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Reputatation Management Systems Analysis Presentation

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Managing Corporate Reputation in the Era of Consumer Generated Media: A systems approach to reputation management with an emphasis on data collection techniques and non-linear analysis.

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Reputatation Management Systems Analysis Presentation

  1. 1. Managing Corporate Reputation in the Era of Consumer Generated Media “It takes 20 years to build a reputation and five minutes to ruin it. If you think about that, you'll do things differently.” ~ Warren Buffett Presented by: Jason Voiovich Ecra Creative Group 651.209.2778 or jason@ecracreative.com
  2. 2. Buffett couldn’t be more right… •  Collapse of Bear Stearns •  Best Buy’s “Peep Squad” •  Microsoft Vista revolt •  Starbucks’ coffee ranked below McDonald’s
  3. 3. Poll: Your View of Reputation How do you view “reputation” in the business context? (select the option that best describes your view) [1] Primarily a business risk to be avoided [2] Primarily a business asset to be leveraged
  4. 4. Why is Reputation Important? Reputation management (or the failure thereof) presents tangible downside risk. However, proper reputation management - at the same time - presents significant upside opportunity.
  5. 5. The Illusion of Control The real conundrum? Corporate reputation is, largely, out of management’s direct control. Blogosphere & Insta-Pundits Blackberry, iPhone, and Email 24-hour News Cycle
  6. 6. The Real Problem: The World Changed Years ago, PR professionals learned the source/receiver model:
  7. 7. The Real Problem: The World Changed The 1990s saw the wide dissemination of the feedback loop:
  8. 8. The Real Problem: The World Changed The reality is now clear: Reputation is created - in large part - due to interaction between people and groups.
  9. 9. Problems with the PR Model Why are most public relations departments failing to protect reputation assets? They are “marketing-centric”: There are too many people now involved. They are too slow: They just can’t keep up They are not seen as authentic: We really do live in a ‘Spin Zone’
  10. 10. A New Model is Needed A new model for reputation management must account for three basic truths: 1. Reputation is in the hands of all stakeholders, not only management 2. Reputation changes quickly - it is fundamentally unpredictable 3. Reputation follows a non-linear model
  11. 11. Poll: What percentage of plastic water bottles are recycled? More than 80 percent 20 to 80 percent communities with special facilities Less than 20 percent
  12. 12. Case Study: The Plastic Water Bottle We’ll explore the transformation of the water bottle from “health symbol” to “eco-disaster” using three key techniques: 1. Identifying and clustering stakeholders and issues into meaningful groups 2. Managing change in reputation over time 3. Non-linear analysis methods
  13. 13. Market Overview Bottlers have reason for concern: •  $11 Billion US Market •  Average American consumed 28.3 gallons of bottled water last year (lots of room) •  2nd most consumed beverage in the US (after soft drinks)
  14. 14. Step 1: Identification •  Establish a baseline for ongoing measurement and analysis •  Try to hit the “high points” (not leave anything out) •  Identify internal stakeholders, external stakeholders, and key issues •  Begin to “Cluster”
  15. 15. Stakeholder Identification Major Stakeholder Group Sub-Group Sub-Group Baseline RIF Nestle Coca-Cola PepsiCo Environmental Organizations US Government Waste Mgt/Recyclers Major Media Social Media Minor Corporate Players Petroleum Industry Health Organizations
  16. 16. Stakeholder Identification Major Stakeholder Group Sub-Group Sub-Group Baseline RIF Nestle Corporate Communications Employees/Staff (14K in US) Corporate Marketing Bottling Plants Distributors/Fleet Executive Management Nestle Retailer Network Corporate Sales Purchasing Company Operations Legal
  17. 17. Stakeholder Identification Major Stakeholder Group Sub-Group Sub-Group Baseline RIF Nestle Corporate Communications Media Relations Consumer Relations Reseller Relations Investor Relations Government Relations Employee Relations
  18. 18. Stakeholder Identification Major Stakeholder Group Sub-Group Sub-Group Baseline RIF Nestle 6 Coca-Cola 6 PepsiCo 6 Environmental Organizations 5 US Government 4 Waste Mgt/Recyclers 7 Major Media 4 Social Media 6 Minor Corporate Players 2 Petroleum Industry 2 Health Organizations 3
  19. 19. Poll: Who have we left out? What major stakeholder(s) can you identify that have been “overlooked” in this chart? (choose all that apply) [ ] Water resource management [ ] General public opinion [ ] Sports organizations
  20. 20. Issue Identification Major Issue Group Sub-Group Baseline RIF Environmental degradation Basic “littering” 8 Landfill space Wildlife poisoning/harm Plastic “nodules” in the oceans Geologic timescale decomposition Misconception of bottles as non- 7 recyclable Use of petroleum in the process 4 New research on “unhealthy” plastic 2 effects on health
  21. 21. Step 2: Tracking & Data Collection •  Deploy the data collection system to a wide group of internal and external stakeholders •  Integrate traditional and social media data inputs •  Track “emergent” stakeholders and issues •  Begin to see patterns of interconnection
  22. 22. Tracking & Data Collection Stakeholder Baseline RIF Adjusted RIF Tagged Issues Conn. Vector Group Strength Waste 8 9 Management/ Recyclers Nestle 6 4.5 Social Media 7 8.5 Major Media 4 3.5 Health 3 6 Organizations Environmental 5 7 Organization
  23. 23. Tracking & Data Collection Stakeholder Baseline RIF Adjusted RIF Tagged Issues Conn. Vector Group Strength Waste 8 9 Basic littering 7 +1 Management/ Landfill space 8 +2 Recyclers Wildlife harm 3 –3 Ocean pollution 8 –5 Decomposition 1 0 Nestle 6 4.5 Social Media 7 8.5 Major Media 4 3.5 Health 3 6 Organizations Environmental 5 7 Organization
  24. 24. Tracking & Data Collection Basic Littering 4.00 Landfill Space 5.5 Waste Decom- Management / position Recyclers 1.25 9 Wildlife Harm Ocean Pollution 6.25 4.25
  25. 25. Step 3: Non-Linear Analysis •  The nature of this data lend itself to chaotic analysis techniques •  Attempting to predict one event from a trend-line belies an underlying unpredictability in this type of complex system •  Key concepts from non-linear analysis can help us (even if the math eludes us)
  26. 26. Concept 1: Attractors •  We see this in “clustering” around issues in our initial identification •  New data points tend to cluster around existing issues during data collection •  Rule of 256 A “strange attractor” - one of the four non-linear attractors
  27. 27. Concept 2: Self-Similarity •  Self-similarity shows us a way to look at the “small picture” to spot larger trends. •  What are the small trends? Health effects of water bottles. A common mathematical example of self-similarity
  28. 28. Case Study: The Plastic Water Bottle In the end analysis, who has done well/poorly? Nestle’s “less-plastic” bottle Primo’s “corn” bottle
  29. 29. Advice for CI professionals •  Track and measure smarter, but understand its limits (allow MANY more people in the organization into the tracking process - collective wisdom works here) •  Sweat the small trends - help your clients understand this and plan for it (counter to quot;executive opinionquot;) •  Set clear and simple rules (executive wisdom works here - creativity of a single quot;headquot;), let the rest self- organize throughout the organization (you can't quot;controlquot; it; don't try - let people interpret the vision as they see it needs to - thinner plastic bottle)
  30. 30. Contact Information Jason Voiovich Principal Ecra Creative Group 651.209.2778 jason@ecracreative.com www.stateofthebrand.com Additional reading: Deep Simplicity, by John Gribbin, © 2004 Random House Complexity Explained, by Peter Erdi, © 2008 Springer

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