Lecture about Artificial Intelligence & Marketing by Hugues Rey & Hugues Bersini 16/11/2017 Examples & Cases Part of the 17 days program of Education: Executive Master in Digital Marketing & Communication - ULB - Solvay Brussels School of Economics

1. A.I. & THE FUTURE
OF MARKETING
EXECUTIVE MASTER
IN DIGITAL MARKETING
2017 – 2018
BRUSSELS – 16TH OF NOVEMBER 2017

2. Hugues Rey
Ø1968 – Ixelles – Married to Geneviève
ØSolvay Business School (1992)
ØCEO Havas Media Group BeLux (2010)
Ø+25 years of experience in communication agencies (WPP, IPG, Havas)
ØActive in digital marketing since 1996
ØFounding Father of Interactive Advertising Bureau (IAB) (1998)
ØCIM: President Strategy (2016 - …), Past-President Internet (2000-2011), TV (2012 -2013)
ØTeach Marketing at Solvay Brussels School: Digital, International Marketing and Integrated
Communication
ØActive Blogger : I’am A Bridge – www.huguesrey.com
Ø@huguesrey

3. Hugues Bersini
Professor in Université Libre de Bruxelles and Co-Director of the IRIDIA laboratory.
Teacher Artificial Intelligence, Object-Oriented technologies (UML, Design Patterns, Java, .Net,
...) and Web Programming (Django/Python) both for academics and for entreprises.
Member of the Belgium Royal Academy of Science
Main research interests are modelling and control of complex systems, neural networks and
fuzzy control, data/graph/text mining, autonomous agents and dynamics of biological networks,
computational chemistry, immune engineering, cognitive sciences, bioinformatics and object-
oriented technology.

4. Agenda
of
today
1. Why Marketing
needs Innovation ?
2. Artificial Intelligence
Concepts & Principles
3. A.I. and Marketing:
Let’s Maximize the
consumer experience !
4. Technical Approach
and Solutions
5. What’s Next ?
Ethical point of view
6. Will A.I. steel my
Marketeer Job ?
7. How A.I. could
empower each stage
of the consumer
decision journey ?
8. Conclusions
Wrap Up

5. Marketing Professional Challenges

6. Why Marketing needs A.I. ?
Real Time
Ultra
Large
Scale
Complexity

7. Complexity
Marketing Channels Multiplication

8. Complexity –
Frangmented
Consumer
Journey

9. Complexity - Data (Volume /Source)

10. Complexity
Fragmentation
of the
technology
stack

11. Real Time is the new norm

12. Digital impact the whole Marketing Process

15. Hyper Personalization vs Ultra Large Scale

16. EXAMPLE OF MARKETING EVOLUTION

17. What Consumers Really Think about AI ?
(6000 interviews by Pega)

19. 1.Marketing meets
Technical Innovation
GARTNER CURVE

20. Le Soir
16/11/2017

21. Mapping
Technologies
Through the
Gartner
Curve
Gartner Hype Cycles are a graphic
representation of "the maturity and
adoption of technologies and
applications, and how they are
potentially relevant to solving real
business problems and exploiting new
opportunities"

22. 5 Phases
Innovation Trigger: A potential technology breakthrough kicks things off. Early proof-of-concept
stories and media interest trigger significant publicity. Often no usable products exist and commercial
viability is unproven.
Peak of Inflated Expectations: Early publicity produces a number of success stories — often
accompanied by scores of failures. Some companies take action; many do not.
Trough of Disillusionment: Interest wanes as experiments and implementations fail to deliver.
Producers of the technology shake out or fail. Investments continue only if the surviving providers
improve their products to the satisfaction of early adopters.
Slope of Enlightenment: More instances of how the technology can benefit the enterprise start to
crystallize and become more widely understood. Second- and third-generation products appear from
technology providers. More enterprises fund pilots; conservative companies remain cautious.
Plateau of Productivity: Mainstream adoption starts to take off. Criteria for assessing provider
viability are more clearly defined. The technology's broad market applicability and relevance are
clearly paying off.

