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Machine learning based Predictive analytics and its applications

by Steven Brown
Machine learning based Predictive analytics and its applications

Research shows that data-driven organizations outperform competitors by 6% in profitability and 5% in productivity. This is possible by leveraging rich data to make real-time data-driven decisions. As enterprises evolve in data-based decision-making, is it possible to predict future trends and be future-ready? Predictive Analytics makes it possible.

It uses historical and current data to predict future outcomes. It leverages statistical and data modeling techniques to analyze past data, find trends and help stakeholders make intelligent data-driven business decisions. It extensively uses Machine Learning (ML) for data modeling as it can accurately process tons of data and recognize patterns and trends.

Let’s find out how machine learning-based predictive analytics helps enterprises predict the future and make informed decisions. You will also find a few important use cases where it can significantly impact.

Data analysis and neural networks:

Before we get into predictive analytics, I would like to walk you through the forms of data analytics and neural networks which is the building block of data analysis.

  • Descriptive analytics â€“ Aggregates big data and gives valuable insights into the past.
  • Predictive analytics â€“ This is the next step in data reduction. It uses many statistical modeling and ML techniques to analyze past data and predict future trends and patterns.
  • Prescriptive analytics â€“ This emerging analytics uses business rules, machine learning, and computational modeling to suggest the best course of action for any pre-defined result.

It is a system of hardware and software that mimic the human central nervous system to define vast unknown input-dependent functions. Architecture, activity rules and learning rules define neural networks. The neural network has a network of nodes just like humans. Each processing node has its small sphere of knowledge, which contains the memory of what it has seen and any rules it was initially programmed or developed for itself. Neural networks can learn the output for a given input from the given training datasets.

Simply put, neural networks are adaptive and can modify as they learn from subsequent inputs. For example, we can take image recognition. Initially, the system is trained with human and non-human image datasets. The output will be human or non-human. After the training, the neural networks remember the attributes of a human image. Next time, when the input image is given, it compares the stored knowledge with the input image to provide accurate information. The accuracy increases as it identifies.

Few general use cases:

The evolution of advanced capabilities Artificial Intelligence (AI) and machine learning paved the way for massive applications of predictive analytics for businesses. Here are a few use cases of predictive analytics.

Customer service:

Customer segmentation:

Segmenting customers based on their responses and purchase patterns can help businesses create marketing strategies tailored to each segment’s characteristics.

Customer retention:

It also assists businesses in identifying customers who are about to leave. Such insights enable businesses to create packages and content that meet the needs of their customers, thereby retaining and attracting new ones.

E-commerce:

To study customer behavior:

Predictive analytics powered by machine learning algorithms can assist retailers in better understanding their customers’ behavior and preferences. E-commerce companies can effectively develop product recommendations and offer to increase sales by analyzing customers’ browsing patterns and click-through rates of specific products. Tailored suggestions and alerts can also assist retailers in retaining customers and building a loyal customer base.

To manage the supply chain:

It simplifies the process and makes the supply chain processes easier to manage. Retailers can use predictive algorithms to manage inventories better, avoid out-of-stock scenarios, and optimize logistics and warehousing.

Healthcare:

Medical Diagnosis: 

In healthcare and medicine, predictive analytics has a wide range of applications. Training algorithms with extensive and diverse data sets improves the understanding of patient symptoms. This can also aid in providing a quicker and more accurate diagnosis.

Healthcare operations:

It streamlines and derives insights from large amounts of unstructured data. This increases operational efficiency and allows for better management in the healthcare industry. Hospitals, for example, can forecast surge issues that result in bed and staff shortages. Such insights and forecasts allow hospitals to provide the best possible patient care.

Sales and marketing:

Many enterprises use predictive lead-scoring algorithms on complex data sets to improve their lead conversion rates. Predictive analytics powered by machine learning can also prioritize known prospects, leads, and accounts based on their likelihood to act. Combining historical data points of customer behavior with market trends can create a 360-degree view of the prospective customer. It also helps businesses excel in performance and streamline their sales and marketing activities into a data-driven endeavor rather than simply taking a chance

Finance:

Finance firms can detect and prevent fraudulent transactions and activities using predictive analytics. It scans historical datasets and identifies risk areas, allowing businesses to make risk-aversion decisions. You can prepare revenue projections like planning goals, objectives, cash flows, potential issues, and so on accurately and ahead of time. Demand forecasting can assist in predicting the sales cycle, allowing businesses to place market-specific products to increase profitability.

Cyber Security:

Gartner predicts that through 2025, 30% of critical infrastructure organizations will experience a security breach that will halt operations and mission-critical cyber-physical systems. Thanks to predictive analytics. Cyber-attacks can now be predicted and prevented. Real-time traffic analysis detects unusual patterns and assists businesses in thwarting attacks beforehand. It can automate gathering data and transform it into reports and actionable insights, reducing human errors when processing large amounts of data.

When you adopt predictive analytics, here are a few things you should keep in your mind. Most importantly, accurate predictions need accurate data. You can only expect predictive analytics to make good predictions if your current records are complete and accurate. Good future outcomes rely on selecting the best predictive modeling techniques when looking for patterns. Ambiguity is unavoidable in predictions, and We cannot predict the future with certainty, particularly regarding customer behavior. We need to know how accurate our model is and how confident we can be in its results. Predictions should provide actionable insights to help your stakeholders make impactful decisions because that is our primary motto. Insights should lead to a progressive impact on your business.

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