DATA SCIENCE IN E-COMMERCE

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APPLYING DATA SCIENCE IN E-COMMERCE
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INTRODUCTION
Data science is now essential to e-commerce
success. Targeting the right audience through
advertising platforms is highly necessary to
boost online sales as customers only want to
look at relevant products or items they need.
Artificial intelligence (AI), with the
assistance of machine learning (ML), helps
determine the target audience based on customer
preferences and past browsing data, which help
bring potential buyers and score inbound sales.
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HOW IS DATA SCIENCE BOOSTING SALES IN E-
COMMERCE?

• Data science powers predictive forecasting using
  various data sources, such as the historical data
  of sales, economic shifts, customer behavior,
  and searches. This empowers e-commerce companies
  by promoting relevant products to potential
  buyers.
• Machine learning (ML) and artificial intelligence
  (AI) make it possible to provide shoppers with
  predictions based on what they like even before
  deciding to look for a product or if they need
  something in particular.

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RECOMMENDATION SYSTEMS
In order to help e-commerce services provide more
relevant and accurate recommendations, data
science powers recommendation systems that are
entirely based on the past data of users. This
method is incredibly effective and appears to
almost recommend goods that customers will
always want to buy or at the very least show
interest in. By putting the right product in
front of the right customers, this results in
higher sales. Recommendation systems are
tailored to individual users and built using data
about users, such as the products they purchase
and the pages they visit.
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CUSTOMER FEEDBACK ANALYSIS
Data science allows e-commerce companies to work
on their shortcomings by collecting the relevant
feedback for each product or service and then
taking action based on the collective analytics.
Methods such as sentiment analysis and brand
image analytics help companies understand what a
customer or the target audience requires,
increasing sales significantly. E-commerce
giants and startups use NLP or natural language
processing, text analysis, text analytics, and
computational linguistics to power analytics of
this kind.
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INVENTORY MANAGEMENT
Data science allows established e-commerce
companies and startups to manage their inventory
more effectively. This also indirectly helps them
not waste capital on unpopular products which
are not selling well and have no need for
restocking. Since e- commerce companies work
with tons of customers and thousands of products
daily, advanced data science is
highly necessary to conduct accurate inventory
management and predictive forecasting for future
requirements.
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CUSTOMER EXPERIENCE AND CUSTOMER SERVICE
Data science helps ease and improve customer
experience by automating a lot of functionalities
and making regular things hassle-free with the
help of feedback and analytics. These
implementations can range from automated
experiences to easier navigation. As per
reports, around 80 of customers are of the
opinion that customer experience is also
important and helps them come back to a specific
site. In addition, determining preferences via
social media can also improve customer service,
and recommendations as many millennials and Gen
Z have discovered products via social media
platforms like Instagram.
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