Generative AI For Marketing
Catching up with the latest Black Mirror series on Netflix (I’m a little behind), the episode titled ‘Joan Is Awful’ - where a streaming network, Streamberry, focuses on the conjunction between AI and entertainment, utterly captured my attention. Whilst it can be argued that the episode predicted the SAG strike and AI could be damaging to some industries, I wanted to delve into how we marketers can effectively use technology to assist with marketing BAU, strategy and processes.
What is Generative AI?
Generative AI refers to a category of artificial intelligence (AI) techniques and models designed to generate new and original content such as text, images, music, videos and more.
Generative AI models use machine learning system (ML) algorithms - particularly deep learning - to understand and replicate the underlying patterns and structures in the data they are trained on. They then use this understanding to generate new content that is similar to the input data but not an exact copy.
How Is Generative AI Different to AI?
Generative AI is a subset of artificial intelligence (AI) that specifically focuses on creating new content or data that is not replicated from the input. While generative AI is a type of AI, they differ in terms of their immediate functions and capabilities:
AI (Artificial Intelligence):
AI encompasses a broad range of techniques and technologies that enable computers to mimic human cognitive functions, such as learning, reasoning, problem-solving and decision-making.
AI systems can be designed for various tasks, including classification, regression, clustering, recommendation and more.
Traditional AI methods often involve explicit programming and rule-based systems to perform specific tasks.
Generative AI (Generative Artificial Intelligence):
Generative AI specifically focuses on the creation of new and original content or data, often resembling human creativity.
Generative AI models, such as GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders), learn patterns and structures from existing data and generate new data that follow those patterns.
These models can generate various types of content, such as images, text, music, videos and more, based on the patterns they have learned.
Generative AI models are less focused on explicit task performance and more on creative output.
In essence, generative AI is a specialised branch of AI that is concerned with creative content generation, while AI in a broader sense encompasses a wider range of capabilities that extend beyond just content generation. Generative AI models use techniques from the field of machine learning, to learn patterns and create new content, making them a powerful tool for various creative and innovative applications.
How Does Generative AI Work?
Data Collection and Preprocessing
The first step is to collect a large dataset of the type of content you want the generative AI model to produce. For example, if you're creating a text generator, you would gather a diverse set of text documents. The data is then preprocessed, which may involve cleaning, tokenisation and other transformations to make it suitable for training.
Model Architecture Selection
Depending on the type of content you're generating, you choose an appropriate generative model architecture. Some common architectures include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Recurrent Neural Networks (RNNs) for sequential data like text.
Model Training
The generative model is trained on the preprocessed data. During training, the model learns the underlying patterns, features and structures present in the data. For example, a text generator would learn the relationships between words, phrases and context.
Sampling and Generation
Once the model is trained, it can generate new content by sampling from the learned patterns. For example, a text generator could start with a seed sentence and use the trained model to predict the next word, then use that prediction to generate the subsequent words, creating a coherent paragraph of text.
Fine-Tuning and Optimisation
Generative models are often fine-tuned and optimised to improve the quality and diversity of generated content. This may involve adjusting model parameters, incorporating user feedback or applying techniques to reduce biases or errors in the generated output.
Evaluation and Iteration
The generated content is evaluated based on various criteria such as coherence, realism, relevance and diversity. The model will most likely go through multiple iterations of training and fine-tuning to improve its performance.
Application and Deployment
Once the generative AI model is trained and optimised, it can be deployed for various applications. For example, a generative text model could be used for content creation, chatbots or language translation.
It's important to note that generative AI models can produce highly creative content, but they are also limited by the data they are trained on and the quality of the training process. Generating content that is coherent, relevant and high-quality requires careful design, training and evaluation.
What Tools Can I Use For Generative AI?
There are several powerful tools and frameworks that you can use for generative AI, depending on the type of content you want to generate and your level of expertise. Here are some popular tools and libraries for different types of generative AI:
Text Generation - For The ‘Droid Dantes’:
OpenAI's GPT (Generative Pre-trained Transformer): GPT-4 and other iterations offer state-of-the-art text generation capabilities.
Hugging Face Transformers: No, not a version of the parasitic lifeform that hatches from Xenomorph eggs, but a library that provides access to various pre-trained language models for Natural Language Processing (NLP), including GPT and Google’s BERT.
TextgenRnn: A simple Python library for generating text using recurrent neural networks (RNNs). A good tutorial demonstrating how to apply this can be found here.
Markovify: A Markov chain text generator for creating random, semi-plausible text based on patterns in the training data.
Bard: Powered by Google’s very own Large Language Model (LLM), Bard is similar to ChatGPT, but can include other mediums such as images in its answers.
Image Generation - For The ‘Robot Rembrandts’:
DeepDream: A tool that uses neural networks to generate artistic and surreal images based on existing images.
StyleGAN2: A popular architecture for generating high-quality and realistic images.
DALL-E: Created by OpenAI, DALL-E generates images from textual descriptions.
RunwayML: A creative toolkit that offers various AI models for image generation and manipulation.
Music Generation - For The ‘Cyborg Chopins’:
Magenta: A research project by Google that offers tools and models for music generation using neural networks.
AI Dungeon: A text-based adventure game that uses GPT-3 for interactive story and music generation.
MuseNet: Created by OpenAI, MuseNet is a deep learning model that generates original music compositions across different styles and genres.
Video Generation - For The ‘Computerised Chris Nolans’:
VQ-VAE-2: Vector Quantized Variational AutoEncoders are a model for high-fidelity video generation that can also be used for image and audio generation.
Artbreeder: An online platform that uses GANs to blend and generate images and videos.
