The Ultimate A-Z of AI and ML for Marketing

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Keeping on top of all the new AI terms is a full-time job. It seems like there's a new article every day about connective-this and behavioural-that and generative-whatsit. So, log out of LinkedIn, and sign out of Twitter. Shut down your MySpace page. Really you should've done that already, it's 2023. 

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So, we've got an extensive list of all the top terms, an A-Z if you will, for the use of AI and ML in marketing. We'll sort through the main technologies of AI and ML, and give you a run-down of all the top tech terminologies. Your AI and ML vocabulary will be top-tier.

Looking for the down low from a download? Check out our AI-Driven Growth Best Practice Guide here! We’ll explore the growth and evolution of AI, examining some of its potential uses, especially in marketing, sales, and customer experience.

Right, let's jump into the ultimate AI and ML glossary!

A

Artificial Intelligence (AI): refers to computer systems that can perform tasks that would normally require human intelligence, such as problem-solving, decision-making, and language understanding. Applications in marketing include predictive analytics, customer segmentation, chatbots, and personalisation.

Automated Marketing: the use of AI and ML to automate marketing processes, such as email campaigns, social media posts, and content creation. This helps marketers save time and improve efficiency.

Algorithm: a set of instructions that an AI system uses to solve a specific task. Algorithms are used in various marketing applications, including predictive analytics and recommendation systems.

B


Big Data: refers to large and complex data sets that traditional data processing methods cannot handle. AI and ML are crucial in handling and processing such data for marketing purposes, such as customer segmentation and predictive analytics.

Business Intelligence: the use of AI and ML to analyse data and generate insights to inform marketing strategies and decision-making. This can include analysing customer behaviour, identifying market trends, and optimising campaigns.

Behavioural Targeting: the use of AI and ML to analyse customer behaviour and target marketing messages based on that behaviour. This can include targeting customers who have abandoned shopping carts, browsed certain products, or clicked on specific ads.

C

Chatbot: an AI-powered tool that can interact with customers through conversational interfaces. Chatbots are used in marketing for customer service, lead generation, and personalized recommendations.

Customer Segmentation: the process of dividing a customer base into groups based on shared characteristics, such as demographics, behaviour, or preferences. AI and ML can be used to analyze customer data and identify relevant segments for targeted marketing.

Content Marketing: the creation and distribution of valuable, relevant, and consistent content to attract and retain a clearly defined audience. AI and ML can be used to optimize content for specific audiences, identify trending topics, and automate content creation.

D

Data Mining: the process of discovering patterns and insights from large data sets. Data mining is used in marketing for customer profiling, market analysis, and identifying opportunities for growth.

Digital Marketing: marketing that takes place online, including email campaigns, social media marketing, SEO, and PPC advertising. AI and ML are used in digital marketing for personalized recommendations, targeting, and optimization.

Decision Tree: a visual representation of a decision-making process that uses a tree-like model of decisions and their possible consequences. Decision trees are used in marketing for lead scoring, customer segmentation, and product recommendations.


E

Expert Systems: AI systems designed to provide expert-level knowledge in a specific domain. Expert systems can be used in marketing for product recommendations, customer service, and lead generation.

Email Marketing: the use of email to promote products or services. AI and ML can be used in email marketing for personalised recommendations, automated campaigns, and predictive analytics.

Ensemble Learning: a machine learning technique that combines multiple models to improve accuracy and performance. Ensemble learning can be used in marketing for predictive analytics, recommendation systems, and customer segmentation.

F


Facial Recognition: AI technology that identifies and verifies a person's identity based on facial features. Facial recognition can be used in marketing for personalised experiences, fraud prevention, and targeted advertising.

Fraud Detection: the use of AI and ML to identify and prevent fraudulent activity, such as credit card fraud, fake reviews, and fake accounts. Fraud detection is important for maintaining trust and credibility with customers.

Feature Engineering: the process of selecting and transforming relevant data features for use in machine learning models. Feature engineering is used in marketing for data analysis, customer segmentation, and predictive analytics.