23. Cloud
Computing
Exemple

25. 3 Trends
A.I. Everywhere
Transparently Immersive Experience
Digital Plateforms

26. AI will impact all
industries
Consumer &
Marketing are the
most impacted
today & in 5 years

28. 1. Transformational
technologies require
exploration — and
patience
2. AI invades
marketing
3. Marketers take
control of data to
enhance experiences
4. Martech and
adtech: An open
relationship
5. Brand safety a top
digital advertising
priority

29. 1. Transformational technologies require
exploration — and patience
Real-time marketing, AI for marketing and customer journey analytics will bring
more power and adaptability to help marketers effectively engage with
prospects and customers.
Although these three technologies are transformative and still in the Innovation
Trigger phase, they will take 10 or more years to mature. The next decade will
require a sense of exploration and experimentation.
Action items:
AI for marketing: Start learning about AI now and engage directly with your
vendors to understand the role AI currently plays.

30. 2. AI invades marketing
AI for marketing comprises systems that change behaviors without being explicitly programmed
based on data collected, usage analysis and other observations, and includes enabling
technologies such as machine learning, deep learning and natural-language processing.
AI-enabled technologies and techniques have the potential to transform the practice of
marketing as we know it.
AI for marketing is in the Innovation Trigger phase, and vendors liberally promote “intelligent”
capabilities within their offerings with varying degrees of precision and truth.
Action items:
AI for marketing: Be wary of lofty claims of AI-powered marketing. Establish test criteria and
conduct pilots and proofs of concept to vet such assertions.

32. 3. A.I. Integration in
Marketing Practices
LET’S MAXIMIZE THE CONSUMER EXPERIENCE

34. 1. Chatbots (text / voice)
2. Recommendations /
Content curation
3. Personalized pricing
4. Ad targeting
5. Computer vision
6. Speech recognition
7. Data analysis
8. Content Generation
9. Search
10. Business Forecasting
10 Major Trends of A.I.
Usage in Marketing

35. 3.A. Marketing
Data Available is
Growing

38. 38
$$$
Advanced TV
Online Video
Display
Search
Social
Digital OOH
Landing Experiences
Direct Channels
Consumers
Merchants
Drive
Awareness
Retailer
Distribution
CROSS-CHANNEL
MEDIA EFFECTIVENESS
Social Signals
Business data, e.g. orders,
registrations, revenue etc.
Other open & proprietary data:
government incentives,
market forecast, surveys…
Web / Mobile site
Search intent
Real-Time
Buying Teams
Data Management
Platform
Where?
(Media)
Who?
(Audience)
Frequency
Management
Verification
Delivery
Audit
Creative
Segmentation
Modeling
Forecasting
Profiles
Day part
Media
Channel
MEDIA INTELLIGENCE
PLATFORM
DATA RESULTSACTIVATIONAUDIENCE
Offline MediaAudience data
1st party (client data)
2nd party (partner data)
3rd party (observed data,
Experian, Acxiom, IXI…)
Web / Mobile site
SMART ONGOING EXPERIMENTATION & TESTING
DMP
Programmatic
Desks
Multiple Source of Data
IOT
Other Unstructured Data

40. 3.B. A.I. Ingredients for
Marketing

41. 3.B.1 – (Chat)Bots
& Robots

43. Research from Forrester showed 5% of companies
worldwide said they were using chatbots regularly in
2016, 20% were piloting them, and 32% were planning
to use or test them in 2017.
Research from Gartner has predicted that 85% of
customer service interaction will be done by means of
artificial intelligence by 2020.

44. 28.2 trillion mobile messages are expected to be sent in
2017. This number is about double the amount of
messages sent in 2012.
The open rate for mobile messages is a near perfect
98%, which is exponentially higher than the 22 percent
rate emails employ.
6 of the top apps used globally are messaging apps.
Retention rate of messaging apps is about double the
amount of other types of apps.

