Customer engagement analytics is the process of collecting, measuring and analysing how customers interact with a business or its services. It involves tracking activities such as website visits, social media interactions, email responses and purchase behaviour. Businesses use these insights to understand customer preferences, improve their services and build stronger relationships with their audience.
Category: Data Science
Knowledge Consolidation Models
Knowledge consolidation models are theories or computational methods that describe how information and skills become stable and long-lasting in memory. They often explain the process by which memories move from short-term to long-term storage. These models help researchers understand how learning is strengthened and retained over time.
Generalization Error Analysis
Generalisation error analysis is the process of measuring how well a machine learning model performs on new, unseen data compared to the data it was trained on. The goal is to understand how accurately the model can make predictions when faced with real-world situations, not just the examples it already knows. By examining the difference…
Knowledge Mapping Techniques
Knowledge mapping techniques are methods used to visually organise, represent, and share information about what is known within a group, organisation, or subject area. These techniques help identify where expertise or important data is located, making it easier to find and use knowledge when needed. Common approaches include mind maps, concept maps, flowcharts, and diagrams…
Knowledge Transfer Networks
Knowledge Transfer Networks are organised groups or platforms that connect people, organisations, or institutions to share useful knowledge, skills, and expertise. Their main purpose is to help ideas, research, or best practices move from one place to another, so everyone benefits from new information. These networks can be formal or informal and often use meetings,…
Temporal Feature Forecasting
Temporal feature forecasting is the process of predicting how certain characteristics or measurements change over time. It involves using historical data to estimate future values of features that vary with time, such as temperature, sales, or energy usage. This technique helps with planning and decision-making by anticipating trends and patterns before they happen.
Anomaly Detection Pipelines
Anomaly detection pipelines are automated processes that identify unusual patterns or behaviours in data. They work by collecting data, cleaning it, applying algorithms to find outliers, and then flagging anything unexpected. These pipelines help organisations quickly spot issues or risks that might not be visible through regular monitoring.
Bayesian Hyperparameter Tuning
Bayesian hyperparameter tuning is a method for finding the best settings for machine learning models by using probability to guide the search. Instead of trying every combination or picking values at random, it learns from previous attempts and predicts which settings are likely to work best. This makes the search more efficient and can lead…
Feature Interaction Modeling
Feature interaction modelling is the process of identifying and understanding how different features or variables in a dataset influence each other when making predictions. Instead of looking at each feature separately, this technique examines how combinations of features work together to affect outcomes. By capturing these interactions, models can often make more accurate predictions and…
Symbolic Knowledge Integration
Symbolic knowledge integration is the process of combining information from different sources using symbols, rules, or logic that computers can understand. It focuses on representing concepts and relationships in a structured way, making it easier for systems to reason and make decisions. This approach is often used to merge knowledge from databases, documents, or expert…