HubSpot Pipeline Management: Cheat Sheet 2026

Why pipeline health is a data problem

Your pipeline isn't a sales visualisation. It's a data structure — and when it's broken, every downstream process lies. The full guide makes the case in depth. This cheat sheet gives you the reference tables.

The pattern repeats in every portal: deals stuck in stages they shouldn't be in, close dates from three months ago, "Negotiation" deals with zero activity. The pipeline looks full. The forecast is fiction. Pipeline reviews become status updates instead of problem-solving sessions.

The 6 deal health signals

Every healthy deal shows these six signals. When signals drop, the deal is at risk — regardless of what stage it's in or what the rep says about it.

Signal	What to look for	HubSpot check	Healthy	Warning	Critical
Next step exists	Scheduled future commitment	hs_next_activity_date populated	Within 14 days	>14 days or vague	No next activity
Activity velocity	Frequency + recency of touchpoints	Deal timeline activity count, last activity date	2+/week, last <7 days	<1/week, last 7-14 days	No activity >14 days
Multi-threading	Multiple buyer contacts engaged	Associated contacts count + roles	3+ contacts with roles	2 contacts, no roles	Single contact
Access to power	Decision-maker involved	Contact role = "Decision Maker" in recent activities	Active in last 2 weeks	Listed but not active	Not identified
Stage velocity	Time in stage vs. benchmark	hs_date_entered_[stage] or days since entry	Below average	1-1.5x average	>1.5x average
Close date stability	How often close date moved	Custom counter property or manual check	0-1 pushes	2 pushes	3+ pushes

For the reasoning behind each signal and how to interpret them, see the full pipeline health guide. If your data quality foundations are shaky, the data quality audit guide will help you fix the inputs before you try to read the signals.

Pipeline warning thresholds by sales cycle

When a deal triggers one of these thresholds, it needs attention. Thresholds vary by how fast your sales motion runs.

Warning	Short cycle (<45 days)	Mid-market (45-90 days)	Enterprise (90+ days)
No activity	7 days	14 days	21 days
Stalled in stage	10 days	20 days	30 days
Ghosted (no inbound from prospect)	4 days	7 days	14 days
Close date pushed	2+ times	2+ times	3+ times
Cycle length exceeded	>1.5x average	>1.5x average	>1.5x average
Overdue close date	Any deal past close date	Any deal past close date	Any deal past close date
Single-threaded	Only 1 associated contact	Only 1 associated contact	Only 1 associated contact

HubSpot implementation — Deal Warnings property

Build a multi-checkbox property called "Deal Warnings." Create workflows that check each condition and add the matching warning value. Filter saved views by this property to surface at-risk deals before every review. This single property becomes your central risk signal — every saved view and pipeline review can filter on it.

9 HubSpot saved view recipes

These views cover the core pipeline management use cases. Each one uses HubSpot-native filters, columns, sorting, and grouping. Build them once, assign to your team.

Rep views

1. My Deals This Quarter

Purpose: Personal pipeline focus for reps. See your open deals closing this quarter with risk indicators.

Setting	Value
Filters	Owner = Me, Close Date = This Quarter, Stage = all open stages
Columns	Deal Name, Close Date, Amount, Stage, Time in Stage, Last Activity Date, Next Activity Date, Deal Age
Sort	Close Date (ascending)
Group by	—

2. Deal Hygiene

Purpose: Surface deals with missing or stale data. The cleanup view.

Setting	Value
Filters	Stage = all open stages, Close Date = This Quarter + Next Quarter
Columns	Deal Name, Owner, Amount, Close Date, Stage, Associated Contacts, Last Activity Date, Next Activity Date
Sort	Last Activity Date (ascending) — stalest deals first
Group by	Owner

What to look for: Deals with $0 amount, no associated contacts, close date in the past, no activity in 14+ days.

Manager views

3. Deal Review

Purpose: Manager's inspection view for structured pipeline reviews. Shows health indicators at a glance.

