Discover the challenges in your industry & how we can help you deal with them
Data science provides a great opportunity for retailers to take advantage of the customer data they own and turn it into actionable insights that will end up boosting revenue. Some use cases are:
Recommendation Engines
Powering Augmented Reality
Personalized Marketing
Price Optimization
Intelligent cross-selling and upselling
Inventory management
Foretelling trends through social media
Managing real estate
Customer lifetime value prediction
Applying data science technologies like AI, NLP, and machine learning algorithms can help banks in several areas like fraud detection, risk management, customer sentiment analysis, and personalized marketing.
Data science is disrupting the banking sector like never before. Banks are sitting on piles of data and harnessing the volumes of data is helping banks in various ways, from process automation, process improvements to exploring new delivery models and introducing new services. Use cases for banking:
Recommendation Engines
Powering Augmented Reality
Personalized Marketing
Price Optimization
Intelligent cross-selling and upselling
Inventory management
Foretelling trends through social media
Managing real estate
Customer lifetime value prediction
The manufacturing industry is undergoing a huge transformation supported by today’s digital age that requires greater agility for the customers, business partners, and suppliers. The increasing scale and speed can be challenging for manufacturers, and this is where data science comes in. Here is a list providing the major applications of data science in manufacturing:
Predictive Analytics or Real-time Data of Performance and Quality
Preventive Maintenance and Fault Prediction
Price Optimization
Automation and Robotization in the Smart Factory
Supply Chain Optimization
Product Design and Development
Inventory Management and Demand Forecasting
Efficiency and Computer Vision Applications
Big data was developed in order to analyze different data sets at a scale beyond the capacity of most data warehouses. However, big data analytics are only valuable when the big data use cases are well defined. If you can clearly identify what information you are looking for and the data sources are best suited to provide the proper insight, big data can be extremely valuable. And no group may have more need for more big data analytics than the public sector.Some use cases for the public sector:
weather patterns
social services
regulatory compliance
health services
smart surveillance
law enforcement
education
infrastructure
In the course of time, data science has proved its high value and efficiency. Data scientists find more and more new ways to implement big data solutions in daily life. Nowadays data is a fuel needed for a successful company.
Telecommunication companies are not an exception. Due to these circumstances, they cannot afford not to use data science. Within the telecom industry data science applications are widely used to streamline the operations, to maximize profits, to build effective marketing and business strategies, to visualize data, to perform data transfer and for many other cases. Key activities of the companies working in the telecommunication sector are strongly related to data transfer, exchange, and import.Some use cases for the telecom sector:
customer churn prevention
lifetime value prediction
network management and optimization
recommendation engines
customer sentiment analysis
customer segmentation