General Generative AI Frameworks:
TensorFlow: A versatile open-source deep learning framework with tools for various types of generative models.
PyTorch: Another popular open-source deep learning framework that provides flexibility for implementing generative models.
Keras: A user-friendly high-level neural networks API that can be used with TensorFlow or other backends.
These tools and libraries provide a starting point for your generative AI projects, whether you're interested in text, images, music or other types of content generation.
Keep in mind that working with generative AI can require a solid understanding of machine learning concepts, so it's beneficial to have some background knowledge or access to resources to help you get started.
Can Generative AI Improve My Marketing Strategy?
Absolutely. Generative AI can bring a creative and innovative dimension to your marketing strategy. Here are several ways you can apply generative AI to enhance your marketing efforts:
Content Creation:
Generate Blog Posts: Use generative AI to create informative and engaging blog posts on relevant topics, saving time and effort in content creation.
Social Media Posts: Create compelling social media captions, tweets and posts that resonate with your audience and drive engagement.
Email Campaigns: Generate personalised email subject lines and content to improve open rates and click-through rates.
Search Engine Optimisation: Generate SEO clusters to build pillar pages and content from in order to increase visibility.
Visual Content:
Image Generation: Create unique and eye-catching images for your marketing materials, such as banners, social media posts and advertisements.
Video Editing: Utilise AI-powered tools to edit and enhance videos for marketing campaigns, incorporating effects captions and more.
Infographics: Design data-rich infographics to visualise complex information and statistics.
Chatbots and Customer Interaction:
AI Chatbots: Implement AI-driven chatbots on your website or social media platforms to provide instant customer support, answer FAQs and guide users through the sales funnel.
Personalised Recommendations: Use AI-powered chatbots to recommend products or services based on user preferences and behaviour.
Personalisation and User Experience:
Dynamic Content: Tailor website content, product recommendations and offers based on user behaviour and preferences.
Interactive Landing Pages: Create interactive and immersive landing pages that respond to user input, creating a unique and engaging experience.
Market Research and Insights:
Trend Analysis: Use generative AI to analyse trends in customer behaviour, preferences and sentiment to inform your marketing strategies.
Customer Feedback Analysis: Automatically analyse and categorise customer feedback to gain insights into product improvements and marketing opportunities.
Branding and Creative Campaigns:
Slogan Generation: Create catchy and memorable slogans or taglines for branding and marketing campaigns.
Visual Identity: Generate logo variations, colour palettes and design elements to experiment with branding concepts.
Localised Marketing:
Multilingual Content: Translate and adapt your marketing materials to different languages and cultures using AI-powered translation tools.
A/B Testing and Optimisation:
A/B Test Variations: Use AI to generate A/B test variations for website elements, email subject lines and ad copy to optimise conversion rates.
Data Visualisation:
Data-Driven Content: Generate charts, graphs and visualisations to support data-driven marketing campaigns and presentations.
When incorporating generative AI into your marketing strategy, it's important to ensure that the generated content aligns with your brand identity, maintains quality, and resonates with your target audience. Regular monitoring, testing and human oversight will help fine-tune the AI-generated content to maximise your marketing outcomes.
What Does The Future Of Generative AI Look Like?
The future of generative AI is expected to be filled with exciting advancements and transformative impacts across various industries and domains. While it's challenging to predict every detail, here are some potential trends and developments that could shape the future of generative AI:
Enhanced Creativity and Content Generation:
Generative AI will continue to evolve, enabling more sophisticated and creative content generation across different media types, including text, images, music, videos and beyond. This could lead to AI-generated content becoming seamlessly integrated into various creative processes.
Human-AI Collaboration:
The future may involve more collaboration between humans and generative AI, where AI tools assist and augment human creativity rather than replace it. This could lead to new forms of artistic expression, content production and storytelling.
Personalisation and Customisation:
Generative AI will play a pivotal role in delivering personalised and customised experiences for consumers, ranging from tailored marketing content to personalised product designs and recommendations.
Healthcare and Drug Discovery:
Generative AI could revolutionise healthcare by aiding in drug discovery, protein folding prediction, medical imaging analysis and disease diagnosis. AI models might help researchers generate untried drug compounds and identify potential treatment options more efficiently.
Virtual Worlds and Entertainment:
The entertainment industry could see a significant impact, with AI-generated characters, scenes and narratives enriching video games, virtual reality experiences and other forms of digital entertainment.
Design and Architecture:
Generative AI may play a role in architectural design, generating innovative building designs that are both aesthetically pleasing and functional. It could also assist in urban planning and infrastructure development.
Scientific Discovery:
AI-generated models and simulations might aid in scientific research and discovery by generating hypotheses, predicting outcomes of experiments and assisting in data analysis.
Ethical and Regulatory Considerations:
As generative AI becomes more advanced, there will be an increased focus on ethical considerations, potential biases and the responsible use of AI-generated content. Regulatory frameworks may evolve to address these issues.
Education and Training:
AI-powered educational tools could be developed to create personalised learning experiences, generate study materials and simulate real-world scenarios for training purposes.
Communication and Language Translation:
Generative AI may lead to more seamless communication across languages, enabling real-time translation and interpretation, which could facilitate global collaboration.
Advancements in AI Models:
Continued research and development could lead to even more powerful and efficient generative models, capable of understanding context, generating more coherent content and exhibiting higher levels of creativity.
As the field of generative AI evolves, its applications are likely to expand and diversify, impacting numerous sectors and aspects of our lives. However, challenges related to data privacy, bias mitigation, and human-AI interaction will also need to be addressed to ensure the responsible and beneficial use of generative AI technologies.
