Data Science Use Cases in Retail & Healthcare Industries

1. Data Science
Use Cases in
Retail &
Healthcare
Industries

2. Quick Summary:
Retail and healthcare were the two
major industries that looked after
our health and survival when we
were restricted to our homes during
the COVID pandemic. During this
period, they underwent a speedy
digital transformation and upgraded
their IT infrastructure to stay
prepared for the future. In this blog,
we will analyze data science use cases
in retail and healthcare to get a clear
vision of adopting the right approach
necessary to embrace data science.

3. Introduction

4. As the post-pandemic world refuses to
be at the mercy of fate in dealing with
human suffering, data has emerged as
the most dependable way to alleviate
human suffering in almost all spheres
of life. It has become imperative for
enterprises to make informed
decisions across the industries. It is
possible when they have access to
accurate data, which no longer
remains about names, contacts,
addresses, etc. The enterprises need
to have the data which gives them the
razor-sharp insights into behavior
patterns, thought processes, culture,
social, political, and financial
background of their customers.
The inevitable after

5. math of this acute consumer need is
the rise of data science practices,
methodologies, tools, technologies,
and data science jobs. As per the
salary report published by AIM
Research in June 2021 – “The median
salary of data scientists declined
6.9% to Rs. 13.4 lakhs compared to
the median salary of Rs. 14.4 lakhs in
2020 and in August 2021. However, it
again increased to Rs. 13.6 lakhs and
is expected to increase throughout
2022.”
The data science use cases across the
industries reflect the growing
importance of data science,
increasing focus on MLOps, and the
need for automation within
different industries.

6. Why Do You
Need Data
Science?

7. Data science came into being with the
merging of computer science and
statistics. The products in many
industries that use data science reflect
how it can solve complex business pain
points and enhance customer
experiences. Every enterprise needs to
understand what can data science be
used for and how can data science help a
business. Let’s discuss it in details.

8. Data science enables organizations to
make strategic decisions that impact the
bottom line and enhance ROI.
Data science allows organizations to know
and delight the customer experience at
every stage of the consumer journey to
receive better customer gratification.
The data-driven insights made available by
data science empower organizations to
meet customer demands and design
products that appeal to consumers and add
value.
Sentiment analysis is another aspect of
data science that helps organizations
understand customer mindsets.
Data science bridges the gap between
consumers and businesses by enabling
organizations to understand customer
demand and personalize their buying
experience.

9. Data
Science
Use Cases

10. People say that “Data is the new oil and
data science is the combustion engine.”
Data science for businesses focuses on
collecting, processing, analyzing, and data
visualization. The method or data science
applications usually depend on the
specific business domain. Therefore, it
would be better; if you plan on hiring data
scientists experienced in the particular
skill set.
Every industry, including health care,
finance, energy, media, etc., realizes the
importance of using data science to
streamline operations, enhance ROI, make
precise business decisions, and optimize
processes. We will be exploring data
science use cases in retail and data science
use cases in healthcare.
Let’s discuss the data science use cases in
retail.

11. Data Science Use Cases in Retail
Data science applications in retail
undoubtedly boosts operations such as
assortment; recommendation; logistics,
supply chain management, demand
forecasting, etc. Besides, it also plays a
significant role in optimizing prices for
products/services, predictive
maintenance, churn prediction, and data-
driven product management.
As per the IBM study, 62% of retailers
acknowledged having gained a significant
competitive advantage due to
applications of data science.
For example;
The analytics team of Target started
analyzing the buying trends of its
customers with the help of customized
data science applications

12. It allowed them to know the pregnancy
status of their customer in advance
before the customer knew it.
Furthermore, the predictive analytics
model they created was so powerful that
it could predict the likely due date. This
overwhelming information helped the
retail company target these customers for
selling their fetal items along with regular
products and coupons.
Let’s get into how data science is helping
the retail organizations get the best out of
accurate data:

13. 1. Recommendation Engines
Recommendation engines can be
considered one of the best data science
use cases in retail. It works by filtering
information and providing retailers
with consumer behavior patterns.
Based on this information, retailers
can customize their offers for target
customers interested in buying
products or services. The above
mentioned data science example of
Target, precisely represents the best
way to use recommendation engines
for better outcomes.

14. Collaborative, content-based, and
hybrid are the three major filters
used in recommendation engines.
The collaborative filter offers
recommendations based on user
preferences, while the content-based
filter takes a product-centric
approach, and the hybrid
recommendation uses both these
filters. If retailers can recommend
products/services based on customer
preferences, it will boost sales and
revenues. Therefore, retailers need to
get detailed data science knowledge
for the retail industry if they intend
to adopt data sciences

15. 2. Fraud Detection
The immense growth in online
transactions across the industries has
resulted in serious fraud. The rule-based
approach to fraud detection no longer
works when so much data is involved,
even for committing the crime. The data
science applications are customized to
predict fraud by using data generated
during online transactions. Data Science
and Machine Learning techniques such as
Deep Neural Networks (DNNs) are also
used to detect business transaction fraud.

