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Using Empirical Market Share Data To Measure Industry Disruption
The document discusses the disruption in the retail industry, emphasizing market volatility and changing consumer behaviors. It highlights the importance of adapting innovation strategies beyond product-focused approaches to include various types of innovation for greater success. Key factors influencing this disruption include increased competition, technological advancements, and shifts toward experience-driven economic models.
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Using Empirical Market Share Data To Measure Industry Disruption
- 1. #RIC16 Retail Disrupted: Using Empirical Market Share Data To Measure Industry Disruption Kasey Lobaugh, Chief Retail Innovation Officer, Deloitte Consulting Lance Thornswood, Senior Director, Omnichannel, JCPenney Scott Moore, former SVP, Marketing Best Buy
- 2. #RIC16 Moore’s Law Calculations/Sec per $1000 computer
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- 5. #RIC16 Volatility of Share 0% 5% 10% 15% 20% 25% 06-‐0707-‐08 08-‐09 09-‐10 10-‐11 11-‐12 12-‐13 13-‐14 14-‐15 15-‐16 Weighted Standard Deviation, Brands Contributing Most to Annual Volatility Increasing volatility Share Volatility US Retail Market Share Volatility
- 6. #RIC16 0.69 0.70 0.71 0.72 2009 2010 20112012 2013 2014 2015 Relative Mean… Fragmentation US Concentration of Share Decreasing Concentration 1. Gini coefficient, with 0 being complete share equality and 1 being monopoly. Measured based on top 140 retailers*. * Top US retailers are defined by revenue size within the largest 250 Global Retailers as tracked by Planet Retail. Due to M&A and banners moving in/out of the Global 250 list, the number of Top US retailers within this global list varies by year, between 143-‐158
- 7. #RIC16 18% 23% 28% 33% 38% 2007 2008 20092010 2011 2012 2013 2014 2015 Fragmentation Stock Price Volatility Increasing volatility Weighted Standard Deviation, Top 20 Public Retailers
- 8. #RIC16 Forces at Play COMPETITION CONFIGURATION CUSTOMER Reduced Barriers To Entry Decoupled Value Chain Non-‐Traditional Sources Emergence of Ecosystems Reduced Ownership of Assets & Infrastructure Constantly Connected Experience Driven New Economics New Profit Models New Economics Reduced Barriers To Entry Decoupled Value Chain
- 9. #RIC16 Commoditization Experience Differentiation Product Differentiation HighLow HighLow Performance 2.1%COMPS 6.7% EBITDA 6.5% COMPS 15.8% EBITDA 0.5% COMPS 1.4% EBITDA 2.5% COMPS 10.0% EBITDA
- 10. #RIC16 Network Connections with others to create value Process Signature or superior methods for doing your work Product System Complementary products and services Channel How your offerings are delivered to customers and users Customer Engagement Distinctive interactions you foster Profit Model The way in which you make money Structure Alignment of your talent and assets Product Performance Distinguishing features and functionality Service Support and enhancements that surround your offerings Brand Representation of your offerings and business We discovered that recognizing and constructing across multiple types of innovation produced more robust innovation concepts.
- 11. #RIC16 The average innovator tends to pursue product-‐based innovation which integrates few other types. Average number of types in use: 1.8 types 300
- 12. #RIC16 A most successful innovators looks beyond Product Performance and integrates twice as many types of innovation. Leading innovators Average number of types in use: 3.6 types 300
- 13. #RIC16 “It is not the strongest of the species that survives, but the one most adaptable to change.” Charles Darwin
- 14. #RIC16 Kasey Lobaugh Chief Retail Innovation Officer Deloitte Digital (913) 219 5073 klobaugh@deloitte.com @klobaugh
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