Media Vision Day 13 Sept. 2016 - http://www.grp.be/nl/agenda/inspireren/grp-media-sessions/1/496/grp-media-vision-day-no-screens-no-glory.html

1. Mapping the media
industry’s challenges
1
Olivier Braet
iMinds-SMIT
(V.U.B.)
Mapping the media
industry’s challenges

2. Three parts
I. Media industry challenges
II. Strategic questions
III. Strategic response
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3. I.
Media industry challenges
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1.Shifting revenue streams
2.Expanding ecosystem
3.Technological change

4. I.1. Revenue stream shifts
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5. Direct revenues a) Sales / subscriptions.
Question: “Would you pay for news?”
§ “Never.” – Relatively younger segment
§ Some groups only reachable with ad-based model.
§ Young people: Higher WTP for digital (≠ paper) news
§ Moderate social media users: Highest WTP.
§ Low and heavy social media users: Lower WTP.
§ “If the interface is right.” – Higher earners
§ Read later apps (Pocket, Instapaper)
§ Mobile interface (Facebook ‘Instant articles’ => risk)
§ “Absolutely” – Relatively older segment
§ Long form news content valued higher
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6. Direct
revenues b)
Crowdfunding
Kickstarter
numbers:
Money collected
≠ Success rate
19/09/16 6

7. 19/09/16 7

8. 8
Crowdfunding movies in small markets
(Numbers for Flanders)
Typical
average movie
budgets,
Flanders
Revenue
projection for
movie
crowdfunding
platform
internal income
Separately
crowdfunded

9. Indirect revenues a) advertisements.
“Would you block advertisements?”
https://blog.pagefair.com/2015/ad-blocking-report/
https://www.globalwebindex.net/blog/north
-americans-most-likely-to-block-ads

10. Arms’ race with ad blockers.
(Catblock)

11. Five strategies against ad blockers
1. Ask to turn off ad blocker
§ Unique content needed (No substitutes; Brand sympathy)
2. Pay AdBlock Plus to whitelist website
§ Extortion model, No solution for other ad blockers
3. More ‘native advertising’
§ Tension with editorial side. Saatchi&Saatchi model
4. Marketeers: “Create non-irritating ads”
§ Define ‘irritating’. Personalized non-intrusive ads can irritate
5. Lower dependence on ads, through direct revenues
(subscriprions; piece sale)
§ Quality content needed (as perceived by visitor)

12. Advertising market:
Data-driven risk of break with public
§ Online ad brokers (try to) undercut business
case of traditional ads (*)
§ Is ‘Population that saw specific ad’ = ‘Population that
didn’t see it’?
§ What if the demography of ‘Over-the-top viewers’ ≠
‘Traditional TV-viewers’?
§ Better measurements needed of visitor
characteristics
§ Avoid mismeasurement: ‘Started videos’ ≠
‘viewed videos’
(*) http://davidreiley.com/papers/DoesRetailAdvertisingWork.pdf
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13. Redefining contextual advertising
§ Online ads now only focus on the user profile of
the visitor of a certain page
§ Barely takes into account the content that
appears alongside the advertisement
§ In depth enrichment of content (metadata) can
make an advertisement-content link possible
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14. Indirect revenues b)
E-commerce conversion / create sales
§ Sales conversion ratios are not good (yet)
§ Metadata still has to improve a lot =>
Recommendation algorithms still clunky
§ ‘Amazon x-ray’ on Kindle Fire & Kindle Fire HD
§ ‘Watch with eBay’ app
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15. Youtube recommendation algorithm.
15
Watch one history documentary about Hitler …
On the flip side, at least we can’t complain about
the ‘filter bubble’ if this happens.

16. I.2. Expanding ecosystem
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17. Escalation of complementary viewing
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(Source: Digimeter Panel 2015)
Daily TV viewing

