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2-AnalyticalCompetitiveness.pdf
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Analytical Competitiveness: Right Data vs. Big Data
Professor Jared M. Hansen, Ph.D.
© Jared M. Hansen No redistribution/reusage/etc without permission
Contribution Toward Course Objectives 1. Gain big data and marketing analytics factual knowledge (terminology, classifications, methods, trends). 2. Develop improved ethical reasoning and/or ethical decision making in the context of big data and marketing toward the development of a clearer understanding of, and commitment to, the student’s personal values, good business practices, and human flourishing.
3: Develop specific skills, competencies, and points of view needed by professionals in the field of marketing analytics
4. Learn to apply course material to improve thinking, problems solving, and decisions in strategic marketing as it relates to market segmentation and positioning of products.
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Definition: Analytical Competitor (Noun): An organization that uses analytics extensively and systematically to outthink and outexecute the competition.
--Davenport and Harris
Four common key characteristics…. (of most analytically sophisticated and successful firms)
Survey of 371 medium to large firms indicated rough 5% of firms were ‘full-bore analytical competitors.’
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Analytical Competitor
Distinctive Capability
Enterprise-Wide Analytics
Senior Mgmt. Commitment
Large Scale Ambition
• Support of a Strategic, Distinctive Capability • If analytics are to support competitive strategy, they must be in
support of an important and distinctive capability (which vary by firms and industries)
• If capability is experience based (or intuitive based), difficult to try to compete on statistics and fact-based decisions
Definition: Distinctive Capability (Noun):
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• Support of a Strategic, Distinctive Capability • If analytics are to support competitive strategy, they must be in
support of an important and distinctive capability (which vary by firms and industries)
• If capability is experience based (or intuitive based), difficult to try to compete on statistics and fact-based decisions
• Begin with focus on critical area, then move to additional areas.
Examples: • Marriot: revenue management → to loyalty programs, web
metrics • Netflix: predicting customer movie preference → supply chain,
advertising • Harrahs: loyalty → pricing placement, web site design
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Business Models, Strategies, and Critical Thinking …are covered in the Competitive Intelligence and Data Visualization class
Module 1 (Jan 8): Introduction to Competitive Intelligence & Data Visualization Module 2 (Jan 10): Basic Approaches and Techniques of CI Module 3 (Jan 15): Legal Aspects of CI + Organizational Factors Module 4 (Jan 17): Ethical Considerations/Dilemmas/Templates in CI and Marketing Module 5 (Jan 22): Understanding Markets--Identifying Competitive Business Models and Strategies Module 6 (Jan 24)--Critical Thinking Techniques for CI and Marketing Module 7 (Jan 29): Problem Identification and CI Key Intelligence Topics (KITs) Module 8 (Jan 31): Human Intelligence Gathering Module 9 (Feb 5): Industry/Market Analysis Module 10 (Feb 7): Competitive Profiling Module 11 (Feb 12): Transforming intelligence into data visualizations that aid insight, part I Module 12 (Feb 14): Transforming intelligence into data visualizations that aid insight, part II Module 13 (Feb 19): Transforming Data Visualizations into Business Narratives that Drive Module 14 (Feb 21): Project presentations Module 15 (Feb 26): Project presentations
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Right Data vs Big Data
In business analytics, you’ll often encounter the poorly posed problem:
1. Someone else in the business encounters a problem.
2. They use their past experience and (lack of?) analytics knowledge to frame the problem.
3. They hand their conception of the problem to the analyst as if it were set in stone and well posed.
4. The analytics person accepts and solves the problem as-is.
This can work. But it’s not ideal, because the problem you’re asked to solve is often not the problem that needs solving.
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• Tools are important. They enable you to deploy your analytics and data- driven products.
• But when people talk about “the best tool for the job,” they’re too often focused on the tool and not on the job.
• Software and services companies are in the business of selling you solutions to problems you may not even have yet. • And to make matters worse, many of us have bosses who read stuff like
the Harvard Business Review and then look at us and say, “We need to be doing this big data thing. Go buy something, and let’s get Hadoop-ing.”
• Don’t put the cart before the horse and buy the tools (or the consultants who are needed to use the open source tools) only to then say, “Okay, now what do we do with this?”
• This all leads to a dangerous climate in business today where: • management looks to tools as proof that analytics are being done • providers just want to sell us the tools that enable the analytics • there’s little accountability that actual analytics is getting done
• So here’s a simple rule: Identify the analytics opportunities you want to tackle in as much detail as possible before acquiring tools. • Do you need Hadoop? Well, does your problem require a
divide-and-conquer aggregation of a lot of unstructured data? No? Then the answer may be no.
• Don’t put the cart before the horse and buy the tools (or the consultants who are needed to use the open source tools) only to then say, “Okay, now what do we do with this?”
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• Solve the correct, yet often misrepresented, problem. • This is something no mathematical model will ever say to you. • No mathematical model can ever say, “Hey, good job
formulating this optimization model, but I think you should take a step back and change your business a little instead.”
• And that leads me to my next point: Learn how to communicate.
• You cannot accept problems as handed to you in the business environment. • Never allow yourself to be the marketing analyst to whom
problems are “thrown over the fence.” • Engage with the people whose challenges you’re tackling to
make sure you’re solving the right problem. • Learn the business’s processes and the data that’s generated and
saved. • Learn how folks are handling the problem now, and what
metrics they use (or ignore) to gauge success.
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“studies show that knowledge workers waste up to 50% of time hunting for data, identifying and correcting errors, and seeking confirmatory sources for the data they do not trust.”
• Connect Data Creators with Data Customers • Focus on Getting the Right Data • Put Responsibility for Data in the Hands of Line Managers
