This presentation explains briefly how Artificial Intelligence is involved in all fields specifically in digital marketing and business tactics.

1. AI Revolutionizing
Digital Marketing Karen Elaawar

2. Intro:
Contents and Topics:
o Definition of AI …(slide 3)
o AI fields …(s4)
o Marketers’ goals …(s9)
o Motivations for AI application in
Marketing …(s11)
o Forms of AIM: …(s12)
I. Programmatic Ads …(s13)
II. Web Designing …(s17)
III. Content Marketing: …(s18)
a) Content Generation …(s19)
b) Content Curation …(s20)
IV. Recommender systems …(s21)
V. Email Marketing …(s22)
VI. Voice Search Engines …(s23)
VII. Dynamic Prices ….(s26)
VIII.SEO …(s28)
o Chatbots: …(s32)
I. Types …(s35)
II. Machines Learning Model …(s36)
III. Algorithm …(s37)
IV. Dataset …(s38)
V. Accuracy …(s41)
VI. Misclassification Error …(s43)
VII. Evaluation: …(s44)
• Quantitative …(s45)
• Qualitative …(s46)
o AIM Successes …(s48)
o Humanizing AI …(s53)
o AIM Failure …(s54)
I. Customer Centricity …(s55)
II. Data Analytics …(s56)
III. Budget Constraints …(s57)
IV. IT Infrastructure …(s58)
o Rogue and Risks …(s59)
o Conclusion:
I. Topic …(s61)
II. Pros and Cons …(s62)

3. Artificial Intelligence
‫נּ‬ From a general point of view, AI relates to
robots and machines wiping off work
opportunities, and threatening humane
activities.
‫נּ‬ However when looking through this, AI lies in
the true intelligence of machines having
autonomy and adaptation to the dynamic
environment, therefore boosting humans’
progress and work fluidity/convenience.

4. AI Fields
AI
applications
Travel &
Transportat
ion
Security
Healthcare
Finance
Consumer
Electronics
Robots
MARKETING

5. Marketers’ Goals
Aim
Create costumer value
through personalized
experience.
Gain value and loyalty in
return.

6. Outside- in:
Marketers’ understand markets’ needs
and traits (outside), then create suitable
products or services (in) to suit
respective deprivations. They make
sure to tap on the insights of
customers and understand them more
than they understand themselves!

7. So much
work and
data!

8. AI offers help here!

9. AI marketing (AIM) Overcoming Challenges
‫נּ‬ AIM: The use of customer data,
machine learning and other
computational concepts to predict
a person’s action or inaction.
‫נּ‬ It can take on huge amounts of
data and help marketers easily
segment them, and personalize
customers’ experience

10. Why AI?

11. AI Helps (Motivation)
It also helps
content
marketers
understand
who exactly
their target
audience is,
thereby
creating a
personal
experience for
customers/us
ers.
Tap on Needs
This gives
marketers
and
businesses
more time
to focus
on other
equally
important
tasks.
More Time
The insights
they get in a
shorter time
frame will
help
marketers
boost
campaign
performance
and return on
investment
(ROI) faster.
Save Time
AI Marketing
(AIM) allows
marketers to
crunch huge
amounts
of marketing
data
analytics from
social media,
emails, and the
Web in a
relatively faster
time.
Data Gathering

12. Forms of AIM:
Programmatic Ads
Web designing
Content marketing
Recommender system
Email marketing
Voice search optimization
Dynamic Pricing
Search Engine Optimization
Chatbots

13. 1. Programmatic Ads: What are Programmatic Ads?
• Before, AI marketers were required to do
intense research to figure out the right
platform to market their business (ads).
• Today, this research is done by AI.
• These systems operate autonomously,
placing the right kinds of ads in front of
the right kinds of people based on
complex algorithms and big data.

14. 1. Programmatic Ads: Examples
Audi:
• When Audi was preparing to launch
its new customizable vehicle, the Q2,
the company wanted to personalize
its marketing.
• The goal was to create an ad
campaign that would live up to the
brand’s slogan.
• Therefore, Audi tried programmatic
ads and reached the right customers.

