deal-patterns

Deal Pattern Analysis

When to Use

• Quarterly business reviews when analyzing sales performance
• Refining your ICP based on which deals actually close (vs. which you thought would close)
• Building predictive models for deal scoring and pipeline forecasting
• Training new sales hires on what "good" looks like at your company
• Diagnosing a pipeline problem: "We have meetings but can't close"

Framework

The Deal Pattern Hypothesis

Every company has hidden patterns in their deal data. The companies that find and exploit these patterns close 2-3x more efficiently than those that don't.

The core question: What do your best deals have in common that your worst deals don't?

If you can answer that question with data, you can:

1. Target more prospects that look like your best deals
2. Disqualify prospects that look like your worst deals
3. Optimize your sales process around what actually drives closes
4. Forecast revenue more accurately based on deal characteristics

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The Deal Anatomy: 12 Variables

For every closed deal (won or lost), capture these 12 variables:

Deal Profile Variables

Variable	What to Capture	Why It Matters
1. Deal size	Final contract value	Revenue per deal affects everything downstream
2. Sales cycle length	Days from first touch to close	Longer cycles = higher cost to serve
3. Source channel	Outbound, inbound, referral, event, partner	Which channels produce the best deals
4. Number of touches	Emails, calls, meetings before close	Effort required per deal
5. Decision committee size	How many people were involved	More stakeholders = slower, riskier deals

Account Profile Variables

Variable	What to Capture	Why It Matters
6. Company size	Employee count at time of deal	Which company sizes close best
7. Industry	Vertical/sector	Which industries convert best
8. Growth stage	Seed, Series A/B/C, PE-backed, public	Stage affects urgency and budget
9. Tech stack	Relevant tools they were using	Tech affinity patterns
10. Trigger event	What triggered the evaluation	Which events produce deals

Stakeholder Variables

Variable	What to Capture	Why It Matters
11. Champion title	Who internally pushed for the deal	Which personas champion most effectively
12. Economic buyer title	Who signed the check	Which titles have budget authority

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Analysis 1: Win Pattern Analysis

Pull your last 20-50 closed-won deals and analyze:

Firmographic Patterns

CLOSED-WON ANALYSIS: {{N}} deals

Company Size Distribution:
| Size Bucket | Deals Won | % of Wins | Avg Deal Size | Avg Cycle (days) |
|------------|-----------|-----------|---------------|-----------------|
| 1-50 emp | __ | __% | $__ | __ |
| 51-200 emp | __ | __% | $__ | __ |
| 201-500 emp | __ | __% | $__ | __ |
| 501-1000 emp | __ | __% | $__ | __ |
| 1000+ emp | __ | __% | $__ | __ |

Sweet spot: {{size range with highest win concentration and best unit economics}}

Source Channel Analysis

Channel Performance:
| Channel | Deals Won | Avg Deal Size | Avg Cycle | Win Rate | Revenue |
|---------|-----------|---------------|-----------|----------|---------|
| Outbound (cold) | __ | $__ | __ days | __% | $__ |
| Inbound (content/SEO) | __ | $__ | __ days | __% | $__ |
| Referral | __ | $__ | __ days | __% | $__ |
| Events/conferences | __ | $__ | __ days | __% | $__ |
| Partner | __ | $__ | __ days | __% | $__ |

Best channel by revenue: ___
Best channel by win rate: ___
Best channel by speed: ___

Trigger Event Analysis

What triggered the winning deals:
| Trigger | Frequency | Avg Deal Size | Avg Cycle |
|---------|-----------|---------------|-----------|
| ___ | __% | $__ | __ days |
| ___ | __% | $__ | __ days |
| ___ | __% | $__ | __ days |

Insight: {{which triggers predict the largest, fastest deals}}

Champion Analysis

Who championed the winning deals:
| Champion Title | Frequency | Avg Deal Size | Avg Cycle | Win Rate |
|---------------|-----------|---------------|-----------|----------|
| ___ | __% | $__ | __ days | __% |
| ___ | __% | $__ | __ days | __% |
| ___ | __% | $__ | __ days | __% |

Best champion: {{title that leads to the highest win rate and largest deals}}

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Analysis 2: Loss Pattern Analysis

Equally important — analyze your last 20-50 closed-lost deals:

Loss Reason Distribution

| Loss Reason | Frequency | Avg Deal Size | Stage Lost | Could Have Prevented? |
|------------|-----------|---------------|-----------|---------------------|
| Went with competitor | __% | $__ | ___ | Y/N — How: ___ |
| No decision / status quo | __% | $__ | ___ | Y/N — How: ___ |
| Budget not approved | __% | $__ | ___ | Y/N — How: ___ |
| Timing — pushed to next quarter | __% | $__ | ___ | Y/N — How: ___ |
| Champion left the company | __% | $__ | ___ | Y/N — How: ___ |
| Requirements changed | __% | $__ | ___ | Y/N — How: ___ |

Preventable Losses

The most actionable analysis: which losses could have been prevented with better targeting, qualification, or sales process?

