Advancing personalized marketing content generation

Hyper-personalization in the era of generative AI

“Hyper-personalization” has become a standard term in marketing, particularly with the rise of generative AI and advanced machine learning technologies that enable more precise targeting and personalization. It has become essential for solid customer engagement and loyalty in marketing. Companies now go beyond basic demographics to understand customers’ behaviors and thoughts. They use this information to create marketing messages that seem like personal conversations tailored to each person’s unique preferences.

The interplay of creativity, personalization, and AI

In the current era of technology, where we constantly consume digital data, the attention span is reduced. To capture attention, marketing strategies and content must evolve constantly and align with customers’ needs almost immediately. This poses an interesting problem of how to generate marketing content on the fly that is creative and appeals to users. The rise of generative AI tech has enabled us to develop content creation frameworks to generate such content. With more advances coming in at regular intervals, the quality of content creation will only get better.

Fractal’s framework for market content generation

The future of marketing relies on highly personalized strategies. With advanced frameworks, brands can quickly create content that appeals personally to each customer. One such framework combines usual demographics with other factors to help marketers easily create customized content faster and automatically.

The framework is a wholesome platform designed to enable users to create a wide range of content, such as product descriptions, blog posts, email campaigns, and social media posts. Additionally, it generates theme-based product images to align the product advertisement according to a custom theme.

This framework stands out because it reduces the need for manual trial and error often found in traditional AI interactions. It guides users through creating content by asking for specific details like brand and product names, weight, and central theme. It also lets users target their content to specific audiences by choosing the right language style and incorporating important keywords to reflect the brand accurately. The user-friendly framework allows for the customization of multiple parameters for precise communication. Additionally, users can upload their brand guidelines to keep the content consistent with their brand identity.

The technology behind the framework

The framework uses advanced models such as large language models (GPT series or open-source models) and text-to-image generation models (DALL-E or open-source models). Through rigorous prompt engineering, the framework enables the generation of relevant textual and visual content. A set of guardrails is also in place to restrict the generation of offensive content.

An illustration from the very initial framework is below. The first is theme-based product image generation (see Figure 1). It inputs the product image and theme description and generates the product image within the theme. We provide an option to upload a product image so that the process does not change the product image and maintains its integrity.

The second one is generating the product description and product title based on input variables such as content length, theme, target social platform, target audience, target age group, etc. Through all these variables, brands can generate different content that caters to various audiences. (See Figure 2).

Note that these images are for reference purposes only and belong to the initial development framework. Multiple enhancements have been made, and the integration of many advanced features is in progress. They include more advanced models for better content generation, multimodal content generation (combined text and images such as flyers), etc.

Human touch is necessary!

While our framework is good at making personalized marketing content quickly and in large quantities, we recommend going only partially automatic. Humans bring a special touch to marketing that even the smartest tech can’t match. People can tell if the content is good and relevant, ensuring it feels natural to the audience.

It’s also important to remember that these tools aren’t perfect. Sometimes, they can make mistakes or even create offensive or irrelevant content. It becomes tricky when the type of offense is subjective; hence, having humans in the loop is crucial. They can catch these errors and ensure everything is appropriate before it goes out.

We’ve added features to control how creative the tool gets, but things really come together when humans work alongside the tech. Even with fancy settings, humans still need to check things over. They provide feedback and suggest changes when the content isn’t quite right. It’s a careful process of ensuring the content is perfect before showing it to people.

Integration with Genesis platform:

Genesis, our generative AI platform, offers foundational models (open-source and proprietary) services accessible through APIs, applications tailored for specific tasks, and agents that may be hosted on the platform, utilizing foundational models like LLMs. The three interconnected services within the Genesis platform — models, applications, and agents — are designed to work synergistically, providing a comprehensive and cohesive generative AI experience.

Our framework leverages the foundational models (LLMs or text-to-image models) offered by the Genesis platform. It is integrated into Genesis and offered as a content generation application.

A dive into the future:

As discussed, these frameworks are poised to become markedly better in terms of quality, personalization, and creativity. One exciting direction is to tune these frameworks for personalization and marketing content generation by training the models for the task. At present, they are mainly dependent on pre-trained models and prompt engineering. Another emerging direction is generating multi-model content such as flyers, GIFs, or short videos. Additionally, such frameworks are generic and can be leveraged to generate content for any domain. We might be looking forward to advanced multimodal content generation frameworks in multiple highly personalized domains.