War II. Others maintain that this field has been current only since the advent of high speed computing. SAS/SPSS
OLAP
Blink by Malcolm Gladwell is a great book about the intuitive side of business capability.
Amazon.com : Jeff Bezos
Proctor and Gamble acquired Gilette after using much analytics. RFID (Radio Frequency ID) on every palette and carton. Fair Isaac Corporation Business Objects Cognos Many Americans Sports team managers swear by statistics and analytics
COMPETING ON ANALYTICS
WHAT MAKES AN ANALYTICAL COMPETITOR Four Characteristics 1. Analytics supported a strategic capability 2. The embrace was enterprise wide and not limited to one or two employees. 3. Senior management was committed to the use of analytics. 4. The company made a significant investment based on analytics.
COMPETING ON ANALYTICS
The authors conducted a survey of 371 firms about their analytics capabilities. 10% responded in the affirmative that analytical capability is a key element of strategy. The companies that demonstrate these four companies I Netflix, Harrah’s entertainment, Marriott International, Wal-Mart, Pro sports are all known for applying analytics to distinct capability. FICO score (from Fair Isaac Corporation) is used to Consumer Finance to evaluate the risk of lending to a particular borrower.
Progressive Insurance and Capital One analyse and sift the factors of FICO scores to identify less risky customers out of the poor scoring consumers.
COMPETING ON ANALYTICS
II Enterprise Level Approach BICC Business Intelligence Competency Center: “Cross-functional team with a permanent formal organizationanl structure. It is owned and staffed by the company and has defined tasks, roles, responsibilities and processes for supporting and promoting the effective use of business intelligence across the organisation. This approach seems reasonable when confronted with the ideal of an enterprise wide embrace of analytics. CEO of Scheider National ( a large trucking and logistics company) is a Ph.D in O.R. His BICC unit works on internals and also customers and suppliers.
COMPETING ON ANALYTICS III Senior Management Commitment
Low ranked champions of analytics can influence company attitude to analytics but nothing works as well as endorsement from senior management. IV Betting on analytics Companies have invested on “yield management” and other methods to gain and save millions of dollars.
doc_593552995.ppt