1. DATA ANALYTICS
A N D " R
PRESENTED BY:
ANWESHA KALITA
BIPASHA NAYAK
RECENT ADVANCEMENT IN FASHION STUDIES USING R PROGRAMMING
2. In preparation of our assignment, we had to take the help and guidance of a few respected sources,
who deserve our deepest gratitude. As the completion of this assignment gave us much pleasure, we
would like to show our gratitude towards.
Prof TVSN Murthy , Data Analysis & R Instructor, National Institute of Fashion Technology,
Hyderabad, who, after numerous consultations, guided us well on this opportunistic assignment.
In addition, we would also like to thank him for introducing us to the methodology of work, and
whose passion for the "underlying structures" had lasting effect. We would also like to thank our
parents for motivating us with the assignment.
Many people, especially our classmates have made valuable comments on our assignment which
inspired us to improve the overall quality of it.
ACKNOWLEDGEMENT
3. CONTENT
01 What is R programming?
02
03
04
05
07
08
Advancement of R programming in Fashion industry.
Sales forecasting.
Sustainability analysis.
Textile design.
06
Image analysis.
Visual Merchandising.
Consumer behaviour.
Analyze customer sentiment.
10 Fashion Fingerprints.
11
Naive Bayesian Network (NBN).
12 Conclusion.
09
4. WHAT IS R
PROGRAMMING?
R programming is a popular open-source
programming language and software environment for
statistical computing and graphics. It is widely used for
data analysis, machine learning, and statistical
modeling in a variety of fields, including finance,
healthcare, and social sciences. It provides a wide
range of tools for data manipulation, visualization, and
statistical analysis, making it a powerful tool for
exploring and interpreting large datasets.
5. ADVANCEMENT OF R
PROGRAMMING IN FASHION
INDUSTRY
R programming can analyze consumer behavior and preferences, such
as sales data, online search trends, and social media activity.
R programming can also be used in textile design to create and analyze
complex patterns, generate 3D designs, and simulate fabric draping and
movement.
R programming has also optimized visual merchandising strategies by
analyzing sales data and identifying which products sell best in which
locations.
Overall, the advancements in R programming have enabled fashion
companies to make data-driven decisions, create more innovative
designs, making it an invaluable tool in the fashion industry.
6. SALES
FORECASTING
Sales forecasting is a critical aspect of supply chain
management, as it enables fashion companies to make
informed decisions about inventory management,
production planning, and marketing strategies.
One common method for sales forecasting using R
programming is time series analysis. Time series
analysis involves analyzing historical sales data to
identify trends, seasonality, and other patterns that can
be used to predict future sales.
Another method for sales forecasting using R
programming is machine learning. Machine learning
algorithms can be trained on historical sales data to
identify patterns and make predictions about future
sales.
7. Store Layout Optimization: R programming can be used to
analyze customer movement patterns within a store and optimize
the layout for maximum sales. This analysis can help retailers to
create more effective product displays and optimize inventory
levels.
Customer Segmentation: By analyzing customer data (their
demographics, purchase history, and behavior), retailers can create
targeted visual merchandising strategies.
Predictive Analytics: R programming can be used to predict
future trends in fashion and consumer behavior. Retailers can use
this information to create more effective visual merchandising
strategies that anticipate future trends and stay ahead of the
competition.
VISUAL
MERCHANDISING
8. Many fashion brands have started using R programming to
analyze their data and gain insights into consumer behavior,
design trends, and social media influence.
CONSUMER BEHAVIOUR
Zara has implemented R programming to improve its inventory management and sales forecasting.
The brand uses a custom R-based system called InStock that analyzes sales data and predicts demand
for each product.
The InStock system uses a combination of regression analysis, time-series forecasting, and machine
learning algorithms to analyze data from Zara's point-of-sale system and other sources.
The system is able to make accurate predictions about product demand, Customer buying choices and
patterns, etc., allowing Zara to adjust its inventory levels and production schedules accordingly.
By using R programming, Zara is able to reduce waste and improve its sustainability practices.
Additionally, the brand is able to better serve its customers by ensuring that popular products are
always in stock and available for purchase.
ZARA
9. ANALYZE CUSTOMER SENTIMENT
Levi's has implemented R programming to analyze customer sentiment and gain insights into customer preferences and needs. The
company uses a custom R-based system called Levi's Insights Studio that analyzes customer feedback from various sources,
including social media, customer surveys, and online reviews.
