IIML-Use of AI in Marketing.pdf

Use of Artificial
Intelligence in
Marketing
The Future of Marketing: AI-Enabled Personalization for End Consumers
Group VI

Table of contents
01
04
02
05
03
06
Literature
Review
Identifying AI
Applications
Data Collection &
Preparation
AI Algorithms &
Models
Case Studies &
Practical
Implementations
Challenges &
Future Outlook
This capstone project seeks to explore the integration of AI in marketing to improve customer insights,
personalized experiences, and overall marketing efficiency.

“Machine intelligence
is the last invention
that humanity will ever
need to make.”
~Nick Bostrom

Introduction to AI in
Marketing
In-depth literature review to understand existing AI
marketing practices & case studies on successful AI adoption
in marketing to understand current practices and challenges.

Evolution of Artificial Intelligence in Marketing:
Early Automation- email
marketing platforms and
chatbots.
(2000s)
Predictive Analytics -rise of
recommendation engines
and the increased use of
data for decision-making.
(2010s):
Chatbots and Natural
Language Processing (NLP)
Chatbots and virtual
assistants, driven by NLP
and machine learning,
(Mid-2010s):
Content Personalization-
website content, email
personalization, and product
recommendations
(Late 2010s):
Advanced Analytics and
Attribution Modeling- AI-
powered dashboards and
analytics tools
(Late 2010s - Early 2020s)
AI for Customer
Segmentation and Hyper-
Personalization
Voice Search and AI-
Enhanced SEO, Sales
Automation
(2020s):
Future Trends - Deep
learning, computer vision,
and generative AI.
AI in augmented and virtual
reality
(Ongoing):

Activities Marketers Trust Artificial Intelligence (AI)
https://www.statista.com/statistics/1304112/activities-marketers-trust-artificial-intelligence-to-do-world/
Leading activities marketers trust artificial intelligence (AI) to do for them in selected economies worldwide as of
January 2022*
47%
42%
39%
37%
33% 32%
30%
22% 21%
0%
10%
20%
30%
40%
50%
Target Ads Personalised
content
Optimise
Email
Calculate
Conversion
Probability
Optimise
Email subject
line
Conduct live
chat session
Score leads Write subject
line
Write copy
SHARE
OF
DESPONDENCE

Sectorial overview of AI in Marketing
Source: Artificial-intelligence use in marketing – statistic s& facts , Statista , Apr23
Key Insights
Recent Developments
Key Players
Market size & Projections
241.8
738.8
2023 2028
CAGR
17.30%
➢ Google
➢ Amazon
➢ IBM
➢ Microsoft
▪ The global market revenue of AI is projected to rise from USD 241.8bn in 2023
to USD 738.80bn by 2030
▪ Key Growth Drivers:
▪ Expected fast adoption of AI
▪ Achievement of Open AI by creating chatbot software “ChatGPT”
▪ Large enterprises fuelling money into AI marketing start-ups
▪ ChatGPT & DALL-E- OpenAI’s digital image generating software
▪ H&M, Sephora & Pizza Hut are using chatbots to provide personalized
recommendations, answer queries & process orders resulting in improved
customer engagement & Sales
▪ Coca-Cola, Netflix, Amazon use ML algorithms to analyze customer data &
predict their preference

Major Marketing Activities integration with AI
Enhancing user experience & personalization through AI
Hyper- Personalized
Customer Experience
Action Involved
• Identifies User pattern & user Type
• Recommended customised workflows
Potential Benefit
• Increase Customer lifecycle
• Improve customer satisfaction
Tools:
AI Recommendation
Engine
Offering Few but
most suitable
options
Action Involved
• Help users narrow down products
• Uses demographic, past experience options to
prospects
Potential Benefit
• Enhance conversion rate
Tools:
ML Tools
Making User
Experience more
interactive
Action Involved
• Crafts interactive user engagement through
chatbots, AR & VR, NLP Etc.
Potential benefit
• Increase user engagement
Tools:
AI Chatbots- Chat GPT

