Gives insight into how data analytics is used in the fashion industry.

1. Data analytics in fashion
By ANJALI ROSE ISSAC

2. Cross-selling and
upselling through
personalization
 Companies across industries leverage inexpensive
technologies like Hadoop to analyze massive customer
data, uncover patterns and use the insights to
personalize their offers to their customers. This helps
them roll customer engagement and recommendation
engine to grab market share.
 Customer acquisition, retention campaigns, and
maximization strategies enabled an increase in
customer engagement and loyalty. Sales in select
categories grew by 92% with targeted retail campaigns,
using models like Market Basket Analysis, K-Means,
Churn and Propensity.

3. CRM(Customer
Relationship
Management)
 This is also calculated using various KPIs, one of them
being churn rate. Churn rate refers to number of
customers discontinuing a particular brand.
 Depending on the churn rate of a brand, companies
employ various strategies that influence customer
retention like CLV(Customer life time value), customer
complaints, Net Promoter score(NPS), Repeat purchase
rate(RPR) etc.

4. Predicting
futuretrendsof
fashionstyles
 Analytics is enabling retailers to aggregate fashion
trends and sales information from a wide variety of
sources around the globe—from fashion sites, web
forums, designer runway reports, and blogs tracking
fashion trends—and making it available in real-time –
across menswear, women’s wear, children’s apparel,
accessories, and beauty.

5. Customer
insights
 From leading and famous fashion labels to the new
brands, everyone is leveraging data analytics to gain
crystal clear insights into current trends. From
individual updates on distinctive trends to collective
fashion choices, web crawling can provide access to
critical and crucial information.