Setting	Value
Filters	Close Date = This Quarter, Stage = all open stages, Owner = select team
Columns	Deal Name, Amount, Close Date, Stage, Time in Stage, Deal Age, Last Activity Date, Forecast Category, Associated Contacts
Sort	Close Date (ascending), then Amount (descending)
Group by	Owner

4. Expansion Pipeline

Purpose: Track upsell and cross-sell deals separately from new business.

Setting	Value
Filters	Pipeline = Expansion (or Deal Type = "Existing Business"), Stage = all open stages, Close Date = This Quarter
Columns	Deal Name, Company, Amount, Close Date, Stage, Time in Stage, Owner, Deal Age
Sort	Close Date (ascending), Amount (descending)
Group by	Owner

HubSpot note: If you don't have a separate expansion pipeline, use Deal Type or a custom property to distinguish expansion from new logo deals.

5. Renewal Pipeline

Purpose: Track renewals approaching close. Catch at-risk renewals before they churn.

Setting	Value
Filters	Pipeline = Renewals (or Deal Type = "Renewal"), Close Date = This Quarter + Next Quarter, Stage = all open stages
Columns	Deal Name, Company, Amount, Close Date, Stage, Next Activity Date, Owner, Time in Stage
Sort	Close Date (ascending)
Group by	Owner

Planning views

6. Next Quarter Pipeline

Purpose: Pipe generation health check. Is enough being built for next quarter?

Setting	Value
Filters	Close Date = Next Quarter, Stage = all stages, Owner = all
Columns	Deal Name, Amount, Create Date, Stage, Owner, Deal Age, Next Activity Date
Sort	Create Date (descending), Amount (descending)
Group by	Owner

What to review: Total pipeline value vs. next quarter target. Number of new deals created. Stage distribution — are they front-loaded in early stages or progressing?

7. Recently Created Deals

Purpose: Monitor deal creation velocity by rep and source. Are enough new deals entering the pipeline?

Setting	Value
Filters	Create Date = Last 30 Days, Stage = all
Columns	Deal Name, Company, Create Date, Amount, Stage, Owner, Deal Source
Sort	Create Date (descending)
Group by	Owner

8. Win/Loss Analysis

Purpose: Learn from closed deals. Identify patterns in what wins and what loses.

Setting	Value
Filters	Stage = Closed Won + Closed Lost, Close Date = Last Quarter (or custom range)
Columns	Deal Name, Company, Amount, Close Date, Stage, Deal Age, Associated Contacts, Closed Lost Reason, Closed Won Reason, Owner
Sort	Close Date (descending), Amount (descending)
Group by	Stage (separates won from lost)

What to review: Average deal size won vs. lost. Average cycle length won vs. lost. Common lost reasons. Activity count differences between won and lost deals.

9. Swing Deals

Purpose: Focus on high-value deals that will make or break quota. These get extra attention.

Setting	Value
Filters	Amount > [your threshold, e.g. $50K], Stage = all open stages, Close Date = This Quarter
Columns	Deal Name, Company, Amount, Close Date, Stage, Time in Stage, Last Activity Date, Associated Contacts, Owner
Sort	Amount (descending)
Group by	Stage

Threshold guidance: Set the amount filter to your top 20% of deal sizes. These are the deals where one slip costs the quarter.

Stage design checklist

Before a stage belongs in your pipeline, it should pass all four filters. Use this when building or auditing your deal stages.

• Required — would skipping this step meaningfully reduce the chance of winning?
• Factual — tied to a concrete, observable action (not a feeling or judgment call)
• Inspectable — a manager can verify it by looking at the CRM (logged activity, property value, associated record)
• Buyer-centric — named from the buyer's perspective, not the seller's

