Prospecting
AuraScore 81/100

B2B Micro-Segment TAM Density and Outbound Viability Audit

Conduct statistical TAM micro-segmentation and capacity modeling to prioritize outbound territory allocation.

Use this template when planning territory carving, expansion campaigns, or quarterly outbound sprints across an industry vertical. It calculates market density, unit economics viability, and SDR capacity constraints.

Template

Role: Territory Optimization & Market Intelligence Principal specializing in statistical TAM decomposition and outbound prioritization.

Context

  • Targeted industry vertical and sub-sectors: {{industry_vertical}}
  • Raw account list and firmographic database extract: {{firmographic_dataset}}
  • Historical acquisition metrics and unit economics: {{historical_cac_data}}
  • Extracted regulatory, compliance, and technological shifts: {{regulatory_filings_extract}}
  • Total new annualized outbound quota target: {{target_revenue_quota}}
  • Total allocated business development headcount capacity: {{outbound_capacity_fte}}

Task

Perform a comprehensive multi-variable TAM micro-segmentation and statistical viability audit for {{industry_vertical}}, calculating segment yield density, required outbound volume, and territory allocation models to meet {{target_revenue_quota}}.

Method

  1. Decompose {{firmographic_dataset}} into discrete micro-segments based on company scale, tech complexity, and regulatory exposure from {{regulatory_filings_extract}}.
  2. Calculate the Total Addressable Market (TAM), Serviceable Addressable Market (SAM), and Serviceable Obtainable Market (SOM) per micro-segment.
  3. Apply unit economics from {{historical_cac_data}} (CAC, average deal size, sales cycle length) to model expected unit profitability.
  4. Compute a Micro-Segment Viability Index (MVI: 0-100) using weighted metrics: revenue density (35%), regulatory urgency (25%), historical velocity (25%), and competitive saturation (15%).
  5. Model pipeline generation capacity given {{outbound_capacity_fte}} and historical touch-to-opportunity ratios.
  6. Run a sensitivity distribution mapping the minimum account coverage required to achieve {{target_revenue_quota}}.
  7. Rank all micro-segments and delineate an optimal territory distribution matrix for sales development teams.

Constraints

  • MUST include complete mathematical breakdowns for TAM/SAM/SOM and MVI per micro-segment.
  • MUST calculate the exact pipeline coverage multiple (e.g., 3.5x, 4.0x) against {{target_revenue_quota}}.
  • MUST NOT recommend micro-segments that exceed the operational throughput capacity of {{outbound_capacity_fte}}.
  • The resulting strategy must focus strictly on cold outbound viability rather than inbound or partner channels.

Output format

  • Section 1: Micro-Segment Quantitative Taxonomy (Summary table with Accounts, TAM ($), SAM ($), and MVI Score)
  • Section 2: Mathematical Capacity & Quota Feasibility Model (FTE productivity, required pipeline, coverage ratios)
  • Section 3: Strategic Segment Ranking & Prioritization (Tiered ranking with growth catalysts from {{regulatory_filings_extract}})
  • Section 4: Operational Prospecting Mandates (Account-to-rep allocation rules and qualification hurdle thresholds)

Self-review

  • Ensure calculated SAM and SOM totals do not exceed aggregate TAM figures in Section 1.
  • Confirm capacity limits of {{outbound_capacity_fte}} are mathematically reconciled with required contact volumes.
  • Check that CAC metrics from {{historical_cac_data}} are accurately factored into micro-segment prioritization.
AuraScore breakdown
81/100Provisional
Instruction clarity15/15 · Strong

Explicit role, a named task, and discrete steps the model can follow.

Context architecture12/12 · Strong

Background, inputs and variables the model needs before it starts.

Constraint engineering12/12 · Strong

Hard boundaries — what the model must and must not do.

Output specification6/14 · Thin

A named, field-level shape for the response.

Reasoning structure10/10 · Strong

Ordered work items that force analysis before an answer.

Model compatibility10/10 · Strong

Length and structure that travel across frontier models.

Token efficiency5/10 · Thin

Signal density — instruction weight without padding.

Reusability7/7 · Strong

Documented variables so the scaffold adapts to new inputs.

Robustness3/5 · Adequate

Quality bar, assumptions and behaviour when inputs are thin.

Observed performance1/5 · Thin

How much real usage the template has behind it.

sales
sales-prospecting
complex-reasoning-analysis-math
tam-analysis
market-segmentation
territory-planning