Data Science Use Cases in Retail & Healthcare Industries

Data Science Use Cases in Retail & Healthcare Industries

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.

Introduction

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

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.

Why Do You Need Data Science?

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.

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.

Data Science Use Cases

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.

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 datadriven 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

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:

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.

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

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.

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?

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,

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.

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

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

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.

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.

Data Science Use Cases in Healthcare

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.

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

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

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

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.

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.

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

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.

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

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.

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.

Conclusion

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 datarelated 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.

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