Agustín RejónAgu Rejón is the Founder of Martechbase. He has worked in B2B Marketing Operations and Performance Marketing for more than 10 years and is fascinated by the role of technology in marketing and sales.
Do not index
Do not index
Artificial intelligence (AI) is quickly becoming a major force in modern marketing, from copywriting assistants to photography studios and even new search engines.
OpenAI's public release of models like GPT-4 and DALL-E is giving marketers access to new tools and capabilities that can improve their customer service and generate creative marketing strategies, all while increasing efficiency along the way.
In this blog post, I’ll explore some of the main ways in which AI is being used in marketing right now, key technologies and some of my favourite use cases. For a full glossary of AI marketing terms and buzzwords, check out my other post.
OpenAI: A for-profit research institute and company that focuses on the development of artificial intelligence. It was founded by Elon Musk, Sam Altman, Greg Brockman, and others with the goal of promoting and developing friendly AI in a way that benefits humanity.
Examples of How AI is Being Used in Modern Marketing
1. AI for Content Ideation
One of the primary ways that AI is being used for content creation is through natural language generation (NLG) with tools like GPT-4 and its more user-friendly variant ChatGPT. This technology allows marketers to automatically generate ideas and outlines for any kind of written content including blog posts, emails, social media posts and ads.
For instance, you can provide ChatGPT with a general topic or theme that you want to write about, and it will create a list of potential sub-topics or ideas for articles, social media posts or campaigns. This can be a useful way to brainstorm ideas that can then be develop into fully-fleshed content:
Another way that GPT language models can help marketers is by providing a starting point for written content. For example, you could provide a brief outline or summary with your thoughts on a topic. AI can then generate a draft outline based on this input, which you can then edit and refine as needed.
Here’s how the outline for this post was conceived (and then modified as I developed this piece):
Not limited to ideation, you can also instruct ChatGPT to optimise your content for SEO (which is huge in my opinion), either by asking it to provide the main target keywords you should be targeting or even asking it to rewrite your copy to include those keywords. While it may not consider all of the latest data from Google Search Console or Ahrefs, it can be a valuable resource for considering SEO in your content creation process.
With the recent launch of OpenAI’s plugins, which allow ChatGPT to interact with the internet in real time, there’s even some debate on how soon marketers should start optimising their sites for AI browsing. The principle is simple: If users are able to interact with your site via tools like ChatGPT or Google’s BARD, should you not be structuring, organising and writing your content for these new search engines?
“Hang on a minute, don’t let an AI write my Facebook Ads headlines!” – said no copywriter, ever.
There are countless other use cases for AI-assisted writing; from actually drafting articles and essays to rewriting content in a different tone of voice, correcting grammar and translating to other languages.
Note-taking platforms like Notion have already seen this trend and started to embed generative AI capabilities within their products.
But seriously, can an AI write? Specially when it comes to B2B content? Like Doug said on his recent post, “it ain’t Ann Handley or Jay Acunzo”. To me, it’s all about letting this technology assist you, as opposed to the AI doing the work for you – leading to increased productivity and lower time to market by using AI as a way to inform your own creative thinking and help with the initial research.
ㅤ | Traditional copywriting
✍️ | AI-assisted copywriting
(✨ the sweet spot) | AI-generated copy
🤖 |
Advantages | Creative, original, human authentic. | Time savings from instant research, supposedly non-biased insights and factual. | Very quick turnaround and time to market, prioritizes volume vs quality of content. |
Caveats | Time and skills-dependent, subjective. | Still reliant on good creative (and even technical) skills in order to be authentic and creative. | Robotic, non-creative, likely to be penalised by search algorithms. |
2. AI for Image and Video Creation
Not limited to text, AI-powered tools like DALL-E or Stable Diffusion are helping marketers optimise the visual elements of their content, such as colours and composition, to make it more visually appealing and engaging. More importantly, these tools can create 100% bespoke graphics and hyper-realistic photography from text descriptions (also known as prompts) which can help produce quality marketing collateral in record time (literally seconds).
These straightforward examples of text-to-image creation/optimisation don’t convey the realm of possibilities that this technology offers! If you want to learn more, I highly recommend subscribing to Ben’s or Anita’s excellent AI newsletters, check out projects like ProfilePictureAI or follow some incredible prompt engineers like @javilop on Twitter.
The commercial role that these technologies will play in marketing is still unclear with mixed, so-far reactive responses from incumbents like Getty Images – who is banning AI-generated content amid fears of future copyright claims vs Shutterstock actually partnering with OpenAI to start selling AI-generated images on their site.
Indie makers and startups are also finding innovative applications of this technology every day at a pace that’s frankly hard to follow. Here are some of my favourite examples:
- HeadshotPro lets you get professional business headshots in minutes with AI.
- Photoshoot.ai generates product shots in seconds.
- Vidyo makes short videos from long ones instantly.
- CapsuleScript generates videos from text prompts.
- D-ID's Creative Reality™ Studio turns scripts into a video of a human-like talking avatar within seconds.
- Upscale.media uses AI to upscale and enhance images.
A whole new ecosystem of AI-powered marketing tools is emerging and the news that Zapier now integrates natively with OpenAI (hence allowing anyone to create custom apps without code) will just speed up the pace at which these new products will become available to marketers.
