Conversion Physics

The Hidden Pattern

Beneath the chaos of individual deals, a pattern exists.

Stage 1 to Stage 2 converts at a certain rate. Stage 2 to Stage 3 converts at another rate. These rates fluctuate quarter to quarter but, when qualification is tight and stages are well-defined, they cluster around a stable baseline.

This is your pipeline's mathematical signature. It is as distinctive as a fingerprint and as reliable as gravity, when properly measured.

Most organizations do not know their conversion physics. They know their win rate (closed won divided by all opportunities created). But win rate is a blunt instrument. It collapses a multi-stage journey into a single number, hiding where value is created and where it is destroyed.

Conversion physics unpacks the win rate into its component parts. It answers: Where are deals dying? Where is the bottleneck? Where should intervention focus?

The Conversion Cascade

Imagine a pipeline with 5 stages before Closed Won. One hundred deals enter Stage 1. How many exit as customers?

The journey looks like this:

Stage	Deals Entering	Conversion Rate	Deals Advancing
Stage 1 → 2	100	60%	60
Stage 2 → 3	60	50%	30
Stage 3 → 4	30	65%	20
Stage 4 → 5	20	75%	15
Stage 5 → Won	15	80%	12

Total conversion: 12% (12 won from 100 entered)

This 12% win rate is the product of the stage conversion rates: 0.60 × 0.50 × 0.65 × 0.75 × 0.80 = 0.117

Each stage contributes to the cascade. Each stage has its own physics.

The Diagnostic Power

The cascade reveals where to focus.

In the example above, Stage 2 → 3 converts at only 50%. This is the weakest link. Improving Stage 2 → 3 from 50% to 60% would change the overall win rate from 12% to 14%. A two-point improvement at one stage yields a two-point improvement overall.

Contrast this with focusing on Stage 5 → Won. It already converts at 80%. Improving it to 90% changes overall win rate from 12% to 13.5%. More effort for less impact.

Conversion physics tells you where leverage exists. The bottleneck is where intervention produces results.

Establishing Your Baseline

Conversion physics requires data. Not opinions about how deals progress, but measured rates from historical performance.

The Measurement Process

Step 1: Define the cohort

Select a time period with enough deals to produce statistical significance. For most B2B companies, this means 100+ deals that have resolved (won or lost) from a specific entry window.

Use deals created 6-12 months ago, depending on your sales cycle. Deals must be resolved, not in-progress.

Step 2: Track stage progression

For each deal in the cohort, document:

• What stage did it reach?
• At what stage did it exit (won or lost)?
• How long did it spend in each stage?

Most CRMs track stage history. If yours does not, begin tracking it now. You will need 6+ months of data to establish baselines.

Step 3: Calculate stage conversion rates

For each stage transition:

• Numerator: Deals that advanced to the next stage
• Denominator: Deals that entered this stage (including those that died here)

Example:

• 100 deals reached Stage 2
• 55 advanced to Stage 3
• 45 died in Stage 2 (lost, disqualified, or went dark)
• Stage 2 → 3 conversion: 55%

Step 4: Segment if necessary

Conversion rates may differ by segment:

• Deal size (SMB vs. Enterprise)
• Source (Inbound vs. Outbound)
• Region
• Product line

If you have enough data, calculate baselines by segment. A single blended rate may mask meaningful variation.

The Decay Curve

An important pattern emerges from conversion data: later stages have higher conversion rates than earlier stages.

This is counterintuitive. You might expect conversion to be roughly constant, with deals dying at similar rates throughout the pipeline.

In reality, the Decay Curve shows that:

• Early stages have low conversion (60-70%)
• Middle stages have moderate conversion (50-65%)
• Late stages have high conversion (70-85%)

Why the Curve Exists

Early stages include the broadest population of deals. Many are exploratory. Many have weak qualification. Many will discover internal blockers. The high attrition rate reflects this reality.

Late stages include only deals that have survived. The buyer has invested time, socialized internally, engaged stakeholders. They have sunk costs. The high conversion rate reflects momentum and commitment.

The Implication

Early pipeline is more valuable than intuition suggests. Late pipeline is less certain than stages suggest.

A deal entering Stage 1 has perhaps 15% probability of closing. But that deal, if it survives to Stage 4, will have 75% probability. The value creation happened in the middle stages where the deal proved itself.

A deal in Stage 5 feels "almost done." But 20% of deals at Stage 5 still fail. That is not negligible. The late-stage confidence is often false confidence.

Conversion physics creates humility about late-stage deals and optimism about early-stage volume.

Using Conversion Physics

Application 1: Bottleneck Diagnosis

When revenue is below target, the instinct is to demand "more pipeline" or "better closing." Conversion physics reveals whether these are the right interventions.

Scenario: Revenue is 20% below target.

Analysis options:

1. Is Stage 1 volume sufficient? (Top-of-funnel problem)
2. Are early-stage conversion rates normal? (Qualification problem)
3. Are late-stage conversion rates normal? (Closing problem)

If Stage 1 volume is healthy but Stage 2 → 3 conversion collapsed, adding more Stage 1 deals will not help. The bottleneck is in qualification or discovery.