Levi's Insights Studio uses a combination of sentiment analysis, natural language processing, and machine learning algorithms to
analyze customer feedback and gain insights into customer sentiment, preferences, and behaviors.
By using R programming to analyze customer sentiment, Levi's is able to gain valuable insights into customer preferences and
needs. The company can use these insights to improve product design and development, develop more effective marketing
campaigns, and better understand its customers' needs and preferences.
LEVI'S:
10. NAIVE BAYESIAN
NETWORK (NBN)
Amazon's Echo Look is a personal styling device that uses
NBN to make personalized fashion recommendations.
The device analyzes a user's clothing choices and makes
recommendations based on their style and preferences.
Echo Look's algorithm considers factors like style, color
preferences, body shape, and size to create a comprehensive
picture of a user's fashion preferences.
The use of NBN in fashion personal styling has enabled
Amazon to offer a highly personalized shopping experience
to its customers.
Amazon's Echo Look:
11. TEXTILE DESIGN
Pattern creation: R programming can be used to create complex
patterns, such as repeating designs or intricate motifs.
3D design: R programming can be used to create 3D designs of
textile repetitive prints, such as clothing or upholstery.
Fabric simulation: R programming can be used to simulate how
different fabrics will drape and move on the human body. This
analysis can help designers select the most appropriate fabric for a
particular product, or to identify areas where the fabric may need to
be altered to achieve the desired effect.
Dyeing and printing analysis: R programming can be used to
analyze the impact of different dyeing and printing techniques on
fabric properties, such as colorfastness or durability.
Here are some ways that R programming can be used in textile design:
1.
2.
3.
4.
12. IMAGE ANALYSIS
Color analysis: R programming can be used to extract color
information from fashion images and analyze color trends over
time. This analysis can be used to identify popular color palettes,
analyze the impact of color trends on sales, and optimize
inventory management.
Texture analysis: R programming can be used to analyze the
texture of fashion images, such as the weave of a fabric or the
pattern on a shoe.
Shape analysis: R programming can be used to analyze the
shape of fashion images, such as the silhouette of a dress or the
cut of a jacket.
Product recommendation: R programming can be used to
improve the accuracy of product recommendations by
analyzing customer browsing and purchase behavior.
R programming can be used in fashion image analysis in several
ways:
1.
2.
3.
4.
13. Fashion fingerprints are a way of analyzing a person's fashion
preferences and creating a unique profile based on their clothing
choices. R programming can be used to build predictive models
that can identify a person's fashion fingerprints and make
personalized fashion recommendations.
FASHION FINGERPRINTS
Stitch Fix is an online fashion retailer that uses R programming and machine learning algorithms to create personalized
fashion recommendations for its customers. The company's algorithm analyzes a customer's fashion preferences and creates a
unique fashion fingerprint based on their clothing choices.
Stitch Fix's algorithm takes into account various factors such as style, color preferences, body shape, and size to create a
comprehensive picture of a customer's fashion preferences.
By using R programming to create fashion fingerprints, Stitch Fix is able to offer a highly personalized shopping experience
to its customers. The company's algorithm has helped it to build a loyal customer base and increase customer satisfaction by
providing personalized fashion recommendations that are tailored to each customer's individual style and preferences.
STITCH FIX
14. H&M using fashion fingerprints for product recommendation according to
user's preferences and previous searches, likes and purchases.
15. SUSTAINABILITY
ANALYSIS
Environmental impact assessment: R programming can
be used to conduct a life cycle assessment (LCA) of a
product, which involves evaluating the environmental
impact of its entire lifecycle.
Energy analysis: R programming can be used to analyze
the energy consumption associated with different
production processes and identify opportunities to reduce
energy use and associated greenhouse gas emissions.
R programming can be used for sustainable analysis in several
ways:
1.
2.
3. Supply chain analysis: R programming can be used to analyze the environmental impact of different stages of the fashion supply
chain, such as transportation, manufacturing, and retail. This analysis can help fashion companies identify areas where improvements can
be made to reduce their carbon footprint.
16. CONCLUSION
In conclusion, the recent advancements in Fashion studies
using R programming have brought about significant
improvements in the fashion industry. R programming has
enabled fashion companies to analyze customer behavior,
conduct sales forecasting, perform sustainability analysis,
analyze social media data, and create personalized fashion
recommendations using machine learning algorithms. These
advancements have helped fashion companies to make data-
driven decisions, improve customer experience, reduce costs,
and increase revenues.
