Text Analytics has emerged as a tool that can be applied to any customer material, such as product reviews and chats, as well as content that pertains to consumers, be it about them or affects them

1. TEXT
ANALYSIS
IN RETAIL

2. The retail business operates in an
extremely complex marketing and sales
landscape, with platforms ranging from
traditional stores to e-commerce
websites the latter of which is rapidly
rising.

3. Day after day, more consumers
turn to eCommerce platforms to
acquire their items or, at the very
least, to gather other consumers’
thoughts before visiting a store
most daily users read reviews
before making a purchase
decision.

4. While access to what customers are saying is no
longer difficult, finding the time to read,
analyze, understand, and categories that data
is nearly impossible — especially when firms
attempt to do so with information from many
data sources.

5. This is where text
analytics comes in. As a
result, data is being
generated at an
unprecedented rate, and
its significance is
expanding.

6. This type of data can be analyzed manually
as long as a process is in place, but it
becomes incredibly difficult to conduct when
bigger subsets of data are involved,
particularly those from different structures
and types. Spending the trouble to examine
each text in detail and relate it to the context
until patterns are discovered becomes costly
and prone to inaccuracy.

7. As a result, Text Analytics has
emerged as a tool that can be
applied to any customer material,
such as product reviews and chats, as
well as content that pertains to
consumers, be it about them or
affects them — such as reviews or
blog entries.

8. Application of Text
Analysis in the Food
Industry

9. Text analysis can help
e-commerce sites to gain
extensive insights into their
customers’ behavior and
intentions, which can then be
used to drive sales.

10. Tracking the sentiment of any
new product launches, from
wherever the feedback and
opinions may appear.

11. The insights gathered from this
analysis are used to identify
problem areas and suggest
improvement actions.

12. Target market surveys can
benefit any consumer activity,
and text analysis allows you to
examine open-ended
questions.

13. Retail owners can swiftly
examine thousands of text-
heavy surveys and evaluations
to improve their services.

14. Some Common
Methods of Analyzing
Texts in Retail

15. Intent
Detection
You can analyze text
data to identify the
intent of the customers to
classify and prioritize
customer tickets based
on intent. It helps in
identifying customers
with purchase intent
along with new
prospective customers.

16. Sentiment
Analysis
Analysis of the feedback,
opinions, and suggestions of
customers compiled from
multiple forums and social
media platforms can be
easily done with sentiment
analysis. You can identify and
categorize the sentiments of
users and access your market
reputation.

17. Entity
Extraction
You can leverage entity
extraction to extract named
entities from the search
queries to understand what
your customers are looking
for. With it you can offer
products and solutions
according to their
requirements.

18. Emotion
Analysis
You can extract customer
support data related to your
brand or your competitors
and analyze the emotions
expressed in the text to
identify satisfied customers as
well as those that are worth
retaining.

19. Semantic
Similarities
With semantic similarities you
can unlock market intelligence
with text analytics. Compare all
competitive products and
solutions and check how close
they are to each other.

20. Turn reviews, suggestions, social media posts, news, etc., into
actionable insights. Listen to your customers, understand their needs
as well their dislikes and transform it into sources of focused
improvement.
BytesView’s text analytics solution can
help you process and analyze text data
from multiple sources.

21. Thank You