Business analytics uses quantitative techniques to analyze market trends and provide insights for growth in competitive industries like fashion. Traditional fashion retail lacked data on pricing, trends and competitors. Analytics now helps identify global markets, analyze trends, understand audiences, improve cross-selling, and measure brand ambassador influence. Techniques like social media analysis, markdown optimization, and market basket analysis provide data-backed decisions for designers, retailers and executives to sustain in competition.

1. BUSINESS ANALYTICS IN
FASHION MARKETING
By: Taanya Gupta

2. INTRODUCTION
• Business analytics as the term suggests is understanding and interpreting market
conditions of various industries and providing insights for greater growth for a
business.
• Involve the use of Quantitative technique in determining the trends that pertain in
an Industry.
• With the advent of ecommerce giants like Amazon, Flipkart, Myntra and so on, the
competition has become tremendous which gives rise to extensive usage of analytical
tool to sustain in the market.

3. WHY DO WE NEED ANALYTICS?
• Humans on an average generate a staggering number of 2.5 quintillion bytes of data
every day and 90% of the world’s data that exists today has been generated in the past 2
years alone.
• With the sudden influx of data, the need of the hour for every company is to move towards
a faster and more efficient way of analytics than that done by a human mind.
• Companies are turning to analytical tools to extract meaning out of this huge volume of
data to help improve decision making.
• They are using everything in their capabilities to analyze historical data to make insights
about the future and provide a better path towards greater growth.
• There are a lot of analytical option available today and looking at them can be a daunting
task. For any business to have a holistic view of the market and to sustain the position
they hold in ever-growing competition today requires a robust analytical environment.

4. THE PROBLEM WITH TRADITIONAL RETAIL
ANALYTICS:
• In the past, information about sales were kept private. Companies abstained themselves in
using such information to bring out better results for their growth.
• This meant that they worked in a vacumn and made decisions about design, color, style etc
on basis of unstructured data.
• They lacked other pieces of the puzzle such as pricing policies, competitive analysis, trends
and insights.

5. USE OF ANALYTICS
1. Identify Your Markets:
Trends are influenced largely by culture. What’s trending in one country may be
opposing in another.
As a designer, it is important to know global markets and cultures to understand the
potential of each design that is implemented.
Business Analytics helps collect data, understand it, analyse it and have insights on
preferences of people across the globe and suggest changes for a larger growth.
It also helps target wider audience.

6. 2- Analyse what is Trending
It is hard to keep track of what is trending using traditional monitoring techniques.
Analyses on what is going to be the next “big thing” is what is needed.
Retailers can understand market trends using techniques like the social media sentiment analysis to know
their target audience.
Big data can do a lot more than this. It can provide insights on impact of different trends on people.
It can help firms make right merchandising decision in the future.
3- Know Your Audience
With the increase in ecommerce it is easy to know what one likes and dislikes. Data analytics accumulated
both organized and unorganized data in one place to generate insights which are not easily visible.
It can also be used in analyzing customer behavior like the hour of the day one likes to shop, how they
respond to marketing messages etc.
Based on such behavior insights businesses can adjust their market and design to suit their customers.

7. 4- Convert with DATA
New collection face the issue of capturing market. With the use of markdown optimization, it becomes
easier to convert more customers and increase revenues. This concept analyses customers behavior to
suggest a price that ignites demand and ensure faster stock clearance. This technique leads to more
efficient and effective clearance sale at the end of every season.
5- Uplift New Designers
Well known designers have more say in the fashion industry. Majority of designs by famous designers
bring in good money to retailers. But the issue with such outfits is the high prices and its limited
customer base.
With the help of business analysis and big data, budding designers can analyse designs and access
their impact on the market.
Using such predictions mid size retailers can make procurement decision about new designers in the
industry. This will also help in uplifting and promoting new talent and sales.

8. 6- Measure the Influence:
Picking on a brand ambassador is a crucial task for every fashion company. They prefer to bring in famous
faces to advertise and market their products.
Big data analysis can help understand emotions people associate to various celebrated figures and the
impact of collaborating with them on the company sales.
Using these insights brands can make data backed decisions for a more evident result.
This also helps save time and energy spent in debating the influence of different brand ambassdors by
bringing data into picture.
7- Improve Cross-selling:
It is a concept of selling more goods to existing customers. By using analytical techniques like “market
basket analysis” it is possible to predict what a consumer will buy in the future.
This technique uses historical purchase data to identify products that complement each other.
It can help shop owners to organize their stores, both online and offline.
This can also be used to send more effective marketing messages to consumers. It makes it easy to upscale
revenues by promoting cross-selling.

9. CONCLUSION
• The range of insights that big data analysis can generate for the fashion industry is
highly extensive. Big data is so effective that even mid-size retailers can compete
with the giants if they use the data properly.
• Data analytics generated by tools like Hadoop BI are more than satisfactory to give
anyone a head start. Even if you are not from a statistical background it not difficult
to understand data provided you have good data scientist.
• Big data visualization tools like Tableau and QlikView specialize in presenting data
in ways easily understood by executives.

10. REFERENCES
• https://medium.com/@taanya27/introduction-to-business-analytics-ab89d6fd493f
• https://searchbusinessanalytics.techtarget.com/definition/business-analytics-BA
• https://www.newgenapps.com/blog/7-ways-using-big-data-in-fashion-industry-retail-
merchandising
• https://www.greatlearning.in/blog/how-the-fashion-industry-is-using-data-science/
• https://mbs.rutgers.edu/articles/how-data-analytics-changing-fashion-industry