Generative artificial intelligence (AI) is the hottest topic in the marketing world today. And the numbers agree. According to Statista, the global generative AI industry is expected to grow to USD 207 billion by 2030.
It’s not a secret that companies already use generative AI for marketing. But should you jump in? And how do you ensure everything you do is bespoke and maintains a high level of quality?
That is what this blog is all about.
We’ll explore the following:
Generative AI Basics
What Is Generative AI?
How Generative AI Works
The Evolution of Generative AI
Four Ways To Leverage Generative AI in Marketing
Three Concerns About Generative AI
What the Future of Generative AI in Marketing Looks Like
While generative AI is a powerful tool, it’s not without its challenges. By the end of this blog, you’ll get an answer to the big question: How does generative AI work, and what should you look out for?
Generative AI Basics
Before we dive into the tips and strategies on how to use generative AI in your marketing efforts, let’s first understand what does generative AI mean and how it works.
What Does Generative AI Mean?
As a technology, generative AI falls under the umbrella of artificial intelligence (AI) and refers to a type of machine learning model that is capable of creating original content, such as:
• Images
• Text
• Audio
This means that generative AI marketing tools go beyond simply rearranging existing data. Instead, they create new and unique outputs based on patterns learned from analyzing large datasets.
There are several generative AI examples today. Some of the best AI tools for digital marketing include the following:
• OpenAI ChatGPT: This platform specializes in generating text-based content, such as articles, product descriptions and even conversations.
Image: How the ChatGPT interface looks like
• Claude by Anthropic: Focuses on “constitutional AI,” aligning AI tech with human values like helpfulness, safety and honesty. This is also a technology backed by Google.
Image: How the Claude interface looks like
• Midjourney: Creates images, fine art illustrations and other types of imagery via natural language descriptions.
Image: Community Showcase from Midjourney
• Bard: Google’s take on conversational style text-generation AI to write, research and create content. Its closest competitor would be ChatGPT, Claude and other text-based AI marketing tools.
Image: How Bard’s chat interface looks like
Because of its wide range of applications, generative AI for marketing is indispensable for companies that want to scale their efforts and reach more targeted audiences.
So, How Does Generative AI Work?
Generative AI works by using large datasets to train machine learning models. The technology behind this is called deep learning, which uses artificial neural networks to analyze data and learn patterns. This is then supported by a Large Language Model (LLM) that helps the model create realistic and coherent outputs.
Generative AI examples of datasets can come from various sources:
• Social media posts
• Customer reviews
• Product descriptions
• Images
And the sources are vast. For instance, Google’s Pathways Language Model (PaLM) uses 540 billion parameters to train its AI. OpenAI’s multimodal GPT-4 model, on the other hand, is estimated to have trained on 1.76 trillion parameters.
While the general principle behind how does generative AI work is the same, the way owner companies implement it is different, which is why you could see a variation between the generated outputs and their use case.
The Evolution of Generative AI
Generative AI for marketing has come a long way, but it’s essential to understand its beginnings before we can truly maximize its potential.
Initially, AI was more about rule-based systems. It wasn’t until machine learning and neural networks became famous that we saw the potential for generative AI. Early models were pretty basic and good at specific tasks but not very flexible.
Then came the LLMs, boosting the capabilities of AI in content marketing.
These are large neural networks trained on vast amounts of text, like Google’s DeepMind and OpenAI’s GPT models, which have allowed AI to generate text with high coherence and fluency. They’re called generative because they can generate text, not just analyze it.
If that sounds interesting, then you’ll find GPT-3 to be a game-changer. It was both better in size and underlying architecture, generating various text, such as:
• Articles
• Poems
• Code snippets
While Google has been developing its own LLMs, such as BERT, the company only recently had a viable OpenAI GPT alternative in 2023 with its Bard tool, powered by PaLM 2. It is Google’s “next generation large language model,” which is grounded in Google’s approach of “deploying AI responsibly.”
Unlike GPT-3, PaLM 2 is multimodal. This means it can generate texts, images and code. These are the modern generative AI examples we’re experiencing today, along with OpenAI’s GPT-4, another multimodal AI model that “exhibits human-level performance on various professional and academic benchmarks.”
