Top 7 Use Cases of Data Science

Use Case of Data Science

Data has become the new oil of the 21st century. Companies are generating tons of data. Earlier, they were recorded for posterity. The uses and applications of the recorded data have changed manifold. Today, companies have started drawing valuable insights from data. To explore the unexplored, they use tools like – 

      – Data Wrangling, 

      – Data Analytics, 

      – Data Visualization, and 

      – Machine Learning 

All this has cumulated to a new sphere named Data Science. There are several interesting use cases of Data Science to fascinate you. 

Data Science - Customer Segmentation

1.Customer Segmentation 

Be it an online store or a brick-and-mortar store, there are different classes of customers. Some prefer branded items while others prefer large quantities of goods. There may be some who resort to expensive items, others would like discounted goods. From a close angle, every customer follows some shopping trend. He/she may be a frequent buyer or a seldom shopper. All these things become key data points while serving the customer. A businessman who understands these intricacies enters into a win-win situation. Using different algorithms, customers can be segmented into different categories. 

Thereafter, effective business strategies can be formulated for customers belonging to each segment. Selective targeting helps businesses approach the right set of customers with the right plan of action. 

Data Science - Fraud Mitigation

2. Fraud Mitigation

There have been increasing cases of fraud. Calls asking for bank details and OTPs have become a menace. The financial losses incurred on account of such fraudulent transactions are voluminous. There are other ways too like hacking and intrusion. Thanks to data science, frauds can be detected before it’s too late. Pattern changes are observed to spot any kind of anomaly. Given a dataset, a range of values relating to a particular incident, we can find its mean, median, quartiles, and other statistical aspects. Data points generally falling beyond the denser region are termed as Outliers. 

When the outliers are completely unexpected and unjustified, they form anomalies. These sudden deviations get caught through Machine Learning algorithms or Neural Nets. Later they help in preventing any unwanted action.

Hiring Decisions

3. Hiring Decisions  

From the biggest MNCs to the smallest start-ups, every organization needs employees. All the work can’t be managed by one person alone, and so there are people operating at different levels. Out of these, the Hiring Manager plays the most crucial role.  It is the HR who is responsible for selecting the right candidate out of a pool of candidates. Scrolling through resumes to find the best candidate is tedious and challenging. Interviewing each candidate to filter out the most deserving one is again a task. Data Science can filter out the right candidate for the right profile within a few minutes. 

AI-based interviews can help in monitoring the smallest of details. Every eye movement, grammatical accuracy, body language, topical knowledge, etc. can be used to identify the best fit. Computers can make impartial selections in hiring prospective applicants. 

Natural Language Processing

4. Natural Language Processing (NLP)

NLP or Natural Language Processing has brought wonders to digitalization. Machines gather information, in textual format, from articles such as comments, feedback & descriptions etc. Computers are programmed to see through these blocks and chunks of text. They further extract necessary information. This information further becomes pivotal in applications like Sentiment Analysis and Product Development. On understanding the feelings of customers, one can improvise one’s products and services.

Customer Churn Prevention

5. Customer Churn Prevention

People are usually attracted to those who understand their needs and desires. One may succeed in fulfilling the needs of a customer but must be well equipped to fulfill their aspirations too. For example, if you go to a shop where all the things are unorganized, you feel annoyed. But, if you go to a shop where things are segregated, you feel pleasant. Though both the shops have the power to meet your needs, you are most likely to visit the second shop again. This is because it understands your aspirations. 

It is vital to gain customers, but it’s way more important to keep them for a long period. Data Science can be your key partner in maintaining your customer base. Through data science, customers who are likely to churn i.e., migrate from your organization to some other can be predicted. Since you can have an idea about which customer is unhappy with you, you can adopt ways for retaining him/her.

Recommendation Systems

6. Recommendation Systems 

It helps in targeting customers with products and services tailored to their interests. Hence, by employing Data Science, significant milestones may be achieved.

Best retail location

7. Best retail location

Google Maps has shown how easy it is to navigate to any corner in the world. By now you must have realized how wonderful algorithms can be. So, why not feed the data obtained from maps into one such algorithm? Yes! This is possible. This way, you can analyze which part of the city gets the highest traffic- of your ideal customers. For instance, one may want to begin a confectionary store near a school to target the right audience.  



Thus, Data Science can revolutionize the way we approach life. The use cases mentioned here are not even a drop in the ocean. There is a plethora of things which can be achieved. Data – a passive entity, can help in making your ventures proactive by the effective use of Data Science.