The Power Of Predictive Analytics With AI
Today in the times that we are living in, there is no bigger power than data. Data makes it possible for individuals to streamline their efforts towards success better. Similarly, data enables organizations to determine the cause of their problems in operations too. One of the ways employed by companies to identify risks and opportunities is predictive analytics and this blog will uncover its potential.
What is Predictive Analytics? – Predictive Analytics Definition
Predictive analytics definition is rooted from advanced analytics that makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques and machine learning.
Predictive data analytics is often considered synonymous with big data and data science. Today companies are swimming in data that resides across transactional databases, equipment log files, images, video, sensors or other data sources. To gain insights from this data, data scientists use methodologies like deep learning and machine learning algorithms to find patterns and make predictions about future events. These include both linear and nonlinear regression, neural networks, support vector machines and decision trees.
Machine Learning For Predictive Analytics
Predictive analytics and machine learning aid companies make more promising decisions by predicting what will happen. Both methods can anticipate future outcomes by examining current and past data. As such, the terms machine learning and predictive analytics are sometimes used synonymously, but although connected, they belong to two separate disciplines.
Application of Predictive Analytics in Various Industries
Banking
Financial services employ machine learning with quantitative tools to predict credit risk and for fraud detection.
Healthcare
Predictive analytics in healthcare is used for detecting and managing the care of chronically ill patients.
Human Resources
HR teams in diverse industries apply predictive analytics for identifying and hiring employees, determine labor markets and predict employee performance level.
Marketing and sales
Predictive analytics is also used for marketing campaigns throughout the customer lifecycle and in cross-sell strategies.
Retail
Retailers are using predictive analytics for identifying product recommendations, forecast sales, analyze markets and for management of seasonal inventory.
Supply chain
Businesses use predictive analytics to make inventory management more efficient by helping in meeting demand while minimizing stock.
Predictive Analytics With AI
When the statistical algorithms are combined with internal and external data using Artificial Intelligence (AI) Services to predict the future business trends, the results are more accurate and time-efficient. It benefits businesses in the following ways:
Managing Inventory
By syncing real-time data with AI, one can optimize their inventory management beyond simple reordering. For example, Monitoring technologies like the internet-of-things (IoT) devices can be used in warehouses to provide real-time alerts for low inventory so one can restock products before they go out of stock.
Optimizing Delivery
Route optimization software when paired with AI enables real-time rerouting based on previous factors. It is then capable of even predicting the best times for deliveries, potential delays and other transportation and delivery factors. Moreover, IoT devices are able to gather real-time sensor data on trucks to optimize operational aspects of deliveries. For example, this technology is able to sense object shifts in transit, load imbalances and sudden stops, revealing insights for smarter decision-making.
Actionable Insights Through Data
Data collection is crucial but useless if it does not map out actions. AI data analytics then helps to convert those humongous piles of data into predictive actionable insights.
What’s the Difference Between AI & Predictive Analytics?
The most significant difference between artificial intelligence and predictive analytics is that AI is totally autonomous while predictive analytics depends on human interaction to query data, determine trends, and test assumptions. Therefore, AI has a significantly broader scope and more applications than solely predictive analytics. Similar to predictive analytics, artificial intelligence involves constantly expanding multi-variable algorithms.
Mitigating risk and forecasting campaign success has never been more comfortable thanks to AI and predictive analytics. In spite of the fact that each strategy approaches data analysis differently, both offer advanced insights and 360-degree views of data patterns to help you increase marketing ROI, conversion rates, and customer retention.
Conclusion
There are abundant derivatives one can filter for their business using AI analytics. If you wish to employ this technology for your entity, Nuox offers a set of software tools that help one in creating easy, quick and scalable predictive models. We also provide Blockchain Development and Cyber Security Services. It is time for you to offer your business the boons of AI.
