Whether you’re a seasoned professional or just starting out, navigating abbreviations can be overwhelming. To make things easier, we’ve compiled a list of 50 of the most commonly used acronyms, each with a clear, concise definition. From CRM to ROI, these terms are essential for you to know. Use this guide to stay informed, boost your knowledge, and ensure you’re speaking the same language as your colleagues and clients.

API – Application Programming Interface: Allows different software systems to communicate.	CPM – Cost Per Mille: Advertising cost per 1,000 impressions.	KPI – Key Performance Indicator: A measurable value that shows how effectively a company is achieving its goals.	ROI – Return on Investment: A performance measure used to evaluate the efficiency or profitability of an investment.	TOFU – Top of Funnel: The initial stage of the sales funnel where potential customers first interact with your brand.
B2B – Business-to-Business: Refers to companies selling products or services to other businesses.	CPQ – Configure, Price, Quote: A tool that helps businesses configure product offerings, price them, and generate quotes.	LTV – Lifetime Value: The revenue a business can expect from a single customer over their relationship.	SAL – Sales Accepted Lead: A lead that has been vetted by sales as a potential prospect.	UAT – User Acceptance Testing: The final phase of testing before software is released, where users test it for functionality.
B2C – Business-to-Consumer: Refers to companies selling directly to consumers.	CRM – Customer Relationship Management: A system for managing a company’s interactions with current and potential customers.	MQL – Marketing Qualified Lead: A lead that has shown interest in your product or service and is more likely to convert into a customer.	SLA – Service Level Agreement: A contract that defines the level of service expected from a service provider.	UI – User Interface: The point of interaction between the user and a digital product.
CAC – Customer Acquisition Cost: The cost of acquiring a new customer.	CRO – Conversion Rate Optimisation: Improving the percentage of visitors who take a desired action on a website.	NPS – Net Promoter Score: A metric for customer loyalty and satisfaction, based on a simple survey question.	SME – Subject Matter Expert: An individual with in-depth knowledge of a specific topic or field.	USP – Unique Selling Proposition: The factor that differentiates a product from its competitors.
CDP – Customer Data Platform: A system that collects and manages customer data from various sources.	CTA – Call to Action: A prompt for the user to take a specific action (e.g., “Buy Now”).	OKR – Objectives and Key Results: A goal-setting framework used to define and track objectives and their outcomes.	SQL – Sales Qualified Lead: A lead that has been qualified by the sales team as ready for engagement.	UX – User Experience: The overall experience a user has when interacting with a product or service.
CLV – Customer Lifetime Value: The total revenue a business can expect from a single customer.	CTR – Click-Through Rate: The percentage of users who click on a specific link.	PPC – Pay Per Click: An advertising model where advertisers pay for each click on their ad.	SRM – Supplier Relationship Management: The discipline of managing and optimising supplier relationships.	VAR – Value-Added Reseller: A company that adds features or services to an existing product, then resells it.
CMS – Content Management System: Software used to create and manage digital content (e.g., WordPress).	DMU – Decision-Making Unit: The group of people in an organization responsible for making purchasing decisions.	PRM – Partner Relationship Management: A strategy and system to manage a company’s relationships with its channel partners.	SSO – Single Sign-On: A session and user authentication service that allows users to use one set of login credentials for multiple applications.	WOM – Word of Mouth: Information about products or services passed between people verbally.
CPA – Cost Per Acquisition: The cost to acquire one paying customer. See also CAC	ERP – Enterprise Resource Planning: Software used to manage day-to-day business activities such as accounting, procurement, and supply chain operations.	QBR – Quarterly Business Review: A meeting to review performance metrics and align on business strategy.	SWOT – Strengths, Weaknesses, Opportunities, and Threats: A framework for analysing a business or project.	XaaS – Anything as a Service: Refers to any service delivered over the internet rather than on-premise.
CPC – Cost Per Click: The cost for each click in a pay-per-click advertising campaign.	GTM – Go-to-Market: A strategy used to bring a product to market.	RFP – Request for Proposal: A document soliciting proposals from vendors or suppliers.	TAM – Total Addressable Market: The total market demand for a product or service.	YoY – Year over Year: A method of comparing performance metrics from one year to the same period in the previous year.
CPL – Cost Per Lead: The cost to generate a single lead.	ICP – Ideal Customer Profile: A description of the type of company that would benefit most from your product or service.	ROAS – Return on Ad Spend: A marketing metric measuring the revenue generated for each dollar spent on advertising.	TCO – Total Cost of Ownership: The purchase price of an asset plus the costs of operation over its lifetime.	YTD – Year to Date: The period from the beginning of the year up until the current date.

BONUS – Common AI terms and acronyms: 

• AGI – Artificial General Intelligence: Hypothetical AI capable of understanding, learning, and applying knowledge across a wide range of tasks like a human. 

• AI – Artificial Intelligence: The simulation of human intelligence in machines programmed to think and learn. 

• ANN – Artificial Neural Network: A type of AI model inspired by the structure of the human brain, used in deep learning. 

• AutoML – Automated Machine Learning: A process of automating the end-to-end process of applying machine learning to real-world problems. 

