What is Predictive Analytics?
Predictive analytics definition
The unprecedented amount of data generated by Internet-enabled devices and machines has given rise to predictive analytics, the practice of building analytical models that interpret this data in order to predict the likely outcome of future scenarios. Data scientists mine data for correlations among independent streams of information, then build and back-test models that can produce useful insights.
Why predictive analytics?
Companies can use predictive analytics to understand likely customer behavior, streamline internal processes, monitor and automate IT infrastructure and machine maintenance, and more. A few top use cases include:
- Managing risks: Using statistical models for risk management is nothing new, but predictive analytics takes the practice to a new level of precision. Now technology can uncover an issue and proactively resolve it before your business is impacted.
- Predicting demand: Predictive analytics can help companies smoothly allocate resources and adjust bandwidth or inventory levels to match likely needs, boosting overall operational efficiency.
- Cross-selling and up-selling: Particularly useful for e-commerce, this type of predictive analytics allows retailers to offer additional purchase options during current transactions based on data from previous similar transactions
- Optimizing pricing strategy: Using predictive analytics, companies can understand the micro-dynamics of a given market and tailor pricing to increase sales and/or optimize profit dollars based on likely consumer behaviour.
- Automating system maintenance: The Internet of Things (IoT) makes it possible to capture data at the edge of your network and use predictive analytics to perform preventative maintenance and minimize downtime.
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