Benefits of leveraging AI in Marketing:
Personalised
Customer
Experience
• Analyse
customer data
for predictive
preferences,
resulting in
personalised
recommendation
& experiences
• Increase
Customer
satisfaction,
loyalty & lifetime
value
Better
Content
&
Offers
For
Customers
• Chatbots or voice
assistance
interacts with
customers in real
time, providing
personalized
recommendation
& support
• Optimize content
marketing & SEO
activities
Improved
Performance
Matrix
• Optimize
marketing
campaigns,
improves click
throughs,
conversions &
activation costs
• Automates A/B
testing & quickly
identifies most
effective
messages &
offers
Increased
Productivity
• Automates lead
generations &
customer
segmentation
• Provides
actionable
insights,
streamline
processes &
improve decision
making, leading
better results
Increase
Revenue
Of
Business
• Improve
customer
engagement ,
Optimize
marketing
campaigns,
identifies new
income
opportunities ,
leading to
increase revenue

AI Marketing use cases across industries:
Agriculture Energy Education Government Finance Health
Industrial Media Retail Smart Home Telecom Transport
Achieve higher yields
& increase efficiency
Maximize production
and uptime
Transform
the learning experience
Enhance safety,
research, and more
Turn data
into valuable
intelligence
Revolutionize patient
outcomes
Empower
truly intelligent
Industry 4.0
Create
thrilling experiences
Transform stores and
inventory
Enable homes
that see, hear,
and respond
Drive network
and operational
efficiency Automated driving

Customer Journey during usage of AI
Smart
Content
Creation
Demand risen
with buying
intent
• AI generative content
• Voice Search
Stage 2
• Lead Sourcing
• Client Predictive analytics
• Ads Targeting
First Purchase
Catboats
• Client Personalization
• Re- targeting measurement
Re-purchase
• Dynamic Email
content
• Marketing task
automation
Indeterminate
customer
Loyal
AI Applications

AI has been widely adopted to personalize
marketing efforts. Machine learning
algorithms can analyze customer data and
behaviour to tailor marketing messages and
product recommendations
AI enables marketers to use predictive
analytics to forecast trends, identify potential
leads, and optimize marketing campaigns. It
can help in understanding when and where
to invest marketing resources for the best
ROI.
AI-powered chatbots and virtual assistants
are used to provide instant responses to
customer queries, improving customer
service and engagement.
AI can automate content creation for marketing
purposes. AI is used to segment email lists,
personalize email content, and optimize the
timing of email delivery for better open and
click-through rates.
Personalization
Chatbots and Customer
Service
Predictive Analytics
Content Generation &
Email Optimization:
Common Trends in AI Adoption in Marketing

AI relies heavily on data, and ensuring the
quality, security, and privacy of this data is a
major challenge. GDPR and other data
privacy regulations add complexity to data
usage in marketing.
Many marketing organizations use multiple
tools and platforms. Integrating AI solutions
into existing systems can be a complex and
costly endeavor
There is a shortage of AI and data science
talent in the marketing field. Companies
need to invest in training or hiring experts
to make the most of AI technologies.
AI algorithms can inadvertently reinforce biases
present in the training data, leading to unfair or
discriminatory outcomes. Ensuring fairness and
ethical use of AI is a critical concern. Measuring
the ROI for AI in marketing can be challenging.
Data Quality and Privacy
Talent and Skill Gap
Integration
Algorithm Bias &
Measuring ROI
Challenges in AI Adoption in Marketing:

Integration of AI in
marketing to improve
customer insights,
personalized
experiences, and
overall marketing
efficiency.

AI is a tool. The
choice about how it
gets deployed is
ours.
~Oren Etzioni

Identifying AI
Applications
02

Introduction
Categorizing and prioritizing AI applications such as sentiment
analysis, recommendation systems, chatbots, targeted
advertising, and predictive modeling.

