Generating Actionable, Data-Rich Insights about Technology, Markets, and Business Models to Optimize Strategic Impact in Rapidly Expanding and Changing Environments

1. STRATEGIC
INTELLIGENCE
Generating Actionable, Data-Rich Insights about
Technology, Markets, and Business Models to
Optimize Strategic Impact in
Rapidly Expanding and Changing Environments
JUNE 20, 2018
DAVE LITWILLER

2. INTRODUCTION
• A guided tour through the pivotal moments of my career in
growth stage technology enterprise general management
• A look at the context, necessity and contribution that
could be gained from detailed strategic intelligence in
those situations

3. OVERVIEW
• Motivations for Strategic Intelligence
• Primary Bases of Data Organization and Analysis
• Empirical Early Signaling Value – both of opportunity and threat
• Modeling Tools
• Leading Data Sources
- Break –
- Case Studies
- Getting Strategic Intelligence Right
- Warning Signs of Difficulties
- Sustaining Success: Eternal Challenges of Strategic Intelligence

4. WHY STRATEGIC
INTELLIGENCE?
WHY NOW?
• Growing profit divergence between companies
Source: Economist, March, 2016

5. WHY STRATEGIC
INTELLIGENCE?
WHY NOW?
• Growing labour productivity divergence between companies
Source: OECD, June, 2016

6. WHY STRATEGIC
INTELLIGENCE?
WHY NOW?
• With time pressures and information overload, it has become easy to
insidiously filter our way to information and intellectual silos
• The people we associate with
• The electronic communication filters we apply
• The biases we bring, both conscious and unconscious
• The ideology, methods and short-cuts that worked for us in the past
• Without active offsetting measures, it is very easy to end up in an
echo chamber as time goes on
• The tendency toward a kind of echo chamber can be reinforced by
the drive to build a Cult of X kind of company
• Even if X in itself is not a malign thing
• Who wouldn’t want to build a company that can defy reality a bit,
stretching the boundaries of what should be possible?

7. MOTIVATIONS FOR
STRATEGIC
INTELLIGENCE
• Sort out fact from fiction about the competitive environment
• Ground objectivity about what constitutes best in class
performance versus good, average, or worse
• Triangulate on the most critical strategic inferences to inform
management decision making
• Increase amenity to making changes to get better, when
better is demonstrably possible in the competitive and
benchmark environment
• Provide counterweight to noisy anecdote-driven decision
influence (law of small numbers) and narrative fallacy
• See through bias, filtering and emotional short-cuts that
people might otherwise want to guide them

8. MOTIVATIONS FOR
STRATEGIC
INTELLIGENCE
• Balance the richness, but potential tunnel vision of data
coming from internal MISs, CRMs, development tools and
test equipment
• Counteract the tendency in disordered information
overload to primitively call the arguments on each side of
a debate a wash, and then make an overly emotional
decision
• Keep up in the rapid co-evolution with the environment, to
continue driving for a moving global optimum rather than
settling for a local peak

9. MOTIVATIONS FOR
STRATEGIC
INTELLIGENCE
• Core convictions:
• Informational advantages in the hands of those willing to
act on that advantage can confer a significant leg up in
strategic impact and financial return
• The hundreds of $millions or $billions of economic activity
in and around your sector external to your enterprise can
provide as much or more information as the most detailed
financial and management decision support analytics from
within

10. Getting to Work

11. TRACKING BASIS
• Two primary strategic environment tracking methods:
• By competitor, potential competitor and industry participant
• By industry vertical (application sectors)
• Secondary matters are usually tracked subordinate to the
primary methods:
• Technology and technology convergence over time
• Operations
• Geographic and regulatory variations
• Reference class benchmarking (comparison class)
• Especially in emerging industries, where in-market, reliable
proof points are harder to come by

12. MARKET PARTICIPANT
TRACKING
For each entity of interest, usually a corporation:
• Time series of developments spanning corporate, product,
technology, operations, market, customer, distribution,
financing and staffing
• Similar tracking applies, be it for
• Competitors in a market
• Ecosystems players
• Distributors
• Suppliers
• Complementary product or service providers

13. COBOT EXAMPLE:
UNIVERSAL ROBOTS A/S

14. COBOT EXAMPLE:
UNIVERSAL ROBOTS A/S

15. COBOT EXAMPLE:
UNIVERSAL ROBOTS A/S

16. FOUNDATION OF
STRATEGIC
INTELLIGENCE
• Read prolifically
• Keep detailed notes, including quality of data sources
• Differences over time are one of the most powerful way to reveal
trends and changes which reflect evolving marketplace and
competitor reality
• The goal is to fill in the puzzle, the journey is to enjoy filling in the
pieces
• There is much more in the public domain and near public domain
than people realize; this is not about violating confidences
• Diligence and consistency counts
• People who do this need to be good at making and testing
assumptions and interpolations
• It is like good journalism:
• Be intellectually engaged, but emotionally detached;
• Fallibilism dictates that the purveyor should never be 100% certain

17. BENCHMARKING
• If done right, strategic intelligence activities should
provide an evolving, quantitative model for benchmarking:
• R&D Productivity
• COGS
• Sales Productivity
• Market Share
• Relative and Absolute Growth
• Overall Management Effectiveness
• Financing and Acquisition Valuation
• Make vs. Buy for Incremental and Transformative
Advances

18. BENCHMARKING
Qualitative Benefits (in the early market for a new tech):
• Identification if the company is getting to true KOLs, or is working
with B-list wannabes
• Determining the weightings in the relationship between
technology, manufacturing/operations and distribution to
sustainably win in the market
• Knowing what price-performance thresholds and adoption cues
signal the likely arrival to stay of disruptive new technology vs. the
head fakes of many would be challengers
• Helping the company’s executive and board of directors to guide
and evolve strategy in step with competitive reality
• Identifying the most efficacious ideas and techniques from
competitive, adjacent, and analogous reference class companies
for adapted incorporation in the company’s continual evolution

