The 60-Day Proposal Pileup
Northwind Analytics sells an $18k–$120k annual B2B platform. Its 18 account executives work two pipelines: New Business and Expansion. The New Business pipeline has five stages, but reps routinely create deals at Proposal because a workflow creates a deal when a meeting is booked through a marketing form. As a result, 63% of open deals over $50k have sat in Proposal for more than 45 days. Deal amount is required at creation but is populated with a $10,000 default; 41% of open deals still show exactly $10,000. Managers forecast from a manually chosen forecast category and rep intuition. Last quarter the commit forecast missed by 27%, and the board is asking for a credible number this quarter. Sales ops cannot add more than two required properties per stage, and reps have threatened to abandon stage updates if friction increases. The CRO wants cleaner pipeline data and a forecast that reflects deal evidence, not optimism.
1. Diagnose the most likely causes of the forecast miss. Rank the data and process problems by impact, and explain what evidence you would pull from HubSpot before changing anything.
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The forecast miss is more likely driven by stage inflation and amount noise than by rep optimism alone. A workflow that creates deals at Proposal means the pipeline includes unqualified meetings, so stage probability overstates reality. The $10,000 default corrupts weighted forecasts because small placeholder amounts understate large deals while making totals look precise. Manual forecast categories let reps signal hope without evidence. I would pull four reports before changing rules: (1) amount distribution and count of deals at exactly $10,000; (2) average days in stage by stage, especially Proposal over 45 days; (3) forecast category by stage and historical win rate; (4) deal stage history for closed-won vs closed-lost deals. Rank impact: stage inflation first, amount quality second, forecast category discipline third. Fixing stage entry stops new bad data; cleaning amount improves the number immediately.
2. Design stage exit criteria and lightweight automation for New Business. Specify what must be true to enter Proposal and Contract Sent, which properties to require, and how to avoid rep workarounds.
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Make Proposal mean 'qualified and scoped,' not 'meeting booked.' Entry criteria: discovery call logged, pain or use case documented, budget range or amount validated from a quote or pricing conversation, close date within a reasonable window, and decision-maker identified. Require only two properties: validated amount and close date. Contract Sent should require a quote with line items, a named approver, and documented verbal or written commitment. Move the auto-created deal to a pre-pipeline stage or create it at Discovery, not Proposal. Use stage-transition required properties and a task for missing items; avoid more than two required fields. Watch for workarounds by auditing stage history and deals that jump from Discovery to Contract Sent. Managers should review a weekly stale-deal list and reset close dates rather than let reps push deals forward to satisfy dashboards.
3. How would you clean the existing pipeline and measure whether forecast accuracy improves within one quarter?
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Clean in this order: re-stage every open deal using the new evidence criteria, reset amount from the most recent quote or confirmed budget, and remove or downgrade deals with no recent activity. Do not delete history; use stage changes and a cleanup note. For forecasting, map forecast categories to evidence: Commit requires a quote and agreed close date, Best Case requires a dated next step, Pipeline is everything else. Run weekly forecast reviews by rep and compare to a stage-based weighted forecast. Measure accuracy with forecast-vs-actual variance by rep and category, plus stage conversion and days-in-stage. Expect one quarter to stabilize, not perfect accuracy; the leading indicator is fewer late-stage deals with placeholder amounts and shorter Proposal aging.