Predictive Analytics

Prescriptive Analytics

Advanced analytics that recommends specific actions to achieve desired outcomes based on predictive analysis.

Descriptive Analytics

The analysis of historical data to understand what has happened in the past and identify patterns.

Real-Time Analytics

The discipline of analysing data as soon as it becomes available to support immediate decision-making.

Customer Analytics

The practice of collecting and analysing customer data to understand behaviour, preferences, and lifetime value.

Cohort Analysis

A behavioural analytics technique that groups users with shared characteristics to track metrics over time.

Funnel Analysis

Tracking and analysing the sequential steps users take toward a desired action to identify drop-off points.

Churn Analysis

The process of analysing customer attrition to understand why customers stop using a product or service.

Graph Analytics

Analysing relationships and connections between entities represented as nodes and edges in a graph structure.

A/B Testing

A controlled experiment methodology that compares two versions of a product, feature, or experience to determine which performs better against a defined metric.

Identifying data points that differ significantly from other observations in a dataset.

Techniques such as differential privacy, federated learning, and secure computation that enable data analysis while protecting individual privacy and complying with regulations.

The process of examining, analysing, and creating summaries of data to assess quality and structure.

The process of moving transformed data from a central warehouse back into operational tools such as CRM, marketing platforms, and customer support systems to activate insights.

A technique for determining which input variables have the most significant impact on model predictions.

An abstraction layer that provides business-friendly definitions and consistent metrics on top of raw data, enabling self-service analytics with standardised terminology.

Statistical and machine learning methods for predicting future values based on historical sequential data, applied to demand planning, financial forecasting, and resource allocation.

An interdisciplinary field using scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data.

Machine Learning

A subset of AI that enables systems to automatically learn and improve from experience without being explicitly programmed.