If all stage conversions are healthy but Stage 1 volume is down 30%, the answer is top-of-funnel investment, not sales training.

Conversion physics prevents misdiagnosis. Without it, revenue shortfalls trigger generic responses that may address the wrong problem.

Application 2: Capacity Planning

Knowing your conversion rates enables backward calculation from targets.

Scenario: You need to close 10 deals next quarter.

• Stage 5 → Won converts at 80%. You need 12.5 Stage 5 deals.
• Stage 4 → 5 converts at 75%. You need 17 Stage 4 deals.
• Stage 3 → 4 converts at 65%. You need 26 Stage 3 deals.
• Stage 2 → 3 converts at 50%. You need 52 Stage 2 deals.
• Stage 1 → 2 converts at 60%. You need 87 Stage 1 deals.

To close 10 deals, you need 87 qualified opportunities to enter the pipeline.

This math drives hiring decisions. If your team can generate 50 Stage 1 deals per quarter, you need more capacity, more efficiency, or lower targets. The physics constrain what is possible.

Application 3: Forecasting Foundation

Forecasting based on conversion physics is more reliable than forecasting based on rep opinion.

If you have 30 deals in Stage 3 and historical Stage 3 → Won conversion is 40%, you can project 12 closed deals from this cohort. This projection does not depend on whether reps feel optimistic. It depends on measured rates.

Conversion-based forecasting aggregates deals by stage, applies historical conversion rates, and produces a probability-weighted projection. It can be refined by adjusting for deal-specific factors, but the baseline is mathematical, not emotional.

The Stability Principle

Conversion rates are stable when underlying conditions are stable.

If qualification standards are enforced, the population entering Stage 1 is consistent. If stage definitions are rigorous, advancement reflects real progress. Under these conditions, conversion rates fluctuate within a narrow band.

When rates deviate significantly from baseline, something has changed:

• Qualification has loosened (more garbage enters, early conversion drops)
• Stage definitions have drifted (deals advance without commitment, late conversion drops)
• Market conditions have shifted (buyers are slower, all conversion drops)
• Competitive dynamics have changed (alternative emerged, late conversion drops)

Conversion rate deviation is a diagnostic signal. A sudden drop in Stage 3 → 4 conversion is not random noise. Something caused it. Find the cause.

The Instability Warning

If your conversion rates are wildly inconsistent quarter to quarter, the problem is not the rates. The problem is the underlying architecture.

Unstable rates indicate:

• Qualification is not enforced consistently
• Stages are not defined by buyer behavior
• Deals are advancing based on rep judgment, not criteria
• The data is corrupted by CRM hygiene issues

Fix the architecture first. Until stages are meaningful and qualification is rigorous, conversion rates are measuring noise, not signal.

Case Study: The Conversion Diagnosis

A Remotir client (B2B software, $12M ARR, 8-person sales team) was missing revenue targets despite "healthy" pipeline. Leadership assumed closing skills were the issue and scheduled sales training.

The Diagnosis:

We calculated stage conversion rates for the previous 4 quarters:

Transition	Q1	Q2	Q3	Q4
Stage 1 → 2	62%	58%	55%	51%
Stage 2 → 3	48%	45%	41%	38%
Stage 3 → 4	71%	68%	70%	69%
Stage 4 → Won	75%	78%	74%	76%

The pattern was clear. Late-stage conversion (Stage 3 → 4, Stage 4 → Won) was stable and strong. Early-stage conversion (Stage 1 → 2, Stage 2 → 3) was declining quarter over quarter.

The problem was not closing. The problem was qualification and early-stage discovery.

The Root Cause:

Six months earlier, marketing had launched a new lead gen program that dramatically increased inbound volume. The leads were lower quality but were being converted to opportunities at the same rate as higher-quality leads.

Garbage was entering the pipeline. It was dying in Stages 1-2, where it should have been filtered. But the volume masked the decay until conversion physics exposed it.

The Intervention:

1. Implemented stricter qualification criteria for marketing-sourced leads
2. Added a "Marketing Qualified" vs. "Sales Qualified" distinction
3. Required PAIN Threshold validation before opportunity creation
4. Re-trained marketing on qualification signals

The Results (next quarter):

• Stage 1 volume decreased 25%
• Stage 1 → 2 conversion improved from 51% to 64%
• Stage 2 → 3 conversion improved from 38% to 52%
• Total closed deals increased 18% despite lower volume

The insight: More pipeline was not the answer. Better pipeline was. Conversion physics revealed the bottleneck that intuition missed.

Conclusion: The Math Underneath

Pipeline feels like a qualitative domain. Relationships, conversations, intuition. But underneath the qualitative surface, mathematical patterns govern outcomes.

Conversion physics makes those patterns visible. It transforms "we need more pipeline" into "Stage 2 → 3 conversion dropped 15% and needs diagnosis." It transforms "reps need to close harder" into "late-stage conversion is strong; the problem is upstream."

The physics do not lie. Deals convert at rates determined by qualification quality, stage integrity, and buyer behavior. Measure the rates. Respect the patterns. Intervene where leverage exists.

Companies that understand their conversion physics do not wonder why revenue missed. They know where the cascade broke and how to fix it.

The math is the map.