Example stage progression

Stage	Buyer-centric name	Exit criteria (what must be true)	Required properties
1	Appointment Scheduled	Buyer agreed to a discovery meeting. Calendar invite sent and accepted.	Close Date, Deal Owner
2	Product Explored	Buyer attended demo/discovery, got questions answered, agreed to next step.	Amount (estimated), Primary Contact
3	Solution Evaluated	Buying team assessed fit. Requirements documented. Decision criteria understood.	Decision Maker (contact role), BANT/MEDDICC fields
4	Proposal Reviewed	Buyer received and reviewed proposal. Pricing discussed. Timeline confirmed.	Amount (final), Proposal Date
5	Contract Received	Buyer received contract. Legal/procurement engaged. Close plan documented.	Expected Close Date (confirmed)
6	Closed Won	Contract signed and returned.	Closed Won Reason
7	Closed Lost	Buyer explicitly declined or went dark beyond threshold.	Closed Lost Reason

Adapt to your sales motion. The point isn't to copy these stages — it's to apply the four filters to your own process and trim until every stage earns its place.

For the full stage design framework including naming conventions and probability calibration, see the pipeline health guide.

Pipeline review prep — 5-point deal inspection

Before every pipeline review, check these five data points on each flagged deal. This takes 60 seconds per deal with the right saved view.

1. Close date — Current, overdue, or pushed? How many times has it moved?
2. Time in stage — Within normal range, or stalled? Compare to your average.
3. Next activity — Is there a scheduled next step? When?
4. Activity recency — When was the last bi-directional activity?
5. Contact coverage — How many contacts associated? Is a decision-maker identified?

Pipeline health benchmarks

Metric	How to calculate	Healthy	Warning	Critical
Pipeline coverage	Total open pipeline / revenue target	3:1 - 5:1	2:1 - 3:1	<2:1
Stage conversion rate	Deals progressing to next stage / total in stage	Tracked quarterly, improving	Tracked but flat	Not tracked
Average stage velocity	Days per stage (by pipeline segment)	Known and used for stall detection	Known but not actioned	Unknown
Stall rate	% of deals exceeding 1.5x avg stage duration	<15%	15-25%	>25%
Deal data completeness	% of open deals with all required fields	>90%	70-90%	<70%
Win rate	Closed Won / (Won + Lost) by segment	Known by segment and rep	Known overall only	Unknown
Overdue deals	% of open deals with close date in the past	<5%	5-15%	>15%
Single-threaded rate	% of deals with only 1 associated contact	<20%	20-40%	>40%

Coverage and win rate depend on the same data quality foundation. A 5:1 coverage ratio with 40% of deals missing amounts is not really 5:1 — it's an unknown ratio with a confident-looking number. The forecasting cheat sheet covers this in detail, and the data quality audit guide gives you the framework to fix the inputs.

Is your pipeline healthy? 10-question diagnostic

Answer honestly. Each question maps to a specific pipeline health gap.

• 1. Stage definitions: Every deal stage has written exit criteria that pass the 4-filter test
• 2. Required properties: At least Amount, Close Date, and Primary Contact are required at relevant stage gates
• 3. Deal hygiene: <5% of open deals have a close date in the past
• 4. Activity coverage: >80% of open deals had logged activity in the last 14 days
• 5. Contact associations: >85% of deals are associated to 2+ contacts
• 6. Decision-maker mapping: >60% of deals past qualification have a Decision Maker contact role
• 7. Stage velocity: You know your average time-in-stage per pipeline stage and review it quarterly
• 8. Warning system: Automated warnings (workflow or saved view) flag stalled, ghosted, and overdue deals
• 9. Review cadence: Structured pipeline reviews happen weekly or biweekly with a defined triage process
• 10. Win/Loss analysis: Closed deals are reviewed monthly with lost reasons tracked and patterns identified

Scoring your results

8-10 checked: Your pipeline is well-structured and actively managed. Focus on coaching and optimisation.

5-7 checked: Your pipeline works but has gaps. Prioritise stage definitions (Q1-2) and warning systems (Q8) — they improve everything else.

Below 5: Your pipeline is a list of deals, not a managed process. Start with the full pipeline health guide and fix the architecture before adding process.

For the broader CRM health view beyond deals, a CRM health score aggregates signals across all objects. And for data quality foundations, the data quality dimensions framework applies to every property in every object.

