Improving Data Categorization in Marketo Engage Using Fine-Tuned AI Models
As a Revenue Ops professional, you may be struggling with SPAM form submissions, keyword matching in job titles to determine personas, or messy open-text fields that make it hard to extract insights from your data. These data categorization challenges hinder segmentation, personalization, and reporting, preventing your team from leveraging your data and making it difficult to send tailored content to your audience.
Explore how fine-tuned Large Language Models (LLMs) can help address these persistent data problems. Learn how custom-trained models can significantly boost the accuracy of SPAM filtering, automate persona classification, and intelligently categorize unstructured inputs, and be confident about bringing AI into Marketo Engage.
You will learn about,
- Real-world use cases where AI meaningfully improves data categorization in Marketo Engage.
- How to fine-tune an LLM using your own data (featuring OpenAI as an example).
- Using the Fine-Tuned model in Marketo Engage via Webhooks.
AI Use Cases for Data Categorization
- Spam Detection AI models outperform CAPTCHA, reducing false positives/negatives and saving sales teams time.
- Persona Matching AI accurately maps job titles (even with misspellings or in other languages) to personas, improving lead scoring and segmentation.
- Open Text Field Categorization AI buckets diverse attribution sources, handling misspellings and languages, enabling richer insights and reporting.
- Customization Fine-tuned models allow you to define rules and explanations for each categorization, giving you full control over outcomes.
Additional resources
At a glance
Product: Marketo Engage
Series: The Skill Exchange
Role: User