46. Bots to Create
Seamless User
Experience
Uber Facebook
Bot

48. Bots to Create
Seamless User
Experience
HP Print Bot

49. Bots as Customer Support
The deep learning AI chatbots possess allow them to access
layers of data from neural networks, including customer
information that has been built up over time.
Real-time access to a customer’s preferences and purchase
history gives customer support chatbots a significant edge over
their human counterparts.
A great example of this can be found in the hospitality industry
with the Hyatt chatbot. As a branded Facebook Messenger bot,
Hyatt customers can get up to date information in seconds.

50. Bots to help in
the Sales
Process
H&M

52. Recommendation
Having trouble finding
your next read? Harper
Collins' Epic Reads
chatbot is here to help

53. Bots to Process
Payments
Capital One’s Eno bot is one of the
oldest and most respected.

55. We Chat – 900 Millions users

57. DJ Hardwell
(The Bot
Plateform)

59. Let’s Meet
Thédorore – The
ULB Recruitment
Bot

60. VDAB: DIGITAL PROJECTS
CONVERGENCE A.I. + HUMAN
60

61. OBJECTIF
61
INSCRIPTION RASSURER
Convaincre la cible de
s’inscrire à l’ULB
Faire sauter les barrières
(craintes, questions, problèmes)
retenant la cible potentielle de
s’inscrire
Eleves francophones inscrits en
rhétorique en 2016-2017 dans un
établissement belge, ayant obtenu leur
CESS et désirant poursuivre leur
formation dans un établissement
d’enseignement supérieur.
LA CIBLE
On a décidé de se concentrer sur les étudiants, et non les
parents avec adolescents car ces derniers sont, d’une part,
plus difficiles à cibler et identifier, et, d’autre part,
stratégiquement pour le budget donné, les performances
seront meilleures sur les jeunes sur les réseaux choisis.

62. UNE STRATÉGIE, 3 AXES
62
CONVAINCRE
DE REJOINDRE L’ULB
INFORMER
SUR L’UNIVERSITE
S’INSCRIRE
SUR LE SITE DE L’ULB
Faire sauter les barrières et
informer les étudiants sur la
vie universitaire
Convaincre les étudiants de
s’inscrire à l’ULB, l’université pour
leur futur.
Aider et inciter les étudiants à s’inscrire à l’ULB
avec une communication adaptée

63. LET’S INTRODUCE YOU
TO OUR HOME MADE
CHATBOT

64. In which situations would
you be comfortable with a
company using AI to give
you better customer
service ? (Source: Pega)

65. Bots Handy
Guide
(Source:
Huffington Post)

66. 3.B.2 – Speech
Recognition

67. Began in 2014 ,
the smart speaker
battles are not
really about the
device

68. Voice assistant
The new search engine
Different Strategies:
- Apple Siri is wherever you are.
- Amazon Echo is only at home.
They created the category and
are the first that have a version
with a camera and another one
with a screen.
- Google has experience in
search. They take advantage of
processing the information in
the cloud. Some people do not
like to give them their private
information.
- Microsoft tries to be
everywhere but, by now, only
excels in games.

70. Search is still
key …
According to Google, 20 percent of
searches on its mobile app and on
Android devices are voice
searches.
The total number of voice
searches overall is actually much
higher when you take into account
personal assistants like Amazon’s
Echo (aka Alexa), Google Home (a
direct competitor of the Echo), Siri
and Cortana — tools that are
solely based on voice recognition.

72. Juniper estimates that smart
speakers such as Amazon Echo,
Google Home and the Sonos One will
be installed in over 70 million US
households by 2022, reaching 55% of
all homes.

