HG Insights|Phoenix

Peer Comparison Brief

Open with 'every peer in your cohort runs X — you don't' instead of vague competitive FUD.

Industrial automation cohort · BI platform consolidation

All 4 active peers are evaluating Tableau right now — Your Account is still researching

4 of 4
peers in active eval
Honeywell, Schneider Electric, Emerson, and Thermo Fisher all surfaced Tableau displacement intent in the last 30 days — all flagging IBM Cognos or Power BI as the tool being replaced. Your Account shows the same research signal but no commitment.
Company Tableau Signal Legacy BI Being Displaced
🎯 Your Account Evaluating — no commit IBM Cognos, Power BI
Honeywell High — Displacement IBM Cognos, Power BI
Emerson Electric Medium — Displacement IBM Cognos, Power BI
Thermo Fisher High — Displacement IBM Cognos (intensity 5,940)
Opening line
“All four of your industrial peers moved to active Tableau evaluation this month — each targeting IBM Cognos as the replacement. What’s holding up your team’s decision?”

Overview

Your AEs walk into peer-comparison conversations with vague competitive FUD. This workflow gives them one specific, data-backed gap to lead with: 'every peer in your cohort runs CrowdStrike — you don't' or 'your cloud spend is 40% below the cohort median.' Pulls firmographic, technographic, IT spend, and intent data from HG Insights for a 5-peer cohort, then ranks the gaps a CFO would actually act on.

Use cases

  • Pre-discovery framing for your AEs

    Your AEs are about to pitch a prospect on consolidation, modernization, or expansion. Instead of 'industry leaders are doing X' generalities, this workflow surfaces the named peer cohort (5 companies, ±25% revenue, same industry) and pulls HG technographic intensity scores for each — so the opener becomes 'GE, Honeywell, and Schneider all run X; you don't.' Concrete, defensible, and CFO-listenable.

  • Renewal saves grounded in peer signals

    When a customer wavers on renewal, your CSMs need a reason to upgrade rather than churn. The brief surfaces gaps where the customer is below cohort median on IT spend categories your platform addresses, AND active intent signals across the cohort showing peers are buying. The save becomes 'your peer cohort is consolidating into our category right now — here's where you're behind.'

View workflow prompt
# Peer Comparison Brief

## Parameters

- `{{domain}}` *(required)* — Target company domain HG Insights uses for lookup. Example: `siemens.com`
- `{{peer_count}}` *(optional)* — How many peers to compare against. Example: `5`

## Purpose
You are an AE building a peer-comparison narrative for {{domain}}. The output gives the AE one specific, data-backed gap to lead with: "every peer in your cohort runs X; you don't" or "your IT spend is Y% below the cohort median in this category." Cite every claim.

## Process
1. **Cohort definition** — call `company_firmographic` for {{domain}} to get industry, revenue, employee count, primary geography. Pick {{peer_count}} (default 5) peers using `search_companies`: same primary industry, ±25% revenue band, same primary geography or matched-scale global footprint, employee floor near {{domain}}'s scale. Document each pick + why; flag any that fall just outside the strict band but are the strongest structural matches.
2. **Profile delta** — call `company_firmographic` on each peer. Build a revenue + employees + IT-spend + IT-as-%-revenue table. Calculate cohort median (excluding the target). Surface the 2-3 most striking deltas (target ahead vs target behind).
3. **Tech-stack delta** — call `company_technographic` on target + each peer. Two tables:
   - **Target HAS, peers LACK** — products in the target's top stack that don't appear in any peer's top-40 (the unique-advantage list).
   - **Peers HAVE, target LACKS** — products that 2+ peers have but the target doesn't (the whitespace / competitive-gap list). Note any signal where a peer has the target's own product (e.g., GE running Siemens NX is a cross-sell tell).
4. **Spend delta** — call `company_spend` on each peer. Compare top 12 IT categories vs cohort median. Flag where target is ≥1.5× the median (over-investing) and where target is <0.7× (under-investing). Normalize by revenue ($/B revenue) for a fair comparison across scales.
5. **Intent gaps** — call `company_intent` (last 30 days) on target + each peer. Surface topics where the cohort is universally active but the target isn't — that's a buying signal the AE can credibly anchor "your peers are evaluating X right now."
6. **Closing narrative** — pick ONE gap that combines a spend signal + a cohort-wide intent signal + a target-specific catalyst. The single most compelling thing to lead with on the next call.

## Output Format
Markdown brief with these top-level sections in order:
- `# 🏭 Peer-Comparison Intelligence Brief: {{domain}}` (data-as-of date + sources line)
- `## 1. Cohort Definition` (selection methodology table + the chosen cohort table)
- `## 2. Profile Delta` (revenue/scale snapshot + key observations)
- `## 3. Tech-Stack Delta` ("target HAS, peers LACK" table + "peers HAVE, target LACKS" table + cross-sell signals)
- `## 4. Spend Delta` (cohort comparison table + over/under-investing flags + normalized $/B revenue)
- `## 5. Intent Gaps` (cohort-wide intent table + 3-5 observations)
- `## 6. Closing Narrative` (the single gap + the opening line + why it lands bullets)

Use HG tools first; `web_search` only when HG is sparse for non-US-listed peers. Cite every data point inline (HG company ID, intent topic ID, spend category, web URL).

## Quality Checklist
- Every peer pick includes a *why* (industry + revenue match + geography)
- Tech-stack delta clearly separates "target unique" from "target whitespace"
- Spend numbers are normalized per $B revenue, not raw dollars only
- Intent table covers ≥3 peers (not just target)
- Closing narrative names a specific stakeholder + a specific event/signal, not a generic role
- No fabricated data — flag any peer where HG is sparse and `web_search` was the source