The ability to anticipate events, trends, and behaviours is an essential strategic advantage for modern companies. Adopting a predictive approach makes it possible to base decisions on objective data, reducing uncertainty and improving operational efficiency in every area—from supply chain management to financial planning, from marketing to the maintenance of production assets.
In this context, understanding the differences between forecasting and predictive analytics is the first step toward implementing effective solutions aligned with business goals.
Forecasting and Predictive Analytics: Two Different Ways
to Predict the Future
Both approaches share a common goal: anticipating future events based on data. This apparent similarity often leads to confusion, with forecasting and predictive analytics considered synonymous or variations of the same process. In reality, they are distinct methodologies, each with specific purposes and different approaches.
Forecasting: What It Is, How It Works, and How It’s Used in Business
Forecasting is a predictive technique based on the analysis of historical data aimed at estimating the future evolution of a given variable. In other words, it is a process that produces a quantitative forecast: the output of forecasting is an expected numerical value—a specific data point answering a specific question.
Forecasting is based on the principle that past behaviours—such as trends, seasonality, cycles, or recurring patterns—tend to repeat over time. It builds models that make it possible to project the variable of interest into the future based on a solid and well-defined historical structure.
In business settings, this approach is widely used in various areas, such as demand forecasting for goods and services, sales and commercial strategy planning, inventory management, production scheduling, and the definition of budgets and financial forecasts.
Forecasting is particularly effective when rich, reliable, and relatively stable historical datasets are available, which allow for precise understanding of the phenomenon's dynamics. Technically, implementation can rely on different methods depending on the type and complexity of the problem and the availability of supporting data. Key techniques include time series analysis, causal models, and machine learning algorithms, as well as qualitative methodologies based on expert judgment, contextual analysis, focus groups, and participatory methods.
Predictive Analytics: The Ability to Anticipate Future Behaviours
Predictive analytics refers to a set of analytical techniques within the broader domain of data science, designed to anticipate future behaviours, estimate event probabilities, and classify complex situations based on a wide range of structured and unstructured, historical and real-time data.
Unlike forecasting, which focuses on estimating a specific variable, predictive analytics aims to model scenarios and answer probabilistic or categorical questions: “What is the likelihood that this customer will churn in the next 30 days?” or “Is this transaction fraudulent?” For example, by integrating purchase history, CRM-recorded interactions, and, when available, external data sources, a predictive model can estimate the purchase propensity of a customer segment, identify the most relevant product categories, and provide marketing with insights for personalised campaigns.
The distinctive value of predictive analytics lies in the ability to combine a broad spectrum of variables—historical data, behavioural signals, system logs, open data—to produce insights that support timely decisions. These techniques are widely applied in numerous business scenarios, from customer retention to risk management, from fraud detection to the predictive maintenance of production assets.
Technically, predictive analytics rely on a complex set of algorithms chosen based on the problem type and dataset characteristics. Common approaches include regression and classification models (e.g., decision trees), as well as artificial neural networks.
How predictive analytics works
Building a predictive analytics model requires a structured sequence of steps and a lot of expertise in the world of data science. To summarize, we can identify 4 basic steps.
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- It all starts, logically, with the collection and preparation of historical data, which is the basis for identifying relevant patterns and trends. This is followed by a feature engineering phase aimed at selecting the most informative and useful variables for forecasting purposes.
- This is followed by the choice of algorithm, a key element that depends on the nature of the problem. If the goal is to classify data into predefined categories, algorithms such as Decision Tree, Random Forest, or Support Vector Machine (SVM) are ideal, while for the prediction of continuous numerical values, regression algorithms such as Linear Regression or Generalized Linear Model (GLM) are used.
- The model is then trained through a dataset divided equally into training set and test set to evaluate the ability to make accurate predictions on unknown data.
- The final stage involves optimizing the model through the use of metrics such as precision, F1 score or mean squared error, depending on the type of problem and the business objectives set by the company.
The Differences Between Forecasting and Predictive Analytics
Many differences between forecasting and predictive analytics have emerged in the analysis of each method.
However, there are additional aspects worth considering:
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- Time Horizon: forecasting typically focuses on the medium to long term, while predictive analytics tends to operate over a more dynamic time frame, from short to long term.
- Input Data: forecasting primarily relies on historical data, whereas predictive analytics uses multiple sources, including unstructured and real-time data. These are processed through complex ingestion, transformation, and advanced analysis workflows using specific predictive models to extract useful insights.
- Objective: forecasting aims to provide precise estimates of future values, while predictive analytics has a broader, more strategic purpose: modelling scenarios, anticipating risks, and estimating probabilities of events and behaviours.
Is one approach better than the other? Not necessarily. Both methods are valuable and applicable in specific business contexts. However, it is undeniable that predictive analytics offers a broader scope of action, thanks to its ability to be tailored to virtually any business-relevant question. For this reason, it is a fundamental driver of innovation and efficiency for the most dynamic, smart, and forward-looking companies.
Avvale: A Strategic and Value-Driven Approach
At Avvale, we take a 360-degree approach on artificial intelligence and predictive analytics, integrating advanced technical skills with experience, effective methodologies, and a deep understanding of business processes. The goal is to help companies identify opportunities to apply AI and design solutions that, once implemented, generate tangible value and more competitiveness.
Over the years, we have consolidated numerous success stories by collaborating with large Italian and international companies. For example, we collaborated with a leading Italian asset management company to develop investment strategies based on machine learning. Using predictive models, the company significantly improved the accuracy of forecasts compared to traditional models, gaining key insights for investment decisions.
A different context, but with the same need for accurate forecasts, is one that has seen us collaborate with PVH, a leader in solar tracking systems, to develop a machine learning model that can accurately predict wind gusts. And last but not least, the collaboration with the Hotel Gran Bilbao in Spain saw us engaged with predictive models that enabled the hotel to predict the demand and occupancy of its spaces with very high accuracy.