16. 3. Personalized Marketing
As per the Accenture study, 73% of
consumers want to buy from retailers that
use their information to give them the
shopping experience they desire. Retailers
need to cater to this consumer demand by
using various data science tactics. One of
such tactics is the integration of
personalized recommendations. These
personalized recommendations take into
consideration users browsing history,
past purchases, likes, and dislikes. Many
eCommerce giants are using this one data
science use case in retail.
For example, Netflix.
Have you ever wondered how you get
recommendations for your favorite shows
without doing anything?

17. The truth is Netflix has one of the best
data science applications. It accesses
and collects all the data related to the
viewing habits and content preferences
of its audiences across the globe. Using
ML algorithms and AI models, they
developed data sciences techniques
such as ranking algorithms and
interleaving to recommend the most
relevant content to its target audience.
4. Price Optimization
Price optimization is another significant
data science use case involving various
online tricks and customer approaches.
The data is obtained through multiple
sources and analyzed to pinpoint the
customer demographics, such as age,
gender, place,

18. buying attitude, buying season, and
price expectations (it involves
comparing prices of the same product
on different platforms). All the data
insights help them develop an ideal
price for the product or run a
personalized marketing campaign for
independent customers.
Let’s take the example of Netflix again.
Do you remember how Netflix changed
its pricing plans in India to increase its
subscriber base? Well, Netflix used data
science application to identify the
customer behavior patterns and
optimize the pricing plans suitable to
attract more customers and be more
profitable in terms of absolute revenue
in India than its competitors.

19. 5. Cross-Selling and Upselling
Cross-selling is about recommending
complementary products to
customers for their purchases, while
upselling is about recommending
better products than the ones they
want to buy. Many retailers and
eCommerce giants have already
adopted this data science use case to
increase their revenue and enhance
customer experience.
For example, Amazon.
It gives you recommendations to buy
the chair while buying a table; it is
cross-selling. But, it is upselling when
it shows you a better table than the
one you considered buying. Amazon
made

20. it possible by accessing all of its
customers’ information, such as their
names, search histories, buying intent,
modes of payment, and addresses. This
data allows Amazon to provide
customized recommendations and to
cross-sell and upsell.
6. Customer Sentiment Analysis
Sentiment Analysis is one of the latest and
most advanced data science use cases. It
has replaced the time-consuming
traditional approach of focus groups and
customer pools to analyze customer
experience with the product. The retailers
now get data from social media, and the
feedback consumers leave on online
portals. They analyze this data to retrieve
actionable insights into customer
sentiments, like

21. what they think of the product, their
satisfaction level, the possibility of
recommending it to others, will they
buy again?
This typical data science use case in
retail depends on natural language
processing and text analysis to
perform data analysis. The ratings
and reviews derived from this
technique help retailers customize
their products and services
according to consumer expectations
and sentiments. It results in better
customer retention and gratification.
During the Pandemic, when the
entire world was restricted to their
homes, the healthcare industry
worked day and night to save
people’s lives.

22. During this time the healthcare industry
became mature in technology use and
upgraded its IT infrastructure to prepare
for the future. Let’s explore the data
science use cases in healthcare.

23. Data Science
Use Cases in
Healthcare

24. There are numerous data science use
cases in healthcare. In fact, data science
played a pivotal role in tracking and
preventing the spread of COVID-19.
Although the Coronavirus is still
unpredictable, Janssen Pharmaceutical
Companies of Johnson & Johnson
realized that collecting the data and
analyzing it was imperative to make
informed decisions to save and protect
lives.
Janssen built a global COVID-19
surveillance dashboard to track the
virus by obtaining data from countries,
states, and counties. This dashboard
tracked how the virus impacted certain
geographies on an hourly basis.