Related resources

Resource	What it covers
Your HubSpot Pipeline Is a Data Structure	Full guide: pipeline as data architecture, 6 health signals in depth, stage design, reviews
Why Your HubSpot Sales Forecast Is Wrong	The forecasting data stack, methodology comparison, cadence design
The HubSpot Sales Forecasting Cheat Sheet	5 deal properties, forecast categories, cadence templates, benchmarks
The HubSpot Data Hygiene Cheat Sheet	Data quality across all objects: 6 dimensions, object-level best practices
Your HubSpot Portal Has a Revenue Leak	Orphan records, property sprawl, broken associations, report distrust
CRM Health Scores Explained	Composite health scores: what they measure and how to improve them
HubSpot Data Quality Audit Guide	10-area audit checklist for HubSpot portals
RevOps Maturity Model	5-level maturity framework: where does your portal stand?
Building a Unified Customer View	Why associations matter: deals, contacts, companies connected
AI Readiness for HubSpot	Data requirements for Breeze AI, including deal-level signals

Automate the diagnostic

Everything above can be done manually — set up saved views, build warning workflows, audit stages, run structured reviews. It works. It takes discipline and consistent time, which is the resource most RevOps teams are shortest on.

PortalPilot automates the diagnostic layer. Connect your portal and get:

• Pipeline health score — composite metric across stage discipline, deal signals, and data completeness
• Automated deal health signals — the 6 signals above detected per deal, with severity levels
• Stage velocity benchmarks — your actual average time per stage, with stall detection
• Warning dashboard — at-risk deals surfaced with the reason and recommended action
• Pipeline architecture audit — stage usage, exit criteria compliance, probability calibration

The analysis runs in minutes. You spend your time coaching reps and fixing deals, not building views and counting days.

Frequently Asked Questions

What are the six deal health signals in HubSpot?

The six signals are: next step exists (scheduled future commitment), activity velocity (frequency and recency of touchpoints), multi-threading (multiple buyer contacts engaged), access to power (decision-maker involvement), stage velocity (time in stage versus benchmark), and close date stability (how often the close date has been pushed). All six together determine whether a deal is real.

How many deal stages should a HubSpot pipeline have?

Most effective pipelines have 5-7 stages including Closed Won and Closed Lost. Each stage should pass four filters: required (skipping it reduces win probability), factual (tied to an observable action), inspectable (verifiable in the CRM), and buyer-centric (named from the buyer's perspective). Stages that describe feelings rather than actions should be removed.

What is a healthy pipeline coverage ratio?

A healthy pipeline coverage ratio is 3:1 to 5:1 — three to five pounds of pipeline value for every pound of revenue target. Below 2:1 is critical. However, coverage ratios are only meaningful when deal amounts are populated. A 5:1 ratio where 40% of deals have no amount recorded is effectively unknown, not 5:1.

How do I identify zombie deals in HubSpot?

Create a saved view filtering for open deals with no logged activity in 45+ days (adjust for your sales cycle). Sort by last activity date ascending to surface the stalest deals first. Build a workflow that auto-flags deals exceeding your stale threshold. Review flagged deals weekly and either re-engage or close them to keep your pipeline accurate.

What should I review before every pipeline meeting?

Check five data points on each flagged deal: close date status (current, overdue, or pushed), time in current stage versus your average, whether a next activity is scheduled, when the last bi-directional activity occurred, and contact coverage including decision-maker identification. This takes 60 seconds per deal with the right saved view configured.

Get your free pipeline health analysis at portalpilot.io — connect your portal in 30 seconds, see your pipeline health score in under 5 minutes. No credit card required. View pricing plans for continuous monitoring, automated deal warnings, and exportable pipeline diagnostics.

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Peter Sterkenburg is the founder of PortalPilot, a HubSpot portal health and optimisation platform. He's spent years in scale-up RevOps — redesigning pipelines, running the reviews, and eventually building the tool he wished he'd had.