Predicting future trends or outcomes allows entrepreneurs, professionals, and every stakeholder to make informed decisions, elaborate strategies and prevent possible issues.
But how can a company or an individual predict the future? This is where predictive data analysis comes in. Find out what predictive analytics is and how it works, and reinforce your knowledge with our use cases.
Predictive analytics definition
Predictive analytics is an advanced method of data analysis aimed at predicting future results. In other (simpler) words, it uses historical data to try to answer the question “What could come next?” To do that, it uses big data systems, AI, machine learning, and statistical models to find patterns that could help predict future outcomes.
Companies and organizations can use predictive analytics to predict future trends in their market sector. For example, to forecast which sales channel will be the main one in the apparel industry in 2025. Predict possible outcomes for their business strategies, identify risks, prevent issues and workflow interruptions, and ensure security.
How does predictive analytics work?
Of course, predictive analytics doesn’t actually predict the future. What it actually does is utilize predictive models to identify correlations (patterns) between different elements within the provided set of data. After that, the predictive analytics process provides a statistical model and uses it to generate accurate predictions.
To go into even more detail, the predictive analytics process can be simplified in five main steps.
1_Goal Definition
Any predictive analytics process should start with a definition of goals. What do you want to use the predictive model for? Identify the probability of flooding within an area in case of bad weather? Identify the ideal amount of goods to stock for the next season?
Whatever they are, the identified goals outline the type of data to collect and the predictive model to use.
2_Data Acquisition
As predictive analytics is based on data, it goes without saying that the next step is data acquisition. However, a company could have decades of collected data at its disposal, in this phase, it’s useful to identify what data is relevant to the defined goal and organize datasets in a repository.
3_Data elaboration
Data is useless when not elaborated. In this phase, data is analyzed, correlations between elements are found, and anomalies are removed. This way, data is prepared and made ready for predictive analytics, the next phase.
4_Deployment of Predictive Models
Data scientists can use different techniques to develop predictive models, based on the questions they are trying to answer.
The main techniques of their disposal are:
- Regression Analysis: a technique that estimates the relationship between variables. It is useful to determine possible patterns in large data sets and correlations between inputs. A typical use case for regression analysis is to determine how a price increase could affect the sale of a product.
- Decision Trees: models that locate data into categories based on different variables. They’re called decision trees because they are particularly effective in understanding an individual’s or group’s decisions. Their structure is similar to a tree in which each brand represents a choice and each leaf represents the results of that decision.
- Machine Learning: essentially, they are powerful shape recognition engines, deployed to determine non-linear relationships in data sets, especially when there is no mathematical formula for analyzing data.
- Multiple technologies can be utilized together. For example, machine learning is sometimes used to validate the results of regression models or decision trees.
5_Results Deployment
Once acceptable results are obtained, they are made available for stakeholders through the generation of reports, data dashboards, or similar. The way data are displayed is particularly important for
Predictive Analytics: Use Cases
To better understand what predictive analytics can do for organizations across many sectors, let’s have a look at three use cases that show how it can be implemented in different industries.
1_Hospitality: Predictive Analytics to Determine Staffing Needs
In the hospitality industry, customer influx and outflow depend on various factors such as season, presence of big events in the area, currency exchange rate, and others. As the number of staff members needed depends on such customer flow, regression models can be implemented to predict the number of check-ins on any given day and the consequent staffing needs.
In this sector, predictive analytics becomes a tool to avoid overstaffing (which would translate into higher costs and a waste of resources) or understaffing (which would lower the quality of the service provided).
2_Digital Marketing: Predictive Analytics to Plan Campaigns
In digital marketing, users’ and customers’ behavioral data is abundant. Almost any action that users perform on a given platform can be recorded, stored, and analyzed to predict what is most likely to happen next.
In this scenario, predictive analytics is used to forecast sales trends in the different seasons of the year based on weather conditions, events, competitors moves, and any other relevant fact. All this information is used to plan the digital marketing campaigns accordingly.
3_Manufacturing
In the manufacturing sector, machinery maintenance is essential to ensure continuity in the workflow and production chain.
Here, data gathered by sensors located across the machines can be analyzed to predict when a piece is likely to malfunction.
The algorithm can be set so that, when malfunction criteria are met, alerts are triggered and employees can fix or replace the piece. Preventive maintenance (based on preventing issues instead of waiting for their occurrence) can save businesses a lot of money in repair costs. Not to mention how deploying predictive analytics for preventive maintenance would also result in increased productivity and security.
Conclusion
Predictive analytics offers organizations across various industries the ability to make data-driven decisions that can significantly enhance efficiency, productivity, and optimize costs. As technology continues to evolve, predictive analytics will become essential in shaping the future of business strategies.
