Process of Predictive analytics and its use in the business sector
Data Analytics is a vast world and Predictive analytics holds a major corner in it. A key branch of advanced analytics, Predictive analytics is largely used to predict behavior or pattern to understand and foresee trends or actions, or performances in different areas.
Predictive analytics works by using existing data, data history, statistical algorithms and even AI, in a few cases, to predict possible outcomes and the future.
Predictive analytics has the power to use data to foretell possible future
This very ability to interpret patterns and behaviors using available data is why many businesses and industries are now relying more and more on Predictive analytics.
- Imagine knowing what a customer would buy next or would search for on his/her next visit to a website
- Imagine knowing which customer is likely to be a fraud
- Imagine knowing which patient is likely to develop a certain disease
Predictive analytics identifies risks and opportunities to explore
Data mining, from statistical data to data patterns, plays a vital role in Predictive analytics to perform and predict. Data like age, income, gender, and contact details, along with social media information, activities and other data procured by website forms are combined, combed and analyzed to intelligently predict the relationship between data and human behavior, human emotions and decision making.
The process used in Predictive Analytics
Predictive analytics works step by step from gathering data, analyzing it, and creating predictive models to deploying the outcome. Here are key steps involved in the process of Predictive analytics:
- Defining the project or project definition – Predictive analytics begins with defining what a project is, its outcome, possible deliverables, and objectives according to the business and data required.
- Collecting the data or data collection – This part is quite the crucial phase where necessary data, information, customer details, etc are all collected. The vast amount of data collected could include historical data, structural data, new information, old patterns, etc.
- Data mining and Data Analysis –This is the part where collected data is mined, combed and sorted as per the use. The unnecessary or unwanted data or duplicate data is removed, keeping only the important ones. This remaining data is then analyzed, inspected and explored to conclude a pattern or trend.
- Building a predictive model – The data-based conclusion is run through testing and hypothesis. Then using the data and data-based algorithms, a predictive model is built. The model predicts the possible future.
- Deployment – This is the phase where the findings or analytical result of the predictive model is deployed in a real environment to check or test its effectiveness and to run it effectively.
- Monitoring – Predictive models deployed are regularly monitored and checked for their effectiveness and performance and to see how accurately the prediction-based data is performing when compared to actual data.
How important is Predictive analysis in a business sector?
Business sectors ranging from banking, and healthcare cyber security to IT and government sectors are depending on predictive analysis to improve and grow accordingly. Right Predictive analysis helps in:
Fraud detection – Analyzing the predictive patterns, banks, financial sectors and cyber security can tighten the transaction network and detect possible risk threats and frauds in real time. Businesses seeking risk reduction benefit the most from Predictive analytics.
Marketing & sales – Predictive analytics-based marketing and sales campaigns help a business achieve ROI-boosting numbers. Predictive analytics creates key impacting tools like CRM which help a brand understand the customer, its choices, its complaints, its predictable behavior and where or when a customer would move when it comes to purchasing. This kind of data also helps in creating sales-based opportunities with leads and retaining customers.
Businesses and areas benefiting from Predictive Analytics range from:
- Healthcare
- Banks
- Education sector
- Cyber security
- Fraud detection
- Weather forecasting
- Healthcare
- Social Media Marketing