25. In collaboration with Dr. Dmitri
Bertsimas and his colleagues, the
company built machine learning-based
predictive models at the Massachusetts
Institute of Technology. The key role of
this entire exercise was to predict the
next wave of the pandemic. These
predictive models incorporated the
data obtained by the global
surveillance dashboard that included
information about local policies and
behaviors, traveling patterns of people,
mask-wearing habits of certain
geography, etc. The consolidation of
this data provided invaluable guidance
to the Janssen clinical teams in
planning and pursuing their vaccine
execution.
There are so many areas and
applications of data science

26. in healthcare industry that use data
science. Let’s explore some of them.
1. Data Science for Medical Imaging
Data science use cases in healthcare
have become an integral part of human
lives. The impact of data science has
revolutionized the entire healthcare
and pharmaceutical industry. For
example, various imaging techniques
like X-Ray, MRI, and CT Scan are
nothing but data science use cases in
everyday life.
Earlier, doctors or

27. healthcare professionals examined
these images manually to identify
deformities and irregularities and
reach a precise diagnosis. Yet, these
diagnoses were never accurate. Data
science introduced deep learning
technologies and machine learning
models, empowering healthcare
professionals to identify even the
microscopic irregularities within the
human body.
Apart from the image processing
techniques, several others like image
recognition, image enhancement,
reconstruction, edge detection, etc.,
are also part of the data science use
cases in healthcare. These techniques
are used to improve the quality of the
images and the accuracy of the data.
Besides, several open

28. brain imaging datasets encourage
young data scientists to gain practical
experience in image analysis. These
datasets include BrainWeb, IXI
Dataset, fastMRI, OASIS, etc.
2. Data Science for Genomics
The genomic study is one of the most
sought-after data science use cases in
healthcare. It deals with sequencing
and genome analysis to find any
irregularities in the human genome.
Before the Human Genome Project,
many healthcare companies spent a lot
of time and money analyzing gene
sequences. However, with advanced
data science use cases, the time and
cost of genome sequencing and
analysis have been drastically reduced.

29. Also, the insights derived from these
data science use cases are much better
than the earlier methods. It has helped
scientists identify the disease more
accurately, find out the precise drug
for that disease and provide deeper
insights into the outcome of their
research findings.
The latest discipline in this field is
Bioinformatics which combines data
science and genetics. Additionally,
advanced data science use cases like
genetic risk prediction, gene
expression prediction, etc., are
extensively used by the healthcare
industry to improve human lives.

30. 3. Data Science for Drug Discovery
The pharmaceutical industry used to
take a lot of effort, time, and money to
discover the right and most potent drug.
Data science and machine learning are
now easing the pain. Data science
simplifies the job by making available all
the necessary data and insights like
mutation profiles, disease, patient
history, treatments, and patient
metadata. These insights result in
finding the best drugs suitable for a
particular patient profile in a shorter
time at lower costs.
Moreover, deep learning data science
algorithms make researchers develop
methods that can predict the disease
and help

31. simulate the drug reaction in the human
body. Overall, you save a lot of time,
effort, costs, and tedious laboratory
experiments.
4. Predictive Analyt
Johnson & Johnson’s global surveillance
dashboard, which we discussed earlier in
this post, is a great example of data
science in health analytics. Predictive
analytics is a specific data science use
case that involves using historical data,
learning from it, finding patterns, and
giving accurate predictions. This method
is important in improving the state of
patient care and chronic disease
management.

32. During the Pandemic, when the entire
world was restricted to their homes,
the healthcare industry worked day
and night to save people’s lives. During
this time the healthcare industry
became mature in technology use and
upgraded its IT infrastructure to
prepare for the future. Let’s explore the
data science use cases in healthcare.
Also, monitoring the logistic supply of
hospitals and pharmaceutical
departments helps them increase the
efficiency of supply chains and
pharmaceutical logistics.
5. Tracking and Preventing Diseases
An AI platform developed by the
University of Campinas in Brazil to
diagnose

33. the Zika virus using metabolic markers and
machine learning tools used by companies
like IQuity to detect autoimmune diseases
are the best examples of data science and its
use cases in healthcare to track and prevent
diseases.
It has become possible now to detect
chronic diseases early and prevent them
from worsening with the data science use in
healthcare. It helped the industry optimize
the prices of the drugs as the cost of curing
increases with the growth of the disease.
Additionally, early detection of the disease
accelerated the quality of life.

34. 6. Data Science for Wearables
Wearables have immensely contributed
to making the lives of chronically ill
patients better. These devices use data
science to track the patients’ circadian
cycle, blood pressure, calorie intake,
heartbeat, temperature, and other
parameters prescribed by the doctors.
The data collected is constantly
monitored and helps take appropriate
action in case of irregularities in these
parameters.

35. Conclusion

36. All the studies and the statistics prove
that data is indeed the queen that will
rule the world in the coming days.
Bacancy has geared up to serve
organizations interested in adopting
the data science approach. When our
trusted partners in retail and
healthcare hire data scientists from
Bacancy, we know that their data-
related pain points have finally got the
right solutions, and we are happy to
see them satisfied. Reach out to us to
make the most out of data science and
our experienced data scientists.

37. Thank You !
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