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Analyzing Go To Market Efficiency

ASecurity

Evaluates sales efficiency with CAC payback, sales cycle analysis, channel economics, and pipeline conversion metrics. Use when assessing GTM efficiency, analyzing sales productivity, or evaluating distribution strategy.

22 stars
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Added 9/20/2026
businessgosqlexpressapi

Works with

cliapi

Security Analysis

A100/100

Scanned 9/20/2026

Install to Claude Code

$npx -y skills add lev-os/agents --skill analyzing-go-to-market-efficiency --agent claude-code

Installs into .claude/skills of the current project.

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SKILL.md
---
name: analyzing-go-to-market-efficiency
description: Evaluates sales efficiency with CAC payback, sales cycle analysis, channel economics, and pipeline conversion metrics. Use when assessing GTM efficiency, analyzing sales productivity, or evaluating distribution strategy.
tags:
  - analysis
  - growth-equity
metadata:
  author: casemark
  practice_areas:
    - Growth Equity
    - Expansion Capital
    - Late-Stage Investing
  document_types:
    - Analysis Report
  skill_modes:
    - Analysis
---
# Analyzing Go To Market Efficiency

## When To Use

- Evaluating a growth-stage company's sales efficiency as part of due diligence or portfolio monitoring
- Assessing whether a company's GTM model can scale efficiently before committing expansion capital
- Benchmarking sales productivity across channels, segments, or cohorts
- Diagnosing deteriorating unit economics or lengthening sales cycles in a portfolio company
- Comparing GTM efficiency across multiple investment targets in the same vertical

## Inputs To Gather

- **Revenue data**: MRR/ARR by month for at least 12–24 months, broken out by channel and customer segment
- **Sales & marketing spend**: Fully loaded S&M costs by quarter (headcount, paid acquisition, events, SDR/BDR costs, tooling)
- **Pipeline data**: Stage-by-stage pipeline snapshots with deal counts, values, and timestamps (CRM export preferred)
- **Customer acquisition records**: New logo count by period, source/channel attribution, contract values (ACV/TCV)
- **Sales team data**: Rep count by role (AE, SDR, CSM), ramp timelines, quota attainment distribution, tenure
- **Churn/expansion data**: Logo and revenue churn rates, expansion revenue by cohort (needed for LTV-side of CAC payback)
- **Pricing structure**: Current pricing tiers, average discount rates, deal size distribution

## Workflow

1. **Calculate CAC and CAC payback period**
   - Compute fully loaded CAC = total S&M spend ÷ new customers acquired (use same period)
   - Calculate CAC payback = CAC ÷ (average ACV × gross margin %). Express in months
   - Segment CAC by channel (inbound, outbound, partner, paid) and by customer tier (SMB, mid-market, enterprise)
   - Flag any channel where CAC payback exceeds 18 months [VERIFY against sector-specific benchmarks]

2. **Analyze sales cycle length and velocity**
   - Measure median days from opportunity creation to closed-won, segmented by deal size and channel
   - Track sales cycle trend over trailing 4–6 quarters — increasing cycles signal market resistance or segment mismatch
   - Calculate pipeline velocity = (qualified opportunities × win rate × average deal size) ÷ sales cycle length
   - Compare velocity per rep against quota to identify capacity vs. productivity gaps

3. **Evaluate channel economics**
   - For each acquisition channel, compute: CAC, conversion rate (lead → opportunity → closed-won), and contribution margin
   - Identify channel-level LTV:CAC ratios — target ≥ 3:1 for healthy channels [VERIFY: threshold varies by stage and vertical]
   - Assess channel concentration risk — if >60% of new ARR comes from a single channel, flag dependency
   - For partner/reseller channels, account for revenue share and co-marketing costs in the fully loaded CAC

4. **Assess pipeline conversion and funnel health**
   - Map stage-by-stage conversion rates (MQL → SQL → opportunity → proposal → closed-won)
   - Identify the largest drop-off point — this is the primary efficiency bottleneck
   - Calculate pipeline coverage ratio = total qualified pipeline value ÷ quota target (healthy: 3–4×)
   - Evaluate pipeline aging — deals sitting >2× median cycle length are likely stale and inflate coverage metrics

5. **Benchmark sales productivity**
   - Compute ARR per quota-carrying rep (fully ramped vs. all reps)
   - Measure average ramp time (months to first quota attainment) and ramped rep retention
   - Assess quota attainment distribution — a healthy org has 60–70% of reps at or above quota [VERIFY]
   - Calculate magic number = net new ARR ÷ prior-period S&M spend (>0.75 is efficient, >1.0 is strong)

6. **Synthesize findings and flag risks**
   - Rank channels by efficiency (LTV:CAC, payback period, scalability potential)
   - Identify structural GTM risks: channel dependency, elongating cycles, declining rep productivity, rising CAC
   - Assess whether current GTM model supports the company's growth plan without disproportionate S&M spend increases

## Output

The deliverable is a GTM Efficiency Analysis Report containing:

- **Executive summary**: One-paragraph verdict on GTM efficiency with the 2–3 most critical findings
- **CAC analysis table**: Blended and channel-segmented CAC, CAC payback, and LTV:CAC ratios
- **Sales cycle analysis**: Median cycle by segment, trend over time, pipeline velocity metrics
- **Channel economics matrix**: Per-channel unit economics with efficiency ranking
- **Funnel conversion waterfall**: Stage-by-stage conversion rates with bottleneck identification
- **Sales productivity scorecard**: Rep-level productivity, ramp metrics, quota attainment distribution, magic number
- **Risk flags and recommendations**: Prioritized list of GTM risks with suggested actions

## Quality Checks

- All CAC calculations use fully loaded S&M costs (not just direct acquisition spend) — confirm headcount costs, tooling, and overhead are included
- CAC payback uses gross-margin-adjusted revenue, not raw ACV
- Pipeline metrics exclude churned or disqualified deals that were never truly in-cycle
- Sales cycle calculations use median (not mean) to avoid skew from outlier mega-deals
- LTV estimates account for gross churn and expansion revenue, not just initial contract value
- Channel attribution methodology is documented — flag if attribution model is last-touch only vs. multi-touch
- All benchmark comparisons cite the source and vintage of the benchmark data [VERIFY]
- If fewer than 12 months of data are available, note limited statistical significance in findings

Attribution

lev-oslev-os
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