73. 2015
Campbell’s Is
Offering
Recipes via
Amazon’s
Voice-Control
System

74. 2016
Amazon Echo Can
Now Order Your
Domino Pizza
The process of ordering via Echo
will first require that Domino’s
customers set up a “Pizza Profile.”
Multi-Plateform: desktop,
mobile, SMS, Twitter, Samsung
Smart TVs, Ford Sync,
smartwatches like Android Wear
and Pebble, and its own
native application (where you can
also speak to its virtual assistant,
Dom, by voice).

75. Johnnie Walker: Educating consumers and promoting new
products

76. GE started
allowing Amazon
Echo users
to control their
WiFi-connected
appliances with
voice commands

78. BURGER KING – “”Ok, Google …” - DAVID MIAMI

79. 3.B.3 – Conversional
Marketing / Commerce

82. Having developed out of traditional channels over the past
decades, conversational commerce leads the way towards a
new era of individualized shopping.

83. 3 main conversational commerce to apply
natural interaction to drive purchase
decisions.

88. Higher customer engagement + individual
offerings = higher conversion

89. Conversational Commerce Bot Market
According to MarketsandMarkets, the conversational commerce bot market
◦ Grow from $703 million USD in 2016 to $3,172 million USD by 2021.
◦ Compound annual growth rate of 35.2%.
Ubisend research shows a number of factors are driving this rapid growth.
◦ A change in consumer behavior from social networks to social messaging,
◦ The rise of the on-demand economy and demand for delivering a personal service at
scale

90. Starbucks
Conversional
Commerce
Allowing consumers to
order coffee at home or in
their car using smart
devices such as Amazon
Echo
So far, it has made the
feature available to
roughly 100,000
customers in the US

91. Large players are operating the new
conversational commerce channels
successfully

92. In anutshell
5 Distinguishing Features of Conversational Marketing

93. 13/11/2017

94. Marketeer vs Consumer (Source: Pega)

95. Marketeer
vs
Consumer
(Source:Pega)

96. Recommendation
=
From
Product to
Service

97. 3.B.4 – Image
Recognition

99. CrowdEmotion
CrowdEmotion offers an API that
uses facial recognition to detect the
time series of the six universal
emotions as defined by
Psychologist Paul
Ekman (happiness, surprise, anger,
disgust, fear and sadness).
Their online demo will analyze facial
points in real-time video, and
respond with detailed
visualizations.

100. No emotion is a bad emotion when it comes
to creating content-led marketing campaigns
BBC Case – Take Away:
Trigger serious emotions to meet your campaign objectives e.g. puzzlement,
fear and sadness increase positivity by 35 per cent.
Don’t ignore the lighter emotions. Happiness and surprise increase desire to
purchase, supporting brand image and consideration.
Clearly label your brand – it adds credibility to the content à a positive impact
on perception of the brand.
Showcasing the right emotions will drive social media referrals. 55% of
consumers who express happiness, puzzlement and fear are likely to share
content.

103. You can’t always get what you want (with
text search)
à 74% of consumers say text-based keyword searches
are inefficient for helping them find the right products online
à 67% of consumers say quality of product images is very
important in selecting and purchasing products
à 90% of information transmitted to the brain is visual
à Visual information is processed 60,000x faster than text

104. Slyce – The Want Engine !

106. ASOS Visual Search
Monetize the ‘discovery’ phase of
the shopping journey
Turns the user’s smartphone camera into a
discovery tool, allowing them to take a picture
of a product. By identifying the shape, color, and
pattern of the object, ASOS’ AI technology can
then cross-reference its own inventory of
products and serve up the most relevant results.

107. TheTake uses AI to
identify products,
locations in video

108. Sephora Visual Artist product à
“try on” cosmetic products
Powered by Modiface AI
technology, the Visual Artist can
map and identify facial
features, then use augmented
reality to “apply” the user’s
selected product and shade.
Moreover, it can automatically
apply suggested shades based
on the consumer’s skin tone.