19. REFERENCE CLASS
FORECASTING
a.k.a. Comparison Class Forecasting
• Method for predicting the future based on analogous past
situations
• Counters human bias toward overconfidence and
statistical misrepresentation of past circumstances
• Provides an outside-in perspective on new initiatives,
including the development and marketing of new
technology, rather than an inside-out view of traditional
business planning
“By supplementing traditional forecasting processes, which tend to focus on a
company’s own capabilities, experiences, and expectations, with a simple
statistical analysis of analogous efforts completed earlier, executives can
gain a much more accurate understanding of a project’s likely outcome.”
Source: Lovallo & Kahneman, HBR, July 2003

20. COMPETITOR AND
REFERENCE CLASS DATA
VISUALIZATION
Three variables – ex. unit volume, ASP, competitor size

21. PICKING INSTRUCTIVE
ANALOGS AND ANTI-LOGS IN
REFERENCE CLASSES
• Availability bias issue – People are easily drawn to the most recent or best
understood example, rather than the most instructive
• Meta examples to broaden perspective:
• Agriculture
• Metals and materials
• Petrochemicals
• Electrification and electricity generation
• Biotechnology and pharmaceuticals; medtech
• Automotive
• Aviation and aerospace
• Wireline and fiber optic communication
• Broadcast
• Wireless communication
• Semiconductors – lithography driven vs. analog, RF & MEMS
• Computing – mainframe, mini, PC, mobile/smart phones, IoT,…
• Analytical instruments
• Software, data science and artificial intelligence
• Internet
• Robotics

22. APPLICATION
MARKET PROFILING
Main Issues to Track and Model:
• Size, growth rate, value of success, cost of failure
• Incumbent solutions
• Major accounts, distribution channels, service providers
• Unit economics, especially from early deployments
• Emergent complexities

23. APPLICATION
MARKET PROFILING
Adoption-Rejection Behaviour:
• Reflects RoI & payback time, perceived risk, integration to
existing workflow & tools, social innovation diffusion factors
Source: Zvi Grilliches, Hybrid Corn and the Economics of Innovation, Science, 1960

24. APPLICATION
MARKET
PROFILING
Ex: Service Robots – The Long List of
Identified Applications
• Tracking of technically and operationally proximate sectors
Source: IFR World Robotics – 2017

25. APPLICATION
MARKET PROFILING
• Most application sector tracking effort applied on the
80%/20% rule
• Or, more typically, 95%/5%

26. APPLICATION MARKET
PROFILING
DATA VISUALIZATION
Three variables – ex. growth rate, market share, and merchant value:

27. EARLY WARNING POTENTIAL
OF STRATEGIC
INTELLIGENCE
• Market participant
• Even with private companies, suitable strategic intelligence often can
achieve advance warning of significant business up- or downturns 3
to 6 months in advance of more overt signals
• Market sector
• Often can identify use case and business case strength and short-
term unresolvable issues 1 to 2 years in advance of a more settled
consensus being reached
• Industry character
• More accurate sense of analog and anti-log examples of past
technology industries from which to draw modeling inferences, as
much as several years in advance of the wider opinion
• Counter availability bias which can otherwise overly drive assumptions

28. INDUCTIVE AND
DEDUCTIVE VALUE
• Time-based profiles of market participants, industry
sectors and related technology and distribution trends
• Identification of drivers, context and transpositions to
achieve higher productivity, performance and impact in
your enterprise

29. MODELING TOOLS
• Steady State Market Share:
• Non-networked: #1, 40%; #2, 25%, #3, 10%-15%
• Strong network effects: #1, 90%; #2, 9%; #3, 0.9%
• => Relation to economies of scale in R&D, Operations
• Profit Pool:
• ~85% of the profit pool in an industry usually goes to the
top ~75% market share

30. MODELING TOOLS
• Product and business line extension adjacency
relationship to success rate
• Adjacency by: technology, operations, and distribution

31. MODELING TOOLS
• Lanchester Dynamics for multi-party competitive
dynamics

32. MODELING TOOLS
• It requires $1.50 of total investment to gain $1.00 of annual
market share with mediocre technology in a crowded
market
• Significantly better revenue gains require better
technology, superior go-to-market, and greater influence
over the development of the marketplace

33. MODELING TOOLS
• Payroll cash costs (salary, benefits, payroll taxes) will
represent ~50% of all cash expenses in a software business,
and somewhat less in a hardware business
• As the largest single cost load, headcount over time is the
most difficult measure of performance to deceive
• Fundraising success, a sweetheart large deal or short-term
high margins can allow unsustainably high headcount for a
while
• But, eventually staffing has to come into line with the
revenues, margins and cash flow the business generates
• The total cost of having an individual contributor employee,
including benefits, infrastructure, supervision, etc. is about
3* what the employee is directly paid
• Si-V payroll costs are about 2* those of KW

34. MODELING TOOLS
• Technology Adoption Curve – Everett Rogers
• Head fake potential for insurgent technologies drops
significantly once the early majority enters
• Technology, product and distribution strategy changes
considerably after early majority onset
• Early market share, R&D productivity and API/interoperability
strategy-execution are critical to get the big ride on the wave

35. MODELING TOOLS
• S-Curves (adoption, growth) are smooth in aggregate, but
much more discrete in practice when carefully analyzed
Source:
Unrelenting Innovation,
by Gerard Tellis,
Jossey-Bass

36. MODELING TOOLS
• Precedent and adjacent market adoption pattern
characteristics often have significant predictive value,
especially for the time dimension, because of how
powerful social factors are in the adoption of innovation
• Rise Time: Time from launch to achieve 20%, 50% and
80% penetration
• Time for leading vendor to achieve $X million in annual
sales
• Careful: When did the project really start, vs. when do
executives retroactively report that it started
• Degree of product commonality and product diversification,
and the related technological, manufacturing and
applications engineering distance between the main use
cases

37. MODELING TOOLS
• Power Law:
• First order: 80/20; averages don’t tell much (vs. Gaussian dist’n)
• Second order: 95/5 <- Becoming more common??
• Resources and knowledge pool and co-evolve
• Difference between perception and reality
• Revenue, profit, valuation, distribution power, etc.
• Inference: Winner, keep new categories from emerging
Challenger: Innovate to create new categories

38. MODELING TOOLS
• Fermi Estimates
• Make justified estimates about quantities, including mins
and maxes of many constituent terms
• With fine grained estimation of individual contributing
items, assumptions and biases become clear
• With fine grained estimation, errors of individual items tend
to cancel provide order of magnitude accurate results

39. MODELING TOOLS
Customer Profitability Distribution

40. MODELING TOOLS
Project Failure Rate
Individual Contributor
Productivity
Planned vs. Actual
Schedule

41. MODELING TOOLS
• Comparative longitudinal studies (over time)
• Often reveal insights about competitors and reference class
companies far better than isolated profiles, limited time series,
or vivid recollections
• Best: If comparisons can be done through recent similar time
windows, when comparable participants were operating with
similar technology, distribution, social and economic forces
• Best: Similarity of entrepreneurial drive, resources and
limitations of study group of businesses
• This is a common methodology in business research and
cultural anthropology
• Most Important: Watch what competitors and reference class
companies do, not what they say they’ll do, except to
compare their ability over time to set a forecast and hit it

42. OTHER GO-TO
CHARACTERISTICS
• Most progress comes from the ability to do many small
mutations fast
• Applies to technology, product, organization, distribution,
supply chain, and issue resolution
• Speed responding to the little problems that arise portends
much about the ability to do bigger things well

43. OTHER GO-TO
CHARACTERISTICS
• There is usually a dominant time constant in technology
adoption
• Understand what it is, why it is, and thus how to read the
forward analytics with greatest accuracy
• Informs the interventions that will be most productive to
accelerate adoption
• The issues are often as much social as technical

44. OTHER GO-TO
CHARACTERISTICS
• Profit usually pools disproportionately in parts of the
market web. One example: Smile Curve
• Note: the profit pool vs. market chain is often not smooth
and not monotonic (even in the second derivative)

45. OTHER GO-TO
CHARACTERISTICS
Accounts Receivable and Inventory:
• If reliable financial data over time is available
• Such as from publicly listed entities of interest, or,
• Private companies in jurisdictions where financial statements
have to be publicly disclosed
• Then, changes normalized to sales revenue in:
• Accounts Receivable (A/R), and,
• Inventory, including the ratios between raw materials, work-in-
progress, and finished goods
Often have very high value about the health trend lines of the
profitability of a company under study

46. IF YOU’RE REALLY IN A
PINCH FOR A
PREDICTIVE MODEL
Rene Girard:
• Most of human behavior
is based upon imitation,
rival and differentiating
Charlie Munger:
(Warren Buffett’s partner)

47. LEADING PUBLIC
DATA SOURCES
Google searches are, at best, only a starting point (ditto for
Baidu). Much is not well indexed. Similar for Wikipedia.
Where to go for better public and near-public data?
• News, especially local news, particularly local language from
responsible outlets and journalists
• Financial and securities disclosures, both of subject
companies and partners, especially analyst day, capital
market conference presentations and acquisition filings
• Podcast and Youtube interviews, where people tend to be
less guarded than in print
• Trade show bloggers, tweeters and photographers
• Aspiring industry mavens who leave a lot of on-line residue
and get out to most or all of the main events

48. LEADING PUBLIC
DATA SOURCES
• Corporate profile “leaks” to the business press
• Often done by retained i-banks to cultivate acquisition interest
• Patent filings laid open, primarily US and home country
• Government research grant applications
• Regulatory filings (such as FCC)
• Court filed litigation documents
• Banker’s books and corporate venture investment
solicitations
• Technical conference presentations (both positive- and
negative-space inferences)
• Suppliers and channel partners (subject to de-biasing their
self-interested spin)

49. PRODUCT
BENCHMARKING
• The limits of vendor documentation:
• Hardware: Specifications, term definitions, test methods, test
equipment and results interpretations can vary greatly among
competing vendors, and hide as much information as they reveal
• Software: Many vendors claim similar high level capabilities, but only
when you drill down do the usually significant differences in capability
and usability fully emerge
• Relying on published competitive specifications or lone user
impressions are dangerous as the main inputs for significant
decisions
• Benchmarking performance of competitive products is best done in-
house where consistency of test conditions can be achieved
• Next best is using a 3rd party lab
• Usually the best tracking method is the trajectory over time for each
performance attribute that users find valuable

50. Break

51. CASE STUDIES
• Digital Image Sensors, Cameras and Semiconductors
(’97-’08)
• Technology and Application Sector Focus Choices in
Fragmented, Rapidly Changing Digital Imaging Markets
• Turnaround of Medical and Biotech Imaging Business Unit
• Fix, Sell or Shut: Digital Cinematography Business Unit
• Major New Growth Wave or Head Fake for Industrial
Machine Vision: Smart Cameras
• MEMS Foundry Services

52. CASE STUDIES
• Enterprise Software – Customer Communication Mgmt
(’08-’11)
• Lead, Follow or Get Out of the Way
• Drones (’11-’17)
• Agriculture
• Law enforcement
• DJI – what can be learned about a private company with
work

