> For the complete documentation index, see [llms.txt](https://docs.icustomer.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.icustomer.ai/audience/fire-scoring.md). # FIRE scoring How every account and contact is scored: Fit, Intent, Recency, and Engagement, rolled into Hot, Warm, or Cold. Every account and contact under management carries a FIRE score, so you always know who is worth acting on right now, and why. ## The four components | Component | The question | What moves it | | -------------- | -------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------- | | **Fit** | Is this the right kind of company or person? | Your ideal customer profile, firmographics, role, and the commercial traits you confirmed in your context. | | **Intent** | Are they showing buying signals now? | Pricing-page visits, demo requests, hiring for the problem you solve. Weighted by strength, and capped so one noisy source cannot dominate. | | **Recency** | How fresh is what we know? | Signals decay. Last quarter's interest is not this week's. | | **Engagement** | Have they interacted with *you*? | Opens, clicks, replies, visits: the history of the relationship. | Fit anchors everything: a perfect-intent account that is a terrible fit is still a terrible fit. Fit is set by your context. Intent, Recency, and Engagement move on their own as signals arrive. ## Hot, Warm, Cold The components roll into one tier: **Hot** (act now), **Warm** (worth working), **Cold** (watch, or suppress from spend). A single strong signal can flip a tier, a demo request lifts Intent and can turn an account Hot in the moment it happens, which is exactly when the loop reacts. ## The reason comes attached A score is never a bare number. Each scored record carries its reasoning: which signals moved it, and why. * When a play selects "the top 500 by FIRE," you can open any of them and see the case. * Scores update continuously across your [audiences under management](/audience/building-audiences.md). * Scores are what plays target and what [decisions](/concepts/decisions-and-traces.md) cite. --- # 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 by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.icustomer.ai/audience/fire-scoring.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. 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.