109. Sephora Visual Artist
product à
The Visual Artist is also
hosted on Messenger.
Shoppers can send a
picture to the Sephora
chatbot and it will come
back to them with a
range of suggested
products, along with an
augmented reality image
of how they look once
applied.

110. 3.B.5 Recommendation

112. $6 billion on content this year
◦ If it just presented the most popular selections to everyone, many titles would remain unseen.
◦ Wasted content spend, Less time watching Netflix instead of other networks.
Take-rate on top personalized video recommendations was 3 to 4 times higher than the most popular
videos.
◦ Viewers were exposed to 4 times as many videos using personalized recommendations compared to a list of
the most popular titles.
Recommendations = surface niche titles that wouldn't find an audience on a traditional cable
network, but that its viewers love
◦ Save money on its content & maximize the value of inexpensive titles.
Recommendations lead to meaningful increases in overall engagement with the product (e.g.,
streaming hours) and lower subscription cancellations rates.
◦ Higher lifetime customer values and lower revenue volatility.
◦ Retention rates are already high enough that it takes a very meaningful improvement to make a retention
difference of even 0.1%.

113. Netflix Recommendation
à Generate a Long Tail Approach

114. Netflix – A.I. Expense
Over the past few years, Netflix's technology and development budget has increased slightly
faster than its overall revenue.
2015, it increased 38% to $651 million = 10% of revenue.
Not only in recommendations engine.
Technology and development expenses include "testing, maintaining and modifying our user
interface, our recommendation, merchandising and streaming delivery technology and
infrastructure."

115. The ROI of recommendation engines for
marketing
1. Improving with use (positive feedback loop)
◦ With more data to train its algorithms on (more active users interacting), Netflix has the advantage of
improving its offering faster and faster. This “winner take all” dynamic, is one of the reasons why
“improvement with use” is so important.
2. Improving cart value (profit)
◦ Amazon has integrated many recommendation “entry points” into its online experience to maximize
cart value
◦ Estimation: 35 percent of consumer purchases on Amazon come from product recommendations. In
2016, it offered its open-source AI framework called, DSSTNE for free to encourage the development of
artificial intelligence apps.
◦ 11.11 Shopping Festival 2016, Alibaba used AI to display product recommendations on 6.7 billion
personalized shopping pages à 20 % conversion rate improvement.

116. The ROI of recommendation engines for
marketing
3. Improving engagement and delight (retention)
◦ YouTube’s recommendation engine has been powered by Google Brain since 2015
◦ YouTube had plenty of experiments and redesigns before employing an algorithm that searches for
similarities among different types of video content
◦ Some of the data points that it uses include a user’s viewing history, age of videos, search terms and
location to decide which video will play next
◦ YouTube earns revenues when a viewer watches more than 30 seconds of an ad, or when a user clicks
an entity on the screen or an ad listed on the page.

117. RankBrain is an artificial
intelligence system that
is designed to
understand how people
search and why they’re
searching (or their
intent when searching).
It does this through a
mathematical process
and advanced (and
evolving) understanding
of written language
semantics that’s
intended to help
RankBrain ultimately
“understand” searchers
on a deeper level than
simply matching
keywords

118. 3.B.6 Content
Generation / Creative

120. Did a Human or
a Computer
Write This?
MARCH 7, 2015

121. Did a Human or
a Computer
Write This?
MARCH 7, 2015

122. Did a Human or
a Computer
Write This?
MARCH 7, 2015

123. Did a Human or
a Computer
Write This?
MARCH 7, 2015

124. 20 % of all business-
oriented content will be
generated by machines
by 2018 (Gartner)
Client reports
Product descriptions
Campaign summaries
TV programming
Announcements Ratings
analysis,
Online content for clients
Social media updates
…
Wordsmith, a software platform lets you
create sophisticated templates that turn
raw data into unique pieces of writing
about that data. Wordsmith is a natural
language generation (NLG) platform.