18. Similar complementarity in US
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19. 19

20. Next 10 years (2016-2026):
Telecom subsidizes AV media
§ US Cable TV subscriptions down
§ Basic subscriptions down: 53 million to 45.4 million subs
§ Gross revenue down: $57.7 billion in 2016 to $55 billion
§ Cable broadband subscriptions and revenue up
§ Increase from 63 mio to 71 mio => $35.5 B/y to $47.3 B/y.
§ Revenues per sub up
§ They will hike monthly (!) cable TV fees from $90.84 to $100.02/m/sub
§ Cable broadband residential revenue up from $51.23 to $60.99 per
month per sub.
§ Total: Lose $2.7B on one side … Win $11.8B on the other
(Source: SNL Kagan; Variety.)
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21. Piracy / Cloud replaced OTA
§ Live TV has gone from free over the air (1980s),
to paying monthly subscriptions now.
§ Live content exploitation rights costs rising.
§ Negative feedback loop has economic limits
§ Sport federation ask higher exploitation rights =>
§ Sports channels ask higher retransmission fees =>
§ These will / could be charged to end-users directly, or
indirectly through more/longer ads or higher fee =>
§ Higher revenues leads sports to demand more
exploitation rights license fee.
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22. Piracy/cloud/regulation stimulate OTT
§ Piracy & cloud strengthen move to over-the-top
§ What if European broadcasters negotiate lower fees
for highly torrented shows?
§ Feedback loop: Succesfully negotiating a lower fee
could accelerate HBO going ‘Over the Top’.
§ If the business case for foreign quality content
weakens, local TV will invest in local content.
§ Regulation might cause HBO not selling online
exploitation rights to European broadcasters.
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23. I.3. Technological change
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24. More screens = More data
§ The promise: Global data-processing systems
will be all-knowing and all-powerful
§ Self-driving cars; Industry 4.0; DNA hacking
§ Auto-curated stories; auto-written stories
§ With enough (biometric) data and computing
power, these systems can understand and
predict humans much better than we understand
ourselves.
§ “Ruled by the invisible hand of the dataflow”
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25. Business impact of data
§ Moore’s law can turn low-gross-margin
businesses into software-centric, technologically
innovating, high-margin businesses.
§ Some industries had no new entrants for many
decades.
§ No automotive IPO in U.S. since Henry Ford, until
Tesla.
§ Maybe media will see more big IPO’s in coming
years?
§ How will data from all those screens affect the
media industry?
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26. 26
https://www.fastcodesign.com/1671893/the-secret-sauce-behind-netflixs-hit-house-of-cards-big-data

27. Netflix’ House of Cards is not big data
§ Step 1: Select “Heavy viewers” in a spreadsheet
2: Sort table by “favorite director” => David Fincher
3: Sort table by “favorite actor” => Kevin Spacey
4: Remake a BBC series of 1990
= Good, basic marketing, not dynamic big data driven.
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§ Amoral ‘Chief Whip’ of the
Conservative Party schemes
to become leader of the Tories
and PM of the U.K.
§ He kills a female journalist
§ He has monologues that break
the fourth wall

28. Microsoft AI chatbot ‘Tay’
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29. Beauty contest AI doesn’t like black
people
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“The data the
project used to
establish
standards of
attractiveness did
not include
enough
minorities.”
https://www.theguardian.com/technology/2016/sep/08/artificial-intelligence-beauty-contest-doesnt-like-black-people

30. 30
http://arstechnica.com/business/2016/08/facebook-fires-human-editors-algorithm-immediately-posts-fake-news/

31. The route of
made-up news
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http://conservative101.com/breaking-
fox-news-exposes-traitor-megyn-kelly-
kicks-her-out-for-backing-hillary/
http://www.nationalinsiderpolitics.com/2016/
08/27/breaking-fox-news-exposes-traitor-
megyn-kelly-kicks-backing-hillary/
http://endingthefed.com/breaking-fox-news-exposes-traitor-
megyn-kelly-kicks-her-out-for-backing-hillary.html

32. Explosion of blog-like news outlets
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33. AI is as good as the definitions of
markets, people and services it uses
§ AI is as good as the data set you feed it to learn
§ Careful with frequencies! <=> What about
minority views, or black swans?
§ How will algorithms write interesting stories?
§ How will algorithms decide which stories to
cover? Will the deciding variable for topic
selection just be ‘potential profitability’ /
‘clickability’?
§ Markets can be everywhere, but should they be?
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34. Unintended consequences of AI
§ Letting your automated electric vehicle drive
around blocks is cheaper than parking in New
York City => More congestion by automated cars
driving merry-go-rounds?
§ Automated cars will always struggle with
unpredictable pedestrians and cyclists
§ History learns us: The street lay-out will
adapt to the self-driving cars, not the other
way around. News media outlets will adapt to
auto-written stories, not the other way
around.
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35. Big data & media regulation
§ Filter bubble (see above: Youtube’s algorithm)
§ Net Neutrality => Platform neutrality?
§ Eg. “Can a search engine prioritize its own services
over competing services?” (cfr. Vestager’s actions)
§ ‘Privacy is dead’, except if you share the home
address of a high-level Silicon Valley CEO
§ Illusion of equality of opinion
§ Finally Rupert Murdoch can reach people directly
through his twitter
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36. App permissions: Accept
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Data & AI are not
simplifying life. They
create an additional
layer of bureaucracy
and legalese.
-§-
Screenshot:
Suspicious amount
of data asked by a
flashlight app

