A presentation covering the key contextual drivers and consumer trends affecting how consumers will regard their clothes and the retailers that sell them

1. 1
M U K I D I
WWW.WEBSITE.COM
B O R D E A U X A P R I L 2 0 1 8
NICK CHIARELLI
Director & Client Partner
Foresight Factory
UNDERSTANDING
TOMORROW’S
FASHION CONSUMER
Achieving growth in uncertain times
@nickchiarelli@futurethoughts

2. Understanding the future
Driving Forces
EconomyPolitics
TechnologySociety
Legislation Environment
Who is affected?
B2B
product &
service
providers
B2C
product &
service
providers
Consumers
What changes result?
Service
models
Innovation
Changing
attitudes
Changing
behaviours
Marketing
strategy &
tactics
“Consumer Trends”

3. Agenda

4. Understanding
tomorrow
1

5. Global population
2030 = 8.5 billion
2050 = 9.7 billion
Average life
expectancy
2050: = 77.8 years

6. 2050:
Global urban population 66%
(up from 54% in 2015)
2025:
Asia alone predicted to
have 30 mega cities

7. 2oC global temperature rise by 2030

8. 2025:
1.8 billion people will live under
absolute water scarcity
2050:
Demand for water will have increased
by 55% compared to 2010s levels

9. 2021:
The first robot
pharmacist will
exist in the US 2025:
30% of
corporate
audits will be
performed by AI

10. 80% of businesses
already use or plan to
use chatbots by 2020

11. 2020:
There will be up
to 7 million drones
flying in the sky

12. From “New
Normal” to the
#Nevernormal
2

13. 13
2008 FINANCIAL
CRISIS
DIGITAL
EMPOWERMENT
THE
NEW
NORMAL
Understanding the past decade: the “New Normal”

14. Its not just the Economy: A broader set of uncertainties

15. 15Source: McKinsey & Company, Independent Work, October 2016
Some feel vulnerable to
political change
% who personally feel at risk from Political
instability over the next 5 years [UK]
27%

16. Fearing financial hardship
44%
% who feel personally at risk of
financial hardship in the next
5 years [USA]
70%
69%
67%
60%
50%
USA
Germany
Sweden
GB
China
% who agree “I do not save as much
money as I would like to because the
cost of living is too high”

17. 1717Source: McKinsey & Company, Independent Work, October 2016
30%
derive their primary
income from this
work
people in the US and
EU15 engage in task-
based, short-term
independent work
Precarious work
162m
16%
engage in this work on
top of existing jobs out
of financial necessity

18. WELCOME TO THE WORLD
OF #NEVERNORMAL

19. Revision of
Global
Political Norms
Economic
instability and
uncertainty
4th Industrial
Revolution
Re-ignition of
Climate Change
Debate
Understanding the Never Normal

21. 3 MINUTES
TO MIDNIGHT
Source: International Doomsday Clock, October 2017
The Doomsday Clock
1949

22. The Doomsday Clock
1991
17 MINUTES
TO MIDNIGHT
Source: International Doomsday Clock, October 2017

23. The Doomsday Clock
2017
2 ½ MINUTES
TO MIDNIGHT
Source: International Doomsday Clock, October 2017

24. Key
opportunity
areas for fashion
3

25. FASHION
&
DIGITAL
[1]

26. TREND: Cruise Control
“I try to appear in control of my life at all
times” [UK]
Consumers will increasingly want control of their lives, outfits and wardrobes.
Closet+ GlamOutfit
64%

27. TREND: Customised Reality
of consumers are
interested in using a
digital mirror to
virtually try on clothes
[GLOBAL].
Screen-led AR will become commonplace. Mixed reality, eg Hololens, will
represent the next wave of consumer-facing applications.
55%
75%+
Interest is highest in
India, Dubai and China
where agreement is over

28. TREND: Casual Connectivity = wearable
% who have used a wearable device that
connects to the internet or are interested in
doing in the future [FRANCE]
While the frenzy around fashion wearables seems to have cooled somewhat it
is too early to say that it will not rise again.
Currently
own / have
access
Do not
currently
own but
would be
interested in
owning
Do not
currently
own and not
interested in
owning
15%
23%
62%

29. Half of adults have used a fact
checking service or are interested in
doing so [UK].
TREND: Mechanised Trust
Blockchain technology will provide the engine for increased transparency of
supply chain, internal processes and even pricing

30. TREND: Engineered Empathy
% who have used a chat/messenger
service to speak to a customer service
assistant [Global average]
Brand-customer relationships will be sustained by ever more sophisticated
automated solutions.
59%

31. Fashion & Digital
https://www.youtube.com/watch?v=oFzeQ6Z2CIE

32. FASHION
&
PERSONAL-
ISATION
[2]

33. 22%
42%
Using Interested
64%
33%
35%
Using Interested
68%
NOW 2025
TREND: Customisation & Co-creation
The consumer appetite for customisation and co-creation will continue to grow.
% who have customised/personalised a product in
store/online before buying it [Global]

34. TREND: Me Me World
% interested in a service that analysed my
DNA information to give me personalised
health advice [UK]
52%Gen Y 64%
Brand-led personalisation will stretch into all areas from targeting to design.