125. 1. What kind of data does my brand have access to ?
You’ll need to have some type of structured data—data commonly
arranged in columns and rows, like in a spreadsheet. As an example,
the Associated Press has access to a continuous stream of earnings
data in a structured format. Once it uses an AI tool to create an
automated template for that data, it can apply the template to new
data as it’s generated, provided it’s in the same format.
2. What narratives can I tell with that data ?
What stories are you already manually telling with your numbers?
What stories would be profitable for you to tell if you had unlimited
time or budget? Many different datasets have stories to tell, and
many can be automated. But currently it takes human insight to
determine which stories will be the most useful.

128. Saatchi & Saatchi L.A.
Toyota Mirai – 1.000 ads
The Saatchi LA team spent three months training
it to write first in coherent sentences and then
to customize the communication to a target
demographic.
The target demographic for the campaign was
scientists, engineers and other early adopters of
technology.

129. Saatchi & Saatchi
L.A. Toyota Mirai
Training ‘Watson’
is mandatory
With more training scripts it became better until it finally
produced the sentences, “Yes, it’s for fans of possibility.”
Many more lines came afterward, each focused on target
consumer profiles.
The goal wasn’t necessarily to use Watson to produce higher
quality or more creative output. Saatchi LA could have come
up with compelling concepts and copy.
The difference here was scale.
While each video looks the same, the copy is tailored to more
than 100 different demographics, resulting in thousands of
pieces of content.
The IBM Watson output was
rough at first producing only
unintelligible sentence and
word combinations.

130. Programmatic creative: set of advertising technologies that add
speed, scale, and automation to the creative process.

132. DCO CMP

133. A.I.Men – The
Young Pope:
The Papal Artificial
Intelligence

134. A.I.Men – The
Young Pope:
The Papal Artificial
Intelligence

135. It was funny to see how a 2,500-year-old text, the Holy Bible, could still be relevant in
our modern and hyperconnected world, and show its timelessness in contemporary
issues

137. 3.B.7 Dynamic Pricing

138. Uber: Till 2017 - Prices go up to encourage more drivers to go
online. The increase in price is proportionate to demand

139. First degree price discrimination is …
… the act of charging customers a different
price for the same product or service,
depending on who they are and how wealthy
they seem, to capture as much consumer
surplus as possible.

140. A.I. à From Price to Value

142. Dynamic pricing: Retailers using artificial
intelligence to predict top price you'll pay
You could be paying more than others
Data used by pricing algorithms is gathered from seemingly benign sources such as loyalty cards and
even post codes. But it also increasingly incorporates prior behavior.
As with gambling, the house always wins
In the long run, consumers are likely to be the biggest losers, because dynamic pricing plays on our
desire to find that next bargain.
Moral and ethical questions
It has the potential to worsen social inequality, particularly as the determinations used by retail
algorithms are intentionally non-transparent
Algorithms to negotiate on your behalf
Products like Amazon's Echo or Google's Home are the first tangible versions of the quest to make
artificial intelligence butlers a common household feature. The goal is to persuade consumers that
they can save time and energy by delegating mundane tasks like ordering groceries or booking tickets
to these artificial agents.

143. 3.B.8 Predictive &
Prescriptive Marketing

146. 146

147. Served grill video and purchased grill.
Served geotargeted patio
message Northern Market.
Served geotargeted patio
message Southern Market.
Served STRONG
promotional message
Went to site
but did not purchase.
Served STRONG
promotional message
Ongoing personalized
messaging
CUSTOMER JOURNEY ANALYTICS DRIVE CONTENT CREATION

151. Research / Audience
Management /
Personae Generation

152. FACEBOOK
KNOWS YOU BETTER THAN YOUR…
FRIENDS FAMILY SPOUSE
70 150 300

153. So.Prism
Smart data &
advanced
algorithmes to
deliver relevant
insights
2,000 criteria to describe an audience by psychographic
(people's interests), demographics and mobile behaviours.