37. II.
Strategic questions
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§ Who are you?
§ What will it take to sustain yourself?
§ What will market entry do to you?

38. Who are you?
Are you really ‘only’ NACE 58.1?
§ Definition of ‘relevant market’ defined in a top-
down bureaucratic / technocentric fashion.
§ Public administration: NACE codes
§ Technocentric approach: Technology determines the
industry (eg. cable vs xDSL).
§ Don’t freeze the ‘relevant market’ in the heads of
the customers, not as a regulator, and not as a
business.
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39. What will it take? => Go beyond the
technical. § Where’s “stay more
affordable than the
challengers”?
§ Where’s “offer content
bundles that challengers
can’t match in quantity”?
§ Where’s “have unique
content”?
§ What about market
dynamics (acquisitions,
alliances,…)?
§ What if OTT player can
tap into capital of larger
player, like The
Washington Post &
Amazon?

40. On the supply side, external market
entry is the fundamental issue…
§ Netflix entering broadcasting market
§ (But they will need to invest in more and local
content)
§ Facebook, Apple, Google entering media market
via advertising value chain
§ (Even though ‘video views’ on Facebook seem
miscounted)
§ Amazon entering book distribution market and
newspaper publishing (Washington Post)
§ (Experiments with paper-digital bundle possible)
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41. Degrees of market entry impact
§ Status quo
§ Assimilation
§ Disruption
§ Upheaval
§ (Caused by structural, strategic or
regulatory factors.)
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42. Status quo / Assimilation:
Entry + disappearance or assimilation

43. Disruption: Top (or other) segment
substitution

44. Upheaval: Top (or other) segment
destruction
“The relational database market is a $9 billion a year market.
I want to shrink it to $3 billion and take a third of the market.”
---
Former mySQL CEO Mårten Mickos
https://pando.com/2013/03/07/danny-rimer-how-open-source-helped-create-the-modern-startup/

45. Upheaval: Top (or other) segment
expansion
• Top price segment introduction case: Apple iPhone.
• But entry from bottom segment more common &
much more disruptive.

46. III.
Strategic response
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47. Exploitation and exploration
§ The firm that maximally exploits existing
conditions will be maladapted when those
conditions change.
§ The firm that invests in exploring alternative
strategies might survive change (but at the
expense of not maximizing short-term profits).
§ The best chance of survival in a changing
environment is to pursue mixed strategies.
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48. MEDIA COMPANIES HAVE TO
BRIDGE…
Technologies
Channels
People
Who do you serve?
What are you
offering?
How are you able
to deliver it?
Why won’t it be
copied?
Resources, Timing,
Position, Scale, Agility

49. Need to combine technology insights …
Numbers & Networks

50. … with ‘PUMA’ insights
Policy User Market

51. Cooperation in a media cluster
Knowledge
spill-over
effects
Density
of
suppliers
Thick
labour
market
Access to specialized
service providers
Knowledge
sharing
across space
and time
(retool old
knowledge)
Multiple employers
is good for
employees.
Numerous
employees is good
for employers.

52. Individual companies can’t do it alone
§ Needs: Information/Insights, Innovation,
Incubation and Internationalization
§ Goals:
§ Support corporate responsiveness & strategic
foresight
§ Linking academics with entrepreneurial culture
§ Aid growth and/or innovation ambitions of industry
§ Funding for innovation, internationalization,
frontrunners and edgerunners

53. Strategic media cluster for exploration
§ Testing new media services in a Living Lab
environment (with hundreds of real users)
§ Profile-based customized media content (not just
advertising)
§ Data-driven business modelling & Real-time
advertisement value
§ City-embedded media / Media and IoT
§ Regulatory and policy issues for media (Privacy;
Data protection; Competition)

54. Thank you for your attention
Olivier Braet
iMinds-SMIT (VUB)
olivier.braet@vub.ac.be
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