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Predictive analytics is a branch under advanced analytics primarily used to make predictions about the uncertain future events. Predictive analytics makes use of statistics, modelling, data mining, artificial intelligence, machine language to work on the current set of data provided as instructions and predict the future events.
Through the usage of the historical data and transactional data patterns, risks are identified in addition to exploring opportunities for future needs. Through the usage of predictive analysis businesses can effectively interpret the date available to them for their benefits.
Data mining and text analytics along with statistics enables business users to create a predictive intelligence by unravelling relationships among the data patterns both in the structured and unstructured data. Certain data can be used readily for commencing the analysis like the age, gender, income, sales etc. These data are referred to as structured data. In addition to the structured data we also information available around us like the social media, website information and any other information from which data could be picked and then structured for further analysis. This is called unstructured data. The date collected from such activities are then extracted and used in model building process.
Through predictive analytics organisations tend to get more pro-active and forward looking in terms of planning and apt decision making rather than going by emotions or a gutsy feeling!
Process involved in Predictive Analytics
1. Project Definition: Identify what shall be the outcome of the project, the deliverables, business objectives and based on that go towards gathering those data sets that are to be used.
2. Data Collection: This Is more of the big basket where all data from various sources are binned for usage. This gives a picture about the various customer interactions as a single view item.
3. Analysis: Here the data is inspected, cleansed, transformed and modelled to discover if it really provides useful information and arriving at conclusion ultimately.
4. Statistics: This enables to validate if the findings, assumptions and hypothesis are fine to go ahead with and test them using statistical model.
5. Modelling: Through this accurate predictive models about the future can be provided. From the options available the best option could be chosen as the required solution with multi model evaluation.
6. Deployment: Through the predicative model deployment an option is created to deploy the analytics results into everyday effective decision. This way the results, reports and other metrics can be taken based on modelling.
7. Monitoring: Models are monitored to control and check for performance conformance to ensure that the desired results are obtained as expected.
Applications of Predictive Analytics
1. CRM: Through predictive analytics marketing campaigns, sales, and customer services are objectively achieved. This can be used in analytical customer relationship management throughout the customer life cycle right from the acquisition, relationship growth, retention and customer win back can be better planned and strategically addressed for retaining customers and addressing them more clearly.
2. Health care: Usage of predictive analytics in the health care domain can aid to determine and prevent cases and risks of those developing certain health related complications like diabetics, asthma and other life threatening ailments. Through the administering of predictive analytics in health care better clinical decisions can be made.
3. Collection Analytics: These applications optimise the allocation of collection resources by identifying collection agencies, contact strategies to reach out to them, legal actions to increase recovery and cost reduction of collection.
4. Cross Sell: Through predictive analytics applications attached to various touch points connected to the customers a detailed analysis on the customer spends, usage pattern of certain purchases they make regularly, customer behaviour can obtained with which ultimately to efficient cross sales or selling additional products to customers. This way organisations dealing with multiple products can effectively increase its sales volume and profits ultimately.
5. Fraud Detection: Predictive Analytics can aid to spot inaccurate credit application, deviant transactions leading to frauds both online and offline, identity thefts and false insurance claims saving financial and insurance institutions of lots of security issues and damages to their operations.
6. Risk Management: The best portfolio prediction to maximise returns on the capital invested, probabilistic risk assessment to yield accurate forecasts are some of the important benefits of using predictive analytics.
7. Direct Marketing: Predictive Analytics also aids in identifying the most effective combination of product versions, marketing material, communication channels and the timing to be used to target a given consumer in the current environment where the dynamics are constantly changing and gets challenging for a business to compete and run successfully.
8. Underwriting: Perhaps one of the biggest benefits that can infiltrate into underwriting is providing information about the likelihood of illness, default of loan/insurance and bankruptcy. Predictive Analytics streamline the process of customer acquisition by closely predicting the future risk behaviour of a customer through the application data.
As a result of all these benefits predictive analytics finds a wide range of usage in telecom, insurance, banking, marketing, financial services, retail, travel, heath care, pharmaceuticals, oil and gas and a host of other industries where organisations are getting to take decisions based on data than emotions. In fact organisations are getting to take decisions like “next best offer” or product recommendation capability with this.
Even with those cautious and disciplined approaches we had seen in the process section, it does get pretty astonishing that we can use analytics to predict the future with a great degree of accuracy. All that we have to do now is gather the right data, do build the right type of statistical model, and be careful of our considered assumptions. There enters a new age of analytical predictions where ultimately we will reach that stage in our lives that the results obtained as a result of the predictive analytics are going to yield better results in all our decisions during our course of life that a soothsayers words and predictions may take a back seat!
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