Four Ways To Leverage Generative AI in Marketing
So, what does generative AI mean for marketers? Well, a lot. There are several AI tools for marketing, thanks to generative AI and LLMs. These tools can be used in several ways, and here are some of them:
1. Content Creation and Generation
This is the part that many companies are most excited about: It cuts down the time it takes to write and create quality content, as well as helps them keep up with tight deadlines.
How?
For instance, ChatGPT can be used for topic ideation, market research and sourcing content ideas. Essentially, ChatGPT can look for and pool information about any topic, extract common themes and generate text that follows a specific style or tone.
Image: Generating long-tail SEO keywords for a topic.
(Read more: How To Use ChatGPT for Your Keyword Research and Content Topic Ideation)
Other AI marketing tools like DALL-E and Midjourney create instant images based on human language prompts. Microsoft actually integrates image generation into their AI Co-Pilot, which people can use freely.
Image: Bing AI Image Creator
With AI marketing automation, teams can have more time to focus on other important tasks while still producing high-quality content in a shorter period.
(Read more: AI Isn’t for Words Only: Bing Image Creator Tool)
2. Personalized Marketing and Experiences
If you feel ad fatigue from generic, irrelevant ads, then AI in content marketing can help. All you have to do is feed it with data, and it will give you insights into customer preferences, generating various ad materials, such as:
• Personalized ads
• Targeted emails
• Video production
• In the case of Google and other search engine ads, product recommendations
One industry that could leverage this heavily would be eCommerce and online marketplaces like Amazon, where AI can generate a summary of reviews and listings based on customer data. It’s similar to content creation, but the main focus is to analyze customer behavior and preferences and then tailor marketing materials to individual customers.
If you have a list of customer leads with relevant data, you can use AI tools for marketing to create personalized content, increasing the chances of conversion. AI marketing automation also plays a vital role in building customer loyalty and retention.
3. Insights to Customer Behavior
Being trained with LLMs has its perks.
The best AI tools for digital marketing like ChatGPT can process PDFs and other data sources, while Claude can also analyze other text-based documents.
This is useful when you have tons of data that you need to analyze to get insights into customer behavior and preferences.
For example, let’s say you have a spreadsheet that contains all the purchases made by your customers. With AI for marketing, you can quickly analyze the data and determine patterns in customer behavior like:
• Buying frequency
• Preferred products
• Price range
Instead of manually going through every document, artificial intelligence in marketing can do this in a fraction of the time and give you more accurate results. Out of this data, you can create search engine optimized (SEO) content, target the right customers for promotions and more. All of this ensures that you get maximum ROI from your marketing efforts.
4. Chatbots and Customer Support
Many SaaS companies are also leveraging LLMs to build their chatbots and automate customer support for small to large businesses.
The most popular ones include the following:
• Docsbot
• Botpress
• Drift
Generative AI as a customer support solution is very interesting – it’s more efficient without sacrificing the quality of interactions and service experience.
For instance, you can train a chatbot to answer top frequently asked questions, freeing up the time of human agents to handle more complex queries and tasks. This not only improves overall customer satisfaction but also saves time and resources for the company.
Sounds Great! But There Are a Few Things To Keep in Mind.
Don’t hit the “Generate” button too fast; there are actually crucial issues that you have to remember. While generative AI in itself is harmless, there’s a rising need to use this technology more responsibly.
Here’s a quick rundown of the three most pressing concerns in generative AI:
Concern 1: Ethical Implications
This is perhaps the hottest discussion surrounding AI content – whatever you generate is built upon pre-existing data that’s been fed into the model. And here lies a pressing concern: What if this data contains certain biases? What if it perpetuates harmful stereotypes or promotes discriminatory content?
These are just some questions the 2023 Klion Forum discussed at Columbia Business School.
Whenever you generate new content, ensure that you’re not perpetuating harmful biases, and use inclusive language where possible.
This means four things:
• Heavy human editing is still required: While generative AI can help you automate the content creation process, it’s still necessary to review and edit the output for accuracy and tone before publishing.
• Be mindful of sensitive topics: Certain topics like race, gender and politics are delicate issues. These require careful consideration and thoughtfulness to give generative AI meaning safely. In fact, if you’re writing about these topics, we highly recommend not using generative AI at all.
• Understand your audience: What might be acceptable for one group of people may not be appropriate for another. Consider the impact of your content on different groups and tailor it accordingly.
