Advertising Bidding Algorithms: Automated systems designed to bid on advertising channels in real-time, ensuring maximum efficiency and ROI. They utilize data points such as ad performance, user behavior, and ad spend to refine the bidding process, providing competitive advantage in advertising strategies.

Ad Personalization: Refers to the process of crafting individualized ad content based on customer behavior, interests, and demographics. By leveraging AI technology, this process is automated, ensuring a more personal and engaging ad experience for each user.

AI Chatbots: Automated programs that interact with customers in real-time. Using Natural Language Processing (NLP), these chatbots can understand and respond to customer queries, improving customer experience and satisfaction.

Artificial Neural Networks (ANNs): These are data processing models inspired by the human brain’s network of neurons. ANNs learn from and make decisions based on data, proving invaluable in predictive modeling within the marketing realm.

Attribution Modeling: A strategy that determines which marketing channels are contributing to lead and sales conversions. By employing Artificial Intelligence, this process is made more accurate and efficient, identifying the most effective channels based on extensive data analysis.

Augmented Reality (AR): A technology that layers digital information onto the real world. AI-powered AR provides immersive and interactive customer experiences in marketing, driving engagement and sales.

Automated Content Creation: Generates written content for marketing purposes using AI-powered Natural Language Generation (NLG) algorithms. These algorithms can create text with human-like fluency, thereby increasing effectiveness.

Behavioral Analytics: The analysis of customer behavior data, such as browsing patterns, purchase history, and interactions. AI tools facilitate these analyses with increased accuracy and predictive capabilities, enhancing the understanding of customer behavior.

Big Data Analytics: The process of analyzing large and complex datasets to uncover patterns, trends, and insights. With the help of AI, these large datasets are processed more efficiently and accurately, making big data analytics a cornerstone of modern marketing strategies. 

Conversational AI: Conversational AI is a subset of AI that allows machines to understand and respond to human language in a conversational manner. It’s typically employed in chatbots and voice assistants, improving customer interactions and service.

Customer Relationship Management (CRM): AI technologies are integrated with CRM systems to enhance customer data analysis, predictive analytics, and automation of tasks. This leads to more effective CRM (like Salesforce) strategies and improved customer relationships.

Customer Sentiment Analysis: Involves the use of Artificial Intelligence, particularly NLP, to analyze customer feedback and determine the emotional tone behind words. It provides valuable insights into public opinion and customer satisfaction, shaping communication and product strategies.

Data Mining: The extraction of patterns from large datasets. It’s leveraged in marketing to gain insights into customer behavior and market trends, informing strategic decision-making.

Data Visualization: Presenting data in an easily understandable visual format. With AI, this process is automated and enhanced, enabling complex data to be interpreted quickly and effectively (see Tableau).

Deep Learning: A subset of machine learning that mimics the human brain’s processing of data. In marketing, it’s used for image and speech recognition, NLP, and recommendation systems, improving marketing efficiency and personalization.

Demand Forecasting: Demand forecasting uses AI to predict future customer demand for products or services based on historical data. This enables businesses to better manage inventory and operations, reducing waste and improving profitability.

Dynamic Pricing: An AI-driven strategy that allows prices to fluctuate based on variables such as market demand, customer behavior, and more. This optimizes pricing for profitability and competitiveness, ensuring a more responsive business model.

Emotional AI (Emotion AI): Emotion AI consists of systems that recognize and interpret human emotions. By enhancing customer profiling and ad targeting in marketing, it allows for more empathetic and effective marketing campaigns.

Generative AI: Artificial intelligence technologies that utilize machine learning algorithms to generate novel content, including images, text, or sound. By leveraging the power of deep learning networks, such as GANs (Generative Adversarial Networks), these AI systems can create high-quality and realistic outputs, often indistinguishable from human-made creations.

Influencer Network Analysis: This AI-driven process identifies influential individuals within social media networks for potential marketing collaboration. It optimizes influencer marketing strategies, maximizing reach and impact.