> For the complete documentation index, see [llms.txt](https://docs.akkio.com/akkio-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.akkio.com/akkio-docs/demo-models/demo-models/lead-scoring.md). # Lead Scoring {% embed url="" %} Machine learning has revolutionized the way companies approach lead scoring, allowing them to leverage the power of data to identify the leads that are most likely to convert into paying customers. Akkio is a powerful machine-learning platform that can help users get the most out of lead-scoring their data, providing valuable insights and predictions that can be used to optimize sales efforts and increase revenue. The tech company lead scoring dataset is a collection of 10,000 leads that have been scored based on their likelihood to convert into a customer. The dataset contains 13 columns, including job title, years of experience, company size, industry, location, website visits, resources downloaded, attended webinar, email open rate, email click rate, response to the survey, days since last interaction, and positive lead. Use this dataset to understand how Akkio can make predictive lead scoring work for you. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.akkio.com/akkio-docs/demo-models/demo-models/lead-scoring.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.