​Predictive Analytics: Why The Future Doesn’t Need To Be Left To Chance

A tool that can predict a downturn in business or make that all important intervention to prevent a bad investment is no longer a thing of fantasy


Applying analytical and statistical techniques to historical data makes it possible to identify the patterns that highlight potential business development opportunities or risks. This has become a bedrock for the kind of proactive and intuitive decision-making that can translate into a competitive advantage.

As businesses that use analytics in this way achieve a higher ROI than those that don’t, it’s not surprising that we are seeing this employed across a number of sectors. In retail, for example, predictive analytics routinely provides insights into purchasing behavior and preferences that can prove to be a game-changer in terms of tailoring the offering and driving a far more personalized and enriched customer experience. Then there’s the ability to establish detailed intelligence on a customer’s potential lifetime value. This allows us to assess to what extent a group of customers or prospects should be pursued to both optimise their loyalty and spend, or perhaps to understand the likelihood of their defection to another retailer.

If we look to the world of finance, predictive analytics is now regularly harnessed for fraud detection and managing risk. While in manufacturing, analytics ensure that production is optimized and that the routine maintenance of machines can be carried out on time, with issues tackled before they escalate into problems that could affect the smooth running of mission critical systems.

Indeed, as we are on the cusp of the Fourth Industrial Revolution, manufacturers are now seeing predictive analytics as disruptive technology, which is driving meaningful insights on products, processes, productions and maintenance in real-time, or even in advance. While different manufacturers will have varying data environments and data needs, there remain four key areas where predictive analytics could really help to optimise operations: product quality, forecasting demand, machine utilisation and factory maintenance. Adopting such technology is helping to drive target-oriented decisions and proactivity, which in turn lead to growth and greater profitability.

Putting specific verticals to the side for a moment, we also need to consider the application of predictive analytics across the disciplines of customer marketing, mobile asset management, supply chain management or route optimisation – a broad selection of disciplines that barely scratches the surface of areas that can benefit from predictive analytics. The applications are undoubtedly diverse, but a common denominator is the significant competitive advantage that can be gained, or business risks eliminated, through considered application of predictive analytics.

Unravelling the myths

For those who didn’t major in data science, however, predictive analytics can all too easily appear to be a mystical process. Indeed, as a narrative with a focus that usually falls upon the outcomes and end result, for many, the actual mechanics remain an unknown quantity. But, it really is worth considering what is actually involved.

Predictive analytics is a process that starts as soon as the project and business objectives are defined, and the scope of work is laid out to determine the relevant data. This data is captured, then checked, cleaned up and transformed, before an exploratory data analysis (EDA) is conducted and statistical tests undertaken to verify the relevant assumptions and hypotheses.

We can then drill down further to the data modelling, which combines classic statistical models such as regression, to build linear relationships with supervised/unsupervised machine learning models, such as support vector machine, tree based and artificial neural networks. The chosen model, based on the evaluation of error matrices, is then back-tested for validity and if necessary corrected with the subsequent results used for daily decision-making processes. Decisions based on these results, can be automated and the choice of statistical method is selected depending on business requirements.

It is this behind the scenes complexity – invisible to the business user – that has helped to simplify real-time data for these business experts and is the foundation from which an era of flexible, intuitive and data-supported decision-making can then fully flourish.

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