Artificial intelligence and machine learning has changed the way we look at the world today. It continues to push the boundaries of human imagination in all the ways that matter.
With the onset of generative artificial intelligence and tools like ChatGPT and DALL-E, the approach we take to marketing has also changed.
Read along to discover how generative AI can solve various marketing use cases and propel AI-driven marketing.
Generative AI refers to a subset of artificial intelligence that mainly focuses on creating content, instead of just analyzing it. This mainly includes text, designs, music, audio, and video.
It uses deep learning models (called foundational models) that are trained on large amounts of data and are capable of performing multiple tasks in a very human-like manner.
Unlike other AI technologies trained to perform a single task, generative AI possesses a broader range of capabilities.
You only have to enter a text based prompt to generate unique content that resembles the training data. With the rising interest in generative AI, the number of industries using it has also increased, especially in the field of marketing and advertising.
Here are a few interesting stats that show their adoption and implementation rates:
There are many other AI use cases, with marketing not being the only sector that it is being employed in. These are some of the others:
A survey by Mckinsey reports that 90 percent of marketing leaders expect to see an increase in the use of generative AI tools over the next two years.
Companies that continue to implement AI in their efforts will definitely see benefits in the near future, if we were to believe the stats published.
That said, these are the major benefits you can expect:
Innovation and creativity
Inspire new concepts and designs. Marketers can do away with old content types and experiment with fresh ideas that might improve conversions.
Make better decisions. Companies can get valuable insights to help them along all customer touchpoints and find unique solutions that address their pain points.
Speed up product testing and development. Developers can automate repetitive tasks, bring in diversity, and create a customized product.
Personalized customer experience
Eliminate the one-size-fits-all marketing. You can analyze customer data to tailor content and visuals to meet individual tastes. All at the click of a button.
Time and cost efficiency
Reduce creative time frames. AI systems can generate content in less than a minute and give companies the liberty to computerize simple marketing tasks.
In a perfect world, using generative AI and marketing would not raise any concerns. It would give you the ability to personalize your marketing efforts, giving quick and actionable results.
However, integrating AI in marketing is not as easy as it sounds.
The potential risks far outweigh the benefits, ranging from biases and inaccuracies to issues of copyright infringement and data privacy.
Everyone has tried out ChatGPT or Midjourney at least once since they were launched. You must have noticed that the output is not very accurate sometimes.
This is a major problem with AI-generated content.
Although it’s vast and limitless, chances of it being incorrect are equally high. Your AI marketing content could contain misleading information, which if put out in public could damage your credibility.
And this is not the only issue.
Since generative AI cannot fully understand human emotions and culture, it might produce responses that are offensive to certain groups of people. Funnily enough, even though it is wrong, the output is framed in a way that sounds just right.
So it becomes all the more important to thoroughly review and process any AI content before you approve it for use.
We know that AI models learn from existing datasets. The same is true for generative AI. Now imagine if this data is influenced or has some cultural, social, or political biases.
What happens then?
AI will generate outputs that will undoubtedly contain stereotypes. If you choose to use them as part of your marketing strategy, it would be really bad for business.
This is secondary to the hit your brand reputation will take if you create offensive content that is biased and promotes homogeneity instead of diversity.
Here’s an example. Take DALLE, OpenAI’s image generator.
Suppose it was trained on data that assumed all doctors to be men. The next time someone asks DALL-E to generate an image of a doctor, it might create images of only men in white coats.
Thus reinforcing gender biases and ignoring the multidimensional aspects of the medical profession.
It goes without saying that your company will need to put up strict rules and policies in place when it comes to AI to stop this from happening and avoid any legal complications.
Generative AI is transforming marketing in more ways than one. You get a ton of information right at your fingertips, with all the resources necessary for a successful marketing campaign.
But do you actually know where this data comes from? Not really.
It is also hard for customers to distinguish between human-made and AI generated marketing content. And buyers expect authenticity and transparency from the brands they follow.
Even if you do make use of AI, you have to be upfront about it with your customer because they deserve to know it.
As stated earlier, nobody really knows where AI models get their data from. Literally everything they create, from music to videos to text, is based on existing material that belongs to someone else.
