Needle Movers Overview
Needle Movers are leading indicators of churn or expansion that FunnelStory surfaces from your connected conversations, product usage, support data, and related signals—early enough for your team to act while outcomes are still in motion. This section covers how detection fits into your workspace, how data feeds the pipeline, and where to go for day-to-day workflows.
For the conceptual model, UI walkthrough, and relationship to Predictions, start with Needle Movers in Core concepts. The pages here focus on operations: managing the lifecycle, notifications, and how AI summaries are produced.
How detection fits together
FunnelStory continuously ingests data from your connections and models (accounts, users, product activity, tickets, meetings, chats, notes, and more). On each processing cycle, needle-mover detection evaluates new and updated activity against patterns that historically preceded renewal, expansion, or churn for your customer base—not a generic template.
What you see in the product—type (for example pricing, competitor, personnel change), impact (risk vs opportunity and severity), title, and timeline entries—is the output of that pipeline. You do not configure individual rules in the UI; the system learns from outcomes and surfaces candidates for your team to review, assign, and close.
What feeds a Needle Mover
Needle Movers are grounded in evidence from your workspace. Typical inputs include:
| Input | Role |
|---|---|
| Conversations and chats | Transcripts and threads from connected communication tools |
| Support tickets | Subjects, descriptions, and status changes from helpdesk integrations |
| Product activity | Usage signals tied to accounts and users from your product analytics or warehouse models |
| Meetings | Summaries and excerpts where your workspace ingests meeting content |
| Notes | Human-written context on accounts (see Notes) |
When the same underlying theme appears across multiple sources, FunnelStory can consolidate it into one needle mover with multiple sources on the timeline.
Cold start and ongoing refresh
Cold start refers to the period right after you connect data: the graph is still accumulating history, so the model has fewer comparable renewal and churn examples. Expect confidence and volume of needle movers to grow as more refresh cycles complete and outcomes (renewals, churns, expansions) are observed in your data.
Ongoing refresh means each model and connection refresh can add new timeline entries, adjust severity, or occasionally merge or supersede older signals when the situation changes. You do not need to manually “re-run” detection; staying on a regular refresh schedule keeps the queue current.
Where to go next
| Topic | Page |
|---|---|
| List and detail views, assign, comment, close, playbooks | Managing Needle Movers |
| Email, Slack, Teams, mentions, and workspace alerts | Needle Mover notifications |
| AI Summary, titles, and timeline excerpts | AI summaries |
| Built-in Slack/Teams routing for account events | Notifications overview |
| Automating multi-step responses | AI Agents overview |