What it does
Every Monday morning, snapshots the pipeline - deals by stage, weighted forecast, committed pipeline, forecast-vs-actual variance for trailing periods - delivers it to sales leadership via email and Slack, and freezes the snapshot for historical comparison.
You get
a frozen
comparable weekly forecast snapshot delivered automatically
How it works
Build Forecast Snapshot Report
build_insights_reportBuild an insights report 'Weekly pipeline forecast snapshot' computing: (a) total open pipeline ARR by stage; (b) weighted forecast (each stage × its historical win-probability); (c) commit-category breakdown (Commit / Best Case / Pipeline / Omitted — sourced from deal-level forecast_category attribute); (d) week-over-week pipeline movement (deals added / advanced / lost / closed-won); (e) forecast-vs-actual for closed quarters (was last week's forecast accurate?). Freezable: each Monday's report is preserved for historical comparison.
Build Weekly Snapshot Email Content
Generate ContentGenerate a weekly forecast snapshot email for sales leadership. Structure: executive summary at top (one sentence per: weighted forecast vs. quota, week-over-week pipeline change, top deal moves this week), then the full report rendered as a scannable HTML table. Tone: concise, exec-ready. Send-from: the CRO or VP Sales address (not from a no-reply system).
Build Weekly Rollup Workflow
Create WorkflowCreate a scheduled workflow firing every Monday at 7am local time. Step sequence: (1) refresh the forecast snapshot report; (2) freeze the snapshot to historical store (so 'last week's forecast' is queryable); (3) compose the weekly snapshot email with the latest data; (4) deliver to sales leadership list (CRO, VP Sales, AE managers); (5) post a condensed Slack version to #revenue with the top-line numbers + link to the full report; (6) for AEs specifically, send each AE a personalized snippet with their own pipeline movement (separate channel — don't make exec emails AE-personal).
Build Forecast Accuracy Dashboard
Build DashboardCompose a forecast accuracy dashboard reading from the historical snapshot series: forecast-vs-actual variance by week (sub-10% = excellent, 10-20% = solid, 20%+ = forecast process needs work), variance by rep (which AEs forecast accurately vs. who's optimistic / pessimistic), and forecast-category accuracy (do Commit-tier deals actually close at 90%+? Best-Case at 50%? — calibration health). Surfaces patterns leadership can coach on.
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