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Predictive analytics can be defined as a classification of data analytics whose purpose is to make possibly accurate predictions about future outcomes based on historical data and analytics techniques such as statistical modelling and machine learning. The data/results gained from predictive analytics can create future insights with a considerable level of accuracy. With the help of sophisticated predictive analytics tools and models, any organization can now use past and current data to reliably forecast trends and behaviours, days or even years into the future.
Predictive analytics has become quite popular to a wide range of industries and organizations, which is projected to reach the global market at approximately $10.95 billion by 2022 according to a 2017 report issued by Zion Market Research.
How predictive analytics work?
big data, data mining, statistical modelling, machine learning and assorted mathematical processes are a few of the wide range of methods and technologies that predictive analytics uses.
It is used by organizations to help them detect possible trends and predict events or happenings that could happen using current and historical data and based on supplied parameters.
With predictive analytics, organizations can identify, and develop patterns contained within data in order to identify potential risks and opportunities. For example, models can be designed, to find relationships between various behavior factors. Such models enable the evaluation of either the promise or risk presented by a particular set of conditions, guiding informed decision-making across various categories of supply chain and procurement events.
Benefits of predictive analytics
Just as the name suggests, predictive analytics aims at looking ahead, and this tool makes looking ahead into the future much more convenient, reliable, and even accurate than any other older tools.
Used by different industries in different ways targeting different analyses or results, predictive analytics serve the purpose of improving operational efficiency. It is used by retailers to predict inventory requirements, manage shipping schedules etc. Hotels, restaurants etc. uses the technology of predictive analytics to predict the possible number of guests to be expected at a specific time period and thus ensure maximum revenue from it.
When marketing campaigns are optimized with predictive analytics, new customer responses can be created along with promoting newer opportunities for sales, all leading to assist in attracting, retaining, and growing valued customer base.
Not just helping to improve operations, sales and customer base, predictive analytics can also be used to identify and possibly terminate various types of criminal behaviour before it can cause any serious damage. This is possible by using predictive analytics to study user behaviours and actions, this helps an organization to identify activities that are out of the ordinary, which could range from credit card fraud to corporate spying to even cyberattacks.
Predictive analytics models
The templates that allow users to turn past and current data into actionable insights, creating positive long-term results are the models of predictive analytics, which can be defined as the foundation of the same. Some common types of predictive models include:
- Customer Lifetime Value Model: This model pinpoints those customers who are most likely to make better and more investments in products and services.
- Customer Segmentation Model: This model groups customers based on their similar characteristics and purchasing behaviours.
- Predictive Maintenance Model: This model serves the purpose of forecasting the chances of essential equipment turning obsolete or being damaged.
- Quality Assurance Model: This model is to spot and prevent defects to avoid bad reputation and extra costs when providing products or services to customers.
Getting started in predictive analytics is a task that almost any business can handle as long as one remains committed to the approach and is willing to invest the needed time and funds to get the project moving. It is ideal to begin with a budgeted limited scale starting project, before diving too much deep in. Once a model is put into action, it usually requires little maintenance as it continues to bring out actionable insights for many years.