15. 1. Programmatic Ads: How do they work?
https://youtu.be/Cqki_mlQmkI

16. All of this
happens in
1/10 of a
second.
So this helps
specify a
target market,
budgets and
goals for a
campaign.
Time Saved
The demand side
platforms DSPs,
companies that
represent the
specific interest
of advertiser
including specific
bids budgets,
target markets
etc.. Matches
these interest to
the ad spaces’
features.
Complementing
Then the
independent
source will
create bidding
and auctions
on all of the
sets of
demand side
platforms (the
ads that are
ready to be
taken).
Auction
First we have an
independent
source that is
responsible for the
ad exchange. It
waits for the
websites to work,
when the website
(container/ad
space) is loading it
will send an ad
request to the
independent add
source.
Request

17. 2. Web Designing
• Developing a website
without knowledge of
HTML, CSS, and JavaScript
seems like an impossible
thing.
• AI has made it possible.
• All we need to feed in is
the content, call-to-action,
images, and page layout

18. 3. Content Marketing
It is a proven truth that out of all marketing strategies, content
marketing offers the highest return on investment ROI.
Content
Marketing
Content Curation
Content Generation

19. 3. Content Marketing:
Content Generation
• Content is usually generated by getting
inspired by other similar pieces of content.
• AI can also be used to search the content
relevant to our topic of interest.
• They help search content that is currently
trending and accordingly plan the future
content, rebuild the existing content,
schedule it, and then distribute it.
Eg: Concured tools

20. 3. Content Marketing:
Content Curation
• Content curation is about finding high-
quality relevant content from external
sources and promoting it to help build
brand authority and engagement.
• Similar to content generation, curation can
be automated using AI and machine
learning
• Netflix’s movie/tv show recommendations
and Amazon’s product recommendations
are great examples of AI-based content
curation.

21. 4. Recommender systems:
‫נּ‬ A recommender system, or a
recommendation system, is a subclass of
information filtering system that seeks to
predict the "rating" or "preference" a
user would give to an item. They are
primarily used in commercial
applications.
‫נּ‬ RS can:
• Deliver Relevant Content
• Provide Reports
• Reduce Workload and Overhead
• Increase Average Order Value
• Engage Shoppers

22. 5. Email Marketing
In this day and age of auto-generated emails, people are
expecting personalized/tailor-made emails that are relevant
to them
AI can help you send a customized email for your email
marketing campaigns by analyzing user behavior and
preferences.
It can also find the right time, day, and frequency to shoot
the email, which further increases the chances of
conversion

23. 6. Voice Search
• Creates a unique and
optimized customer
experience that will foster
relationships
• Builds brand loyalty
• Voice search interactions take
far less time than text-based
ones

24. 6. Voice Search
Optimization:
Example

25. Forms of AIM:
Programmatic Ads
Web designing
Content marketing
Recommender system
Email Marketing
Voice search
Dynamic Pricing
Search Engine Optimization
Chatbots

26. 7. Dynamic Pricing

27. 7. Dynamic Pricing
• This AI is often referred to as personalized pricing. It’s a pricing strategy
wherein a product’s price is determined by demand and/or supply.
• A good example is the prices of ride-sharing apps that increase as
demand rises or when you cannot find a discount when you need to
purchase a product online.

28. 8. SEO:
Search
Engine
Optimization
• SEO is still a major player in a well-rounded digital strategy, with many digital
marketers choosing to specialize in this highly sought-after skill.
• As SEO algorithms change across major search platforms, the insights from
searchable content may become more relevant than specific keywords in the
search process, thanks to AI and ML tools

29. 9. Chatbots
• Chatbots are already on numerous websites,
as they excel at answering customers'
frequently asked questions.
• The key fascination with chatbots is the
impact they can have on the customer
experience.
• For some businesses, there aren't enough
employees or hours in the day to answer
customer queries quickly. Chatbots allow
customers to help themselves.

30. Chatbots not only are part of the
AIM family, but also started relating
to Machine Learning

31. This is the Chatbot
we aim for
Types of Chatbots:

32. Algorithm:
Two algorithms could be used:
• Supervised learning. In this
case, the chatbot software is
trained by a large set of
requests. Each request is
correlated to a specific “tag”,
which represents a specific
user intent.
• Unsupervised learning. In this
case, the chatbot software
relies on a very high number
of examples to independently
identify the requests and
corresponding user intents.

33. Ground Truth Dataset:
Ground-truth dataset used is a
dialogue dataset where it’s split as
follows:
• 80% training dataset
• 20% testing dataset

34. Ground Truth Dataset:

35. Ground Truth Dataset:
Example:
Break: a set of data for understanding
issues, aimed at training models to
reason about complex issues. It consists
of 83,978 natural language questions,
annotated with a new meaning
representation, the Question
Decomposition Meaning Representation
(QDMR). Each example includes the
natural question and its QDMR
representation.

36. Accuracy:
Loup Ventures found that Google
Assistant answered more
questions correctly than Apple’s
Siri or Amazon’s Alexa in its
annual IQ test. The study tested
smartphone-based digital
assistants by asking each the
same 800 questions about
finding: nearby places, ordering
goods, navigating and more.