• BERT – Bidirectional Encoder Representations from Transformers: A transformer-based model designed for understanding the context of words in search and NLP tasks. 

• CNN – Convolutional Neural Network: A type of deep neural network particularly effective for image recognition tasks. 

• CV – Computer Vision: A field of AI that enables computers to interpret and process visual information like images and videos. 

• DL – Deep Learning: A subset of machine learning that uses neural networks with many layers to model complex patterns in data. 

• EDA – Exploratory Data Analysis: The process of analyzing and visualizing data sets to summarize their key characteristics. 

• ELMo – Embeddings from Language Models: A deep contextualized word representation model used in NLP tasks. 

• GAN – Generative Adversarial Network: A type of neural network used to generate realistic images, videos, or audio by pitting two models against each other. 

• GPT – Generative Pre-trained Transformer: A family of large language models developed by OpenAI, capable of generating coherent text based on input prompts. 

• Grounding – Grounded Language: Ensuring AI’s understanding of language is tied to real-world objects, actions, or concepts. 

• Green AI – Sustainable AI practices that focus on reducing the computational power and energy consumption needed for model training and deployment. 

• Hyperparameters – Configuration variables used to control the training process of machine learning models, which are set before training and can significantly impact model performance. Examples include learning rate, batch size, and number of epochs. 

• LLM – Large Language Model: AI models trained on vast amounts of text data to understand and generate human language. 

• ML – Machine Learning: A type of AI that enables systems to learn from data and improve over time without explicit programming. 

• MLaaS – Machine Learning as a Service: A range of cloud-based services that provide machine learning tools and infrastructure. 

• MLOps – Machine Learning Operations: The practice of streamlining and automating the development, deployment, and maintenance of machine learning models. 

• NLP – Natural Language Processing: A branch of AI focused on enabling computers to understand, interpret, and respond to human language. 

• NLG – Natural Language Generation: A subfield of NLP focused on generating human-like text from data or algorithms. 

• NLU – Natural Language Understanding: A subfield of NLP that enables machines to understand the meaning and intent behind human language. 

• OCR – Optical Character Recognition: Technology that converts different types of documents, such as scanned paper documents or images, into editable and searchable data. 

• RL – Reinforcement Learning: A type of machine learning where an agent learns to make decisions by performing actions and receiving feedback. 

• RAG – Retrieval-Augmented Generation: A hybrid model that combines pre-trained generative models with retrieval mechanisms to enhance the generation of accurate responses. 

• RNN – Recurrent Neural Network: A type of neural network designed to recognize patterns in sequences of data, such as text or time series. 

• SAR – Self-Attention Mechanism: A technique used in transformer models, allowing them to focus on relevant parts of the input when making predictions. 

• SVM – Support Vector Machine: A supervised learning algorithm used for classification and regression tasks. 

• T5 – Text-To-Text Transfer Transformer: A model that treats all NLP tasks as a form of text-to-text generation. 

• TTS – Text-to-Speech: AI that converts written text into spoken voice output. 

• TF-IDF – Term Frequency-Inverse Document Frequency: A statistical measure used to evaluate how important a word is to a document relative to a collection of documents. 

• Transformer – Transformer Model: A type of neural network architecture widely used for NLP tasks, which improves the model’s ability to capture context across sequences. 

• Zero-shot Learning – A type of learning where the model makes predictions for categories that it has never seen during training. 

• Bias – AI Bias: When AI systems make unfair or skewed decisions based on the data they were trained on. 

• Overfitting – When a machine learning model learns the training data too well, capturing noise and irrelevant details that negatively impact its performance on new data. 

• Underfitting – When a model is too simple to capture the underlying patterns in the data, leading to poor performance. 

• Explainability – The degree to which the workings of an AI model can be understood and explained by humans. 

• Tokenisation – The process of breaking text into smaller units (tokens) that can be processed by machine learning models. 

• Embedding – A way of representing words or other data types in a continuous vector space, where similar items are closer together. 

• Ethics – AI Ethics: The moral implications of AI and the need to ensure fairness, transparency, and accountability in its development and deployment. 

• Prompt – In NLP, the input text provided to a language model to generate a response. 

• Bias Mitigation – Techniques to reduce or remove bias in AI models, ensuring more fair and balanced outcomes. 

• Pre-training – The process of training a machine learning model on a large dataset before fine-tuning it for a specific task. 

• Transfer Learning – A technique where a model trained on one task is reused or adapted to improve performance on another task. 

• Fine-tuning – The process of taking a pre-trained model and adapting it to perform better on a specific task with further training. 

• Multimodal AI – AI systems capable of processing and integrating data from multiple modalities, such as text, images, and audio. 

• Synthetic Data – Artificially generated data used to train or test AI models when real-world data is unavailable or limited. 

• Data Augmentation – Techniques used to increase the diversity of training data without actually collecting new data, such as by modifying existing samples. 

• Gradient Descent – An optimisation algorithm used to minimise the loss function in machine learning models. 

• Meta-learning – “Learning to learn” – the process where an AI model learns to adapt quickly to new tasks with minimal training data. 

• Explainable AI (XAI) – A set of techniques designed to make AI model decisions more interpretable and understandable to humans.