2. How Technology will help evaluate itself
• Patterns exist in how people embrace new concepts
– We follow the same approaches to evaluate, invest and build over and over
• Markets reveal if they are ready for innovation
– Innovation can be ahead of market readiness, so it’s not just technology; it’s behavior
• Humans also create, follow patterns based on what they desire
– We know what we want, act on it and show it via our data trails online
• Predictive models can now help identify innovative concepts, market
niches and human change agents at an earlier stage.
– We need to look around the next corner to find the new edge for opportunity
3. A new index can now be created to help us understand the
significance of human behavior, technology advances and
investment decisions in the face of transformational change
The Social Disruption Index
4. What Is The Social Disruption Index?
• We are currently in a revolution, driven by social/mobile
technology, that is transforming how we communicate in every
part of life, both personally and in business.
• This transformation is both destroying and creating businesses and
business models; every industry is, or will be, affected
• It is not a question of IF an industry will be disrupted, but WHEN.
• The Social Disruption Index (SDI) measures relative disruption by
industry segment.
• The SDI measures and analyzes whether an industry is in a pre-
disruptive state, currently being disrupted, in a post-disruptive
state, or anywhere along that timeline (its "Disruption Stage").
6. How Do We Measure Disruption?
• Dozens of attributes within three key factors for any given
industry as an indication of its Disruption Stage, such as:
• Start-Ups – how many, how big, how much momentum?
– Start-ups reflect perceived opportunity
• Investors – How much $ is flowing, at what stage
– Investing is the arms race of innovation
• Marketplace – How restless are customers, leaders; how
are their habits driving change(or not)?
– Has technology advance reshaped action or just rhetoric
9. The 1,9,90 Model
1 9 90
Influencers
• Top thought leaders – 1%
or less who define the
conversation.
Advocates
• 2nd concentric circle of
influence – where the top
influencers interpret and
share influencer
information.
Enthusiasts
• Like-minded people who
unconsciously react to and
shape markets based on what
they find online or hear via
friends/networks
10. We will also look at the elasticity of innovation
Markets are not all equal in response to technological disruption
11. What Factors Enable or Stall Disruption?
• All enterprises and industries pressured by the new connections
among people and how information is disseminated
• Some sectors naturally more prone to disruption than others.
• The SDI will analyze multiple factors, some of which accelerate
while others stall disruption, including:
– Presence of new start-ups / delta of venture investment
– Commodity vs. specialized nature of products or services
– Relevance of higher information velocity
– Legal and regulatory framework and barriers
12. A Visual Representation of Change
Business Model Human Resources Industry Turnover
Status of Disruption
New
Entrants
Commodity
Services
Info
Velocity
Facilitating / Dampening Factors
Legal/
Regulatory Overall Disruption Stage: 3 (Early)
13. Can Also Measure Start Up Potential
• Openness to disruption vs. number of start-ups
• Best opportunity space: high disruption/few start-ups
• Worst opportunity space: regulatory issues, many
contenders
• Can’t measure individual start-up success, but can
measure comparative market potential
• Where are start-ups most likely to penetrate next?
14. SDI Has Marketing Impacts
• Channel effectiveness will vary by disruption stage
• Messaging modes will shift
• Power of consumers will rise as disruption deepens
• Radical shifts in tactics as disruption takes hold
• A new normal emerges in post-disruption
15. Netflix Social Data vs. Actual Revenue
Data
15
20,000
22,000
24,000
26,000
28,000
30,000
32,000
34,000
36,000
38,000
0
1000000
2000000
3000000
4000000
5000000
6000000
7000000
8000000
9000000
10000000
Members vs. Predictive Variable
All Mentions Total members at end of period
16. Predicted vs. Actual Netflix Revenue
Data
16
20,000
22,000
24,000
26,000
28,000
30,000
32,000
34,000
36,000
38,000
December
31, 2012
March 31,
2013
June 30,
2013
September
30, 2013
December
31, 2013
March 31,
2014
June 30,
2014
Revenue
Actual Revenues Social Predicted Members Market Forecasts
17. Next Steps
• We want your help!
• Join us as early partners and see SDI before anyone else
• Initial release by June 2015
• Available to W2O clients & select investors
• Summary data available to media
• Post-release, 1 category deep dive each quarter
• By end of 2015, release of broad quarterly cross-category
disruption index
Editor's Notes
The 1-9-90 Rule, which states 1% of people online create content, 9% share content, and 90% lurk and learn, is the foundation to the structure of our analysis.
The 90% represents the content consuming audience whose expressions are covered in the listening segment of our analysis. We carried out this analysis for each of the three brands.
The 9% and the 1% are investigated by the Influencer Meme and the Muse Analysis with emphasis on the concentric circles of influence surrounding topics related to the brands. We identify who creates and shares content.