Over the past decade, tech innovation has transformed the way consumers buy and
businesses operate. With the arrival of technology, there isn’t a doubt that
all choices we make should be absolutely data-driven. The fashion industry can
be most disrupted through the effective use of data science.
So let's take a look at how data science is
changing the way we buy clothes.
1. Advent of online shopping:
Physical stores are now
being largely replaced by e-commerce websites. Online as well as offline
retailers are now utilizing artificial intelligence (AI) to understand their
customer’s preferences better. With data science, fashion stylists and
designers are able to identify trends and meet the expectations of their
2. Sentiment analysis:
In the field of fashion,
through data science, a user’s likes or dislikes, details of items purchased
and exchanged, delivery timelines, optimal supply chain management, and a huge
variety of additional information can be gathered and acted upon to make sure
that the users have a great consumption experience.
3. Personalised fashion
The future of fashion depends on
personalization. Given that fashion consumption is highly subjective, customers
do not desire to see countless items when they shop instead, they wish to be
shown only the relevant items. The use of data becomes a remarkably potent tool
in providing true personalization. With the help of data science, brands can
now process several hundred data points and learnings about their consumers in
a few seconds to figure out their consumption preferences.
4. Predicting trends
With data science, brands can keep collecting
data about the ever-changing preferences of their consumers. This info can then
be utilized by designers to forecast trends and curate products that are in
line with customer demands. With the arrival of social media, buyers are
revealing what they buy, their likes and dislikes, the scope of improvements,
and more. This information enables the fashion brands to make necessary changes
in their offerings and products.
5. Minimizing wastage and managing inventory
Data science plays a pivotal role in forecasting
the shopping behavior of customers. For brands with large-scale production, it
can help them predict the demand for certain products and minimize the
production of product lines that are in less demand. On the flip side, it also
enables retailers to maintain sufficient inventory levels. With the help of data
science, retailers can control inventory issues and offer products that are in
6. Fewer returns
For online shoppers, finding the best fit
and look is usually tricky despite the wide selection of products. If the
customer gets a product that doesn’t meet their expectation, it leads to a
dissatisfied shopping experience. On the other hand, for sellers, the return
and reimbursement process is an added cost. With data science, fashion brands
can comprehend the nuances of a buyer’s preferences to create products that
match a consumer’s expectations.
7. An ideal experience
Supply chain management, predictive &
sentiment analysis, and overall transformation, lead to a seamless user experience by the integration of rigorous data inferences derived by the
machine along with human creativity to produce unique ideas on data
application. Although, it is important to note that while data can be very
effective for your business, it is what you do with those insights that
distinguish you from your competitors.