53. MACHINE VISION IMAGE
SENSORS AND CAMERAS
• Circa 1997, a local tech company had grown its image
sensor and digital camera business predominantly in
document scanning and postal sorting to ~$30M in sales
• The company was becoming a big fish in a finite sized
pond based on those beachhead applications
• It needed to continue to generate strong growth, having
gone public in 1996
• The company had toeholds in a number of additional
markets, through early adopter cross-over uses of its
standard (catalog) products, and custom developed
products
• The overall market for machine vision sensors and
cameras was large, but very amorphous in product
requirements across many sectors

54. MACHINE VISION IMAGE
SENSORS AND CAMERAS
• The main questions to answer at the time:
• What candidate market verticals to target next?
• To get to that answer, needed to understand for each
vertical:
• Size, growth rate, and system level technical trends
• Competitors and competitive intensity
• Leverage and gaps in current technology capability
• Extensibility of current operations and distribution
• Studied ~70 discernable application sectors
• Everything from Astronomy to various X-Ray Imaging uses

55. MACHINE VISION IMAGE
SENSORS AND CAMERAS
• Desirable sweet spot for growth:
• Markets large enough to fuel significant growth for years to
come, but not so large as to take on behemoth competitors
the company couldn’t handle
• Verticals requiring technology and operational capability
packages where the company’s existing assets were
already 80% to 90% complete
• Bound risk and time to success, especially when requiring
financially material investments in a public company
setting

56. MACHINE VISION IMAGE
SENSORS AND CAMERAS
• Added challenge:
• As an OEM component provider, the time from product
concept through development and release, and then
system customer design-in through release and ramp
could take 3 to 5 years in total
• Technology and market forecasting had to be sound, both
for direction and magnitude
• Sunk cost investments to go after major new applications
and technologies were significant

57. MACHINE VISION IMAGE
SENSORS AND CAMERAS
• Diversity of applications, technology requirements,
markets
Source: RIA/AIA 2008 Machine Vision Mkt. Study

58. MACHINE VISION IMAGE
SENSORS AND CAMERAS
Example Outcome: Semiconductor wafer, mask and reticle inspection
KLA-Tencor
Hitachi
Applied
Materials
Rudolph
Technologies
Nanometrics
Veeco
Therma-
Wave
ADE
Leica
Bio Rad Other
Inspection & Metrology
Fractal-like market concentration and technical diversity in the sector, much as
the larger machine vision industry

59. MEDICAL AND BIOTECH
IMAGING SYSTEMS
Setting:
• The company had traditionally not been a strong vendor
of imaging technology for ionizing radiation imaging, and
slow-scan, high dynamic range sensors and cameras
• This sector was poised to grow significantly in the years
to come, at or above the rate of the company’s traditional
industrial machine vision markets
• To fill this gap, in the early 2000’s the company acquired
an early stage developer and manufacturer of x-ray
imaging sensors and cameras in the US

60. MEDICAL AND BIOTECH
IMAGING SYSTEMS
• After a few years, the acquired business was struggling for growth
• CRM analytics and management regularly suggested an upswing
was near, but the horizon kept receding as time rolled forward
• Issue: The misunderstanding of the real rate of progress of internal
account development was mirrored by misunderstanding of the
external environment
• Hopeful optimism for a quick rebound at the onset of difficulty had
institutionalized and amplified into a number of specious beliefs and
dogmas about the internal and external situation
• Action: Bottom-up external re-evaluation of the key target markets,
and major prospects in each sector
• Separate fact from fiction to get a reliable handle on attainable
growth, desirable target accounts, and near-term levers for an
operating-level turnaround of the business

61. MEDICAL AND BIOTECH
IMAGING SYSTEMS
• Findings
• Three major customers the business relied upon for its near
term vitality
• Digital Mammography: In financial and regulatory difficulty,
declining market share it its end market, and unclear ability to
afford to stay competitive through next generation R&D of its
system product
• Protein Crystallography: Substantially cash cowing the
product line in which it used the company’s sensors, and
would not significantly reinvest to get more competitive
• Small Animal CT: The customer was healthy, but small, and its
growth alone could not reflate the business as it was
• Action: Expedited next generation product development of a
broadly applicable CMOS x-ray sensor and camera

62. DIGITAL
CINEMATOGRAPHY
• Time Period: 2000 – 2008
• Era: Advent of digital cinema photography camera usage in principal
photography of movies, episodic TV and big budget commercials
• Issues:
• Ability for 4K digital image capture to supplant film, on aesthetic and
technical grounds
• Adoption speed proclivities in project-based film industry
• CMOS vs. CCD image capture
• Disruptive potential or head-fake of $30K ASP insurgent camera system
price from a new vendor vs. deemed $300K-$400K legacy mechanical
film camera price and targeted CCD camera price
• Finite size of global market and profit pool relative to the inexorably rising
cost of up-front product and market development
• Little revenue or total production cost leverage from using digital cameras
vs. film
• Rate of likely obsolescence of digital cinematography cameras vs. legacy
mechanical film cameras, and business model implications for ecosystem

63. DIGITAL
CINEMATOGRAPHY
Key Decision Inputs:
• Market penetration of digital cameras, and penetration rate vs. previous digital
production and post-production technologies
• Early adopter to mainstream tipping point
• Market share
• Use by the most respected of the priesthood of directors of photography
• Separating real from would-be key opinion leaders (KOLs)
• Relative and absolute penetration of digital cameras by:
• Legacy mechanical film camera producers
• Cross-purposed high end broadcast cameras
• Up-performance DSLR cameras
• Insurgents pursuing low cost, high performance accessible to most at the price of a
legacy camera rental, for the benefit of ownership
• Market size, profit pool, growth rate, and implications for required market share to
return cost of growing capital investment
• Positive and negative externalities in movie production costs from the advent of
digital image capture during principal photography
• High statistical fall-out of movie projects going from concept to green-lit status

