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    Predictive analytics: what it is and how it can help you predict the future of your business

    Predictive analytics: we’d all like to know what the future has in store for us. And for those who run a company, and those who invest in it, it’s even more important. It means knowing how to best use resources to exploit future events and so improve operational efficiency, reducing risks and mistakes. The competitive advantage that this offers is very significant.

    According to research by Gartner, bad decisions by managers costcompanies more than 3% of profits, with 3.9 billion blown by Italian SMEs alone in 2021.

    Predictive analytics uses big data, statistics and modelling techniques to make predictions about future results and performance. In practice, it looks at current and historical data patterns to determine the likelihood of these patterns emerging again.

    Predictive analytics, what it’s used for.

    To understand how important using predictive analytics techniques could be for your company, just imagine being able to buy raw materials, at the best time and in the right quantity, for an order that… doesn’t yet exist, but has been predicted by a virtual assistant. There would be a host of significant benefits: savings in purchasing costs, reduced inventory and customer waiting time, increased efficiency in executing the order, direct margin growth for the company, elimination of time and decision costs in the process. With predictive analytics, in practice, you can do away with assumptions.

    All this can be achieved through predictive analytics and so-called “predictive models”, which can be used for numerous applications. 

    Predictive models are particularly useful for companies to manage their inventory, develop marketing strategies and forecast sales. In highly competitive business environments, their use is a real asset for a company. Operating on the basis of information that can predict what will happen in the future significantly reduces potential risks and mistakes.

    This is because, technically, predictive analytics models determine relationships and structures in the data that can be used to draw conclusions about how changes in the underlying processes, which in turn generate the data, will change results.

    Methods for predicting future actions using predictive analytics include:

    • predictive maintenance;
    • risk of abandonment (churn rate);
    • demand planning;
    • fraud identification.

    Predictive analytics, techniques used.

    What is the basis of this critically important tool for your business? Predictive analytics is based on a number of techniques that draw on artificial intelligence (AI), data mining, machine learning, modelling and statistics.

    The three most common techniques used in predictive analytics are: 

    • decision trees: the most simple of models because they are easy to understand and dissect, particularly useful when a decision needs to be made quickly;
    • regression: the most commonly used model in statistical analysis;
    • neural networks: part of artificial intelligence and able to handle complex data relationships, particularly useful when too much data is available.

    Predictive analytics, advantages and disadvantages.

    As we have already seen, the use of predictive analytics offers numerous advantages for a company. In particular, this type of analysis can be helpful when the need arises to make predictions about results and there are no other sources for the answers.

    Using predictive models can have a significant impact on cost reduction and this is especially true when companies use predictive analytics to determine the likelihood of success or failure of a product before it is even launched.Predictive analytics using artificial intelligence can also have some disadvantages. Its use has been criticised and, in some cases, is even legally restricted, due to perceived inequalities in the results it produces. The issue here is statistical discrimination against racial, ethnic or gender groups (so-called “bias”) in areas such as credit scoring, lending, human resources or assessing the risk of criminal behaviour.

    The various types of data analysis. Predictive analytics answers the question: “what will happen?”