FAQ
Frequently asked questions
Clear answers about wallet credit, usage, subscriptions, and how Tycoon charges for work.
How is this different from ChurnZero, Vitally, or Gainsight?
Those are CS platforms — dashboards for your CS team to manage accounts. Tycoon is the CS team that runs the plays. ChurnZero will surface a health score; Tycoon will surface the score, diagnose the root cause, pick the right save play, draft the email in your voice, and send it. For companies under 50 employees that don't have a dedicated CS team, Tycoon replaces the 'team' part of Customer Success — you don't need Gainsight plus three CS hires. For companies with existing CS teams, Tycoon augments them: CS people handle the high-judgment saves, Tycoon handles the long tail.
How accurate is the prediction — do I trust it enough to act on it?
Accuracy improves with your data and calibration. Cold-start (first month) precision is around 60-70% — meaning 30-40% of flagged accounts would not have churned. That's still worthwhile because the intervention is cheap (an email) relative to the save. By month 3, calibrated against your actual churn history, precision typically reaches 80%+. You're never making irreversible decisions on the prediction; you're deciding whether to send a check-in email, which has near-zero downside if wrong.
What about large enterprise customers where the signal is contract renewal rather than usage?
For enterprise the health model changes: usage matters less, stakeholder health matters more. AI Data Analyst can track stakeholder stability (champion still at the company via LinkedIn), email thread sentiment over time, NPS / CSAT responses, and renewal dates. 120 days before a renewal, the AI starts the renewal workflow: scheduling the QBR with the champion, preparing the business value recap, surfacing expansion opportunities. Enterprise churn prevention is more about relationship intelligence than product usage, and the AI adapts to that.
We're B2C with 10K users. Is per-user health scoring overkill?
Yes — for B2C consumer SaaS, per-user scoring is usually overkill. The right abstraction is cohort-based: 'users who signed up in week X are churning at 40% vs 25% for earlier cohorts — what changed?' AI Data Analyst can run cohort churn analysis, identify problem signups, and trigger bulk save campaigns (discount email to cohort X, onboarding re-trigger to cohort Y). Individual customer save plays only make sense when LTV justifies the 1:1 attention, which usually means B2B or high-value B2C ($50+/mo).
Can it handle win-back campaigns for already-churned customers?
Yes — and this is an underused workflow. AI Data Analyst tracks churned customers and triggers win-back plays at specific intervals: 30 days after churn ('we shipped the thing you asked for'), 90 days after churn (new pricing tier offer), 365 days after churn (gentle re-introduction as lapsed user). Win-back conversion rates of 5-15% are normal, and these are customers you already acquired so the cost to re-activate is a fraction of new acquisition. Tycoon keeps the churn list warm without your time.