64. DIGITAL
CINEMATOGRAPHY
Social and Structural Issues:
• Lateral nature of Hollywood (rather than vertical integration) making it
harder to get everyone on the same page for changes that cross
organizational boundaries
• Maturity of the industry, meaning much spending power and
management time among major players are dedicated to similar
problems
• Oligopoly of the major studios, dampening competitive intensity to try to
get ahead with new technologies, and,
• Project-based nature of content creation
• Teams re-form from project to project, changing stakeholders
• Decision team reconstitution brings “solidarity of three” problem in risk
assessment about carrying innovations from past projects to new ones
(single missionary advocate for a risky position faces several opposers)
• Also, project-based work with team reform each project means IP moves
around, lessening the incentive for businesses to invest in differentiating
technological or work-process IP beyond a minimum competitive
threshold

65. DIGITAL
CINEMATOGRAPHY
Adoption timescale benchmarking example
(predecessor, competitor and adjacent techs):
Red - Major Release Lensing
HD - Episodic TV Digital HD Camera Use in US, English
Year Share
Year Years
Since
Launch
Event Share of US Episodic
TV Capture
2007 2.50%
2008 7% to 14% HD - 2/3" Major Release Lensing
1999 0 Introduction 0.0%
2000 1 Launch 0.0% Genesis - Major Release Movie Cinema Photography Lensing
Year Share of Major Release
Principal Photography
2001 2 Experiments, Commercials 0.0% Viper, F900/950, Varicam
2002 3 0.0%
Year Years
Since
Launch
Event Share of Major Release
Principal Photography
Number
of
Cameras 2002 0.5%
2003 4 First HD Series 2.0% 2003 0.5%
2004 5 11.0% 2004 0 Introduction 0.0% 2004 1.0%
2005 6 20.0% 2005 1 Launch 2.5% 12 2005 2.0%
2006 7 31.5% 2006 2 8.5% 50 2006 7.0%
2007 8 40% 2007 3 11.5% 90
Avid Film Composer Digital Editing Suite - Partial Editing Avid Film Composer - Full Editing, Feature Length Movies 3-D - Major Release Production
Year Years
Since
Launch
Event Market Share Year Years
Since
Launch
Event Market Share
1992 0 Launch 0.0% 1992 0 Launch 0.0% 2005 0.5%
1993 1 First Movie 0.2% 1993 1 2006 1.0%
1994 2 Two Movies 0.5% 1994 2 2007 2.0%
1995 3 Dozens of Movies 5.0% 1995 3 2008 5.0%
1996 4 10.0% 1996 4 2009 7.5%
1997 5 20.0% 1997 5 5.0% 2010 10.0%
1998 6 50.0% 1998 6 2011 12.5%
1999 7 60.0% 1999 7
2000 8 65.0% 2000 8
2001 9 70.0% 2001 9 12.5%
2002 10 75.0% 2002 10
2003 11 80.0% 2003 11
2004 12 85.0% 2004 12
2005 13 87.5% 2005 13
2006 14 90.0% 2006 14 60.0%
Full Digital Intermediate in Wide Release Hollywood ProductionsFull Digital Intermediate in India (Bollywood) Productions 4K Digital Intermediate in Wide Release Hollywood Productions
Year Years
Since
Launch
Event Market Share Year Years
Since
Launch
Event Market Share Year Market Share
1993 0 Commercials 0.0% 1993 0
1994 1 Music Videos 0.0% 1994 1
1995 2 0.0% 1995 2
1996 3 0.0% 1996 3
1997 4 0.0% 1997 4
1998 5 0.0% 1998 5
1999 6 0.0% 1999 6
2000 7 First wide release 0.0% 2000 7
2001 8 0.3% 2001 8
2002 9 7.0% 2002 9 Introduction
2003 10 19.0% 2003 10 0.2%
2004 11 32.0% 2004 11 2.0% 2004 0.5%
2005 12 50.0% 2005 12 6.0% 2005 1%
2006 13 66.0% 2006 13 15.0% 2006 5%
2007 8%
2008 10%

66. DIGITAL
CINEMATOGRAPHY
Adoption timescale example inferences:
• 10% of ultimate market share can happen 3-5 years after launch of working product
• 50% of ultimate share takes 6-9 years for partial use of a systemic new technology,
and a 10-15 years for use that totally displaces the incumbent
• Order of magnitude cost reduction is the standard for driving faster adoption within
these ranges
• Sustained high growth rates come from being associated with distinctive audience
experiences in the highest grossing projects (herd dynamics)
• 50%-80% share of market is ultimately possible with exceptional performance and
execution
• Point technologies (incremental) in production workflows move much faster (2* to
4*) than those requiring systemic change
• 4K (next generation tech) lags 2K (current generation tech) by about 5 years
• The incremental benefits of 4K in DI are much less vs. 2K, than 2K was relative to
the incumbent technique DI replaced. 4K DI is being adopted at roughly half the
pace of 2K DI.