154. COMPETITIVE
AVANTAGE
Data Processing
Real Personas Generation
In Depth Mapping

155. Exemple - Leveraging social insights to
detect appropriate TV contexts
Discover unique insights about your consumers’
behavior through the power of social profiling.
Collecting Customizing Matching Profiling
Basic-Fit clients data
base 18-45 y.o
Create a custom
audience based on
client email adresses
Match Basic-Fit
Custom audience with
Soprism total audience
18-45 y.o
Identify interests in affinity
with the audience
Examples
- The Voice: 125 aff
- Cyril Hanouna: 135 aff
- Ozark Henri: 116 aff
- Pukkelpop: 144 aff
- Viceland: 122 aff
- …

156. Creating Persona
Data
collection and analysis
of data
Persona descriptions
Scenarios for problem
analysis and idea
development

157. Persona Generation

158. Let’s Meet Lucy
an invaluable
member of the
marketing team
Lucy can
à hunt down facts about a
potential market
à pull together customer profiles
from social media
à draw up media models in
moments.
She performs previously time-
consuming tasks in an instant,
empowering marketers to do
much more in far less time, and
the more she does the more she
learns, growing and developing
according to a campaign’s needs.

159. Lucy
A new employee feeded by
Data
She is already populated
with data from sources such
as SEMRush, Spinn3r,
SimilarWeb, US Census and
Clearbit and can crunch
reams of data to answer any
marketing question. Her easy
to use interface also means
that marketers can ask
questions in a free text
format, simply and directly.

161. Communication
Campaign Management

162. Cosabella is using multiple A.I. Media Campaign Tools
… with success !

164. A.I. empower Cosabella CRM Campaign

165. Cosabella + Emarsys – Factor of succes
The right structure
Marketing team and merged with the e-commerce team & IT / digital director took over marketing
From a design-led marketing to a technology-led marketing.
◦ determine how to crunch data, develop customer profiles, connect with audience in a rapidly changing marketplace..
◦ How do we bring that function in-house without hiring 200 people ? à A.I.
The right implementation strategy
Knowing that non-technology people would be primarily responsible for using the platform day to day, it was
important for Emarsys to gear the deployment to the team’s needs and knowledge levels. Ease of use and
approaching the implementation using laymen’s terms were early priorities.
Moving the needle to confidence
The list of potential uses for AI in retail is almost limitless, and the technology is still maturing. Forget the human-
driven guesses that have sculpted past marketing efforts; today’s AI is all about knowing.

166. A.I. empower Cosabella CRM Campaign
How does it Work ?

167. Aditionally, Cosabella use Albert
Autonomous Media Buying A.I.
After processing high-level KPIs, parameters, and creative materials, Albert quickly identified
emerging user behavior and patterns across paid search and social media.
He then analyzed the behaviors to produce insights and suggestions for optimal performance and
scalability. Most crucially, he then autonomously executed on the suggestions, delivering previously
unseen results across channels including:
◦ Proactive creative and messaging insights & recommendations.
◦ Micro-campaigns of infinite campaign variables.
◦ Autonomous media buying using predictive analytics and deep learning.

168. A.I. Capacities = (Human Capacities)
x
A.I. (Albert) can create user
segments based on:
Contacts: People in the brand’s existing CRM,
which can be broken down into segments such
as ‘parents', 'highest lifetime value',' newsletter
subscribers' or 'loyalty club members'.
Visitors: Website visitors who, for example,
added something to their cart, viewed a
product, or made a purchase (these were not
applicable in Dole's case).
Lookalikes: New audiences resembling
previous high-value customers.
Based on its incessant testing, A.I. (Albert) can
determine :
What media to invest in, at what times and in what formats.
How much budget to spend on each channel and for each ad format.
Which creative + headline combinations to use for each micro-segment.
Which ad combinations to place where.
Which cities, audience types and audience behaviours to prioritize and
target.
Which cities, audience types, and behaviours to eliminate.

