How AI affects fashion trends and forecasting

1. AI FOR
PREDICTING
FASHION TRENDS
Prachi Mohapatra &
Sukanya Lodh

2. CONTENT
Fashion trends
Fashion trends
forecasting?
Positive and
negative points?
Fashion
retailers?
Traditional
method of trenfd
forcasting ?
Ai for trend
forecasting?
Architecture?
Case study?
Conclusion.

3. FASHION
TRENDS?
• A fashion trend occurs when
a particular item,
silhouette, color, or other
new look rises in
popularity.
• Expressions of trends
• Socio-culturally grounded
• In aesthetic spheres
• By means of visual and
haptic characteristics
applied to clothing and
apparel products and are
often considered fads or
short-term trends.

4. TREND
FORECASTING
Trend forecasting is the act
of predicting fashion trends
including
 colors,
 fabrics,
 silhouettes,
 patterns,
 styles
and more for clothing
collections in upcoming
seasons

5. FASHION TREND FORECASTING FOR
FASHION RETAILERS
• Meeting customer demands
• Inventory management
• Competitive advantage
• Design and Production
Efficiency
• Marketing and promotion

6. Consequences of Inaccurate Trend
Forecasting on Retailers
• Excess Inventory and
Waste
• Decreased Profitability
• Resource Misallocation
• Negative Brand Image
Impact on
Sustainability and
Carbon Emissions
• Increased Environmental
Footprint
• Carbon Emissions:
• Sustainability
Commitments

7. Fashion Forecasting Timeline
1950s: Fashion forecasting
was a manual process that
involved trend spotters
traveling the world to
gather information about
emerging trends.
1960s: The first fashion
forecasting agencies were
established. These
agencies used a variety of
methods to forecast
trends, including trend
spotting, expert opinion,
and market research.
1970s: The first computer-
based fashion forecasting
systems were developed.
These systems allowed
fashion forecasters to
collect and analyze data
from a variety of sources,
including social media, e-
commerce, and fashion
shows.
1980s: The use of
technology in fashion
forecasting became more
widespread. Fashion
forecasters began to use
computers to collect and
analyze data, and they
also began to use
technology to create trend
boards and presentations.

8. traveling the world to
gather information about
emerging trends.
methods to forecast
trends, including trend
spotting, expert opinion,
and market research.
1970s: The first computer-
based fashion forecasting
systems were developed.
These systems allowed
fashion forecasters to
collect and analyze data
from a variety of sources,
including social media, e-
commerce, and fashion
shows.
1980s: The use of
technology in fashion
forecasting became more
widespread. Fashion
forecasters began to use
computers to collect and
analyze data, and they
also began to use
technology to create trend
boards and presentations.
1990s: The internet made
it possible for fashion
forecasters to collect and
analyze data from a global
audience. This led to a
more accurate and timely
forecasting process.
2000s: The rise of social
media gave fashion
forecasters a new way to
collect and analyze data.
Social media platforms
allowed fashion
forecasters to track
trends in real time, and
they also allowed them to
connect with trendsetters

9. collect and analyze data
from a variety of sources,
including social media, e-
commerce, and fashion
shows.
computers to collect and
analyze data, and they
also began to use
technology to create trend
boards and presentations.
1990s: The internet made
it possible for fashion
forecasters to collect and
analyze data from a global
audience. This led to a
more accurate and timely
forecasting process.
2000s: The rise of social
media gave fashion
forecasters a new way to
collect and analyze data.
Social media platforms
allowed fashion
forecasters to track
trends in real time, and
they also allowed them to
connect with trendsetters
and influencers.
2010s: The development of
artificial intelligence (AI)
and machine learning (ML)
revolutionized fashion
forecasting. AI and ML
algorithms can now be used to
collect and analyze data from
a variety of sources,
including social media, e-
commerce, and fashion shows.
This data can then be used to
identify emerging trends and
patterns, which can then be
2020s: AI and ML are
becoming increasingly
important in fashion
forecasting. Fashion
brands are now using AI
and ML to identify
emerging trends, to
develop new products, and
to target their marketing
campaigns.

