While AI and marketing may seem like science fiction, there are already many products and organizations using AI in different ways. As a marketing ops professional it’s critical you have a strong understanding of the applications of AI marketing so you can set your organization up for the future.
What is Artificial Intelligence Marketing?
Marketing Ops defines Artificial Intelligence Marketing as:
The science of using smart machines to perform marketing tasks.
Within AI there are several different sub-categories such as Machine Learning (ML) and Deep Learning. Both have different applications for how they can be used in marketing.
Machine Learning (ML)
Machine learning is the use and development of computer systems that are able to learn and adapt without following explicit instructions. Some examples of ML marketing include using algorithms and statistical models to either gain marketing insights or improve customer targeting.
Due to the nature of ML using algorithms and modeling, this skill set tends to sit within an organization’s data team.
Deep Learning and Artificial Neural Networks (ANNs)
Deep learning is a subset of machine learning that focuses on artificial neural networks (ANNs).
All artificial neural networks rely on an input, a process or a ‘hidden layer’, and then an output. The process part consists of different layers and nodes that allow for sophisticated logic. It’s these series of layers in artificial neural networks that give the term ‘deep learning’.
Once these artificial neural networks are designed, they can perform sophisticated tasks such as image identification, navigation, generating content and more.
AI Research Labs
These are organizations that focus on the research and development of AI technologies. Research labs are funded by grants as opposed to selling products, however, some research labs allow the use of their technology via APIs and have a pricing structure.
- OpenAI – DALL-E (Image generation)
- OpenAI – ChatGPT (Information generation)
- OpenAI – Codex (Code generation)
- Midjourney (Image generation)
How to use artificial intelligence in marketing?
Marketing Ops identifies several different ways of using AI within a marketing context.
A key part of being a marketer is managing and developing creative content and as such marketers tend to be experts in developing creative work (either in-house or with external help). For many marketers, this creative element of the role is one of the reasons they love working in marketing. Developing videos, imagery, and copy is part of the marketing development process.
However, there are now many AI products available that write copy, and generate videos and imagery causing much disruption to the creative industries.
There are many applications of generative AI from a creative development perspective, including creative concept development, graphic design, brand design, web/app design, and more.
AI communication and chatbots
Natural Language Processing (NLP) focuses on computers and human language. In a marketing context, this includes generating copy, text-to-speech, speech recognition (transcripts), chatbots, grammar correction, summarising text, conversations with humans, and much more.
We have seen a rapid advancement in this space, with there being many AI-powered chatbots available to implement as part of servicing customers.
An advantage of a bespoke AI chatbot is the depth of knowledge it can access such as case numbers, history of communications, escalation after certain criteria, and even identifying customer moods/tones. However, like all AI it also requires access to accurate source data and thorough testing for it to be able to assist the majority of customer inquiries.
Video communication platforms and avatars allow for quick video creation that delivers messages and in multiple languages. The application for these AI videos ranges everything from explainer videos, event management, news, user experience, tutorials, and more.
AI service improvement
Irrespective of content development, marketers also strive to improve their product services and experiences. This also includes automating marketing processes and tasks whether this is using models to trigger email sends, personalization, research, identifying insights, collection and cleaning of data, improved analytics, algorithms to improve targeting, and so on. These AI platforms assist in the back-end processes as opposed to creative development.
End-to-end AI marketing
End-to-end AI marketing is using an independent AI to manage the overall processes, optimization and performance of marketing initiatives. Currently, this has not yet been achieved by any organization, however, there are steps to take to make sure your organization continues to innovate for AI.
End-to-end AI marketing requires the below foundational steps to achieve an effective result.