169. AI caused a
fruit-cocktail
sellout for Dole
(APAC)
blew through its primary KPI
in half the allotted time;
led to temporary sellout
conditions in some stores;
drove an 87% increase in
store sales once stocks were
replenished;
and garnered the brand
60,000 additional Facebook
followers.

170. Example actions that Albert took during
Dole's campaign à Agnostic & Apolitic
à After determining that the average cost per page "like" was 18 % better in one city than in
another, allocated resources on social according to optimal performance and pricing.
After discovering that user engagement on Facebook was 40 % higher for mobile users than
desktop users, shifted resources to target mobile and adjusted bids accordingly.
Noticed an over-index in responses in the Metro Manila region and therefore started focusing
more spend in that area.
Found that video ads on Facebook increased clickthrough rates and would expand reach to more
potential customers, so requested more video ads for Facebook.
Discovered that videos on organic pages (such as a Facebook profile page, as opposed to an ad)
produced a 165 percent increase in page engagements over seven days, so recommended
introducing more video posts to those pages.
Identified Saturday and Sunday as performing lower than the rest of the week

171. Harley Davidson New York City use A.I.
Challenge:
Harley-Davidson of NYC (dealer) understood the impact digital could have on its business but was
unable to realize the full potential on its own given the size of their team and the resources available.
Solutions:
With Harley-Davidson NYC’s KPIs, business logic and minimal past campaign performance data, Albert
discovered trending behavior at a pace only a machine can handle, and used continuously incoming
info to optimize and scale performance including:
◦ Identifying unknown audiences.
◦ Identifying top-performing creative and messaging combinations and prioritizing them across all digital
channels.
◦ Pricing and budget allocation across all digital channels (on the fly marketing mix modeling).

172. A.I. Capacities = (Human Capacities)
x
Autonomous Test. Execute. Report.

173. Planning is A.I.
Compatible …
What about
Strategy ?

177. 74%
PEOPLE WOULD NOT
CARE IF
DISAPPEARED
OF BRANDS
66%
27%
LESS THAN
NOTABLY
IMPROVE OUR
QUALITY OF LIFE
OF BRANDS
26%

178. Should companies and brands
play a role in improving our
quality of life and wellbeing?
Growing
expectations
61% 70%
69% 75%
Are companies and brands
working hard at improving our
quality of life and wellbeing?
Weak
performance
39%
30%
40%
30%
… but few brands are delivering yet
Increasing number of people want
brands to be meaningful…
2015 2017

179. is not meaningful
to consumers
60% of the content
provided by brands
And more than half of content fails to deliver

180. 6. A.I. Benefits in your
Marketing MIX and
Communication Process
HOW A.I. COULD EMPOWER EACH STAGE OF THE CONSUMER
DECISION JOURNEY ?

181. Consumer
Decision
Journey:
4 Battle
Ground

182. Consumer
Decision
Journey:
A.I. ?

183. - Bots
- Recommendation Tools
- Speech Reco
- Image Reco
- Voice assistant
- Dynamic Pricing
- Content Production
- DCO
- A.I. CRM
- Predictive /
- Prescritive Analytics

185. 7. Conclusion & Wrap Up

186. A.I. Is definitively moving 4P’s to S.A.V.E.
in a Hyper Personalized Relation
Product PlacePrice Promotion
Service x Product
= Bespoke Offer
Value Based
Pricing
Access x
(Learn + Apply)
= Experience
Engage, Entertain,
Educate = Unique
Relationship

187. Phygital vs Transhuman
187

189. General Conclusions
àA.I. is already integrated in Marketing
àConsumer is not completely perceiving that reality
à We (Human) + A.I. = 3
à Let’s Human Concentrate on Strategy and
Creative Big Idea
à Test now is a must – Full productivity is about 10
Years

190. hugues.rey@havasmg.com
@huguesrey
+ 32 496 26 06 88
www.huguesrey.com
THANK YOU
FOR
YOUR ATTENTION !