10. 1990s: The internet made
it possible for fashion
forecasters to collect and
analyze data from a global
audience. This led to a
more accurate and timely
forecasting process.
2000s: The rise of social
media gave fashion
forecasters a new way to
collect and analyze data.
Social media platforms
allowed fashion
forecasters to track
trends in real time, and
they also allowed them to
connect with trendsetters
and influencers.
2010s: The development of
artificial intelligence (AI)
and machine learning (ML)
revolutionized fashion
forecasting. AI and ML
algorithms can now be used to
collect and analyze data from
a variety of sources,
including social media, e-
commerce, and fashion shows.
This data can then be used to
identify emerging trends and
patterns, which can then be
used to forecast future
trends.
2020s: AI and ML are
becoming increasingly
important in fashion
forecasting. Fashion
brands are now using AI
and ML to identify
emerging trends, to
develop new products, and
to target their marketing
campaigns.
As technology continues to develop, we can expect to see even more reliance on AI and ML in fashion forecasting. These
technologies will allow fashion forecasters to collect and analyze data more quickly and accurately, which will lead to more
accurate and timely forecasting.

12. HOW AI IS USED FOR TREND
FORECASTING
• Image Recognition and Analysis
• Natural Language Processing (NLP)
• Social Media and Web Scraping
• Generative Adversarial Networks (GANs)
• Recommendation Systems

13. ARCHITECTURE OF AI FOR FASHION TREND
FORECASTING
• Data collection and analysis
• Pattern recognition
• Prediction
• leveraging large-scale data & advanced
algorithms
• Data Collection:
• Data Processing and Cleaning:
• Feature Extraction:
• Pattern Recognition and Analysis
• Prediction and Forecasting
• Validation and Evaluation:
• Visualization and Reporting:

15. CASE STUDY/EXAMPLES
STITCH FIX
• Stitch Fix is an online personal
styling service that uses AI
algorithms to recommend clothing items
to its customers.
• The company collects data on
customers' style preferences, size,
and budget, and uses machine learning
to generate personalized
recommendations for each customer.
• Customers can provide feedback on the
recommendations, which helps to
improve the accuracy of future
recommendations.

16. CASE STUDY/EXAMPLES
• Rare Carat Creates First AI-Powered
Jeweler. Diamond website Rare
Carat uses AI to compare prices of
diamonds across numerous retailers
to find the best deal for each
customer.
• Rocky walks customers through the
entire process to provide a similar
online experience to what customers
would get in a brick-and-mortar
jewelry store.
• The AI algorithm takes many diamond
factors into account and shows
customers the trade off for
different factors, such as
sacrificing clarity for a lower
price.
• With Rocky, customers can feel
RARE CARAT

17. What do we mean by fashion recommendations?
Personalized fashion recommendations refer to suggestions or advice for fashion items that are tailored to an
individual's preferences and style.
Personalized recommendations take into account a person's taste, style, body shape, and other factors that
influence their fashion choices.
Importance of Personalized Recommendations​ ?
Fashion retailers and e-commerce companies use personalized fashion recommendations to provide a more
tailored and engaging shopping experience to their customers.
• Increased customer satisfaction,
• Repeat purchases
• Brand loyalty
It help customers to
• Discover new styles
• stay up-to-date with the latest fashion trends
• Create outfits that fit their individual style and preferences

