Franklin

Innovating Analytics : How the Next Generation of Net Promoter Can Increase Sales and Drive Business Results.

Author/Creator:
Freed, Larry.
Publication:
New York : John Wiley & Sons, Incorporated, 2013.
Format/Description:
Book
1 online resource (290 pages)
Edition:
1st ed.
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Subjects:
Word-of-mouth-advertising.
Form/Genre:
Electronic books.
Summary:
How does a CEO, manager, or entrepreneur begin to sort out what defines and drives a good customer experience and how it can be measured and made actionable? If you know how well the customer experience is satisfying your customers and you know how to increase their satisfaction, you can then increase sales, return visits, recommendations, loyalty, and brand engagement across all channels. More reliable and more useful data leads to better decisions and better results. Innovating Analytics is also about the need for a comprehensive measurement ecosystem to accurately assess and improve the other elements of customer experience. This is a time of great change and great opportunity. The companies that use the right tools and make the right assessments of how to satisfy their customers will have the competitive advantage. Innovating Analytics introduces an index that measures a customer's likelihood to recommend and the likelihood to detract. The current concept of the Net Promoter Score (NPS) that has been adopted by many companies during the last decade-is no longer accurate, precise or actionable. This new metric called the Word of Mouth Index (WoMI) has been tested on hundreds of companies and with over 1.5 million consumers over the last two years. Author Larry Freed details the improvement that WoMI provides within what he calls the Measurement Ecosystem. He then goes on to look at three other drivers of customer satisfaction along with word of mouth: customer acquisition, customer loyalty, and customer conversion.
Contents:
Intro
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.
Notes:
Description based on publisher supplied metadata and other sources.
Local notes:
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2021. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
Other format:
Print version: Freed, Larry Innovating Analytics
ISBN:
9781118779507
9781118779484
OCLC:
858968211