AI Applications In Marketing
Categorizing and prioritizing AI applications such as sentiment analysis, recommendation systems, chatbots, targeted advertising, and
predictive modeling.
APPLICATION
BENFIT
• Analyzing social media posts,
customer reviews, and other
text data to determine
sentiment (positive, negative,
neutral).
• Understanding customer
opinions, gauging brand
perception, and identifying
potential issues
• E.g. Social websites likes FB
• Using emoticons , Locobuzz
Sentiment
Analysis
APPLICATION
BENFIT
• Suggesting products, content,
or services to users based on
their preferences and
behavior
• Enhancing user experience,
increasing engagement
• Eg. Netflix , YouTube, Tinder
etc.
APPLICATION
BENFIT
• Using AI-driven chatbots to
engage with customers,
answer queries, provide
support, and automate
routine interactions.
• Improving customer service,
reducing response times, and
enhancing user experience
• E.g. Whatsapp chatbot by
Jiomart, Trip planner - Triangle
Recommendati
on Systems
Chatbots

APPLICATION
BENFIT
• Using AI to analyze user data
and behaviour to deliver
personalized ads to specific
target audiences.
• Increasing ad efficiency,
improving click-through rates,
and optimizing ad spend
• E.g. Facebook Ads, Amazon
Ads
Targeted
Advertising
APPLICATION
BENFIT
• Leveraging historical data
and machine learning to
forecast future trends,
customer behaviour, and
outcomes.
• Optimizing marketing
strategies, resource allocation,
and campaign performance
• E.g. Tensorflow : Insurance ,
Banks , Customer churn
APPLICATION
BENFIT
• Using AI to generate content,
such as product descriptions,
blog posts, and social media
updates.
• Saving time and resources in
content creation, maintaining
consistency, and increasing
content output
• Eg. Chat GPT, Jasper.ai
Predictive
Modelling
Content
Generation

APPLICATION
BENFIT
• Employing computer vision to
analyze and tag images and
videos for marketing
purposes
• Enhancing visual content
searchability and organization, and
improving visual content
recommendations
• Eg. Tryolabs - Traffic monitoring,
Parking slot , Healthcare etc.
Image &
Video
Analysis
APPLICATION
BENFIT
• Using AI to monitor and
analyze market trends,
competitor strategies, and
consumer behavior.
• Gaining insights for informed
decision-making and staying
competitive in the market.
• E.g. Google Trends,
Similarweb
APPLICATION
BENFIT
• Analyzing social media data
to track engagement, monitor
brand mentions, and identify
influencers.
• Enhancing social media
strategies, understanding
audience preferences, and
measuring brand impact.
• E.g. Flick , Canva
Market &
Competitive
Analysis
Social Media
Analytics

Align the prioritization with your overarching
business goals, whether that's improving
customer engagement, increasing revenue,
reducing costs, or enhancing customer
support.
Assess the availability of budget, data, and
technical expertise. Some AI applications
may require more substantial resources than
others
Understand your target audience and their
preferences. Prioritize AI applications that
directly address their needs and pain
points. Analyze what your competitors are
doing with AI in marketing.
Ensure that the prioritized AI applications
comply with relevant regulations and ethical
guidelines, particularly in areas like privacy
and data security.
Business Objectives
Customer Needs &
Competitive Landscape
Available Resources
Regulatory and Ethical
Considerations
AI Applications In in Marketing:
Prioritization of these applications should be based on your specific business goals and constraints