37. Accuracy:
• Google Assistant correctly
answered 93% of the questions
and understood all 800.
• Siri came in second, answering
83% correctly and
misunderstanding two
questions
• Alexa answered 80% correctly
but only misunderstood one,
according to the study.

38. Misclassification Errors:
Problem #1: Broken Script
Inevitably, chatbots that draw replies
from IF/THEN scripts will run into a
question or request that wasn’t
accounted for.
Problem #2: Impersonal Interactions
Some chatbots are designed to perform
a specific duty with great efficiency. For
many tasks, this is a good thing, but
some jobs require a more sympathetic
touch.

39. Evaluation of ML Algorithm:
• Quantitative
• Qualitative

40. Chatbot Activity
volume
Bounce rate Retention Rate Target Audience
session volume
Response
volume
Goal completion
rate
Non-response
rate
Quantitative Evaluation:

41. Comprehension
Level
Knowledge Base
Self service rate User feedback
Qualitative Evaluation:

42. So, how does AIM
specifically solve
marketers’ issues?

43. AIM Successes
• Increase Campaign ROI
• Leverage Customer Relationships & Real-Time Personalization
• Enhance Marketing Measurement
• Make Decisions Faster

44. AIM Successes
• Sales forecasting
• Customer understanding
• E-commerce boosting
• Behavior Analysis And Predictive Analytics

45. If leveraged correctly, marketers can
use AI to transform their entire
marketing program by extracting the
most valuable insights from their
datasets and acting on them in real
time.
AI platforms can make fast decisions on
how to best allocate funds across
media channels or analyze the most
effective ad placements to more
consistently engage customers, getting
the most value out of campaigns.

46. But, Marketing also helps AI!

47. Marketing can humanize AI!

48. Humanizing AI:
• The first wave of AI
development has been led
tech-outward.
• The second wave will have
to be human-centric and
human-outward, and that’s
where brands and marketers
can play a really big role
because they bring their
human perspective to the
technologists

49. Incomplete AIM: (Failure)
AIM as all AI, still needs enhancement.

50. Incomplete AIM
Customer Centricity:
Outsourcing entire AI projects led to a
near complete focus on data and analytics
and a near loss of empathy and customer
experience journey expectations.
Solution:
Partial AI efforts can be outsourced, but a
brand must continue to own in the
process.

51. Incomplete AIM
Solution:
• Specify data and conduct outline stick to the
point.
• Consider incorporating operational and
customer data across the organization to
enhance the visibility of effect of marketing AI
efforts.
Data Analytics:
• Nearly every article out there speaks to the
need for better data and use of analytics
when leveraging AI.
• Most of the data and analytics KPIs were
too broad or poorly outlined.

52. Incomplete AIM
Solution:
• Review the budget by cross-referencing some
of the lessons here along with clear value
objectives for AI orchestration, insights or
discovery.
Budget constraints:
• Companies end up not having enough
budget or account for the cost. Not that
it’s expensive, but too many unknown
factors lead to mistakes or unforeseen
issues during education, implementation
or deployment.

53. Incomplete AIM
Solution:
• While large enterprises may opt for developing and
running their own AI marketing software, companies
with less impressive resources can opt for cloud-
based solutions.
• Cloud software vendors provide all the IT
infrastructure and employees needed to run AI
software in exchange for an affordable monthly or
yearly fee.
IT Infrastructure:
A successful AI-driven marketing strategy needs a
robust IT infrastructure behind it. AI technology
processes vast quantities of data. It needs high-
performing hardware in order to do this.

54. Rogue and Risks of AIM
AIM risks
Sharing data
and privacy
Protecting
data will to
further
legislative
regulations
Reinforcing
Bias and
prejudice
Misinforming
with fake
media
Dehumanizin
g brand
identity

55. Conclusion:
• AIM is a major booming area that
has a potential of changing
businesses drastically and facilitating
the marketing process.
• In return, marketing is the hope key
to the huge concern of
dehumanized AI. With the right
customer insight, AI will appeal in a
more humane way.
• However, companies should mind
its rogue and imperfections.

56. Pros:
• Increase Campaign ROI
• Leverage Customer Relationships
& Real-Time Personalization
• Enhance Marketing
Measurement
• Make Decisions Faster
• Sales forecasting
• Customer understanding
• E-commerce boost
• Behavior Analysis And Predictive
Analytics
Cons:
• Privacy and data sharing
• Legislation concerning data
protection
• Reinforcing bias and prejudice
• Fake media and disinformation
• Dehumanizing brand identity