Using it for inspiration is one thing. But directly copying the content gen AI churns out and calling it yours? Outright plagiarism.
It’s no wonder that there are intellectual property and copyright infringement lawsuits against companies behind generative AI. Case in point, the New York Times versus OpenAI.
Now there aren’t any federal laws in place that address this particular subject.
However, users should be careful in the way they employ AI because even the prompts you feed into Bing Chat (or any other tool) are recycled and used to train the model.
Leveraging AI in marketing to improve customer experience involves the analysis of large sets of data.
A lot of personal and private user data, which obviously raises privacy and security concerns. Especially with GDPR and CCPA restrictions in place.
Now, not all generative AI tools have permissions to store sensitive customer data. Unauthorized data can pose great risks to the companies employing it, leading to severe penalties and data breach.
So before you start with AI, it's crucial to address its biases and prioritize transparency, accuracy, and privacy.
AI should compliment and not replace human creativity.
While it is efficient and can speed up your work, generative AI lacks the empathy, emotional intelligence, and cultural nuances that should be the foundation of all your marketing activities.
Although there are risks involved with using generative AI in marketing, one cannot ignore the benefits. It has multiple uses, from content creation to customer segmentation and personalization.
You yourself can learn how to use generative AI models in different marketing scenarios, starting with the seven outlined below.
AI content is all the hype today and is excessively utilized in content marketing. Because why not? It speeds up the process by giving you new ideas along with a variety of content to work with.
In fact, content generation is one of the most common uses of AI and machine learning. For example, AI-generated text can be used to:
AI text generators allow you to generate both short-form and long-form content at scale. This saves a lot of time and gives you the creative liberties to work with.
Naturally, the content quality is subpar and needs excessive edits. But paid marketing tools, like Jasper AI, solve this problem to some extent by giving you prompt templates for different types of ad copies.
Tools like Runway and Midjourney can generate images and videos from textual prompts. They make use of generative adversarial networks (GANs) that help them with text to image translation.
This ability can help marketers do the following things:
In addition, you can insert AI voiceovers and music to create engaging marketing videos, which can help increase brand awareness and conversions.
A thorough keyword research is mandatory for a good SEO project. Experts need to analyze tons of keywords, their competitors, and user intent to build an SEO campaign that works.
AI makes this process easier by sorting out keyword data and listing high performing keywords. Furthermore, you can:
All in all, a content marketer can learn about the topics, subjects, and words their audience searches for online and cater to the same with relevant content.
According to a survey by BCG, 41% of CMOs harness the power of generative AI for better targeting. Better targeting comes with proper customer segmentation.
Marketing segmentation with AI involves analysis of large amounts of customer data in short periods of time. This process can be automated and in turn aid marketers:
Once you have a firm understanding of your target audience, you can offer tailored experiences.
Marketers can use generative AI to develop personalized marketing campaigns. With user likes and dislikes at their fingertips, they can shift the focus on the customer and give them what they want, right where they want it.
They will further be able to:
Since buyers demand personalization at every step of the buyers’ journey, it is crucial that brands provide it. This is the only way to ensure customer loyalty and retention in the present times.
Conversational AI tools can respond to and solve customer queries. AI can handle all types of inquiries via chatbots, social media, and even over the phone.
It is quick, efficient, and can optimize your customer service models. Additionally:
Chatbots can enhance your overall customer experience and give your customer support teams more time to focus on other important tasks, ultimately boosting operational efficiency.
Cookieless marketing doesn’t rely on browser cookies for targeting users. It’s in the vogue today since many platforms (like Chrome and Safari) are limiting the use of third-party cookies.
For those who don’t know, cookies are bits of data stored in your web browsers that track your online activity and help advertisers with ad retargeting.
With them out of the picture, your only option is to use first party data in conjunction with generative AI technologies to:
Of course, you need to ensure that you collect data with explicit user consent and comply with existing privacy regulations.
You must be familiar with the concept of buyer personas. They are fictional representations of your ideal customers that give you an idea about their goals, challenges, motivations, behavior, and interests.
