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16 Metrics to Track the Health of Your Sales Pipeline

Pete Furseth 8 min read
sales pipelinepipeline metricsCRM data qualitysales analytics
16 Metrics to Track the Health of Your Sales Pipeline
Home/ Blog/ 16 Metrics to Track the Health of Your Sales Pipeline

16 Metrics to Track the Health of Your Sales Pipeline

By Pete Furseth

When most people think of the sales pipeline, they picture the classic funnel. Opportunities enter at the top as Qualified, narrow through Proposed and Negotiate, and exit at the bottom as Closed Won or Lost. The widest part is at the top, and the narrowest is at the bottom.

That is the theory. The reality is very different.

Your Pipeline Is Probably Shaped Like a Vase

At ORM, we have analyzed the pipelines of dozens of companies. Most of them are not shaped like a funnel at all. They are shaped like a vase: more opportunities in the Proposed stage than the Qualified stage.

The primary reason is that opportunities enter the pipeline at advanced stages like Proposed or Negotiate instead of Qualified. Sometimes there are legitimate reasons for this, but more often it means deals are being worked before they are entered into the CRM. Reps qualify and progress a deal through conversations, emails, and meetings, and only log it when they feel confident enough to put it on the board.

The problem with this is clear: as a sales manager, opportunities being worked outside the CRM deny you visibility. You cannot predict how long it takes to move deals through the process if you do not see the early stages. You cannot identify pipeline velocity issues if the data starts halfway through the cycle.

The Sales Pipeline Health Check

Based on analyzing pipelines of many shapes, we created this health check. These 16 metrics should be evaluated weekly and compared through time to determine whether your CRM quality is trending in the right direction.

Data Quality Metrics (1-7)

These are foundational CRM metrics that measure the overall quality and completeness of your opportunities. Reviewing them weekly catches anomalies before they corrupt your forecast.

1. Total open opportunities in the CRM. Your baseline count. If this number drops suddenly, it could signal a data issue or a process change. If it climbs steadily without corresponding revenue growth, you may have a zombie pipeline problem. 2. Opportunities without selling stage designation. Every opportunity should have a stage. Deals without a stage cannot be included in stage conversion analysis or weighted pipeline calculations. This is a basic hygiene metric. 3. Opportunities with missing or no products. If your business requires product-level detail on opportunities, missing products mean you cannot forecast by product line or segment. Track the percentage of opportunities with this gap. 4. Opportunities with zero value. A deal worth $0 is either not real or not yet qualified. Either way, it introduces noise into your pipeline metrics. Flag these weekly and require reps to update them or remove them. 5. Opportunities missing a value entirely. Different from zero value. These are opportunities where the amount field is null. They cannot be included in pipeline value calculations at all. 6. Opportunities with an expected close date before the opportunity create date. This is a data entry error, but it happens more often than you would expect. These records corrupt your sales cycle length calculations and should be flagged immediately. 7. Opportunities with past-due close dates. Deals with close dates that have already passed but are still marked as open. This is one of the most common CRM quality issues. Every past-due opportunity is a deal your team has not properly updated. It inflates your pipeline and degrades forecast accuracy.

Stage Transition Metrics (8-16)

These metrics are based on Closed Won or Lost opportunities. They are analyzed separately from your open pipeline. They tell you whether your selling process uses all stages as intended and whether deals migrate the way you expect.

8. Total opportunities Won and Lost. Your baseline for all conversion analysis. Track total count and total value. 9. Qualified to Won/Lost. Deals that moved directly from Qualified to close without passing through Proposed or Negotiate. A high number here might mean your stages are not needed, or it might mean reps are skipping stages. 10. Proposed to Won/Lost. Deals that entered at Proposed and went straight to close. Combined with metric 9, this tells you how much of your pipeline skips early stages entirely. 11. Negotiate to Won/Lost. Deals that entered at Negotiate and closed. These are the late-stage entries that give you almost no forecasting lead time. 12. Qualified, then Proposed to Won/Lost. Deals that used two stages before closing. This is a partial use of the process. 13. Qualified, then Negotiate to Won/Lost. Deals that skipped the Proposed stage. This might indicate that Proposed is not a meaningful stage in your process, or that reps do not see value in updating it. 14. Proposed, then Negotiate to Won/Lost. Deals that entered at Proposed, progressed to Negotiate, and closed. This is the "vase" pattern, where the pipeline is widest at the middle. 15. Qualified, Proposed, then Negotiate to Won/Lost. The full pipeline journey. In a well-functioning process, this should be the most common path. If it is not, your process has gaps. 16. Straight to Won/Lost. Deals that went directly from creation to close without any stage transitions. These often represent deals that were worked entirely outside the CRM and logged after the fact. A high number here is a red flag for pipeline visibility.

What to Do With These Results

Evaluate all 16 metrics weekly. Compare each week to the previous week and to the same week in previous quarters. You are looking for two things:

Trends. Is data quality improving or deteriorating? Are more deals following the full stage path or fewer? Anomalies. A sudden spike in zero-value opportunities or past-due close dates signals a process breakdown that needs immediate attention.

Use this health check alongside your broader sales pipeline KPIs to get a complete view of pipeline quality and quantity. Data quality is the foundation that every other pipeline metric depends on. If the inputs are wrong, the outputs will be wrong.

For a comprehensive look at how to connect pipeline health to forecasting, resource planning, and financial metrics, see our guide to 22 sales operations metrics.

The Shape of Your Pipeline Tells a Story

Is your pipeline a funnel, a vase, or a hexagon? Has the shape changed over time?

Answering these questions gives you visibility into how your sales team actually works, not how you think they work. The gap between process design and process execution is where revenue leaks hide. These 16 metrics help you find them.

Frequently Asked Questions

What metrics should you track for pipeline health?

Track 16 metrics in two groups: seven data quality metrics (total opens, missing stages, missing products, zero-value deals, missing values, impossible dates, past-due dates) and nine stage transition metrics that show how deals move through your pipeline.

Why are most sales pipelines shaped like a vase instead of a funnel?

Because many opportunities enter the pipeline at the Proposed or Negotiate stage instead of Qualified. Reps work deals outside the CRM before logging them, which means you lose visibility into early-stage activity and cannot accurately predict deal progression.

How often should you review pipeline health metrics?

Weekly. Reviewing these 16 metrics every week and comparing them through time helps you catch data anomalies early and measure whether CRM quality is trending in the right direction.

What are stage transition metrics?

Stage transition metrics track the path deals take from creation to close. They show whether deals pass through all stages (Qualified, Proposed, Negotiate) or skip stages, and what percentage of deals take each path.

How do pipeline data quality issues affect forecasting?

Opportunities with missing values, zero amounts, or past-due close dates introduce noise into your forecast model. Every data quality issue reduces your confidence in pipeline metrics like win rate, cycle length, and weighted pipeline value.

PF
Pete Furseth
Sales & Marketing Leader, ORM Technologies
Pete has built custom revenue forecast models for B2B SaaS companies for over a decade.

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