67. DIGITAL
CINEMATOGRAPHY
Bottom Line Near-Term Indications for Action:
• Show revenue enhancement for customers from early projects
using tech (or at least significant cost savings)
• Show how all players felt early projects were de-risked or
creative control was enhanced from using the tech
• Show learning curve for getting superior results from the new
tech to be << one project
Longer-Term Indications:
• Stop-loss threshold, should operating improvement not
materialize

68. DIGITAL
CINEMATOGRAPHY
Epilogue
• By 2006:
• R&D productivity was competitively, objectively sub-par;
technology and manufacturing leverage were proving elusive
• Real KOLs were adopting competitive digital camera
products, for mainstream wide-release movies, episodic TV,
and high profile commercials
• Order of magnitude lower price digital CMOS imager cameras
were at an advanced state of development, pending release
• Success of adjacent DSLR CMOS cameras (Canon D30 and
later models from multiple vendors) since 2000 indicated
advent of CMOS was probable
• Adoption of alternate digital cameras (relative to typical 15%
tipping point threshold)
• TV and commercials – 31%
• Wide release movies – 9% - arguably, still up for grabs

69. DIGITAL
CINEMATOGRAPHY
Epilogue
• By 2007:
• Adoption of alternate digital cameras in principal photography
• TV and commercials – 40%
• Wide release movies – 14%
• Price busting 35mm CMOS imager digital camera launched
by Red Digital Cinema
• 2008:
• The company persisted and continued to press ahead
• Successful activist shareholder intervention to revamp board
of directors and reform strategy
• Digital cinematography business shut down
• Over $60M went into this venture since its inception

70. MEMS FOUNDRY
SERVICES
Time: 2002-2008
Issues:
• Acquired semiconductor foundry to secure access to production for
specialized image sensor fabrication
• Captive image sensor production though was too small to consume
enough of the foundry capacity to be viable (<20% of output)
• Needed to avoid lithography and wafer size based competition
• Most promising growth market matched to equipment and process
capabilities: Micro-Electro Mechanical Structures (MEMS)
• Application and customer focus
• Context: history of “MEMS Death Spiral” for multi-customer foundry
services, where new equipment, materials and processes had to be
added faster than the rate of sustained revenues, margins and cash
flow

71. MEMS FOUNDRY
SERVICES

72. MEMS FOUNDRY
SERVICES
• Decisive Issues to Profile about Competitive Environment
• Threshold of R&D and CapEx to be sustainable, adjusted for
technology node, process mix and product diversity
• Strategic intelligence impact
• Clear dichotomy of profitability and sustainability based on scale
• Objective sense of the time to conceive, develop and achieve volume
production of entirely new processes and devices
• Outcome strategy drivers
• Process transfer for small volume work, sidestepping much of the
R&D and CapEx
• Work with a portfolio of large potential volume emerging
opportunities, with bounded material and process limits matched to
current capabilities

73. SMART CAMERAS
Time: 1997-2008
Era:
• Advent of low cost machine vision appliances
• Integrating camera, frame grabber, image processing
software
• Small, low cost package
• To access heavier industry, consumer packaging, and
pharma/med device sectors where more expensive, larger,
and complex to deploy machine vision had previously tried
and struggled to gain traction

74. SMART CAMERAS
Issues about which confusion was complicating management decision
making:
• Supplier market share and fragmentation
• Fuzziness in the perceptions after the top 2 or 3 players
• Relative leverage of technology vs. distribution vs. customer service
• Real rate of penetration (market adoption-rejection behavior), and
attainable rate of change
• Unit deployment economics and business model w/ less tech’l users
• Use of income statement geography and selective memory loss to
make some vendors appear to be performing much better than they
really were
• Hangover effect from 2000-2003 tech bust when some of the
traditional markets for industrial machine vision were declared dead
and not coming back
• The industry was trying to will into existence a new savior product
category and market

75. SMART CAMERAS
Clouding issues further:
• Cult of Dr. Bob issue
• Widespread industry envy of the leading North American
player’s public awareness, valuation and bravado
Source: Cognex
Annual Reports

76. SMART CAMERAS
Inferences
• Busy, crowded space
Sources: RIA/AIA 2008 Machine Vision Mkt. Study, Fuji-Keizai Group

77. SMART CAMERAS
Basis of Research and Analysis
• Research to build rapid comparative profiles for all market
participants
• Half a page each, covering:
• Participant size and growth rate, and years since inception
• Relative prevalence in value prop and differentiation of
technology vs. distribution vs. operational prowess
• Financial performance and market performance
• Model and assets for each of distribution, operations and
R&D
• Analysis:
• Pair-wise comparisons among the market participant profiles
to reveal the correlations, anti-correlations and likely causality
that led to the strongest vs. average vs. worse outcomes

78. SMART CAMERAS
Key Strategic Intelligence Outputs to Drive Management Decisions:
• Accurate baseline market share and trends
• Thresholds of competitive efficiency in R&D
• Relationship between product diversity, distributor diversity and size
of customer base
• Pragmatic leverage assessment of existing distribution assets
• Ramp-up time and availability of new distributors
• Ability to set a forecast one to three years out, and largely hit it
• Cash Curve: Time to breakeven and payback time
• A more characterized and quantified estimate of the opportunity
available from continuing to invest in smart cameras (small market
share line of business) vs. other business units with more
significant market shares

79. CUSTOMER
COMMUNICATION
MANAGEMENT
Key Issue: Does the market want what we’re selling?
Prime Frame of Reference: Recent predecessor, competitor
and adjacent product vendor success trajectory
0
10
20
30
40
50
60
1 2 3 4 5 6 7
Revenue(US$MM)
Years Since Inception
Reference Company Growth Trajectory
Aprimo
Document Sciences
Eloqua
Exstream
InSystems
StreamServe
ThunderHead
Unica
XMPie

80. CUSTOMER
COMMUNICATION
MANAGEMENT
Sometimes, you get lucky:
• Private company in the space, but HQ’d in the UK
• UK companies have to file financials in the near-public
• Some like to show off a bit (for future partners and
potential acquirers?)