• Stay updated with ethical guidelines: Stay current with the latest ethical guidelines and regulations regarding AI-generated content. This will help you avoid any legal or reputational issues.
It’s not just text-based content, though; AI-generated images may also contain biases. When using AI content, it’s your responsibility to thoroughly review and be aware of any potential biases in the images you create.
Concern 2: Data Privacy Issues
Another primary concern is privacy and security. After all, you’re using customer data to create personalized content for your audience.
How do you deal with this?
While legal frameworks and industry standards are still crafted, it’s crucial to be proactive with your practices around how generative AI works. The usage of customer data alongside artificial intelligence in marketing is actually very similar to the rest of your marketing efforts.
Therefore, you should:
• Obtain explicit consent: Ensure that your customers are aware and have given you explicit permission to use their data for AI in advertising content. This should be clearly stated in your privacy policy.
• Be transparent: Be open and honest about how you collect, store and use customer data to give generative AI meaning. Your audience has a right to know what information is being used to create content for them.
• Protect customer data: It’s your responsibility to ensure that customer data is stored securely and protected from any potential cyber threats. This includes implementing appropriate security measures and regularly updating them as needed.
The marketing industry has been pivoting towards first-party data as opposed to third-party cookies, and this trend is likely to continue. While that presents tons of opportunities for personalization, it’s also an added responsibility to handle customer data with care.
Concern 3: Potential Misuse
The potential for misuse of AI in advertising is a grave concern. Its ability to create realistic outputs can be exploited for malicious purposes, such as cyberattacks.
For instance, WormGPT, a generative AI tool, is used for sophisticated phishing and business email compromise attacks. Its capability to create personalized fake emails significantly raises the success rate of these attacks.
Moreover, the emergence of techniques like PoisonGPT, which involves modifying open-source AI models to spread disinformation, and the development of polymorphic malware using generative AI are further cause for concern.
What the Future of Generative AI in Marketing Looks Like
Despite these challenges, generative AI holds tremendous potential, and we can see that with how companies like Google, Amazon and Microsoft are integrating this technology into their existing solutions.
So, what will it look like from here? Here are some of our predictions.
AI-Powered Search Engines
With Bing Co-Pilot and Search Generative Experience (SGE) already being used by a lot of users, we can only expect that AI search engines will become the norm.
But it’s not just content generation that makes AI search engines special.
They’re built with the same principles that search engines are known for: organic traffic and visibility. This means that as Google, Microsoft and other search engines refine AI search, you’ll stand to benefit from them as long as you follow their best practices.
In fact, Google has already released its own guidelines for artificial intelligence in marketing. If you’re exhibiting expertise, experience, authoritativeness and trustworthiness (E-E-A-T), and your content is helpful and people-first, they will not lose their ranking potential.
(Read more: How Google Treats AI Content and Should It Be Labeled?)
It’s Not a Total Robot Takeover
We’ve already seen so much AI in advertising and organic content that users can now detect which ones are and which are not. That is good news for businesses that take pride in creating authentic, original and creative content.
AI may be good at spitting out different content variations, but it can’t create something entirely new. You’ll still need human experts to curate each piece to make it impactful and unique. This means that editorial processes and quality assurance frameworks within content and marketing teams are more important than ever.
Humans bring creativity, empathy and strategic thinking to digital marketing – qualities that AI currently cannot replicate and will not achieve in the foreseeable future.
Manage Your Expectations
This leads us to our next point: the quality of AI-generated content often falls short of the hype.
It’s important to temper expectations and view generative AI as an assistant rather than a complete solution for marketing. AI can automate routine tasks and provide data-driven insights, but it lacks a nuanced understanding of human emotion and culture.
Therefore, it’s crucial to be strategic when using generative AI. It’s ideal for initial drafts, data analysis and trend prediction, but human oversight is essential for finalizing content.
Stand Out With Thrive’s Human-Led Digital Marketing
How can you set yourself apart in an era of fluff and robotic-sounding content?
The answer is simple: human intelligence to give generative AI meaning, depth and relevance.
With a healthy mix of automation, informed and responsible use of AI and human oversight, the Thrive way of digital marketing ensures your content doesn’t drown in monotonous, generic noise. Thrive Internet Marketing Agency is a 200-strong company of human experts, creatives and strategists that craft and drive content to its fullest potential.
Experience the difference that human-led digital marketing can make to your business.
