Ad Optimization: Leveraging AI to optimize ad campaigns in real-time.

Algorithm: A set of rules or procedures a machine follows to complete a task.

Anomaly Detection: Identifying patterns in data that do not conform to expected behavior.

Artificial Intelligence (AI): Machines programmed to mimic human intelligence processes.

Bias: Errors or unbalanced weight in training data leading to skewed outcomes.

Chatbots: Software that can simulate conversation with human users, typically for customer support or gathering information.

Churn Prediction: Identifying customers who are likely to discontinue using a product or service.

Computer Vision: Enables machines to interpret and decide based on visual data.

Content Generation: Using AI tools to assist or automate the creation of content.

Conversational Marketing: Engaging customers in one-on-one conversations across platforms.

Customer Lifetime Value Prediction (CLV): Predicting the net profit attributed to the entire future relationship with a customer.

Data Mining: The process of discovering patterns and knowledge from large amounts of data.

Deep Learning: A subset of ML that utilizes neural networks with many layers.

Dynamic Pricing: Adjusting prices in real-time based on market demand, competitor prices, and other external factors in the environment.

Feature Engineering: The process of selecting the variables used in an ML model.

Generative Adversarial Networks (GANs): Two networks (the generator and the discriminator) are trained simultaneously through adversarial processes.

Image Recognition: The process of identifying and detecting objects or features in a digital image or video.

Look-alike Modeling: Identifying new potential customers who resemble a company’s best existing customers.

Machine Learning (ML): A subset of AI where computers learn from data without explicit programming.

Natural Language Processing (NLP): A field of AI that helps machines understand and respond to human language.

Neural Networks: Algorithms inspired by the human brain designed to recognize patterns.

Personalization Engines: AI tools that analyze data to provide individualized content or product recommendations.

Predictive Analytics: Using historical data to predict future outcomes.

Propensity Modeling: Predicting the likelihood of a specific event happening.

Recommendation Systems: Algorithms that suggest products to users based on their previous interactions.

Reinforcement Learning: Machines learn by trial and error, getting rewards or penalties for actions.

Retargeting: Using AI to promote products to users based on past behaviors.

Segmentation: Dividing a market into distinct groups of buyers with different needs or behaviors.

Sentiment Analysis: Determining the emotional tone behind words to gain an understanding of the attitudes, opinions, and emotions expressed.

Supervised Learning: Training machines using well-labeled data.

Transfer Learning: Using pre-trained models on a new but related problem.

Unsupervised Learning: Allowing machines to learn from unlabeled data.

Voice Search Optimization: Optimizing for voice-activated searches.