Describe your churn risk process: we'll build you a step-by-step automation spec you can run today.
First agent free · No account required
You find out an account is at risk two weeks too late. The signal was there (usage dropped, support tickets spiked, the champion went quiet) but it was spread across four systems no one was watching at once. BYOBot maps your churn risk detection into AI automations that surface the signal, score the risk, and draft the save play before the window closes.
Describe your churn risk process: we'll build you a step-by-step automation spec you can run today.
First agent free · No account required
Common churn risk automations
Tools CS and RevOps teams live in
How it works
Paste an account's usage data, support history, and CRM notes into Claude or ChatGPT, get a health assessment and a draft save play back immediately. No setup: CS managers can use this today to prepare for at-risk account conversations.
Works with ChatGPT, Claude, Gemini, or any LLM: free to startA browser agent that opens Mixpanel or Amplitude, pulls usage metrics for each account, cross-references with support ticket volume in Zendesk, scores each account against your health model, and drafts the weekly at-risk report: with you reviewing before it goes to the pipeline review.
Works with any AI browser agent toolAn API-driven pipeline that pulls signals from Mixpanel, Zendesk, Gainsight, and Salesforce on a daily schedule; scores each account; routes alerts and save plays to the responsible CSM via Slack; and updates the health score in Salesforce: without any manual review unless an account crosses the escalation threshold.
Connects to Salesforce, Gainsight, Mixpanel, Zendesk, or custom APIsIs BYOBot worth it?
A chat answer dies in the thread. Your agent is a file you own: run it in ChatGPT, Claude, or Gemini, drop it into a browser agent, or hand it to your stack, as many times as you like.
No prompt engineering degree required. Describe what you do, the tabs you open, the data you move, and BYOBot asks the questions that turn your knowledge into the spec.
Every build ships with smoke tests and pass, warn, and fail checks for each step. You'll know it ran right, not just that it ran.
The math
CS teams spend 2 to 3 hours per at-risk account each week: pulling usage data, reading support history, writing the save play, preparing the QBR brief. At 20 at-risk accounts in a portfolio, that's 40 to 60 hours of reactive work every week. BYOBot maps each step into an AI bot that monitors continuously and surfaces the signal the moment it appears. First build free. Five more for $25: five workflow specs, ready to run across your entire account portfolio.
5 build credits · one-time purchase
No subscription · card, PayNow, or GrabPay · your first build is still free
Adjacent roles
What you get
Churn risk detection is a multi-signal problem. A single metric (usage drop, ticket spike, login frequency) is unreliable. The signal is in the combination: low usage in a feature that used to be a core use case, plus an unresolved support ticket, plus the champion's name missing from the last three meeting invites. Those signals live in Mixpanel, Zendesk, and Salesforce. No single tool combines them automatically. BYOBot maps the multi-source health scoring workflow: pulls the relevant signals from each system, applies your weighting logic, generates a composite score, and routes the account to the right intervention.
The difference from a CS platform like Gainsight or ChurnZero is the layer BYOBot operates at. Those platforms track health scores and trigger playbooks within their own system. BYOBot automates the work around the CS platform: synthesising signals from tools not in your CS stack, drafting the personalized save outreach based on the specific risk signal rather than a generic template, aggregating account context for the QBR, and building the risk report that RevOps and CS leadership need for the pipeline review. Every BYOBot build produces one portable agent: a prompt for any AI tool, a browser agent for monitoring and alerting, and a pipeline for fully automated daily detection.
Save plays are where churn risk automation has the most direct revenue impact. A generic "just checking in" email is worse than no email when the account is at risk: it signals you're not paying attention. BYOBot maps the signal-to-play logic: identifies the specific risk indicator and generates the contextually appropriate response for that account at that moment. If usage dropped on the feature they purchased for, the save play addresses that feature. If a champion departed, the play is a relationship-building email to the new stakeholder. The save play is calibrated to the signal, not the template.
Related automations: revenue ops workflow automation covers the pipeline and forecasting layer, and reporting workflow automation covers the CS metrics and performance reporting.
Got questions
Describe your churn risk process. BYOBot maps it into a bot: score, alert, save play, renew.
Build Your Churn Risk Bot Free →