35. TREND: Surprise Me
The best personalisation algorithms will stretch consumers beyond their comfort zones
11%
40%
Very interested
Quite Interested
50%
NOW
10%
33%
22%
Using
Very interested
Quite Interested
65%
2025
% interested in a service that provided surprise product
recommendations based on lifestyle habits [Global]

36. Biometrics: Contextual Personalisation
1 in 2consumers are interested
in a device that
monitored your stress
levels throughout the
day [Global]
Next-gen personalisation will recognise in-the-
moment needs such as mood or physical state

37. TREND: Consumer Capital
63%
24%
14%
14%
49%
It’s personal
This group is
explicitly willing to
share data for a
wider range of
personalisation
features, including
tailored advice,
discounts and
improved product
suggestions.
Discount only
This group is happy to provide personal data to
brands, but only in order to receive better and
more relevant discounts from brands.
Function favourers
No explicit data sharing with
brands, but shows interest in
data-led personalised
products or services, so will
likely share if the proposition
is right and data used is
obviously relevant.
Private preference
No explicit interest in data
sharing with brands and
minimal interest in data-led
services or products covered
by our research.
Consumers will need to be incentivised
to give up their information.
Segmentation of consumers with regards to their attitudes
to data sharing [UK]

38. Fashion & Personalisation
https://www.youtube.com/watch?v=9X_fP4pPWPw

39. FASHION
ON
DEMAND
[3]

40. TREND: Life on Demand/Direct to Consumer
“I am often under
time pressure in
everyday life” [UK]
46%
Gen Y 53%
Consumers will have little or no patience. Annoyance at delay will soon evolve into lost
sales as competitors and startups recognise “now” as a key business opportunity.
have used a delivery
service that delivers to a
specific location in less
than 2 hours [China]
are interested in
doing so in the future
34%
54%
13%
35%
Very interested
Quite interested
48%
NOW
7%
39%
19%
Using
Very interested
Quite Interested
64%
2025
Future prediction for auto-replenishment
(global)

41. Life on Demand examples

42. 57%
are interested in a device that
detected your location and
suggested interested things to
do in the nearby area [Global]
Locational Living: Contextual Personalisation
Next-gen personalisation will need to be able recognise in-the-moment
location as a way of predicting consumer needs.

43. Fashion on demand
Mobymart
https://www.youtube.com/watch?v=W9IqJOpfa5c

44. A glimpse into
the future…
4

45. 45
Bolt Threads – Synthetic Spider Silk:
one of many new lab-engineered or bio-sourced materials

46. 46
Orcam MyEye 2.0

47. 47
Cyborg Neil Harbisson

48. 48
Nectome

49. For now though, 3 key challenges for the fashion industry
DIGITAL PERSONALISATION ON DEMAND
Connection
Control
Immersion
Trust
Empathy
Customisation
Prediction
Data
Mood
Surprise
Immediacy
Flexibility
Automation
Location

50. B O R D E A U X A P R I L 2 0 1 8
NICK CHIARELLI
Director & Client Partner
Foresight Factory
UNDERSTANDING
TOMORROW’S
FASHION CONSUMER
Achieving growth in uncertain times
@nickchiarelli@futurethoughts

51. APPENDIX:
Data prediction methodology

52. Step 1: Select which factors are linked with current interest and usage. This can
include demographic factors
Step 2: Determine how each possible response may influence future interest and
uptake in 5 or 10 years time
Step 3: Combine these factors to create a multiplier score, unique to every
respondent
Step 4: Multiply current usage by multiplier score
Step 5: Regroup forecast score into levels of interest/agreement/usage
 Step 5b: If dealing with an interest question, determine at what point high
interest would translate into usage (taking into account future availability and
infrastructure of technology in each country)
Step 6: Sense check that prediction is realistic and in line with existing trends and
projections
Foresight Factory: data prediction methodology

53. Influencing factors: An example
Acceptance
of mobile
and
contactless
payment
Subscription
services
Uptake of
voice-
commands
Time
pressured
lives
Anti waste
mindset
Interest in
personalised
recommend
ations
Smart home
features
Data sharing
concernsAppeal of
in-store
shopping
IN TER EST IN
A U TO - R EPLEN ISH MEN T

54. 2025NOW
Source: nVision Research | Base: 4838 online respondents aged 16+, GB, 2016 February
Data prediction: Future interest in auto-
replenishment
Interest in a service that automatically bought and delivered basic household supplies (e.g. toilet paper,
washing up liquid) when they ran out
8%
25%
Very interested Quite interested
11%
19%
17%
Most likely using Very interested
Quite Interested
INFLUENCING
FA C TOR S
 Acceptance of mobile and
contactless payment technologies
 Subscription services
 Uptake of voice-commands
 Time pressured lives
 Anti waste mindset
 Interest in personalised
recommendations
 Smart home features
 Data sharing concerns
 Appeal of in-store shopping
47%33%

55. Source: nVision Research | Base: 4838 online respondents aged 16+, GB, 2016 February
Future interest in auto-replenishmentInterest in a service that automatically bought and delivered basic household supplies (e.g. toilet paper,
washing up liquid) when they ran out
33% 33% 33%
56%
51%
43%
28%
18% 13%
47% 46% 47%
59% 61% 59%
46%
33%
30%
11% 12% 10%
17% 21%
15%
9% 5% 3%
0%
20%
40%
60%
80%
100%
Total Male Female 16-24 25-34 35-44 45-54 55-64 65+
2016 - Interested 2025 - Interested or most likely using 2025 - Most likely using
Right-click on chart and select Edit Data for demographic data