Predictive modelling, also known as predictive analytics, is a discipline that uses statistical, mathematical and artificial intelligence techniques to predict future outcomes based on historical data.
Unlocking Business Insights
- We start by defining the business problem to be solved. Whether it’s forecasting demand, optimising inventory, or predicting customer behaviour, the goal is to make informed decisions based on data.
- Data Preparation: historical data is collected and pre-processed. This includes cleaning, transforming, and organising the data for modelling.
- Model Building: we create predictive models by generating ad hoc machine learning algorithms. Common types include:
- Classification and clustering: Categorising data into predefined classes (e.g., identifying potential high-value customers), or grouping similar data points (e.g., segmenting users based on behaviour).
- Regression: Predicting numerical values (e.g., estimating future sales revenue).
- Anomaly Detection: Identifying unusual patterns (e.g., detecting non-conformities in a production line or machine malfunction).
- We use different types of algorithms including statistical models (linear and logistic regression), ML algorithms (supervised and unsupervised), neural networks, deep learning techniques and XAI techniques.
- Real-Time Integration: once the model is trained, we can start to make predictions quite soon. Our client companies can then use these insights in real time to add value to their business areas.
Predictive AI: adding strategic value
Predictive modelling is key to empower businesses to make smarter, data-driven decisions, leading to growth and efficiency.
-
- Maximising revenues: accurate predictions allow businesses to optimise resource and cost allocation, as well as generate newly data-enhanced product lines.
- Cost Reduction: efficient inventory management, maintenance scheduling, and supply chain planning save costs and use of resources, resulting in higher sustainability.
- Competitive Edge: companies leveraging predictive analytics outperform competitors by staying ahead of market trends.
Our use cases for predictive models
Predictive AI models play a crucial role in business optimisation and decision-making. Here are some typical key use cases we can help your company with:
Building robust and scalable AI models
Predictive models are not static but under continuous learning. They automatically adapt as new data becomes available, so that we can make then scalable.
We focus on building highly performant optimised AI models that minimise the use of computational resources, resulting in lower costs and environmental impact.
Also, we always evaluate AI use cases within the specific company industry to make sure predictive AI empowers businesses to make data-driven decisions, optimise processes, and stay ahead in a dynamic market landscape.
We mostly use Predictive Models in the following case studies:
