What is martech?

What is martech?

Let's just get right into it, shall we?

Martech is defined as:

“a set of software solutions used by marketing leaders to support mission-critical business objectives and drive innovation within their organizations.” (Gartner)

If it’s technology and you can use it for marketing purposes, it’s highly likely it’s a form of martech.

Some common areas of martech include:

  • Search and Social advertising
  • Display and programmatic advertising
  • Mobile Marketing
  • Video Advertising
  • Content Advertising
  • Marketing Automation
  • Content Management Systems
  • Search Engine Optimisation
  • Email Marketing
  • Digital Asset Management
  • Marketing Resource Management software
  • Product Information Management
  • Optimisation personalisation and testing
  • Interactive content
  • Social Media marketing and monitoring
  • Customer Relationship Management
  • Customer experience service
  • Community and Reviews
  • Live chat and chatbots
  • Advocacy and Loyalty
  • Referrals
  • Analytics
  • eCommerce platforms
  • Data integration and tag management
  • Data enhancement and data science
  • Customer intelligence, dashboards and data visualisation
  • Customer Data Platforms
  • Data Management Platforms
  • Work collaboration

The martech landscape is broad and constantly growing and evolving. According to Chiefmartec, there are over 8000 marketing technology solutions.

Martech can include marketing technology platforms built within an organisation or outsourced, or a combination of both.

What is a martech "stack"?

A marketing technology stack is a grouping of technologies that marketers leverage to conduct and improve their marketing activities.

Stacks are important in martech as they represent the different types of technology your organisation uses and how those technologies works together.

There is no such thing as a perfect stack, and different organisations have different stacks based on their marketing and technology needs.

We go deeper into stack design principles and best practices in the stack design section.

These are terms every martech professional needs to know.

Here is a list of some of the more common acronyms and jargon used in the martech space. Learning these terms will help you navigate the martech space.

We know this is a long list. However we definitely recommend learning these terms (and you get to impress everyone in that meeting when you explain what these mean too).

Grab a coffee and let's go over the terms.

Common Martech terms

CRM – Customer Relationship Management is the process which an organisation interacts with its customers. This can include software for capturing users details, information, sales leads, and triggering digital messaging such as email.

CDP –  Customer Data Platform is a bundled platform which combines 1st party data, processing, segmentation and activation.

DMP – Data Management Platforms ingest and process 3rd party data.

CDMP – A Customer Data Management Platform is a combination of 1st party and 3rd party data (CDP & DMP).

MRM – Marketing Resource Management is a work collaboration platform that manages different team resources and approvals.

DAM – Digital Asset Managers focus on the storage, access and delivery of digital marketing creative assets.

SEO – Search Engine Optimisation is the process of improving the quality and quantity of web traffic from search engines.

CMS – Content Management Systems is typically software that is used to manage content or website development.

MDS – Modern Data Stack is a suite of tools used for data integration that has a fully managed ELT pipeline, a cloud-based columnar warehouse or data lake, a data transformation tool and a business intelligence or data visualisation platform.

Headless CMS – Headless Content Management Systems is a back-end only content management system that acts primarily as a content repository and its content can be accessible via an API.

EDW – Enterprise Data warehouse pulls together data from many different sources into a single data repository for sophisticated analytics and decision support.

Data Warehouse -A data warehouse pulls together data from many different sources into a single data repository for sophisticated analytics and decision support.

Data Lake – A data lake is a system or repository of data stored in its natural format.

Data Lakehouse – A data lakehouse is a combination of a data lake and data warehouse and is a repository of both raw and structured data.

Data silo – A data silo is a group of data that is accessible by one part of the organisation and isolated by the rest.

TMS – Tag Management Systems manage the tags used to track activity on websites and other digital properties.

ERP – Enterprise Resource Planning technology manages business processes such as human resources, procurement, financial and other operations.

SaaS – Subscription as a Service is a common business model of many martech solutions.

No-code – No-code development platforms allow people to create application software through graphical user interfaces instead of needing to use code.

Marketplace – Online marketplace where marketing technology can be purchased as add-ons, tool features and applications.

Dynamic content – Content that is variable based on its target audience or other data.

CDIM – Cross Device Identity Management is managing the users identity across multiple devices.

RTIM – Real-time Interaction Management aims to deliver communications in real time, also known as ‘in the moment’ communications.

Attribution – Attribution is assigning credit for conversions to different marketing activities.

Data and analytics

Zero party data – Data that a customer intentionally shares with a brand, such as user preferences, personal context, and purchase intentions.

1st party data – Data that is collected directly from its customers and it also owns.

2nd party data – First party data that you acquire from another organisation.

3rd party data – Data that is collected from organisations that don’t have a direct relationship with their customers.

Cookies – HTTP cookies is data created by a web server while a user is browsing a website and placed on the user’s computer or other device by the user’s web browser.

Known data – Type of user or customer data that is personally identifiable.

Unknown data – Type of user or customer data that is not personally identifiable.

PII – Personally identifiable information is data that can identify an individual such as a name or email address.

Attributes – A data attribute is a description of a value within a data object.

Structured data – Structured data is neatly formatted into rows and columns and mapped to predefined fields.

Unstructured data – Unstructured data is not organised into rows and columns, making it more difficult to store, analyse and search.

Raw data – Raw data is the data collected from the source but in its initial state and has not yet been processed.

Big data – Big data is a term that describes extremely large datasets.

Event – An event is a data point recorded when an action takes place.

Event Streaming – Event streaming is a constant flow of data, each containing information about an event or change of state.

ESP – Event Streaming Process is the practice of taking action on a series of data points that originate from a system that continuous create data.

Batch processing – when processing and analysis happens on a set of data that have already been stored over a period of time.

OLTP – Online Transaction Processing captures, stores, and processes data from transactions in real time.

OLAP -Online Analytical Processing uses queries to analyse aggregated historical data from OLTP systems.

CDC – Change Data Capture is a process that identifies and tracks changes to data in a databased.

Activation – Term used for activating or delivering data to marketing/activation channels.

Data replication – Data replication is when data is replicated across systems intentionally or unintentionally.

Data accuracy – The level of accuracy the data reflects reality.

Data completeness – The level of completeness and minimal missing information.

Data consistency – The level of consistency when compared to sources elsewhere.

Data timeliness – The level in what time the data can be used.

Data validity – The level in which the data correctly conforms to its attributes.

Data uniqueness – The level of whether it is the only data is in which it appears in the database.

Data integrity – The level of whether it stays intact when being moved from a database.

Data swamp – A data swamp is an unmanaged data lake that is either inaccessible to intended users or provides little value.

Data debt – Data debt is the conceptual loss of data or its quality due to deferring a software feature or choosing an easy/quick solution instead of a more data focused solution.

Data decay – Data decay is the gradual loss of data within a system.

Data wrangling – Data wrangling is the process of taking raw data and transforming it into a format that is compatible with established databases and applications.

Data cleansing – Data cleansing is the process of removing or correcting errors from a dataset, table or database.

Data mining – Data mining is the act of extracting useful information from large datasets.

Data modelling – Data modelling is the practice of diagramming how data will flow into and out of a database

Data mapping – Data mapping is the process of matching fields between different data structures or databases.

DBMS – Database Management System is a software toolkit that provides a storage structure and data management facility for database management.

Relational database – A relational database is a type of database that organise data into tables.

Customer resolution – The method to standardise customer data so there is a universal set of data fields.

Identity Resolution – Unifying the available information about an identity to accurately reflect the user.

Ring-fence – Ring-fencing data is creating a virtual barrier that prevents specific data from being impacted from outside access.

Data mesh – An architecture framework of an operating model for domain-driven development of data products and applications.

Data virtualisation – Data virtualisation provides companies with a unified view of all enterprise data across disparate systems and formats in a virtual data layer.

Integrations and connections

Data portability – Data portability is the ability to move data among different applications, programs, computing environments or cloud servers.

API – Application Programming Interface is a connection between computers or between computer programs.

API Specification – API Specification is a document that describes how to build or use a specific connection.

SDK – Software Development Kit is a collection of software development tools in one installable package, often for the purpose of integrations or connections.

Pipelines – A data pipeline is a set of tools or processes used to automate the movement of and transformation of data between a source system and a target repository.

