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

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

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

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.

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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.

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