Building Generative AI into Marketing Strategies: A Primer

*This blog is part of a series of blog posts. Check out:

• For a practical deep dive into how to build a generative AI marketing portal using AWS Services and microservice architecture: Building a generative AI marketing portal on AWS

Introduction

Artificial Intelligence has undoubtedly shaped many industries and is poised to be one of the most transformative technologies in the 21st century. Among these is the field of marketing where the application of generative AI promises to transform the landscape. This blog post explores how generative AI can revolutionize marketing strategies, offering innovative solutions and opportunities.

According to Harvard Business Review, marketing’s core activities, such as understanding customer needs, matching them to products and services, and persuading people to buy, can be dramatically enhanced by AI. A 2018 McKinsey analysis of more than 400 advanced use cases showed that marketing was the domain where AI would contribute the greatest value. The ability to leverage AI can not only help automate and streamline processes but also deliver personalized, engaging content to customers. It enhances the ability of marketers to target the right audience, predict consumer behavior, and provide personalized customer experiences. AI allows marketers to process and interpret massive amounts of data, converting it into actionable insights and strategies, thereby redefining the way businesses interact with customers.

Generating content is just one part of the equation. AI-generated content, no matter how good, is useless if it does not arrive at the intended audience at the right point of time. Integrating the generated content into an automated marketing pipeline that not only understands the customer profile but also delivers a personalized experience at the right point of interaction is also crucial to getting the intended action from the customer.

Amazon Web Services (AWS) provides a robust platform for implementing generative AI in marketing strategies. AWS offers a range of AI and machine learning services that can be leveraged for various marketing use cases, from content creation to customer segmentation and personalized recommendations. Two services that are instrumental to delivering customer contents and can be easily integrated with other generative AI services are Amazon Pinpoint and Amazon Simple Email Service. By integrating generative AI with Amazon Pinpoint and Amazon SES, marketers can automate the creation of personalized messages for their customers, enhancing the effectiveness of their campaigns. This combination allows for a seamless blend of AI-powered content generation and targeted, data-driven customer engagement.

As we delve deeper into this blog post, we’ll explore the mechanics of generative AI, its benefits and how AWS services can facilitate its integration into marketing communications.

What is Generative AI?

Generative AI is a subset of artificial intelligence that leverages machine learning techniques to generate new data instances that resemble your training data. It works by learning the underlying patterns and structures of the input data, and then uses this understanding to generate new, similar data. This is achieved through the use of models like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer models.

What do Generative AI buzzwords mean?

In the world of AI, buzzwords are abundant. Terms like “deep learning”, “neural networks”, “machine learning”, “generative AI”, and “large language models” are often used interchangeably, but they each have distinct meanings. Understanding these terms is crucial for appreciating the capabilities and limitations of different AI technologies.

Machine Learning (ML) is a subset of AI that involves the development of algorithms that allow computers to learn from and make decisions or predictions based on data. These algorithms can be ‘trained’ on a dataset and then used to predict or classify new data. Machine learning models can be broadly categorized into supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

Deep Learning is a subset of machine learning that uses neural networks with many layers (hence “deep”) to model and understand complex patterns. These layers of neurons process different features, and their outputs are combined to produce a final result. Deep learning models can handle large amounts of data and are particularly good at processing images, speech, and text.

Generative AI refers specifically to AI models that can generate new data that mimic the data they were trained on. This is achieved through the use of models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). Generative AI can create anything from written content to visual designs, and even music, making it a versatile tool in the hands of marketers.

Large Language Models (LLMs) are a type of generative AI that are trained on a large corpus of text data and can generate human-like text. They predict the probability of a word given the previous words used in the text. They are particularly useful in applications like text completion, translation, summarization, and more. While they are a type of generative AI, they are specifically designed for handling text data.

Simply put, you can understand that Large Language Model is a subset of Generative AI, which is then a subset of Machine Learning and they ultimately falls under the umbrella term of Artificial Intelligence.

What are the problems with generative AI and marketing?