GPT-3: Generative Pretrained Transformer 3 (GPT-3), is a state-of-the-art language processing model developed by OpenAI. It has the ability to generate human-like text and perform a variety of language tasks, such as translation, summarisation, and question-answering.
ChatGPT: A large language model trained by OpenAI that uses a variant of the GPT-3 architecture to generate human-like text responses to user input. It can be used for a wide range of applications, from generating responses to customer inquiries in a chatbot to creating personalised content for web, social media or other applications.
DALL-E: A variant of GPT-3 (also developed by OpenAI) that instead of being trained solely on text data is trained on a combination of text and image data. This allows DALL-E to generate original images from text descriptions, a capability known as "conceptual art".
Stable Diffusion: A deep learning model that converts text into images. It was first introduced in 2022 and is mainly used to create detailed images based on text descriptions. However, it can also be used for other tasks such as inpainting, outpainting, and generating image-to-image translations with a text prompt. Similar to DALL-E.
Midjourney: A research lab that creates its own AI program that generates images from text descriptions. Also similar to OpenAI's DALL-E and the open-source Stable Diffusion.
Hugging Face: A firm and AI community that creates tools for building machine learning applications. It is best known for its Transformers library, which is used to support natural language processing models (like Stable Diffusion), and its platform that lets users share machine learning models and datasets.
👉 Get the full AI Marketing Glossary here.
3. Personalisation of Marketing Campaigns
Another key application of AI in marketing is through personalisation of marketing campaigns. With AI, marketers can analyse vast amounts of data from multiple sources to gain a deep understanding of their customers, including their preferences, behaviours, and interests. This lets teams create marketing campaigns that are tailored specifically to each individual customer, making the marketing more relevant and effective.
Tools like Clearbit allow you to personalise multiple stages of your marketing and sales funnel, like personalising forms and website copy for better conversion rates.
Programmatic SEO platforms like the.com and PageFactory are also gaining popularity as a way to offer hyper-relevant content to web visitors, taking into account a variety of data points, to dynamically change the content displayed on the screen.
Another key practice (that has been around for some time) is the use of chatbots. These are AI-powered bots that can engage with customers in real-time, providing them with information and support, and helping to guide them through the sales process. Chatbots can be integrated into a company's website or social media channels, and can help to improve the customer experience by providing quick and accurate responses to common questions and inquiries.
Programmatic SEO: Not strictly AI-driven, Programmatic SEO involves the creation and publication of large quantities of unique, high-quality pages using a template and database.
4. Analysing Customer Behaviours and Trends
With the ability to process and analyse vast amounts of data in a fraction of the time it would take humans, AI can provide valuable insights that can help marketers better understand their customers and make informed decisions.
By using machine learning tools, such as Google Cloud AutoML and Amazon SageMaker, marketers can use machine learning algorithms to identify patterns and trends in customer and social data, such as purchasing habits or website interactions. For example, a business might use machine learning to analyse customer purchase history and identify products or services that are frequently purchased together, or to identify customers who are likely to make repeat purchases.
Another way machine learning can be used in marketing is through the use of predictive analytics – using tools like Marketo, HubSpot or Lattice to make predictions about future events or outcomes based on past data. For example, a business might use predictive analytics via lead scoring models to identify customers who are likely to make a purchase in the future, or to predict the likelihood of a customer returning to the business in the future.
Machine Learning: A subset of artificial intelligence that involves the use of algorithms and statistical models to enable computers to learn and make decisions without being explicitly programmed. In the field of marketing, machine learning has the potential to revolutionise the way businesses analyse and understand customer behaviour and trends.
Predictive Analytics: A branch of data analytics that involves using statistical models and machine learning algorithms to make predictions about future events or outcomes based on past data. In the field of marketing, it can help businesses better understand and forecast customer behaviours.
Challenges and Limitations
While AI has the potential to revolutionise marketing efforts, it also raises a number of ethical considerations and can potentially replace the human creativity and touch that is vital to many marketing campaigns.
One of the main considerations is the potential for bias. AI algorithms can sometimes reflect the biases of those who created them, leading to unfair or biased outcomes. This is a major concern in the use of AI in marketing, as it can lead to discrimination against certain population demographics.
Another big challenge of using AI in marketing is the potential loss of human creativity and the human touch that traditional creative roles bring to the table. Marketing campaigns rely on the creativity and personal touch of marketers to connect with customers and build relationships. AI algorithms, while efficient, cannot replicate this human element (at least not today).
There are also some practical challenges to using AI in marketing. Implementing and maintaining AI technology can be costly, and small businesses may not have the resources and skills to invest in it. AI also requires high-quality data to make accurate decisions, which in the context of tactics like web personalisation raises concerns about data privacy.
Conclusion
Overall, the benefits of using AI in modern marketing are undeniable. With the right tools and practices, marketers can use AI to gain a deep understanding of their customers and create highly effective marketing campaigns and those who embrace AI in their marketing efforts will undoubtedly have a competitive edge in today's fast-paced, data-driven world.
The risk is for that to come at the expense of human creativity and the personal touch that traditional creative roles bring, as these technologies get more sophisticated every day.
Are the robots finally here?
Written by
Agustín Rejón
Agu Rejón is the Founder of Martechbase. He has worked in B2B Marketing Operations and Performance Marketing for more than 10 years and is fascinated by the role of technology in marketing and sales.