Loss Category	Prevention Strategy
Lost to competitor	Improve competitive positioning. Build battlecards. Address differentiators earlier.
No decision (stuck)	Better qualification upfront. Identify urgency drivers earlier. Set mutual action plans.
Budget rejected	Validate budget authority earlier. Build ROI models. Multi-thread (reach the economic buyer).
Champion left	Multi-thread from day one. Never rely on a single contact.
Bad fit discovered late	Tighten qualification criteria. Disqualify faster.

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Analysis 3: Speed-to-Close Patterns

Which deal characteristics correlate with faster closes?

SPEED ANALYSIS:

Deals that closed in < 30 days:
- Common traits: ___
- Avg committee size: ___
- Typical trigger: ___
- Champion title: ___

Deals that took 90+ days:
- Common traits: ___
- Avg committee size: ___
- Typical friction point: ___
- What slowed them down: ___

Speed accelerators:
1. {{factor that correlates with faster closes}}
2. {{factor}}
3. {{factor}}

Speed killers:
1. {{factor that correlates with slower closes}}
2. {{factor}}
3. {{factor}}

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Analysis 4: Deal Size Patterns

What predicts larger deal sizes?

DEAL SIZE ANALYSIS:

Deals > ${{threshold}} (top quartile):
- Company size: ___
- Industry: ___
- Champion title: ___
- Trigger: ___
- Average number of stakeholders: ___

Deals < ${{threshold}} (bottom quartile):
- Company size: ___
- Industry: ___
- Champion title: ___
- Trigger: ___
- Average number of stakeholders: ___

What correlates with larger deals:
1. ___
2. ___
3. ___

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Analysis 5: The Ideal Deal Profile

Combine all analyses into a single "ideal deal" composite:

THE IDEAL DEAL PROFILE

Company:
- Size: {{range}}
- Industry: {{verticals}}
- Stage: {{growth stage}}
- Trigger: {{what happened}}

Stakeholders:
- Champion: {{title}} — enters at {{stage}}
- Economic buyer: {{title}} — enters at {{stage}}
- Committee size: {{number}}

Deal characteristics:
- Expected size: ${{range}}
- Expected cycle: {{days}} days
- Source channel: {{channel}}
- Number of touches to close: {{range}}

Red flags (walk away if):
- {{red flag 1}}
- {{red flag 2}}
- {{red flag 3}}

Green flags (accelerate if):
- {{green flag 1}}
- {{green flag 2}}
- {{green flag 3}}

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Feeding Deal Patterns Back into Outbound

The ultimate purpose of deal pattern analysis is to improve your targeting and messaging:

Pattern Insight	Outbound Action
Best deals come from Series B companies	Prioritize Series B in your ICP matrix
Deals triggered by "new VP Sales" close 2x faster	Build a trigger campaign for leadership changes
VP Sales as champion has highest win rate	Target VP Sales as primary persona
Deals with 4+ stakeholders take 3x longer	Qualify for committee size early, multi-thread from day one
Referral deals close 40% faster	Build a referral ask into your sequence Step 4
Lost deals often cite competitor X	Address competitor X positioning in Step 2-3

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Analysis Cadence

Frequency	Analysis	Minimum Sample
Monthly	Quick review of new wins and losses	All deals that closed this month
Quarterly	Full deal pattern analysis (all 5 analyses)	Last 50+ closed deals
Annually	Trend analysis: how are deal patterns shifting?	Full year of data
Ad hoc	Specific investigation (e.g., "why are enterprise deals stalling?")	Relevant subset

Templates

Deal Pattern Report

# Deal Pattern Report
# Period: {{date_range}}
# Deals Analyzed: {{won}} won, {{lost}} lost
# Analyst: {{name}}

## Key Findings

### The Ideal Deal Looks Like:
{{1-2 sentence summary of the ideal deal profile}}

### Top 3 Win Predictors:
1. {{predictor}} — {{evidence}}
2. {{predictor}} — {{evidence}}
3. {{predictor}} — {{evidence}}

### Top 3 Loss Predictors:
1. {{predictor}} — {{evidence}}
2. {{predictor}} — {{evidence}}
3. {{predictor}} — {{evidence}}

### Recommended Changes
| Area | Current | Recommended | Expected Impact |
|------|---------|-------------|----------------|
| Targeting | ___ | ___ | ___ |
| Qualification | ___ | ___ | ___ |
| Messaging | ___ | ___ | ___ |
| Process | ___ | ___ | ___ |

Tips

• You need at least 30 closed deals (combined wins and losses) for patterns to be statistically meaningful. Below that, treat findings as hypotheses, not conclusions.
• The most common mistake: only analyzing wins. Your losses contain just as much — often more — actionable intelligence. The pattern of WHY deals die is the pattern of what to fix.
• Don't confuse correlation with causation. If your biggest deals come from referrals, it might be because referrals come pre-qualified — not because the referral channel itself is magic. Test before restructuring.
• Share deal pattern insights with everyone, not just sales. Marketing needs to know which triggers to target. Product needs to know which features close deals. Leadership needs to know which segments to invest in.
• The ideal deal profile should directly inform your ICP matrix, your trigger campaigns, your persona development, and your lead prioritization. If your deal patterns say one thing and your targeting says another, one of them is wrong.

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Progressive disclosure: load CRM-specific data extraction queries and advanced statistical analysis frameworks only when running deal pattern analysis on a specific dataset.