Use Cases And Benefits Of Data Annotation For Retail And eCommerce

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Use Cases And Benefits Of Data Annotation For
Retail And eCommerce
In the era of the experience economy, every
customer wants tailor-made products that fit
their needs. eCommerce and online retail
platforms are now using Al-based solutions to
intrigue customers. AI helps these companies
deliver refined search results and suggest
relevant products to online shoppers based on
their preferences. Data annotation is imperative
to all Al/ML-based operations. The data
annotation tools market is expected to reach over
10 billion USD by 2028. It refers to the
categorization and labeling of data based on its
specific use. High-quality and accurate data
annotation requires human effort where users
label and classify information. Based on this
data, AI/ML models then apprehend essential
attributes from the database. With the help of
data annotation, Al also analyzes different
product attributes and recommends related
products in online and offline shops.
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Data Annotation - Use Cases And Benefits To The
Retail And eCommerce Industries Following are
the ways in which data annotation can take retail
and eCommerce businesses to the next level. By
powering the AI-ML engines with the right
quality data, retailers and eCommerce businesses
can create a tremendous competitive
advantage. Hyper-Personalized Marketing
Campaigns Data annotation in eCommerce can be
used to improve the online shopping experience
by targeting each customer with specific
campaigns picked based on their interests. Since
it is impossible to customize marketing
campaigns for each customer manually, retailers
can leverage Al-based automation to scale such
tasks. Hyper-personalization of marketing
strategies provides benefits in terms of
improved productivity, efficiency, and revenue
generation. According to research by PWC, 12 of
online shoppers preferred buying from a retailer
that sent them personalized offers. Visual
Search Data annotation enables visual search by
appropriately labeling similar product images.
This makes it easy for ML models to recognize the
product when a customer searches for them
online. To make this happen, a large amount of
data needs to be labeled to identify and track
various facial attributes to recognize faces.
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Demand Forecasting Most eCommerce businesses use
AI-based retail analytics solutions for demand
forecasting. Such solutions help businesses
analyze and optimize sales performance. Sales
leaders can dive deeper into their data using
interactive dashboards and slice and dice the
data for deeper analytics. They accumulate all
the data to create a complete picture of all
transactions. This includes mode of purchase,
customer demographics, discount coupons applied,
returns, and refund queries. All such analysis is
possible only through correctly labeled
data. Trend Identification And
Recommendation In retail, identifying customer
trends is crucial for a successful business.
Companies analyze huge amounts of data to
understand their customers, data generated by
creating multiple customer touchpoints. This
helps them interpret what their customers want so
that they can push out relevant offers to
attract more and more buyers. This process of
trend identification can be made easy using
annotated data that is created based on a
specific use case. Email Marketing Data
labeling can also be used to design email
marketing strategies by analyzing customer
patterns. Specific target campaigns, as discussed
above, can hence be deployed based on the
customers preference. This not only helps
retailers bag new sales but also ensures active
engagement and retention of existing customers.
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Semantic Segmentation Semantic segmentation is a
process of assigning labels to images. However,
unlike traditional labeling, it treats different
objects of a single image as separate entities.
It can even be used to provide different labels
for every pixel in an image. This type of data
annotation can be leveraged by online retailers
to better model a clothing item to produce a
better fit and a detailed output image. This
also helps in maintaining a consistent quality
score across different products. Market-Basket
Analytics Market-basket analytics refers to the
use of data annotation to pair related products.
For example, if a customer buys a mobile phone,
they most likely would like to invest in a cover
and tempered glass as well. AI engines can
predict and recommend such combinations to
customers providing them with a convenient
purchasing experience. This also improves sales
conversion by marketing multiple products at
once. SCM Analytics And Inventory
Management Data annotation in SCM and inventory
management plays an important role in developing
robust demand forecasts. Why is it important?
Let's take the example of the COVID-19 pandemic.
There was a major stockout situation for several
products like toilet paper. Although such a rise
in demand increases short-term profit, in the
long run, it simply causes walkouts. It is the
worst situation for any retailer. Such a
situation can be avoided by using AI solutions
that can perform real-time data analytics across
multiple outlets.
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It prevents the out-of-stock inventory situation
and ensures a seamless experience for its
customers. These AI solutions need to be trained
based on accurately annotated data. Image-Produc
t Tagging In online retailing, it is important
to synchronize product images with their
descriptions. While images should aim to get
customers interested in the product, their
descriptions should compel them to make a
purchase. AI-based data annotation can automate
this process by optimizing image-product
tagging. Conclusion In online retail and
eCommerce, data annotation fuels the AI system to
automate product search and purchase. At EnFuse,
our team of data annotation experts provides
highly intelligent platforms to identify and
annotate specific data, so businesses can
leverage machine learning and artificial
intelligence. Connect with us to know more! Read
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