Predictive analytics: what you need to know - IBM SPSS

Predictive Analytics: shaping our futures

No one needs to be told that being able to predict the future grants a remarkable advantage.

Predictive analytics allows you and your clients to discover insights about future possibilities, leading to better informed and smarter decision making.

Based on the analysis of trends, patterns and relationships in data, predictive analytics combines statistical algorithms and machine learning to go beyond understanding what happened and why – it will now provide a best assessment of what will happen further into the future.

Through a combination of ad-hoc statistical analysis, modelling, data mining, analytical techniques, optimisation, cognitive computing, and real-time scoring, predictive analytics can reliably forecast future business trends.

It is both helping solve difficult challenges and uncovering new opportunities.

 

Enhancing every customer interaction

Once they’ve integrated predictive analytics, companies can strengthen customer loyalty and retention, while also reaching out to new customers.

As predictive models can both forecast inventory and manage resources, hotels can estimate the number of guests for any given night to maximise occupancy and increase revenue. Airlines now use predictive analytics to set ticket prices.

When used to determine customer responses or purchases, predictive models help businesses attract, retain and grow their most profitable customers.

 

Driving more intelligent marketing

When advanced analytics are applied to marketing attribution, marketing can be made more efficient, and promotional opportunities optimised.

The most likely opportunities for growth and competitive advantage can be quickly identified, enabling an efficient recognition of the most effective combination of product versions, communication channels, and timing that should be used to target a given consumer.

 

Minimising and mitigating fraud

It’s increasingly important to protect an organisation’s revenue and reputation by detecting and nullifying any attempts at fraud as soon as possible.

Combining multiple analytics methods can improve pattern detection, preventing any possible threat of criminal action as it highlights fraudulent transactions both on and offline. It will also discover inaccurate credit applications and identify thefts and false insurance claims.

Abnormalities that may indicate fraud, zero-day vulnerabilities, and advanced persistent threats can be spotted in real-time by such high-performance behaviour analytics.

 

Risk management and underwriting

Yielding accurate forecasts, predictive analytics can identify the best portfolio to maximise returns through swift comparisons of capital asset pricing models and probabilistic risk assessments.

By predicting the chances of illness, default, and bankruptcy, as well as predicting the future risk behaviour of a customer using application level data, it also streamlines processes such as underwriting.

 

Propel research analysis

Predictive analytics, working in combination with ad hoc analysis, hypothesis testing, and geospatial analysis, provides a fast and powerful solution to business and research challenges.

It ensures a company can understand data, analyse trends, and forecast and plan to validate assumptions and drive accurate conclusions.

IBM SPSS Statistics, for example, works inside a single, integrated interface to run descriptive statistics, regression, advanced statistics, and a host of other technologies, enabling the creation of publication-ready charts, tables and decision trees.

 

What is analytics? The Data Analysis definition

Data that has been gathered by a company can be transformed into a vital asset through the deployment of AI technologies.

This can be structured data, featuring information like age, gender, marital status, income, sales, etc. But it can also be unstructured data, as found in call centre notes or on social media.

Data Analysis involves the inspection, cleansing, and modelling of the data, in preparation for its transformation into a means of arriving at better informed and more insightful decision making.

Put simply, this can be broken down into three important areas:

 

Reporting Analysis

What happened. Why it happened

 

Monitoring

What is happening now

 

Predictive Analytics

What is going to happen in the future

 

Making the analytical life cycle work for you

What would you like to know about the future?

Many things, undoubtedly.

To get started, you need to define the project outcomes you’re hoping for, as well as identifying the data sets that can be used.

Next comes the Data Collection, drawn from multiple sources to provide a complete view of the process including customer interactions.

In the Data Analysis stage, the data is inspected, cleansed, transformed, and undergoes modelling, with the aim of discovering useful information that will arrive at insightful conclusions. Statistical Analysis follows, using standard statistical models to test and validate assumptions and hypotheses.

The ability to automatically create accurate predictive models is enabled through Predictive Modelling, with options to choose the best solution with multi model evaluation. Predictive Model Deployment integrates the analytical results into the everyday decision-making process, suggesting actions deriving maximum benefit from the predictions.

Now your company is anticipating outcomes and behaviours based upon data rather than assumptions, the business can quite literally become more proactive and forward looking.

 

Predictive analytics in an easy-to-use package

The combined advanced techniques of the entire Predictive Analytical process – planning, data collection, data mining, analysis, modelling, reporting, and deployment – have all been ingeniously addressed in a simple to use package.

IBM has brought together an extensive library of machine-learning algorithms and statistical analyses into a single flexible and scalable platform for one-to-many analytics.

It enables powerful model-building, evaluation, and automation, along with Linear Regression analysis capabilities that can be used to predict the value of a variable, based on the value of another variable. It also combines Logistic Regression (also known as logit model) for machine learning extensions.

In this video, Nancy Hensley, Director of Technical Marketing for the IBM Analytics Platform dispels the common misconceptions around predictive analytics.

Conclusion

Predictive analytics solutions provide organisations with a way to analyse data and, through deploying prescriptive analytics, transform it into recommended actions almost instantaneously, opening up new opportunities, improving efficiency, and minimising risk.

By successfully applying predictive analytics an organisation can effectively interpret data for their benefit, including accurately assessing risk, placing decision making on a firm base, and making predictions regarding future developments.

In what ways will predictive analytics be important to you and your clients?

You can explore IBM SPSS Statistics in more detail here.

Then discover the remarkable capabilities of IBM SPSS Modeler.

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