MarketingExpanding marketing use cases for fine-tuned language models

Expanding marketing use cases for fine-tuned language models

Fine-tuning the language models allows marketers to improve the results of generative AI use cases where standard prompt engineering and prompt optimization are not delivering optimal results.

The actual fine-tuning of language models such as OpenAI's GPT 3.5 is a relatively straightforward process. The biggest work and time bottlenecks are, as in machine learning projects generally, the collection of high-quality data and the curation and testing of fine-tuned language models.

Marketing platforms as a data source

Fortunately, many marketing platforms are full of pre-classified data that can be used to fine-tune language models. For example, many marketing automation systems such as Salesforce, Hubspot, Adobe Campaign or Mailchimp store data that you can combine with performance metrics and use for algorithm training.

Data classified by humans

We the humans are good at judging, for example, how well the generated content eg. matches brand guidelines or the target audience's preferences. But classification done by people is often time-consuming, especially if the amount of data needs to be large.

The challenge of modern marketing: lack of concrete examples and use cases

A typical example of using fine-tuned language models in marketing is the creation of a distinct brand tone of voice. This is an important factor in marketing communications, but marketing is full of other uses that can benefit from fine-tuned language models such as:

• The formatting of the content for different advertising channels, such as optimizing headline, primary, and description texts on Facebook or Google Ads
• Best practices for email subject line or preview texts
• Removing prohibited words from content
• Keeping SEO and reader-friendly format in blog texts
• Optimizing content structures and modules (eg. article text structure)
• Pre-testing marketing messages before a campaign launch

Business challenge: how to make ROI with fine-tuned language models?

When you have successfully fine-tuned a language model, the next significant bottleneck is the integration into marketing channels and the creation of valuable business applications and services. So, what should a marketing team do with these fine-tuned language models? Create another chatbot or custom chat UI?

Superlines - The AI Platform for Marketing connects fine-tuned models with concrete use cases across channels

On the Superlines platform, we have solved these bottlenecks: Superlines learns based on the feedback given by users and fine-tunes its own language models to fit the needs of each user and use case. Giving feedback is easy and takes place directly in the user interface.

The best part is that the fine-tuned language models are already integrated into a wide range of marketing use cases, from content creation to analysis and optimization, which are available for marketers to use in their everyday work.