81. CUSTOMER
COMMUNICATION
MANAGEMENT
Challenges:
• 2008-’10 financial crisis in full swing
• Cult of X issue around a pre-crisis sale of sector participant
Exstream Software to HP (since divested and acquired by
OpenText)
• Frame of reference moving what had been a professional
services business to a more scalable technology business
Inferences:
• Various insights about product, technology and go-to-market
tune-ups to reposition business to a sustainable trajectory

82. DRONES IN AG
The Knowable, Likely Head Fake of Precision
Agriculture
• The False Hope to Become a Big Market for Surveying Drones

83. DRONES IN AG
• 2013: Initial breathtaking projections (US example)
Source: AUVSI Economic Report,
2013
• Careful analysis:
• Specious extrapolations from pesticide application drones in
Japan
• Assumption and reliance on system-level capability to exploit
high temporal and spatial accuracy crop data from drones

84. DRONES IN AG
• 2013: Research to make one question a little more deeply
• Slow precedent adoption rate and low ASPs of of GPS-
enabled precision ag technologies in the US
• Widespread adoption achieved only when ASPs of precision
ag techs dropped below $20K, and in some cases, under $2K

85. DRONES IN AG
• 2014: Empirical evidence of unexpected application
complexity and hidden costs
• Crop varieties -> Substantive technical differences to
activate value from drone imaging
• Conservative, cost conscious farmers
• Complexity of flying drones and post-processing data to
generate actionable insights
• Challenges of acting on those insights with existing crop
input application technologies
• Opportunity cost vs. alternative yield-cost enhancing
potentials

86. DRONES IN AG
• 2014-2015: Gold rush of services providers
• Effort to make the technology and workflow more
approachable to end farmers
• But, under observation, in less subsidized markets, anemic
service provider growth rates when studied over short- and
medium-term

87. DRONES IN AG
• 2015-2015: Sober experience of those having used drones in
ag for several years
• Jurisdictions with earlier regulatory relaxation for commercial
use of drones, such as Brazil and France
• Yield impact and RoI are marginal, not fundamental
• Realization that it takes multiple years to fully evaluate the
impact of any new precision ag technology, given the
variability of weather factors
• Attribution difficulties
• Precipitous price drops to try to get traction, not just for the
drones but for the payloads too

88. DRONES IN AG
• 2015-2016: Elevated churn rates of early adopters farmers
using SaaS for crop management drone analytics
• Primary strategic emphasis of many SaaS image analytics
company shift to insurance, mining, surveying and
construction
• Viable agricultural applications in the early market limited
to high value crops such as viticulture and almonds
• Other factors: Growing competition from higher resolution
satellites and microsatellites with more frequent passes
and denser coverage

89. DRONES IN AG
• 2017: Capitulation
• “Encouraging farmers to adopt drones also proved harder
than expected,” notes Chris Anderson of 3D Robotics. “The
agricultural use of drones sounds good in theory—feed the
world, save the planet—but is difficult in practice. The market
is very fragmented and conservative, with many subsidies
and distortions, and some of the social goods that flow from
using drones, such as reducing run-off of chemicals, do not
benefit farmers directly.”
• The agricultural market “is littered with struggling technology
companies that have tried to break in, says Jonathan Downey
of Airware.
• “What good are unmanned aircraft systems for agricultural
remote sensing and precision agriculture?” - USDA

90. DRONES IN LAW
ENFORCEMENT
• Reference class law enforcement analysis:
• Surveillance cameras
• Body cameras
• Digital imaging SaaS and video evidence admissibility
• License plate camera readers
• Encrypted radios
• Police helicopters and imaging payloads
• Night vision and thermal imaging systems

91. DRONES IN LAW
ENFORCEMENT
• The power of social media to track market penetration,
growth, and share by both units and $’s very closely
77%
12%
3%
3% 1%
1%
1%
1% 1% 1%
Announced or Completed US Law
Enforcement sUAS Deployments from Jan
1 to Dec 15 2016, N=138
DJI Manufacturer not Disclosed
Competitor A Competitor B
Competitor C Competitor D
Competitor E Competitor F
Competitor G Competitor H

92. DRONES IN POWER
GENERATION, T & D
INSPECTION
Unit Economics:
• Industry Consortia: Often can only agree to work on near-
term, shared challenges (to protect IP), and frequently
publish extensively, including on unit economics

93. DRONES - DJI
“The harder I work, the luckier I get.” – Samuel Goldwyn
500
2,700
200
150
250
DJI 2017(F) Sales by Product Line, US$ Million, made in mid-2016
Mavic Pro Phantom Inspire Matrice Other
Mavic and
Phantom Sales –
Multiple company
executives in
Chinese language
interviews
Inspire – Promotion
from 20,000th unit
sold during retail
store opening in
Hong Kong
Higher end
systems based on
extrapolation of US
FAA commercial
registry relative to
Inspire
Triangulation -
Amazon and
Alibaba sales
statistics
Actual DJI 2017 sales: US$2.9B

94. DRONES - DJI
• Forecasting:
• Watch headcount
• Other measures of corporate development are more easily
deceived
• Even headcount needs to be watched carefully; there’s a lot
of out of date information in circulation
Wikipedia, downloaded June 7/’18:
DJI, Aug. ’17:
• “DJI now has over 11,000 staff worldwide..” – Frank Wang,
Founder

95. Bringing Strategic Intelligence
All Together

96. GETTING STRATEGIC
INTELLIGENCE RIGHT
• There’s sometimes remarkably little support for what is
generally accepted wisdom
• Careful research and analysis over time reveals deeper, more
reliable facts
• Many people in an ecosystem want to create an aura of being
insiders, often trading to build personal credibility on what
they would like to believe or portray is privileged information
• Instead, what they emit is often just gossip of unconfirmed
veracity, but attention-getting specificity, or lightly reheated
public information
• Detailed strategic intelligence sorts the contenders from the
pretenders among sources and supposed facts with the ability
to corroborate the likely and certain and separate out the
unlikely and uncertain

