Using Empirical Market Share Data To Measure Industry Disruption
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#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
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Transcript of Using Empirical Market Share Data To Measure Industry Disruption
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Retail Disrupted: Using Empirical Market Share Data To Measure
Industry Disruption
Kasey Lobaugh, Chief Retail Innovation Officer, Deloitte Consulting
Lance Thornswood, Senior Director, Omnichannel, JCPenneyScott Moore, former SVP, Marketing Best Buy
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Moore’s Law
Calcu
latio
ns/Sec per $1000 com
puter
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Scarcity to Abundance
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Volatility of Share
0%
5%
10%
15%
20%
25%
06-‐07 07-‐08 08-‐09 09-‐10 10-‐11 11-‐12 12-‐13 13-‐14 14-‐15 15-‐16Weighted Standard Deviation, Brands Contributing Most to Annual Volatility
Increasing volatility
Share Volatility
US Retail Market Share Volatility
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0.69
0.70
0.71
0.72
2009 2010 2011 2012 2013 2014 2015
Relative Mean …
FragmentationUS 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
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18%
23%
28%
33%
38%
2007 2008 2009 2010 2011 2012 2013 2014 2015
FragmentationStock Price Volatility
Increasing volatility
Weighted Standard Deviation, Top 20 Public Retailers
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Forces at Play
COMPETITION
CONFIGURATION
CUSTOMER
Reduced BarriersTo Entry
Decoupled Value Chain
Non-‐TraditionalSources
Emergence ofEcosystems
Reduced Ownership ofAssets &
Infrastructure
ConstantlyConnected
ExperienceDriven New Economics
New Profit Models
New Economics
ReducedBarriersTo Entry
Decoupled Value Chain
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Commoditization
Experience Differentiation
Product Differentia
tion
HighLow
High
Low
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
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NetworkConnections with others to create value
ProcessSignature or superior methods for doing your work
Product SystemComplementary products and services
ChannelHow your offerings are delivered to customers and users
CustomerEngagementDistinctive interactions you foster
Profit ModelThe way in which you make money
StructureAlignment of your talent and assets
Product PerformanceDistinguishing features and functionality
ServiceSupport and enhancements that surround your offerings
BrandRepresentation of your offeringsand business
We discovered that recognizing and constructing across multiple types of innovation produced more robust innovation concepts.
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The average innovator tends to pursue product-‐based innovation which integrates few other types.
Average number of types in use:
1.8 types
300
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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
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“It is not the strongest of the species that survives, but the one
most adaptable to change.”Charles Darwin
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Kasey LobaughChief Retail Innovation OfficerDeloitte Digital
(913) 219 [email protected]@klobaugh
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