Netflix is well-known for its sophisticated AI-driven content
recommendation system. By analyzing user viewing habits,
preferences, and ratings, Netflix's recommendation algorithm
suggests personalized content to keep users engaged. This
has led to increased user satisfaction and longer subscription
durations.
Spotify uses AI to create personalized playlists and
recommend songs to its users. By analyzing user behaviour
and preferences, the platform keeps users engaged,
increasing listening time and subscriber retention
Uber employs machine learning algorithms for dynamic
pricing. It adjusts prices in real-time based on factors like
demand, traffic, and weather conditions. This strategy
maximizes revenue and driver availability during peak
times
Amazon's Alexa, powered by AI, has transformed the way
people interact with technology. It's not just a smart speaker
but a platform for various skills, including shopping. Users can
order products through voice commands, demonstrating how
AI can influence purchasing behaviour.
Netflix:
Content Recommendation
Uber:
Dynamic Pricing
Spotify:
Music Recommendations
Amazon:
Alexa and Echo
Case Studies -How AI Is Applied In Various Aspects Of Marketing

AI applications in
marketing are
diverse and can
offer various
benefits to
businesses.

Predicting the future
isn’t magic, its
artificial
intelligence .
~Dave waters

Data
Collection &
Preparation
03

Introduction
Data collection and preparation are crucial steps in AI and ML, as the
quality and suitability of data can significantly impact the
performance of AI models.

Data Collection and Preparation in AI
To address data privacy and ethical concerns while collecting and preparing data for AI implementation, it's
essential to follow these key steps:
Transparency
& Informed
Consent
• Communicate
data usage
clearly and
obtain
informed
consent from
individuals.
Compliance
with
Regulations
•Adhere to data
protection
laws and local
regulations,
such as GDPR
and CCPA.
Purpose
Limitation
•Collect data
only for
specified and
legitimate
purposes
Data
Minimization
•Collect only
necessary
data to avoid
excessive
data
collection
Security
Measures
•Implement
robust security
to safeguard
data from
unauthorized
access.
Data
Encryption
•Use
encryption
for data in
transit and at
rest to ensure
its protection.

Data Collection and Preparation in AI
Audits and
Compliance
• Conduct
regular
assessments
to maintain
compliance.
Ethical Data
Sourcing
• Source data
ethically and
legally
Bias Mitigation
•Address
biases in data
to prevent
discriminatory
AI outcomes.
Employee
Training
•Educate
employees on
data privacy
and ethics.
Documentation
& Record-
keepings
•Maintain
detailed
records for
transparency
and audits.
Incident
Response Plan
•Plan for
addressing
data breaches
or privacy
incidents.
By following these steps, we can prioritize data privacy and ethics, build trust, and ensure
responsible AI implementation.

Data is the new
science.
Big data holds the
answers.
~Pat Gelsinger

Introduction
AI algorithms and models are the heart of artificial intelligence
systems. These algorithms and models are designed to learn from
data, make predictions, solve problems, and perform various tasks

Algorithms learn from labelled data to make
predictions or classifications.
• linear regression
• Decision trees,
• Support vector machines.
Algorithms analyze data without labeled
outcomes, often used for clustering and
dimensionality reduction.
• K-means
• Clustering
• Principal component analysis.
Combines elements of both supervised and
unsupervised learning by using a small
amount of labeled data and a larger amount of
unlabeled data.
Algorithms learn through trial and error,
interacting with an environment and receiving
rewards or penalties for actions
• Game playing
• Autonomous systems
Supervised Learning
Algorithms
Semi-Supervised Learning
Unsupervised Learning
Algorithms
Reinforcement Learning
Algorithms
AI Algorithms And Models :
AI algorithms commonly used in marketing

• linear regression
• Decision trees,
• Support vector machines.
• Named Entity Recognition (NER)
• Text Classification
• Machine Translation
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs
• Gated Recurrent Unit (GRU) Networks
• Object Detection- YOLO
• Facial Recognition
• Image Generation- GAN
Machine Learning Models
Deep Learning Models
Natural Language Processing
(NLP) Models
Computer Vision Models
AI Algorithms And Models :
AI Model commonly used in marketing

When evaluating AI
algorithms and models
for marketing tasks,
consider factors such as
the type of task ,the
amount & quality of data
and computational
resources available

“A baby learns to crawl,
walk and then run. We
are in the crawling stage
when it comes to
applying machine
learning.”
~Dave Waters.