97. GETTING STRATEGIC
INTELLIGENCE RIGHT
• Knowing what information is missing, which would be
most valuable to know, allows the most incisive, least
damaging, and likely to succeed information exchanges
when they need to occur with others in the industry
• When potentially significant new information comes to
light, its likelihood of being true can be quickly
ascertained based on other information already
marshalled, to allow the correct vector of response

98. GETTING STRATEGIC
INTELLIGENCE RIGHT
• Brains are not the problem in growth stage tech
• More difficult to manage are: Assumptions, ambition, frame
of reference/context, the relative merit of various analogs
and anti-logs, clique dynamics, ideological filtering, and
biases to justify past decisions and positions
• Can-do people, in can-do companies, in can-do industries
• Competitive drive can become neurotic
• People can act in know-ably flawed or unduly risky ways,
just to take action quickly with the tools they have
• Counter: Institutionalize thinking fast and slow
• Counter: At least a few people with broad awareness,
across the industry, adjacent industries, and over time

99. TACTICAL CHALLENGES
TO STRATEGIC
INTELLIGENCE SYSTEMS
Those opposed to the trends and likelihoods indicated, on
the grounds of bias or time pressures:
• Visionary:
• Interest may only last until the next shiny bright object
appears
• GSD Operator:
• Often wants to revert to whatever is tactically quickest, to
get the most to-do list items checked off fast
• Process Oriented Team Member:
• The changes in organizational priority and behaviors
implied can be anathema to the desire for an ordered,
predictable routine

100. WARNING SIGNS
• Uncomfortable inferences for change are nervously
acknowledged or superficially refuted based on little
contrary evidence, and then things stay the way they were
before
• The rate of change inside the company is significantly
outpaced by the competitive environment
• The company’s bureaucracy resists or factionalizes
necessary shifts
• There’s more loyalty to functions, department heads and
key people than to the overall performance of the
business

101. WARNING SIGNS
• Greater value is placed on executing the current strategy
with declining productivity than on finding a new strategy
• The bandwidth dedicated to many smaller issues pushes
out a response to the larger issues at hand
• People are transfixed by the wrong data or indices
• More value is put on iterating narratives quickly than the
facts, their quality, and the thoughtful inferences they
imply
• Critical viewpoints are purged
• The entrepreneurial optimism that success will be just
around the next corner is linked to weaker and weaker
arguments, usually of an increasingly aspirational nature

102. THE ETERNAL LONG-TERM
CHALLENGE OF STRATEGIC
INTELLIGENCE SYSTEMS
• People come to trust those who bring forth information
that largely confirms prior views
• Those who bring forth discouraging or contradictory
information over time tend to become viewed as
misinformed or unsuited to the work, no matter how
strong the factual basis for their divergent views

103. THE ETERNAL LONG-TERM
CHALLENGE OF STRATEGIC
INTELLIGENCE SYSTEMS
• A chauvinistic intellectual and filtering bias can set in,
unless leadership signals that it actively seeks and is
willing to act upon uncomfortable truths
• Ultimately, only the CEO can protect constructive strategic
intelligence, especially in times of dissent
• Otherwise, the challenge to estimates becomes a
challenge of honesty and intelligence, and then fitness for
duty
• The need for discipline becomes greatest when
uncomfortable inferences from strategic intelligence start
impinging upon deeply held beliefs of the leaders
• Even the paranoid have to trust something , to bound
the complexity of the world in which they work

104. TAKEAWAYS
• Headcount of competitors, suppliers and distributors is the
long-term, non-financial measure of success which is most
difficult to deceive
• Staffing levels are most important to track in a landscape
composed predominantly of private companies
• Change detection is powerful
• Different information sources will be more powerful at
different phases of an industry’s growth and development
• There is a lot more information in the public domain than
most people realize, if they are willing and able to look for it,
record it, and periodically synthesize the overall picture
• Foreign language outlets, local journalism, and social media
sources provide rich sources of information, subject to care
about bias and the potential for misinformation

105. TAKEAWAYS
• Over time, an increasing volume of organized data provides a
strong ability to observe what people do, which is more
significant than what they say (except to compare with earlier
projections)
• 3rd party market research reports are often a waste of $ other
than informing the major segments and logos to track, or
entities to target as customers
• Discipline and detail in strategic intelligence can help avoid
spurious and overly localized actions, as well as
unnecessary guesswork, by having a strong grasp of all the
empirical evidence that can reasonably be obtained
• Over time, the ability to quickly add, retrieve, sort and filter
information about the strategic environment becomes more
significant than almost any one data point (no matter how
good that one point may be)

106. TAKEAWAYS
• The most valuable industry participants to track are those
that achieve and sustain the hat trick of:
1. Early (though perhaps not first) with major innovations
2. Pacesetting rate of incremental improvements
3. As zealous about reducing cost, as increasing performance
• If those three capabilities do not exist in one competitor,
ecosystem or reference class company, then
1. There is usually a significant untapped opportunity
2. Best in class performance can by modeled in hybrid from
individual players that exhibit those traits
• Benchmarking drives advancing expectations, objective
yardsticks of performance, productivity and management
performance

107. TAKEAWAYS
• Especially today with information overload and increased
risk of biased information affecting decision making,
strategic intelligence helps get people outside of any
intellectual silos they may fall into by re-grounding
discussion in evidence-based facts and likelihoods

108. FURTHER
DISCUSSION
For arrange further private discussion of any of today’s
topics or related matters:
dave.litwiller@communitech.ca