Predictive analytics is a key digital trend that is realized through big data statistical techniques, modeling and machine learning. Its function is to extract historical data to generate predictions. This is especially important in the business world and brings many advantages because it allows identifying opportunities and detecting risks early.
It is an advanced analysis that helps to examine data to know what is most likely to happen in the future, interpreting predictions, which is valuable in processes and business. It also allows you to identify trends and determine whether they are likely to recur.
Want to know all about predictive analytics? We explain in detail how it works, the best tools for it and some efficient strategies.
Predictive analytics is mainly based on advanced machine learning techniques. Through them, data experts use deep learning and algorithms to analyze different variables and create predictive models.
These predictive models are used to identify correlations between different elements in selected data sets. Subsequently, data collection is completed, a statistical model is formulated, trained and modified to generate predictions.
The workflow for predictive analytics is as follows:
A prediction starts with a thesis and a set of requirements. Clearly determine what you want to predict and what the business value of that prediction is.
Based on this, you can gather relevant data that can be influenced, such as past sales, customer behavior or economic indicators.
Companies may have decades of data to draw from or a stream of data from customer interactions.
Before a predictive analytics model can be developed, data streams must be identified and organized in a repository for better management and control.
Raw data is valuable, but not very valuable.
To prepare it for predictive analysis models, it is necessary to eliminate anomalies, missing data points or extreme outliers, which could be the result of input or measurement errors.
Data scientists have at their disposal many tools and techniques to develop predictive models according to the problem to be solved and the nature of the data set.
Machine learning, regression models and decision trees are some of the most common and efficient.
It is very important to check the accuracy of the model and make adjustments accordingly.
When acceptable results are available, they should be made available to stakeholders through an application, a data dashboard or a website.
Decision trees are a widely used predictive modeling tool because of their simplicity and effectiveness.
They work by dividing the data set into smaller subsets based on the most important features for prediction.
Each node in the tree represents a feature, and each branch represents a possible outcome. This structure allows decision trees to handle both categorical and continuous variables.
One of the main advantages of decision trees is their ability to handle data with multiple variables and their ease of interpreting the results. In addition, they do not require a large amount of data preprocessing and can handle nonlinear relationships between variables.
It is a technique inspired by the functioning of the human brain. It consists of layers of interconnected nodes, each representing a “neuron” that processes information and transmits signals to other neurons.
Neural networks are particularly effective at detecting complex, nonlinear patterns in data, making them ideal for tasks such as image recognition and natural language processing.
One of the greatest strengths of neural networks is their ability to learn and improve with large volumes of data. They are also highly flexible and can be applied to a wide variety of predictive problems.
This is one of the oldest and most widely used predictive analytics tools. It is used to model the relationship between a dependent variable and one or more independent variables, assuming that this relationship is linear.
The objective is to fit a straight line (or hyperplane in the case of multiple variables) that minimizes the sum of the squared errors between the predictions and the actual values.
Used to predict binary outcomes (yes/no, true/false) based on one or more independent variables. Unlike linear regression, which predicts continuous values, logistic regression uses a logistic function to model the probability of a specific outcome.
One of its advantages is that it is simple and easy to interpret. The coefficients obtained in the model indicate the relationship between the independent variables and the probability of occurrence of the outcome of interest. In addition, it is a relatively robust technique and easy to implement.
To achieve effective predictive analytics, it is crucial to follow a series of strategies to ensure the accuracy, relevance and applicability of the models developed:
You must set specific goals for what you want to achieve, such as predicting future sales, identifying potential risks or improving customer satisfaction.
It is critical that these objectives are aligned with what the company is strategically seeking, to ensure that the results of the analysis are useful and actionable in decision making.
Quality data collection and preparation is essential. Use multiple data sources, both internal and external, to obtain a complete and detailed picture.
It is also important that you perform thorough data cleaning, eliminating duplicates, correcting errors and handling missing values appropriately.
Data must be transformed and standardized to ensure that they are consistent and suitable for analysis. Quality is critical, as predictive models can only be as good as the data on which they are based.
Exploratory data analysis (EDA) is a critical step that should not be overlooked. It uses data visualization techniques and basic statistical calculations to better understand the relationships and patterns present in the data.
This preliminary analysis will allow you to identify key variables and potential problems that could affect the performance of the predictive data model.
EDA helps to uncover trends, detect outliers and establish relationships between variables, providing a solid foundation for predictive modeling.
Choosing the right predictive model is essential. You must select the most appropriate type of model for your specific problem, be it regression, classification, clustering, among others.
Subsequently, the model must be trained using a subset of the data and then validated with a different subset to assess its accuracy.
It is important to adjust and optimize it as necessary to improve its performance. Selection of suitable algorithms and hyperparameter tuning play a crucial role at this stage.
Subsequently, the model must be implemented in the company's operating systems.
This process has to be careful and well planned so that it is fully exploited and can be used in real time, obtaining useful and accurate results.
In addition, the model has to be monitored and maintained to ensure that it remains relevant and functional.
To achieve this, external professional advice can be sought, with the objective of having an external expert eye that considers all the key aspects of a business and detects the potential for improvement, creating a roadmap that impacts results.
Communicate the results of the analysis in an understandable way. This will allow you to explain to stakeholders the potential impact of the predictions on the business and provide recommendations based on the results, which ensures that the decisions made in the predictive analysis are informed and strategic.
Clarity in communication helps stakeholders understand and trust the results of the analysis.
Continuous review and improvement is essential. Gather feedback from model users and make adjustments to improve its accuracy and usefulness.
It is important to keep the model updated with new data to ensure its relevance over time, through constant feedback and new data that helps refine and improve predictive models, ensuring that they remain accurate and useful in an ever-changing business environment.
Adaptability and responsiveness to changing needs and market conditions are critical to long-term success.
Predictive analytics is a very valuable tool when making decisions, since it is based on what may happen in the future, allowing you to anticipate and be prepared for different scenarios.
For this, London Consulting Group can help you by facilitating the comprehensive understanding of daily operations through the information generated. The focus is on implementing the right technological tools to obtain reliable and accurate data.
Everything, hand in hand with your teams so that they are prepared and can take control over time.
If you want to keep growing and adopt new trends that deliver results, such as predictive analytics, contact us! And start the transformation of your organization.