The way we produce and consume content is changing due to AI applications in marketing. Now that strong new generative AI tools are accessible, marketers may increase productivity and expand their audience reach. Even while these technologies could appear overwhelming, if we know how to use them appropriately, they can present a wealth of opportunity.
This blog looks at seven intriguing generative AI applications in this piece, some of which are very useful for marketers. It will describe each one’s features, basic operation, and offer some suggestions for testing and using it in your company. Here, goal is to show how, rather than taking the place of human creativity, these tools may be utilized to augment and enhance it.
How Generative AI is Changing Content
Content has always been at the core of AI marketing campaign but creating high-quality stuff has never been easy. It takes time, talent and a lot of effort from competent writers to craft engaging blog posts, social media updates, website copy and more. But all that is changing now thanks to impressive new technologies like generative AI systems.
These advanced computational tools are using techniques like deep learning to analyse massive datasets and learn patterns in language, images, video and other media formats. By understanding how humans construct and communicate through various types of content, AI is gaining an ability to generate new materials that resemble what people create.
While AI applications in marketing may not be perfect, generative systems can produce initial drafts, designs and other building blocks that save marketers significant time normally spent writing from scratch. With some review and editing by people, the end results can be nearly indistinguishable from fully human-made works.
This allows companies to scale up their content operations without needing huge teams of writers or designers. AI assistants take on routine content chores so people are free to focus on more meaningful tasks that machines still can’t do like strategy, community management and customer care.
Content volumes are no longer constrained by limited staff sizes too. Generative systems never get tired or distracted, letting ai marketing campaign teams pump out a constant stream of new blogs, social updates, emails and other nurturing content on the regular without burning people out.
Quality should stay high even as quantity increases also. AI is consistent in a way humans can’t match, adhering to style guides and voice/tone standards every single time without variance. And machine generated materials may conversely inspire more original human works.
Generative AI Applications for Marketers
Following are the 7 promising generative AI applications for marketers and the opportunities they present:
Text Generation
Perhaps the most well-known form of generative AI is text generation. Systems like GPT-3 have shown they can write blog posts, product descriptions, social media captions and more based on a brief prompt.
How it works: Using a technique called transformer models, these systems analyse patterns in huge language datasets to learn the structure and flow of written communication. They can be prompted to continue a piece of text or start from scratch on a topic.
How marketers can use it: Test auto-generating initial drafts of various types of content to save on writing time. Have the system suggest topics or angles to explore based on your business. Review and refine the outputs to develop a unique voice.
Image & Graphic Generation
Moving beyond text, systems like DALL-E and Midjourney can now generate realistic images, graphics and even videos based on text prompts. They have come a long way.
How it works: Using generative adversarial networks (GANs), these tools essentially have two neural networks – one generates images while the other critiques them to produce increasingly higher quality outputs over time. Lots of data fuels this process.
How marketers can use it: Explore auto-generating images for social posts, explainer graphics for content or prototypes for product mock-ups and designs. Review the results and consider post-editing to fine-tune images before publishing.
Keyword Research
Finding the best keywords to target for SEO can be tedious. AI can streamline this by suggesting large volumes of relevant keyword ideas.
How it works: Systems analyse keyword datasets and use techniques like topic modelling to automatically group related keywords together by topic or theme. They can then suggest expansive keyword lists tailored to your business based on things like existing content or competitors.
How marketers can use it: Leverage AI to augment and expand upon your keyword research lists. Use the outputs to find new opportunities and niche terms to consider for optimization that you may have missed otherwise.
Video Transcription
Getting video and audio content transcribed is a big time investment. AI is making this process largely automated.
How it works: Leveraging speech recognition models, these tools can listen to videos and audio files and generate accurate written transcripts in real-time or shortly after upload. Advanced systems even index and allow searching within transcripts.
How marketers can use it: Have AI transcribe interviews, webinars, podcasts and other audiovisual assets on your channel. Use the transcripts to boost SEO, develop new article content or make your media more accessible.
Predictive Analytics
AI is helping marketers better understand customer behaviour and identify opportunities through predictive analytics.
How it works: By applying machine learning techniques to datasets full of customer interactions, purchase histories and more, these tools uncover subtle patterns that reveal things like who is most likely to convert or what they might purchase next.
How marketers can use it: Leverage predictive insights for segmentation, targeting high-value customer groups, optimizing AI marketing campaign and personalizing the customer experience. Continually feed new data into models to improve accuracy over time.
Chatbot Development
Bots are becoming very useful at automating basic customer support, lead generation and other conversational tasks. AI streamlines their creation.
How it works: Through techniques like neural semantic parsing, dialog management and Natural Language Processing (NLP), generative AI can analyse AI advertising examples conversations to automatically develop the semantic rules and flow needed to build new chatbots with minimal manual effort.
How marketers can use it: Quickly prototype and deploy basic chatbots for FAQs, lead capture forms or product recommendations. Focus on more complex bot development where human interactions deliver most value. Continually refine bots based on ongoing user data.
Video Generation
While still early, AI is showing promising results at auto-generating video content from text or image prompts alone. Resolution is improving rapidly.
How it works: Advanced GAN networks and techniques like diffusion models allow systems to realistically render video scenes, simulate movement and even generate lip sync from minimal inputs. Massive datasets power highly realistic yet slightly synthetic video generation.
How marketers can use it: Carefully experiment generating prototype explainer videos, product demonstrations, social media clips or other basic motion content as a starting point. Consider post-editing generated videos before wider use. Monitor advances in quality and implementation guidelines.
Advancing Generative AI In A Responsible Manner
It is more crucial than ever that we carefully analyse how to use these potent tools for good as generative AI’s capabilities grow. A few best practices to bear in mind are as follows:
- By collaborating with people and AI systems, augment human talents rather than replacing them.
- Be open about the content’s machine involvement and supervision. Give outputs clear attributes.
- Rather than attempting to outperform humans by automating repetitive work, concentrate on using AI to enhance human potential.
- Use quality control procedures, feedback data, and monitoring to continuously hone and enhance systems.
- Respect policies around dangerous uses, misinformation, bias, privacy and more through responsible data practices.
- Continually evaluate new guidance from experts, regulators and within communities on ethical AI development and usage.
With care and prudence, generative AI offers huge opportunities to turbocharge content production and open new doors for connecting with audiences. By focusing on augmentation, transparency and oversight, marketers can begin benefiting today while helping ensure these technologies progress responsibly. The future remains unwritten, so our job is to guide it wisely.
Conclusion
Unquestionably, generative AI applications in marketing is changing how information is produced and seen in the modern world. As models improve, the seven applications covered here only scratch the surface of what is feasible. Even if things are changing quickly, accountability must drive development.
Communities like MyW3Magic can be of great assistance to marketers who are looking to gain but are unsure of how. With the help of clear instructions, AI advertising examples, and knowledgeable commentary, they offer current instruction on newly developed instruments. Through collaborative learning in an open forum, we help one another grasp and securely apply new technology for the benefit of everybody.
Protecting people while empowering members to take advantage of the potential that generative AI offers—from boosting outputs to establishing one-to-one connections—is the top priority for MyW3Magic. Participate in this movement that emphasizes human potential, consciousness, and compassion. If wise people guide the future, it will unfold; compassion and knowledge will illuminate the path.