Case Studies &
Practical
Implementations
05

Introduction
Studying real-world examples of companies effectively using AI in
marketing to measure its impact on customer engagement and
ROI.

• 15 % of Global internet traffic.
• 80% content played comes
from the recommendation
algorithm.
• 10-fold increase in No. of active
subscribers in a decade.
• 238 M subscribers currently.
1 3
2
Challenges Solution Deployed
Outcome
• Personalization Consultation: In early 2000 Netflix
introduced a personalized movie recommendation
system; The algorithm was called Cinematch.
• This enabled great viewing experience for the
customer.
• Also this data helped Netflix to design their own
content.
• Exponential hike in total
Revenue 32 B$
• 460 M$ Ad Revenue by 2023
Forecast: Ad revenue 4 B$ till
2030
*Source: https://www.statista.com/statistics/1361989/netflix-ad-
revenue/#:~:text=According%20to%20projections%2C%20Netflix%20is,U.S.%20doll
ars%20in%20advertising%20revenue.
Case study: NETFLIX-Personalized Content Recommendation.
• Building a search experience for a streaming media.
• To create customer centric content and personalizing
their viewing experience.

1 3
2
Outcome
• Personalization Consultation: Provide on-
demand facility to connect with grooming support
over WhatsApp, adding customer to pick &
reserve best available products.
• Online “IN-store” experience: Develop
Sephora bot to allow store pick ups, reserve
products, returns, store timings etc.
• Mongoose media was unaware about Solutions
post googles content updated related to
ai-generated content
• Goal:
• Align industry best 24x7 retail experience across
online & offline channels
• Adopt omnichannel method & ensure identical
support & assistance
>6000
New users on bot in Singapore
& 3000 in Malaysia in 1 year
250,000+
Conversation session in
Singapore & 82K in Malaysia
within 1 year
$30
Average incremental revenue
generated every month
*Source: https://www.gartner.com/en/marketing/insights/videos/two-
minute-case-study-artificial-intelligence-sephora-newest-beauty-advisor
Case study: SEPHORA using AI content planning tool

Outcome
• Stitch Fix’s efforts in using predictive algorithms
not only to recommend clothing, but inform site
content strategies.
• Using Customer Data for an Enhanced
Beauty Experience
*Source: https://www.gartner.com/en/marketing/insights/videos/two-minute-
case-study-stitch-fix-predictive-algorithms
Case study: STITCH FIX using AI Predictive Algorithms

These examples highlight how AI can positively impact customer
engagement and ROI in various marketing contexts.
• Other Examples Of Companies Effectively Using AI In Marketing

The common thread
among these success
stories is the ability of AI
to deliver personalized
experiences, optimize
marketing campaigns,
and improve the
efficiency and
effectiveness of
marketing strategies.

“I am telling you, the
world’s first trillionaires
are going to come from
somebody who masters
AI and all its derivatives,
and applies it in ways
we never thought of.”
~Mark Cuban.

Challenges &
Future Outlook
06

Introduction
Identifying obstacles in adopting AI in marketing and
proposing strategies to overcome them while
discussing future trends

Source: NASSCOM and EY joint study on “Can enterprise intelligence be created artificially?”
Key Hurdles in AI Adoption:
NASSCOM and EY recently launched a joint study on “Can enterprise intelligence be created artificially?”
The report is based on responses from over 500+ CXOs who participated in our survey that was conducted during the period
January to March 2020. The survey respondents were largely the CEOs and CIOs from large and mid-sized companies across
India.