G

Generative Adversarial Networks (GANs): a type of machine learning model that uses two neural networks, one to generate new data and another to discriminate between real and fake data. GANs can be used in marketing for generating personalised content, product design, and advertising.

Geofencing: a location-based marketing technique that uses GPS or RFID to create a virtual boundary around a physical location. Geofencing can be used in marketing for sending targeted messages or promotions to customers who enter or exit the boundary.

Gradient Boosting: a machine learning technique that builds a predictive model by combining multiple weaker models. Gradient boosting can be used in marketing for lead scoring, customer segmentation, and product recommendations.

H

Hyperpersonalisation: a marketing technique that uses AI and ML to create highly personalized experiences for individual customers. Hyperpersonalization can be used in marketing for personalized product recommendations, tailored content, and targeted advertising.

Heat Maps: visual representations of user behaviour on a website or app, showing which areas receive the most clicks or engagement. Heat maps can be used in marketing for website optimization, A/B testing, and user experience design.

Hierarchical Clustering: a machine learning technique that groups data points into clusters based on their similarity. Hierarchical clustering can be used in marketing for customer segmentation, market analysis, and product recommendations.

I


Image Recognition: AI technology that identifies and categorizes objects, scenes, and patterns in images. Image recognition can be used in marketing for visual search, product recommendations, and brand monitoring.

Influencer Marketing: a marketing technique that involves partnering with influential individuals on social media to promote products or services. AI and ML can be used in influencer marketing for identifying relevant influencers, tracking performance, and measuring ROI.

In-Memory Computing: a data processing technique that stores data in memory instead of on a disk, allowing for faster processing and analysis. In-memory computing can be used in marketing for real-time analytics, personalization, and recommendation systems.

J


Journey Mapping: the process of mapping out a customer's journey through different touchpoints, from initial awareness to purchase and beyond. Journey mapping can be used in marketing for identifying pain points, improving customer experience, and optimizing campaigns.

Just-In-Time Marketing: a marketing technique that delivers targeted messages or offers at the right moment, based on real-time data and behaviour. Just-in-time marketing can be used in marketing for increasing engagement, improving conversion rates, and reducing churn.

Joint Probability: the probability of two or more events occurring together. Joint probability can be used in marketing for predicting customer behaviour, identifying cross-selling opportunities, and optimizing campaigns.

K


K-Means Clustering: a machine learning technique that divides data points into clusters based on their similarity. K-means clustering can be used in marketing for customer segmentation, market analysis, and product recommendations.

Knowledge Graphs: a type of database that stores knowledge as nodes and edges, allowing for efficient query and retrieval of information. Knowledge graphs can be used in marketing for customer profiling, product recommendations, and personalised content.

Keyword Extraction: a natural language processing technique that identifies the most relevant keywords or topics in a text document. Keyword extraction can be used in marketing for content optimization, SEO, and social media monitoring.

L


Lead Scoring: the process of ranking leads based on their likelihood to become a customer. Lead scoring can be done using AI and ML techniques, such as predictive analytics and decision trees, and can be used in marketing for lead nurturing, sales prioritisation, and campaign optimisation.

Lookalike Modeling: a machine learning technique that identifies new customers who are similar to existing customers, based on shared characteristics or behaviour. Lookalike modelling can be used in marketing for customer acquisition, targeting, and campaign optimisation.


M

Machine Learning: a subset of AI that involves training algorithms to learn from data and make predictions or decisions without being explicitly programmed. Machine learning can be used in marketing for customer segmentation, product recommendations, and predictive analytics.

Multi-Armed Bandits: a machine learning technique that balances exploration and exploitation in decision-making, often used in A/B testing and advertising. Multi-armed bandits can be used in marketing for optimizing ad spend, improving conversion rates, and increasing revenue.

Markov Models: a statistical model that uses probability to predict future states based on current state and transition probabilities. Markov models can be used in marketing for customer lifetime value prediction, churn analysis, and recommendation systems.