18. How AI is used to create Personalized Recommendations
Personalized fashion recommendations can be achieved using AI techniques like machine learning and deep learning
The AI system can learn from a user's preferences, past purchases, browsing history, and other data points to recommend products that the user is likely to be
interested in. Here are some ways AI can be used to provide personalized fashion recommendations:
• Collaborative filtering
• Content-based filtering
• Image recognition
• Natural Language Processing (NLP)
Examples / case studies
Stitch Fix: Stitch Fix is an online personal styling service that
uses AI algorithms to recommend clothing items to its
customers. The company collects data on customers' style
preferences, size, and budget, and uses machine learning to
generate personalized recommendations for each customer.
Customers can provide feedback on the recommendations,
which helps to improve the accuracy of future
recommendations. Nike: Nike uses AI to provide personalized
recommendations to its customers through its
Nike Fit platform. The platform uses computer
vision to analyze customers' feet and recommend
the best-fitting shoes based on their individual
measurements.
https://www.forbes.com/sites/blakemorgan/2019/01/24/the-7-best-examples-of-artificial-intelligence-to-improve-
personalization/?sh=67218943c4ed
Rare Carat Creates First AI-Powered Jeweler
Customers looking to buy a diamond can now turn to
Rocky, the world’s first AI-powered jeweler. Diamond
website Rare Carat uses AI to compare prices of
diamonds across numerous retailers to find the best
deal for each customer. Rocky walks customers
through the entire process to provide a similar online
experience to what customers would get in a brick-
and-mortar jewelry store. The AI algorithm takes
many diamond factors into account and shows
customers the trade off for different factors, such as
sacrificing clarity for a lower price. With Rocky,
customers can feel confident they’re getting the right
diamond for their needs at the best price.
Virtual Stylist of LEVI's brand uses natural language
processing to find out what each customer is looking
for in a pair of jeans. The bot learns about each
customer’s lifestyle and fit preferences. That
information is combined with actual training that
human stylists receive to provide accurate
recommendations. After the chatbot recommends a
pair of jeans, customers can share it with friends to
get their opinions.

19. AI for Fashion Design and Fabric Selection
• Importance of Fabric Selection and Fashion Design
• How AI is used for Fashion Design and Fabric Selection
• Examples of companies using AI for Fashion Design and Fabric
Selection(CASE STUDY)
• Stylumia B-2-B

20. AI for Inventory Management
• Importance of Inventory Management
• How AI is used for Inventory Management
• Examples of companies using AI for Inventory Management(CASE
STUDY)

21. AI for Supply Chain Optimization
• Importance of Supply Chain Optimization
• How AI is used for Supply Chain Optimization
• Examples of companies using AI for Supply Chain Optimization(CASE
STUDY)

22. CONTENTS
1. What are fashion trends?
2. the importance of accurate predictions
3. Mention the role of AI and machine learning in revolutionizing trend forecasting
4. What is Trend Prediction?
5. Definition of trend prediction in the context of the fashion industry Importance of
understanding and anticipating consumer preferences
6. Traditional Methods vs. AI Overview of traditional trend forecasting methods (e.g.,
expert opinions, historical data analysis)Introduction of AI as a game-changer in
trend prediction, leveraging large-scale data and advanced algorithms
7. Data Collection and Analysis. Explain how AI gathers and analyzes fashion-related
data, such as social media posts, online shopping patterns, runway shows, etc.
Highlight the benefits of using AI in collecting and processing vast amounts of data
quickly and efficiently.
8. Machine Learning Techniques Overview of machine learning techniques used in fashion
trend prediction (e.g., natural language processing, image recognition, deep
learning)Discuss how these techniques extract insights and patterns from data to make
accurate predictions
9. Case Studies Present real-world examples of AI applications in fashion trend
prediction Showcase successful companies or projects that have utilized AI to forecast
fashion trends effectively
10. Future Trends in AI Fashion Forecasting Explore the potential future developments in
AI for fashion trend prediction: Integration of AI with virtual reality (VR) and
augmented reality (AR)Personalized trend recommendations
11. Future Trends in AI Fashion Forecasting Explore the potential future developments
in for fashion trend prediction: Integration of AI with virtual reality (VR) and
augmented reality (AR)Personalized trend recommendations
12. Ethical considerations and sustainability in AI-driven fashion
13. Conclusion Summarize the key points discussed in the presentation Emphasize the