Innovating Analytics: Word of Mouth Index-How The Next Generation of Net Promoter Can Increase Sales and Drive Business Results -- Copyright -- Contents -- Introduction -- Chapter 1: Customer Experience 2.0 -- Accelerated Darwinism -- Chapter 2: NPS-What It Is and What It Does Well -- Chapter 3: NPS-Fundamentally Flawed -- Accuracy -- Margin of Error -- Oversimplification -- Detractors Don't Always Detract, and Promoters Don't Always Promote -- Where's the Growth? -- Insufficient Information -- Simple Is Just . . . Simple -- Chapter 4: WoMI-The Next Generation of NPS -- WoMI Distinguishes between Positive Word of Mouth and Negative Word of Mouth -- Negative Word of Mouth -- The WoMI Research Approach and the Validity of the Results -- Phase 1: ForeSee Independent Research -- Phase 2: Initial Client Testing -- Phase 3: Later Client Testing -- WoMI Testing Results -- The Discourage Question -- Detractor Overstatement -- NPS and WoMI Score Differences -- Recommend and Discourage Scores -- Continuing Implementation -- In Virtually Every Industry, We See a Massive Overstatement of Detractors -- Using WoMI with NPS -- Chapter 5: The Four Drivers of Business Success -- Customer Retention -- Purchased Loyalty -- Convenience Loyalty -- Restricted Loyalty -- Competitive Retention -- True Loyalty -- Upsell -- Marketing-Driven Customer Acquisition -- Word-of-Mouth-Driven Customer Acquisition -- Customer Intent and True Conversion Rate -- True Conversion Rate -- The Common Thread -- Chapter 6: Why the Customer Experience Matters -- Why Measure Customer Experience? -- How to Measure the Customer Experience and Answer the Big Three Questions -- How am I doing? What is my performance? -- Where should I focus my efforts? Where will I get the largest return on my investment? -- Why should I take action? Is the payback worth the effort?.

Measuring the Customer Experience at the Brand Level -- 1. How am I doing? -- 2. Where should I focus my efforts? -- 3. Why should I take action? -- Measuring the Customer Experience in Contact Centers -- 1. How is iMicro doing? -- 2. Where should iMicro focus its efforts? -- 3. Why should iMicro take action and make these changes? -- Measuring the Customer Experience in Stores -- 1. How is Daisy Chain doing? -- 2. Where should Daisy Chain focus its efforts? -- 3. Why should Daisy Chain take action and make these changes? -- Measuring the Customer Experience on Websites -- 1. How is the Daily Reporter doing? -- 2. Where should the Daily Reporter focus its efforts? -- 3. Why should the Daily Reporter take action and make these changes? -- Measuring the Customer Experience with Mobile Experiences -- 1. How is Rest Well doing? -- 2. Where should Rest Well focus its efforts? -- 3. Why should Rest Well take action and make changes? -- How to Measure the Multichannel Consumer -- Chapter 7: The Customer Experience Measurement Ecosystem -- Behavioral Data -- Getting Sticky -- Mobile Complexity -- Challenging Behavioral Metrics -- Bounce Rate -- Shopping Cart Abandonment -- Beware of False Positives and False Negatives -- Voice of Customer Feedback Metrics -- Observation and Usability -- Voice of Customer Measurement -- Chapter 8: Best Customer Experience Practices -- Amazon -- ForeSee Analysis of Amazon -- Customer Complaints -- Zappos -- Delivering Happiness -- Company Culture and Core Values -- Social Media -- Panera Bread -- The Customer Experience -- Government Agencies -- Eddie Bauer -- Touch Points -- Nutrisystem -- Customer Analytics -- House of Fraser -- The Early Years -- Consistent Customer Experience -- ABC -- iPad App -- Testing New Store Programs Impact on the Customer Experience -- Word-of-Mouth Index (WoMI) -- A Multichannel Retailer.

Consumer Products Company -- Insurance Company -- Best Practices -- Chapter 9: Big Data and the Future of Analytics -- Big Data Volume -- Big Data Variety -- Big Data Velocity -- The World of Big Data -- Big Data Creates Value -- Big Data and Retail -- The Trap of Big Data -- Innovation -- Afterword: Measuring Customer Experience-A Broader Impact and the Start of a Journey -- Appendix A: Satisfaction, WoMI, Net Promoter, and Overstatement of Detractors for Top Companies -- The Top 100 U.S. Brands -- The Top 100 U.S. Online Retailers -- Top 40 U.K. Online Retailers -- Seven Largest U.S. Banks -- The Top 29 U.S. Retail Stores -- The 25 Top Mobile Retail Sites and Apps -- Seventeen Mobile Financial Services Sites and Apps -- Twenty-Five Mobile Travel Sites and Apps -- Appendix B: Are Those Least Likely to Recommend Actually the Most Likely to Discourage? -- Least Likely to Recommend: 1s -- Least Likely to Recommend: 1s and 2s -- Low Likelihood to Recommend: 1 to 3 on a 10-Point Least Likely Scale -- Low Likelihood to Recommend: 1 to 4 on a 10-Point Scale -- Low Likelihood to Recommend: 1 to 5 on a 10-Point Scale -- Low Likelihood to Recommend: 1 to 6 on a 10-Point Scale -- Appendix C: Eleven Common Measurement Mistakes -- Common Measurement Mistake #1: Drawing Conclusions from Incomplete Information -- Common Measurement Mistake #2: Failing to Look Forward -- Common Measurement Mistake #3: Assuming That a Lab Is a Reasonable Substitute -- Common Measurement Mistake #4: Forgetting That the Real Experts Are Your Customers -- Common Measurement Mistake #5: Confusing Causation and Correlation -- Common Measurement Mistake #6: Confusing Feedback and Measurement -- Common Measurement Mistake #7: Gaming the System -- Common Measurement Mistake #8: Sampling Problems -- Common Measurement Mistake #9: Faulty Math.

Common Measurement Mistake #10: Measurement by Proxy -- Common Measurement Mistake #11: Keep It Simple-Too Simple -- Appendix D: An Overview of Measurement and Model Analysis Methods -- Introduction -- The Three Essential Questions for Managers -- The Theoretical Framework -- What Are the Technology Platforms Used by ForeSee? -- Measurement -- Exhibit 1: Difference between Precision and Power -- Model Analysis Provides Prescriptive and Prognostic Capabilities -- Prescriptive Guidance: Impacts versus Importance -- Standardized versus Unstandardized Impacts -- The Multicollinearity Problem -- The Prognosis of Future Outcomes -- Summary Table -- The Use of 10-Point Scales -- Criteria for Evaluating Scales and Supporting Evidence -- Information Content -- Mean Squared Correlations -- Why Does ForeSee Use Three Indicators of Customer Satisfaction? -- Single- versus Multiple-Item Measures -- Differences between Single- and Multiple-Item Measures -- How Individuals Respond to Questions in a Survey -- Research Evidence -- Bibliography -- Acknowledgments -- Index.