N


Natural Language Processing: AI technology that enables computers to understand, analyse, and generate human language. Natural language processing can be used in marketing for sentiment analysis, chatbots, and content creation.

Neural Networks: a type of machine learning model that is designed to recognise patterns and make predictions, based on the structure and function of the human brain. Neural networks can be used in marketing for image recognition, natural language processing, and personalised content.

Next-Best Action: a marketing strategy that suggests the optimal action to take in real time, based on customer behaviour, preferences, and historical data. Next-best action can be used in marketing for lead nurturing, cross-selling, and retention.

O


Omnichannel Marketing: a marketing approach that integrates multiple channels, such as email, social media, and mobile, to create a seamless and consistent customer experience. AI and ML can be used in omnichannel marketing for personalisation, automation, and predictive analytics.

Outlier Detection: a statistical technique that identifies data points that deviate significantly from the norm. Outlier detection can be used in marketing for fraud detection, customer segmentation, and anomaly detection.

Optimisation: the process of finding the best possible solution or outcome, often using algorithms and data analysis. Optimisation can be used in marketing for A/B testing, campaign management, and pricing strategy.

P

Predictive Analytics: the use of statistical models and machine learning to make predictions about future events or behaviours. Predictive analytics can be used in marketing for customer lifetime value prediction, churn analysis, and personalised recommendations.

Personalisation: a marketing strategy that tailors content, offers, and experiences to the individual customer, based on their preferences, behaviour, and history. AI and ML can be used in personalisation for product recommendations, content creation, and email marketing.

Positioning: the process of creating a unique and compelling identity for a product or brand, relative to its competitors. Positioning can be used in marketing for market research, brand development, and advertising.

Q


Query Optimisation: the process of optimising database queries for faster and more efficient retrieval of information. Query optimization can be used in marketing for real-time analytics, personalization, and recommendation systems.

Quality Score: a metric used in search engine advertising to measure the relevance and performance of ads and keywords. Quality scores can be used in marketing for optimizing ad spend, improving click-through rates, and increasing ROI.

Quantitative Analysis: the use of statistical methods and data analysis to quantify and measure marketing performance, such as customer acquisition cost, conversion rate, and revenue per user.

R

Reinforcement Learning: a type of machine learning that involves training an algorithm to make decisions by receiving feedback in the form of rewards or punishments. Reinforcement learning can be used in marketing for ad optimisation, email marketing, and recommendation systems.

Regression Analysis: a statistical technique that identifies the relationship between two or more variables, and predicts a continuous outcome based on that relationship. Regression analysis can be used in marketing for price optimisation, demand forecasting, and customer lifetime value.

S


Segmentation: the process of dividing a market into smaller groups of consumers who share similar needs, characteristics, or behaviours. Segmentation can be used in marketing for targeted advertising, personalised recommendations, and content creation.

Sentiment Analysis: the use of natural language processing to identify and analyse the emotions and opinions expressed in text data, such as social media posts or customer reviews. Sentiment analysis can be used in marketing for reputation management, customer feedback analysis, and social media monitoring.

Supervised Learning: a type of machine learning that involves training algorithms on labelled data, with the goal of making predictions on new, unlabeled data. Supervised learning can be used in marketing for lead scoring, customer churn prediction, and recommendation systems.

T


Targeting: the process of selecting a specific audience or group of consumers to receive a marketing message or offer. Targeting can be used in marketing for improving conversion rates, reducing customer acquisition costs, and increasing ROI.

Time Series Analysis: a statistical technique that analyses data over time to identify trends, and patterns, and forecast future values. Time series analysis can be used in marketing for demand forecasting, pricing strategy, and inventory management.

Topic Modeling: a natural language processing technique that identifies the underlying themes or topics in a collection of documents or text data. Topic modelling can be used in marketing for content creation, social media monitoring, and customer feedback analysis.