ETL – Extract, Transform, Load are three sequential processes that in combination move data from one source to another.

ELT – Extract, Load, Transform is when data is immediately loaded to a destination upon extraction and transformation step is moved to the end of the workflow.

Reverse ETL – Reverse Extract, Transform, Load is the process of copying data from a central location to operational systems of record.

Immersive technology

VR – Virtual Reality is a computer generated environment making the user feel they are immersed in their surroundings.

Fully immersive VR – Fully immersive simulations give the users the most realistic experience possible including sight, sound and touch.

Semi-immersive VR – Provides partially virtual environment to interact with.

Non-immersive VR – A virtual reality that does not have immersion, such as a typical video game experience.

AR – Augmented Reality is an interactive experience of a real-world environment, such as seeing layered visuals on top of a real object.

Artificial intelligence

AI – Artificial Intelligence is intelligence demonstrated by machines opposed to natural intelligence displayed by animals.

Machine learning – Machine learning is a branch of AI and computer science which focuses on the use of data and algorithms.

Deep learning – Deep learning attempts to mimic the human brain and is a neural networking with three or more layers.

ANNs – Artificial Neural Networks is a computational model that mimics the way nerve cells work in the human brain.

CNNs – Convolutional neutral networks are used in computer vision and image classification applications and can detect features and patterns within an image.

RNNs – Recurrent neutral networks are used in natural language and speech recognition applications.

DS/ML – Data science / Machine Learning

Algorithm – An algorithm is a set of instructions for solving a problem or accomplishing a task. Algorithms are used as specifications for performing calculations and data processing.


iOS – iOS is a mobile operating system created and developed by Apple.

Android – Android is a mobile operating system based on a modified version of the Linux Kernal and other open source software.

Hybrid apps – Hybrid apps are developed across all platforms.

Native apps – Native apps are developed for specific platforms instead of multiple platforms.


Privacy Act – The Privacy Act 1988 is the principle piece of Australian legislation protecting the handling of personal information about individuals.

APPs – The Australian Privacy Principles are the cornerstone of the privacy protection framework in the Privacy Act 1988 (Privacy Act).

Consent Management – Consent management is the management of data relating to a user or customer’s marketing consent.

GDPR – The General Data Protection Regulation is a regulation in EU law on data protection and privacy in the EU and European Economic area.

Spam Act – The Spam Act 2003 is an Act passed by the Australian Parliament in 2003 to regulate commercial e-mail and other types of commercial electronic messages.

ATT – App Tracking Transparency is Apple’s opt-in privacy framework that requires al iOS apps to ask users for permission to share their data.

Coding Languages

SQL – Structured Query Language is a domain-specific coding language used for managing data in relational database systems. ‌‌‌‌

Python -Python is useful for web development, creating enterprise application, GUIs and is highly effective for artificial intelligence, machine learning, and data analytics.

Java – Java is a prerequisite for android developers and allows to create apps such as banking, electronic trading, e-commerce and apps for distributed computing.‌‌‌‌

Javascript – Javascript is useful for running apps across a range of devices such as smartphones, cloud, containers, micro-controllers and hundreds of browsers.

HTML – Hypertext Markup Language is a standardised system for tagging text files to achieve font, colour, graphic and hyperlink effects.

AMPscript – AMPscript is Marketing Cloud’s proprietary scripting language for advanced dynamic content in emails, landing pages, SMS, and push messages.‌‌‌‌CSS – Cascading Style Sheets is a style sheet language used for describing the presentation of a document written in a markup language such as HTML.

XML – Extensible Markup Language is a markup language and file format for storing, transmitting and reconstructing arbitrary data.

C/C++ – C and C++ allow access to hardware and have been used to create a variety of applications such as real-time systems, internet of things, games, and more.

C# – C# is used for application and web development using the .net ecosystem.

Ruby – Ruby is used for web scraping, static site generation, command-line tools, automation, DevOps and data processing.

R – R is used for data analysis, data science and on machine learning projects.

PHP – PHP is used to create tools like CMS (Content Management Systems), eCommerce platforms and web applications.

Swift – Swift is used for mobile app development on Apple iOS.

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