Part 2: Generative Artificial Intelligence and How It Changes Content Strategies and Ad Creative
In Part 1 of our blog series on the topic of Artificial Intelligence, we discussed how AI plays a role behind the scenes to improve optimization of digital marketing campaigns. In part 2 we will discuss a different area of AI technology advancement called generative AI. Artificial intelligence (AI) can be broadly classified into two categories – narrow AI and generative AI. Narrow AI systems are designed to perform specific tasks within a limited domain, and are proficient at executing these tasks. In contrast, generative AI systems can perform a wide range of tasks and can learn and adapt to new situations. A great example of Narrow AI are the first versions of chatbot assistants (Apple’s Siri, the Google Assistant, and Amazon’s Alexa), these chatbots are only able to provide responses they are programmed with initially. Whereas Generative AI (like ChatGPT) uses deep learning algorithms and neural networks to simulate the human brain’s learning and decision-making processes. The primary difference between these two types of AI is that narrow AI is designed to excel at specific tasks, while generative AI has the potential to be more versatile and adaptive. Generative AI can create various types of content, including text, imagery, audio, and synthetic data.
One of the most common use cases of generative AI is chatbot conversations. Chatbots are becoming increasingly popular in industries such as e-commerce, customer service, and healthcare. These chatbots can answer customers’ queries, provide assistance, and even make recommendations. The underlying technology that powers these chatbots is generative AI, which uses natural language processing (NLP) to understand and respond to users’ queries in a conversational way. Chatbots continue to rise in popularity also because they help bridge the gap for businesses that are not able to hire a full customer service team. Chatbots can help customers get the answers they need before involving customer service. In fact, “virtual customer assistants help organizations reduce call, chat, and email inquiries by 70%” (Gartner).
Another application of generative AI is in creating lookalike audiences. In the world of digital marketing, advertisers use generative AI algorithms to find other people that “look like” their first-party customer set. The AI algorithm continually learns and refines its knowledge about people’s online activity, creating highly targeted audiences for advertisers.
Marketing Copy and Written Content
Generative AI has also found its way into the world of marketing copy and written content. With the proliferation and democratization of AI, tools have emerged that use generative AI to write ad copy and blog content based on a detailed prompt. These tools use machine learning algorithms to generate content that is not only grammatically correct but also highly engaging and persuasive. This is especially helpful for assistance with creating larger sets of ad copy campaigns. When there are more ad copy sets for Google or Facebook to choose from with their responsive ads (also thanks to AI), campaigns can be more optimized and adaptable.
ChatGPT is a model that is rising in popularity to help fill the gap in marketing content and written copy. By leveraging its natural language processing capabilities, ChatGPT can analyze data and generate text that is tailored to the target audience and effectively communicates the desired message. Whether it’s creating product descriptions, social media posts, email marketing campaigns, or website copy, ChatGPT can provide valuable assistance in developing content that captures the attention of customers and drives sales. Additionally, with its ability to generate natural-sounding language, it can help businesses save time and resources by streamlining the content creation process.
ChatGPT can also work within various Microsoft apps such as PowerPoint, Word, and others. In these apps, ChatGPT can assist with tasks such as generating text, suggesting language or content, and aiding in the creation of presentations and documents. For instance, ChatGPT can help users draft compelling presentations by suggesting language that is engaging and informative. Additionally, ChatGPT can assist with research by providing relevant information on a particular topic or subject. In Word, ChatGPT can provide suggestions for grammar, sentence structure, and vocabulary to help users improve the quality of their writing. With its advanced language processing capabilities, ChatGPT can understand the context and intent of the user’s requests and provide tailored suggestions that can save time and improve the overall quality of their work.
As an AI language model, ChatGPT can work within Microsoft’s Copilot as a coding language assistant. When working in Copilot, it can provide suggestions for code completion, offer relevant code snippets, and assist with writing code documentation. By analyzing the context of the code being written and the programming language being used, ChatGPT can provide tailored suggestions that can save developers time and improve the quality of their code. Additionally, ChatGPT can assist with debugging and help developers identify and resolve errors in their code. With its advanced natural language processing capabilities, ChatGPT can communicate with developers using natural language and help bridge the gap between human and machine communication in the coding process.
AI is also making strides in generating audio content. With the advent of AI-powered tools like Descript, technology platforms can learn to sound like a unique person’s voice and generate speech that mimics them. This technology has immense potential in fields like podcasting and audiobook production, where it can be used to create high-quality audio content at scale.
Finally, generative AI is also making waves in the field of image generation. Tools like Dall-e 2 and Mid Journey use generative AI to generate incredibly detailed and unique images and artwork based on a detailed prompt. Even companies like Adobe are entering this AI space with their latest release of Adobe Firefly. Unlike Dall-e, Adobe Firefly will also have 3D capabilities and an emphasis on copyright protection. All of these tools have been trained on vast amounts of visual data and use deep learning algorithms to create highly realistic and intricate images.
The rise of generative AI comes with its pros and cons. The quick advancement of this technology is also raising concerns about potential unintended consequences, such as bias, privacy violations, and safety issues. The lack of regulation and oversight to govern AI development and use is creating legal and ethical challenges, highlighting the need for careful consideration of the societal implications of AI. At the same time, generative AI is transforming the way we create content, whether it’s in the form of chatbot conversations, marketing copy, audio content, or visual content. As technology advances, we can expect to see more exciting use cases emerge, making generative AI a critical tool for content creation in the future. It will just be a matter of balancing this creation and its uses as it advances, that will be imperative.
ChatGPT co-authored this post, can you tell which areas were written by AI and which were not? Stay tuned for Part 3, where we will deep-dive into more about ChatGPT and Google’s Bard will change the landscape of search engine optimization.
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