While generative AI holds immense potential for transforming marketing strategies, it’s important to be aware of its limitations and potential pitfalls, especially when it comes to content generation and customer engagement. Here are some common challenges that marketers should be aware of:

Bias in Generative AI Generative AI models learn from the data they are trained on. If the training data is biased, the AI model will likely reproduce these biases in its output. For example, if a model is trained primarily on data from one demographic, it may not accurately represent other demographics, leading to marketing campaigns that are ineffective or offensive. Imagine if you are trying to generate an image for a campaign targeting females, a generative AI model might not generate images of females in jobs like doctors, lawyers or judges, leading your campaign to suffer from bias and uninclusiveness.

Insensitivity to Cultural Nuances Generative AI models may not fully understand cultural nuances or sensitive topics, which can lead to content that is insensitive or even harmful. For instance, a generative AI model used to create social media posts for a global brand may inadvertently generate content that is seen as disrespectful or offensive by certain cultures or communities.

Potential for Inappropriate or Offensive Content Generative AI models can sometimes generate content that is inappropriate or offensive. This is often because the models do not fully understand the context in which certain words or phrases should be used. It’s important to have safeguards in place to review and approve content before it’s published. A common problem with LLMs is hallucination: whereby the model speaks false knowledge as if it is accurate. A marketing team might mistakenly publish a auto-generated promotional content that contains a 20% discount on an item when no such promotions were approved. This could have disastrous effect if safeguards are not in place and erodes customers’ trust.

Intellectual Property and Legal Concerns Generative AI models can create new content, such as images, music, videos, and text, which raises questions of ownership and potential copyright infringement. Being a relatively new field, legal discussions are still ongoing to discuss legal implications of using Generative AI, e.g. who should own generated AI content, and copyright infringement.

Not a Replacement for Human Creativity Finally, while generative AI can automate certain aspects of marketing campaigns, it cannot replace the creativity or emotional connections that marketers use in crafting compelling campaigns. The most successful marketing campaigns touch the hearts of the customers, and while Generative AI is very capable of replicating human content, it still lacks in mimicking that “human touch”.

In conclusion, while generative AI offers exciting possibilities for marketing, it’s important to approach its use with a clear understanding of its limitations and potential pitfalls. By doing so, marketers can leverage the benefits of generative AI while mitigating risks.

How can I use generative AI in marketing communications?

Amazon Web Services (AWS) provides a comprehensive suite of services that facilitate the use of generative AI in marketing. These services are designed to handle a variety of tasks, from data processing and storage to machine learning and analytics, making it easier for marketers to implement and benefit from generative AI technologies.

Overview of Relevant AWS Services

AWS offers several services that are particularly relevant for generative AI in marketing:

How to build Generative AI into marketing communications

Dynamic Audience Targeting and Segmentation: Generative AI can help marketers to dynamically target and segment their audience. It can analyze customer data and behavior to identify patterns and trends, which can then be used to create more targeted marketing campaigns. Using Amazon Sagemaker or the soon-to-be-available Amazon Bedrock and Amazon Titan Models, Generative AI can suggest labels for customers based on unstructured data. According to McKinsey, generative AI can analyze data and identify consumer behavior patterns to help marketers create appealing content that resonates with their audience.

Personalized Marketing: Generative AI can be used to automate the creation of marketing content. This includes generating text for blogs, social media posts, and emails, as well as creating images and videos. This can save marketers a significant amount of time and effort, allowing them to focus on other aspects of their marketing strategy. Where it really shines is the ability to productionize marketing content creation, reducing the needs for marketers to create multiple copies for different customer segments. Previously, marketers would need to generate many different copies for each granularity of customers (e.g. attriting customers who are between the age of 25-34 and loves food). Generative AI can automate this process, providing the opportunities to dynamically create these contents programmatically and automatically send out to the most relevant segments via Amazon Pinpoint or Amazon SES.