Customer personas have sort of revolutionized marketing, enabling marketing organizations to build targeted marketing campaigns.
However, it’s hard to design them yourself unless you use automatic persona generators.
Generative AI can help you create personas manually. ChatGPT and Bing Chat are some of the tools out there that can be employed for this purpose. With these services in place, you can:
Keep in mind that initial outputs might be inaccurate since the data is random and entirely dependent on the prompts you use.
We have discussed some of the applications of AI in marketing. You know that you can create blogs, emails, visuals, and even produce videos for ads and product demos.
AI tools use generative adversarial networks (GANs) or variational autoencoders (VAEs) to process data and give out such results.
There are about a million of them in the market, but these are the best ones.
ChatGPT plus is the advanced version of ChatGPT, which uses the GPT-4 model. It is apparently the strongest text generator there is, outperforming all of the others.
Alternatives: Bing Chat
As mentioned before, Jasper AI is a marketing tool based on the GPT-3 model that allows users to create copy for all types of content, like blogs, social posts, and website landing pages.
Wordtune is another tool that you can use to diversify your written work. It understands the context of the text you enter and suggests corrections in real-time.
DALL-E is OpenAI’s image generator that creates designs based on textual descriptions. DALL-E2 is the upgraded version trained to produce better outputs.
Similar to DALL-E, Midjourney is an AI image generator based on machine learning algorithms.
Firefly is a generative AI program developed by Adobe that allows users to create and edit all types of graphic designs with text prompts.
Runway is a platform that has developed a text-to-video model, Gen-2, that allows users to create videos with prompts (sometimes using your own images).
Synthesia is another text-to-video platform that lets you create high-quality AI video content quickly.
Most of these tools ease up your work and guide you in the right direction. Additionally, you can use marketing automation tools like Hubspot and Mailchimp to boost work efficiency.
There are a million ways to use generative AI but you need to know the proper way to do it. You cannot just haphazardly integrate it in your marketing workflow and jeopardize your campaign.
Here's a simplified procedure to follow before you get started:
Start by building a cross functional team to spot areas where generative AI can be used, like content creation or data analysis. Mainly focus on repetitive and time consuming tasks that can be automated.
You should clearly define the business objectives you want to achieve with generative AI. It will help you choose appropriate tools and craft prompts that align with your goals.
Establishing a test environment is necessary to check out the way AI functions and find errors, if any, before deploying it. You should also constantly test your AI models to ensure that they give accurate results over time.
It is a crucial step to maintaining privacy, security, and cost-effectiveness. Put up proper AI regulations in place to prevent distribution of harmful content and input of sensitive customer data into AI tools.
It is important that your employees are familiar with the way AI operates so that they feel confident when it comes to using it. Conduct workshops to educate them on the basics of generative AI and its potential applications.
Many companies have joined the generative AI phenomenon. While some have started using it to streamline customer interactions, others have utilized it to create striking visual content.
Atlassian is a software company known for its collaborative solutions that help developers and project managers efficiently work with each other.
It has recently introduced Atlassian Intelligence, an AI virtual assistant. Built with OpenAI LLMs, the AI assistant can:
We cannot have a discussion on generative AI without mentioning creative ad campaigns. ‘Create Real Magic’ is one such movement by Coca-Cola that combines AI with art and customer engagement.
The campaign makes use of GPT-4, DALL-E, and Coca-Cola brand assets to promote creators from diverse markets.
People can visit createrealmagic.com and develop art with Coca-Cola assets. If they make something extraordinary, their artwork will get featured on billboards in places like NYC and London.
Being all inclusive, ‘Create Real Magic’ helps the brand achieve the following objectives:
Duolingo is one of the most famous language learning apps out there. It has partnered with OpenAI to incorporate GPT-4 into its services and personalized learning in a way not seen before.
Leveraging data provided by the 500 million students who use the platform, the integration is used to power two new features.
Generative AI is poised to disrupt the world, but in a good way. As is evident from its uses in design, content, and messaging, it will surely be a gamechanger in years to come.
While its short-term impact is slightly overestimated, it won’t hurt to be fully prepared. After all, human creativity enhanced by AI can give results that marketers could only imagine in the past.