U


Unsupervised Learning: a type of machine learning that involves training algorithms on unlabeled data, with the goal of identifying patterns or structures within the data. Unsupervised learning can be used in marketing for customer segmentation, anomaly detection, and clustering analysis.

User-generated Content: content created by consumers, such as reviews, social media posts, or videos, that can be used in marketing to promote a brand or product. User-generated content can be used in marketing for social media campaigns, influencer marketing, and brand advocacy.

Upselling: a sales technique that involves encouraging customers to purchase additional or higher-priced products or services. AI and ML can be used in upselling for personalised product recommendations, pricing optimization, and customer lifetime value prediction.

V


Viral Marketing: a marketing technique that aims to spread a message or content rapidly through social media or other online platforms, using the power of social networks and word-of-mouth. AI and ML can be used in viral marketing for social media monitoring, influencer identification, and content creation.

Visual Search: an AI technology that allows users to search for products or images using visual cues, such as a photo or screenshot. Visual search can be used in marketing for product discovery, visual recommendation systems, and e-commerce.

Voice Search: an AI technology that allows users to search for information or perform actions using voice commands, such as through a smart speaker or virtual assistant. Voice search can be used in marketing for voice SEO optimisation, voice-based advertising, and personalised voice assistants.

W


Web Personalisation: the process of customising a website's content, design, and functionality to match the individual user's preferences, behaviour, and history. AI and ML can be used in web personalisation for personalized recommendations, product discovery, and email marketing.

Word-of-Mouth Marketing: a marketing technique that relies on consumers sharing information and recommendations about a product or brand with their social networks. AI and ML can be used in word-of-mouth marketing for influencer identification, social media monitoring, and sentiment analysis.

Website Optimisation: the process of improving a website's performance and user experience, often using A/B testing, analytics, and other data-driven techniques. Website optimisation can be used in marketing for lead generation, conversion rate optimisation, and customer retention.

X


Cross-Channel Marketing (XCM): a marketing approach that uses multiple channels, such as email, social media, and advertising, to reach consumers and create a cohesive, integrated marketing strategy. AI and ML can be used in cross-channel marketing for audience segmentation, personalised messaging, and attribution modelling.

Cross-Selling: a sales technique that involves offering customers related or complementary products or services to the ones they have already purchased. AI and ML can be used in cross-selling for personalised product recommendations, customer segmentation, and pricing optimisation.

Explainable AI: a type of AI that is designed to provide transparent and interpretable results, allowing users to understand how and why the AI system makes certain decisions or predictions. Explainable AI can be used in marketing for ethical considerations, compliance, and building trust with consumers.

Y

Yield Management: a pricing strategy that involves adjusting prices dynamically based on supply and demand, in order to maximise revenue. AI and ML can be used in yield management for demand forecasting, price optimisation, and revenue management.

YouTube Advertising: advertising on YouTube, a popular video-sharing platform, to reach a wide audience and promote a brand or product. AI and ML can be used in YouTube advertising for audience targeting, ad placement optimisation, and content creation.

YouTube Analytics: a tool provided by YouTube that allows creators and marketers to track and analyze their channel's performance, including views, engagement, and audience demographics. YouTube Analytics can be used in marketing for audience insights, content optimisation, and ad performance evaluation.

Z


Zero-Party Data: data that is provided voluntarily by consumers, such as survey responses or email subscriptions, with their explicit consent and knowledge. Zero-party data can be used in marketing for personalised messaging, customer segmentation, and building trust with consumers.

Zip Code Targeting: a type of targeting that uses the geographic location of a consumer's zip code to reach specific audiences or markets. Zip code targeting can be used in marketing for localised advertising, customer segmentation, and market analysis.

Zoom Surveys: a type of survey that is conducted remotely, often using video conferencing tools like Zoom, to gather insights and feedback from participants. Zoom surveys can be used in marketing for customer feedback analysis, product testing, and audience research.

Want more AI? Check out our AI-Driven Growth Best Practice Guide here! We’ll explore the growth and evolution of AI, examining some of its potential uses, especially in marketing, sales, and customer experience.