Marketing Automation: Generative AI can automate various aspects of marketing, such as email marketing, social media marketing, and search engine marketing. This includes automating the creation and distribution of marketing content, as well as analyzing the performance of marketing campaigns. Amazon Pinpoint currently automates customer communications using journeys which is a customized, multi-step engagement experience. Generative AI could create a Pinpoint journey based on customer engagement data, engagement parameters and a prompt. This enables GenAI to not only personalize the content but create a personalized omnichannel experience that can extend throughout a period of time. It then becomes possible that journeys are created dynamically by generative AI and A/B tested on the fly to achieve an optimal pre-defined Key Performance Indicator (KPI).

A Sample Generative AI Use Case in Marketing Communications

AWS services are designed to work together, making it easy to implement generative AI in your marketing strategies. For instance, you can use Amazon SageMaker to build and train your generative AI models which assist with automating marketing content creation, and Amazon Pinpoint or Amazon SES to deliver the content to your customers.

Companies using AWS can theoretically supplement their existing workloads with generative AI capabilities without the needs for migration. The following reference architecture outlines a sample use case and showcases how Generative AI can be integrated into your customer journeys built on the AWS cloud. An e-commerce company can potentially receive many complaints emails a day. Companies spend a lot of money to acquire customers, it’s therefore important to think about how to turn that negative experience into a positive one.

When an email is received via Amazon SES (1), its content can be passed through to generative AI models using GANs to help with sentiment analysis (2). An article published by Amazon Science utilizes GANs for sentiment analysis for cases where a lack of data is a problem. Alternatively, one can also use Amazon Comprehend at this step and run A/B tests between the two models. The limitations with Amazon Comprehend would be the limited customizations you can perform to the model to fit your business needs.

Once the email’s sentiment is determined, the sentiment event is logged into Pinpoint (3), which then triggers an automatic winback journey (4).

Generative AI (e.g. HuggingFace’s Bloom Text Generation Models) can again be used here to dynamically create the content without needing to wait for the marketer’s input (5). Whereas marketers would need to generate many different copies for each granularity of customers (e.g. attriting customers who are between the age of 25-34 and loves food), generative AI provides the opportunities to dynamically create these contents on the fly given the above inputs.

Once the campaign content has been generated, the model pumps the template backs into Amazon Pinpoint (6), which then sends the personalized copy to the customer (7).

Result: Another customer is saved from attrition!

Conclusion

The landscape of generative AI is vast and ever-evolving, offering a plethora of opportunities for marketers to enhance their strategies and deliver more personalized, engaging content. AWS plays a pivotal role in this landscape, providing a comprehensive suite of services that facilitate the implementation of generative AI in marketing. From building and training AI models with Amazon SageMaker to delivering personalized messages with Amazon Pinpoint and Amazon SES, AWS provides the tools and infrastructure needed to harness the power of generative AI.

The potential of generative AI in relation to the marketer is immense. It offers the ability to automate content creation, personalize customer interactions, and derive valuable insights from data, among other benefits. However, it’s important to remember that while generative AI can automate certain aspects of marketing, it is not a replacement for human creativity and intuition. Instead, it should be viewed as a tool that can augment human capabilities and free up time for marketers to focus on strategy and creative direction.

Get started with Generative AI in marketing communications

As we conclude this exploration of generative AI and its applications in marketing, we encourage you to:

The journey into the world of generative AI is just beginning. As technology continues to evolve, so too will the opportunities for marketers to leverage AI to enhance their strategies and deliver more personalized, engaging content. We look forward to exploring this exciting frontier with you.

About the Author

Tristan (Tri) Nguyen

Tristan (Tri) Nguyen is a Senior Specialist Solutions Architect at AWS. With deep expertise in data science, martech and customer data platforms, Tristan specialises in utilising Machine Learning and Generative AI to craft scalable customer engagement strategies and architectural solutions for customer across the Asia Pacific Region. He holds a Master of Computer Science from the Georgia Institute of Technology, and has extensive hands-on experience with AWS technologies, achieving all 12 AWS certifications. In his spare time, Tristan dabbles in triathlon, hiking big mountains and climbing big rocks.