Team Performance Investigation

Discover what differentiates top performers and create data-driven playbooks for the whole team.

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The Question

"What differentiates top performers?"

Or variations:

• "Why do some reps consistently hit quota?"
• "What makes our best salespeople successful?"
• "What separates high performers from low performers?"
• "How can we replicate top performer behavior?"

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What Scoop Investigates

When you ask about performance differences, Scoop runs a multi-probe investigation:

Investigation Plan:
├── Probe 1: Quantify the gap
│   └── How much do top performers outperform?
├── Probe 2: Activity patterns
│   └── What do top performers do differently?
├── Probe 3: Deal patterns
│   └── What types of deals do top performers win?
├── Probe 4: Behavioral differences
│   └── How do work patterns differ?
├── Probe 5: Predictive factors
│   └── What predicts high performance?
└── Synthesis: Actionable playbook recommendations

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Example Output

Investigation Results: Sales Team Performance Analysis

FINDING 1: Performance Gap
├── Top quartile (5 reps): $2.8M average revenue
├── Middle 50% (10 reps): $1.4M average revenue
├── Bottom quartile (5 reps): $680K average revenue
└── Top performers: 2x average, 4x bottom performers

FINDING 2: Activity Differences
├── Calls per week:
│   ├── Top: 45 calls/week
│   └── Bottom: 62 calls/week (more calls, worse results)
├── Discovery calls:
│   ├── Top: 35 min average duration
│   └── Bottom: 18 min average duration
├── Follow-up speed:
│   ├── Top: 2.4 hours average response
│   └── Bottom: 18 hours average response
└── Multi-threading:
    ├── Top: 3.2 contacts per deal
    └── Bottom: 1.4 contacts per deal

FINDING 3: Deal Selection Patterns
├── Top performers deal size: $85K average
├── Bottom performers deal size: $42K average
├── Top performers target: 200-1000 employee companies
├── Bottom performers spread across all segments
└── Top performers: 60% in 3 industries vs. 15% for bottom

FINDING 4: Win Rate Analysis
├── Overall win rate: Top 42%, Bottom 18%
├── Win rate by stage:
│   ├── Discovery → Qualified: Top 75%, Bottom 45%
│   ├── Qualified → Proposal: Top 68%, Bottom 52%
│   └── Proposal → Close: Top 82%, Bottom 68%
└── Key gap: Top performers qualify better upfront

FINDING 5: Predictive Patterns (ML Analysis)
├── Strongest predictors of rep success:
│   ├── Discovery call duration (35+ min)
│   ├── Multi-threading (3+ contacts)
│   ├── Industry focus (specialist vs. generalist)
│   └── Follow-up response time (<4 hours)
└── Activity volume NOT correlated with success

RECOMMENDED PLAYBOOK:
1. Quality over quantity: Fewer, longer discovery calls
2. Multi-thread early: Target 3+ contacts per account
3. Specialize: Assign reps to 2-3 industries
4. Response SLA: Implement 4-hour follow-up standard
5. Qualification rigor: Use top performer criteria
6. Deal size discipline: Minimum $50K opportunities

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Sample Prompts

Basic Comparison

"What makes our top salespeople different?"

Specific Metric

"Why do some reps have higher win rates?"

Activity Focus

"What activities predict sales success?"

New Rep Coaching

"What should new reps learn from top performers?"

Segment Focus

"What differentiates top performers in enterprise sales?"

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Follow-Up Questions

After the initial investigation, dig deeper:

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Data Requirements

For best performance analysis, your data should include:

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Tips for Better Results

1. Include activity data - Behaviors matter as much as outcomes
2. Track call quality - Duration often matters more than quantity
3. Capture contacts per deal - Multi-threading is key
4. Include tenure - Control for experience differences
5. Track time metrics - Response times and cycle lengths
6. Log deal characteristics - Size, segment, complexity

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Use Cases

Sales Team Optimization

Identify winning behaviors and create training programs.

Territory Design

Understand what makes certain territories more productive.

Hiring Profiles

Find traits that predict success for recruiting.

Coaching Plans

Create personalized development plans based on data.

Quota Setting

Use actual performance data to set realistic targets.

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Related Patterns

• Pipeline Investigation - Team pipeline health
• Period Comparison - Performance trends over time
• Customer Churn Analysis - Customer retention patterns