In the era of data renaissance and artificial intelligence, predictive analytics is a specialised vertical of data science utilised for extracting future outcomes fairly accurately. Predictive analytics uses historical data, big data mining systems, statistical modelling and machine learning processes.
Organisations use predictive analytics to understand the business risk to face the upcoming challenges more smartly. Predictive analytics can foretell future sales revenue, cash flow and the profit margin.
Besides, predictive analytics also highlights key information regarding project overruns, risks associated with supply chain management, logistics production/execution etc. It also helps to provide a guideline for navigating new business geography.
Types of Predictive Analytics
Broadly, there are ten predictive analytics techniques. These are as follows –
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Classification model
This elementary predictive analytics tool classifies data based on closed-ended queries, whose response may be obtained through’ responses like yes or no.
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Forecast model
This model is also another common model that utilises historical data. Response received to queries in this system is numerical and useful in forecasting sales or revenue estimates.
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Clustering model
This model groups data based on the same or similar features. The collective data from different groups is then utilised to find out the overall outcome of the cluster.
Hard clustering is a process in which data is grouped based on the characteristics which completely match the cluster. However, another type of clustering, namely soft clustering, is also applied based on probability theory. In this case, probability or weightage is added to each data to tag its similarity percentage.
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Outliers model
This model locates if there is any individual unusual data within a pool of given data. This outlying information may have been generated due to some abnormal or abrupt change in the controlling parameters of business or a case of some potential fraud in financial transactions.
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Time series model
This is a predictive analytics tool where historical data over a specific time range is utilised to predict future trends over the same time series i.e. the same months.
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Decision tree algorithm
This predictive analytics model uses graphs plotted based on data obtained from different sources. The purpose of this tool is to identify the different future outcomes based on the different decisions the management undertakes. This compensates for incomplete and missing data and makes it easy for interdepartmental reviews and presentations.
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Neural network model
This model simulates neurons or the human brain through several complex algorithms and provides outcomes from different patterns or cluster data.
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General linear model
It is a statistical tool that can compare two dependent variables over a regression analysis.
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Gradient boosted model
In this model, flaws of several decision trees are corrected and ranked. The outcome is a product of several ranked or boosted decision trees.
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Prophet model
This model may be used along with time series and forecast models to achieve a specific or desired outcome in future.
Predictive Analytics Examples
In today’s world, predictive analytics is a subject that finds application across industries. Below are a few real-world predictive analytics examples for a better understanding of what is predictive analytics.
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Insurance sector
Nowadays, health and all general forms of insurance offerings are guided by predictive analytics. Historical data concerning the percentage of premature claims for customers with similar portfolios are studied.
This tool not only makes the offer more competitive but also helps craft out a better terms package for the client while keeping the profit margin untouched for the insurance company.
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Automotive industry
The neural network model of predictive analytics finds its application in self-driven cars. The car sensors assess and mitigate all safety concerns and challenges a moving vehicle should encounter. Furthermore, historical data can help car dealers or service providers prepare a maintenance schedule for specific car models.
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Financial services
One of the best examples of predictive analytics is its ability to run financial institutions profitably by locating fraudulent activities, identifying potential customers, eliminating loan defaulters and scrutinising other dynamic market scenarios.
Besides the above functions, credit scoring is a major function of financial institutions, and this function is driven by predictive analytics. CIBIL scores for individuals and organisations determine their trustworthiness in securing loans.
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Healthcare
In all modern countries, predictive analytics has become a stable cornerstone for the healthcare industry. Historical records of patient data regarding medicine and surgical techniques with the outcomes have become the backbone of future healthcare systems, ailment-wise. These records have also helped create smooth readmission of patients and immediate diagnosis in each case.
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Marketing and retail sector
Nowadays digital marketing has taken over the age-old traditional marketing practices. Search engines recommend desired products to customers and provide their specifications, prices and past reviews.
Digital marketing techniques target customers based on their recent searches. The retail sector has now become extremely competitive with data-oriented
tailor-made and client-centred products and services.
The target audience may be reached quickly, thereby increasing the sales footprint. Predictive analytics tools also scrutinise client behaviours, purchase power and patterns to improve customer relationships and return on investments.
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Machines and industry automation
Predictive analytics also finds its application in this sector. Machines are prone to breakdowns that result in production downtime and sometimes employee safety risks. Historical data on these machines help in preventive maintenance thereby minimising machine failures improving employee safety factors and boosting workforce morale.
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Energy and utilities
Oil and gas services manage a serious business. Their management must make informed decisions regarding resource allocation and optimum utilisation. Similarly, based on the actual demand based on weather conditions and available supply, these companies must determine the optimum prices for the energy charges.
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Manufacturing and supply chain management
Product manufacturing is directly linked to the demand and supply ecosystem. Predictive analytics take inputs from historical data to predict accurate market demand over a specific time.
Demand depends on factors like market trends, weather, consumer behaviour interests, etc. Past data on manufacturing help the organisation eliminate erroneous or age-old processes, thus speeding up production.
Supply chain and logistics historical data help to speed up and improve the product delivery process to the client, thereby increasing client satisfaction.
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Stock trading markets
Predictive analytics is a very crucial tool when it comes to stock trading. Investing in IPOs and stocks is based on historical data.
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Human resources
The human resource team in an organisation often uses predictive analytics to determine highly productive processes. They also use predictive analytics to analyse the skill requirements in human resources for future business activities.
Besides the above examples, predictive analytics has its footprint virtually everywhere. Even mere typing on the mobile or computer system is supported by a predictive text. Predictive analytics have gained immense importance today and have spiralled as a lucrative career opportunity.
Students are encouraged to pursue a holistic data science course from a good institution. Read about data Scientists and the possible career opportunities to learn more.
Benefits of Predictive Modelling
Today an organisation invests a lot of money in predictive analytics programs to gain the below-mentioned benefits -
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Data security
Every organisation must be concerned with security first. Automation in collaboration with predictive analytics takes care of the security issues by flagging unusual and suspicious behaviours in network systems.
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Reduction of risk
Nowadays, companies consider risk as an opportunity. Thus, mitigation of risk is important and not aversion. Predictive analytics, with the input of historical data, has the capability of risk reduction.
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Operational efficiency
Efficient work processes result in shorter production cycles and hence, better profitability.
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Improved decision making
Last but not least, nobody can deny that an organisation succeed or fails only based on the key decisions made. Nowadays, all key business calls like expansion, merger auction etc. are made based on the inputs from predictive analytics.
Conclusion
Predictive analytics is the future and goal of artificial intelligence. It combines with machine learning to deliver the desired results. The objective of predictive analytics is to forecast future events. The process eliminates past operational errors and suggests a more pragmatic solution in several business sectors.
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FAQs
- What is the predictive model in data mining?
The purpose of applying a predictive model in data mining is to extrapolate the missing data with the help of other available data in the group. The process involves the imposition of statistical models and machine learning algorithms to determine the pattern and relationship of missing data with those available in the system.
- How is data collected for predictive analytics?
Data may be available over various platforms like industry databases, social media platforms and the historical data of the firm planning to conduct the predictive analytics process.
- How accurate is the predictive analytics process?
Subjective expert opinion is an outcome of experience and may vary from one individual to another based on the extent of exposure received. However, predictive analytics is data-driven and forecasts accurate outcomes, provided that no large-scale disruptive events or exceptions come in between.
- Is predictive analytics a part of AI (Artificial Intelligence)?
Predictive analytics is a core attribute of artificial intelligence.