Key Hurdles & Strategies to Overcome AI Adoption:
Problem
Inaccurate predictions and recommendations
Deploy data management tools & procedures
for clean & accurate data
Difficulty in integrating with existing
workflows
Encourage usage of flexible & adaptable
system which can be compatible with AI
Fear of Job replacement & workflow
disruption
Communicate multiple advantage of AI &
provide training & support to company’s key
stakeholders
Inability to develop & implement AI solutions
Invest in L&D programs for current
employees & recruit new talent with AI
acumen
Risk of privacy & data protection issues
Use these technologies in ethical &
responsible manner by following relevant
rules & guidelines
Lack of Data Quality
Integration with legacy
System
Resistance to Change
Lack of Skilled
Professionals
Ethical Consideration
Impact Solution

Future Trends:
Hyper-
Personalization
Conversational
AI and Chatbots
AI-Powered
Customer
Insights
AI-Generated
Content
Real-Time
Optimization
Augmented
Marketing Teams
AI-Driven
Marketing
Automation
AR/VR
Integration

How Will Customer Personalization Look Like In Future
Add data Customised
model
Create Solution True
Recommendation Access
Recommend
ation
Customize
personalize
recommenda
tion
Contextualize
personalize
recommendat
ion
Include customer
events, sign ups,
purchases etc.
Add user meta data
for progressive
modelling.
Choose most ideal
ML algorithm
Train model
accordingly
Select use case
recipe such as
user
recommendation ,
personalise
ranking, etc.
Optimize
according to
revenue , new
item bias etc.
Process of customer personalization which can help marketers deploy this technique in future.

How will customer personalization look like in future
Zomato's hyper-local ad with Hrithik Roshan
delighted users by calling out their names
Personalized
Ads by Gan.ai
*Source: https://gan.ai/

with careful
planning and well-
informed use cases,
we can face down
the challenges and
reap the
considerable
benefits of using AI
in marketing

Conclusion: What Industry Leaders Say About AI
“The playing field is poised to become a lot more competitive, and
businesses that don’t deploy AI and data to help them innovate in
everything they do will be at a disadvantage.”
Paul Daugherty, chief technology and innovation officer, Accenture
“It’s natural to wonder if there will be a jobless future or not. What
we’ve concluded, based on much research, is that there will be jobs
lost, but also gained, and changed.”
James Manyika, chairman and director, McKinsey Global Institute
(MGI)
“As important as it is to educate the new sets of generations coming
in, I also think it’s important to educate the existing workforce, so they
can understand how to have AI serve them and their roles.”
Sarah Aerni, director of data science, Salesforce
“Unfortunately, we have biases that live in our data, and if we don’t
acknowledge that and if we don’t take specific actions to address it
then we’re just going to continue to perpetuate them or even make
them worse.”
Kathy Baxter, ethical AI practice architect, Salesforce
Source: https://www.salesforce.com/blog/ai-quotes/

Conclusion
Overall, the future of AI in marketing is promising, with
advancements expected in personalization, predictive
analytics, ethical AI practices, and the integration of AI
across various marketing channels and platforms.
However, addressing challenges like data quality,
interpretability, and bias will be crucial for realizing the full
potential of AI in marketing

Building Trust And Ethical Practices

Let’s
Revolutionize
Marketing
with AI

Resources
● https://www.statista.com/statistics/1304112/activities-marketers-trust-artificial-intelligence-to-do-world/
• https://www.statista.com/statistics/1304112/activities-marketers-trust-artificial-intelligence-to-do-
world/
• Artificial-intelligence use in marketing – statistic s& facts , Statista , Apr23
• https://www.slideteam.net/ai-powered-marketing
• https://www.gartner.com/en/marketing/insights/videos/two-minute-case-study-artificial-intelligence-
sephora-newest-beauty-advisor
• https://www.gartner.com/en/marketing/insights/videos/two-minute-case-study-stitch-fix-predictive-
algorithms
• NASSCOM and EY joint study on “Can enterprise intelligence be created artificially?”
• https://gan.ai/
• https://www.salesforce.com/blog/ai-quotes/

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