HG Insights|Phoenix

Whitespace Cross-Sell Map

Replace 'what else can we sell them' brainstorms with peer-anchored cross-sell rankings.

Industrial manufacturing · Whitespace cross-sell map

Spend Insights is the strongest whitespace: $4B IT budget with no external benchmark layer

6.0 / 9.0
Spend Insights whitespace rank
$581M
Cloud spend — also untracked
2 of 3
Peers with spend analytics
Product Peer adoption Stack gap Whitespace rank
🥇 Spend Insights 67% (2/3) No benchmark layer 6.0
🥈 Cloud Dynamics 67% (2/3) Dual-cloud, no tracking 5.4
🥉 FAI 33% (1/3) 348-site CRM, no persona layer 2.0
🎯 Your Account already buys Sales Intel + Intent Topics · whitespace = products not yet purchased
Open with this
“Two of your three peers already benchmark their IT spend externally. With $4B in IT budget, what are you using to validate you’re paying the right price?”

Overview

Your account team brainstorms 'what else can we sell them' — and produces wishlists. This workflow ranks the products your customer doesn't yet buy by peer-adoption rate × fit-at-this-customer (stack gap, intent surge, HG company_spend headroom), then names a peer that already runs each top-3 product. The cross-sell becomes a sequenced play with peer evidence, not a brainstorm bullet.

Use cases

  • Account-plan kickoffs grounded in peer behavior

    Your team's account-plan kickoff usually starts with brainstorming 'what else could they buy from us?' This workflow runs at kickoff time and outputs a ranked whitespace map: each missing product scored by peer-adoption rate (5 named peers) × fit-at-this-customer (stack-gap evidence + intent + HG spend headroom). Plans pivot from wishlist to peer-anchored evidence.

  • Quarterly expansion reviews ranked by opportunity, not volume

    Your CS leadership meeting prioritizes expansion by who-shouted-loudest. This workflow batch-runs across the customer book and ranks every account by total whitespace opportunity (sum of peer-adoption × fit across the portfolio). Reviews open with the top-10 list, sorted by data — and budget gets directed where the signals are, not where the most-aggressive AE is.

View workflow prompt
# Whitespace Cross-Sell Map

## Parameters

- `{{domain}}` *(required)* — Customer company domain HG Insights uses for lookup. Example: `siemens.com`
- `{{current_products}}` *(required)* — Comma-separated list of products they currently buy from you. Example: `Sales Intel,Intent Topics`
- `{{portfolio}}` *(required)* — Comma-separated list of all products in your portfolio. Example: `Sales Intel,Intent Topics,Spend Insights,Cloud Dynamics,FAI`

## Purpose
You are an AE building a whitespace cross-sell map for {{domain}}. The customer currently buys {{current_products}} from your full portfolio of {{portfolio}}. The output ranks the *whitespace* (portfolio minus current) by peer-adoption × fit at this customer, then names a top-3 "why now" narrative for each — anchored to specific peers and signals.

## Process

Tool budget is tight. Make ONE call per data source, then reason locally over the response. Do NOT call the same tool repeatedly per product.

1. **Cohort + target firmographic** — call `company_firmographic` once for {{domain}}. Use `search_companies` to pick 3 peers in the same industry, ±25% revenue band. (3 peers, not 5 — keeps the analysis tight.)
2. **Stack snapshot** — call `company_technographic` ONCE per company: 1 call for {{domain}} + 3 calls for the peers. The response carries the full top-N stack; you scan it locally for the products in {{portfolio}}.
3. **Target signals** — call `company_intent` ONCE for {{domain}} (the response covers all category signals you need) and `company_spend` ONCE (covers all IT spend categories). Don't loop these per product.
4. **Per-product ranking (LOCAL — no tool calls)** — for each product in `portfolio - current_products` (the whitespace set), score from the data already pulled:
   - **Peer adoption %** = (peers running this product) / 3, computed from the peer technographic responses.
   - **Stack gap** (0-3): clear gap at {{domain}} = 3; partial overlap = 1; already covered = 0.
   - **Intent signal** (0-3): high-trend topic in target's category = 3; mild = 1; absent = 0.
   - **Spend headroom** (0-3): target's spend in the relevant company_spend category is ≥1.5× cohort median = 3; near median = 1; well below = 0.
   - `whitespace_rank = peer_adoption_pct × (stack_gap + intent_signal + spend_headroom)` — max 9.
5. **Top-3 "why now"** — sort whitespace by rank, take the top 3. For each, write a one-paragraph narrative that names:
   - A peer (by name) that already runs this product
   - The specific stack gap or intent signal at {{domain}}
   - The current event that makes this the moment (M&A, new leader, expiring contract — pull from the data already in hand, not new tool calls)

## Output Format
Markdown with these sections in order:
- `# 🌱 {{domain}} — Whitespace Cross-Sell Map` (header + current vs portfolio)
- `## Cohort` (5-peer table: Peer | Domain | Why Comparable)
- `## Whitespace Ranking` (table: Product | Peer Adoption % | Fit Score (s/i/h) | Whitespace Rank)
- `## Top 3 "Why Now" Narratives` (per product: peer precedent + stack gap + current event)
- `## Recommended Cross-Sell Sequence` (which product first, who to pitch, what trigger to wait for)

Cite peers and signals by name in every narrative.

## Quality Checklist
- Each whitespace product's fit score shows its s/i/h breakdown (stack/intent/headroom)
- Each "why now" narrative names a *specific* peer (not "industry leader")
- Recommended sequence references which stakeholder owns each pitch
- No fabricated peer-adoption numbers — if HG can't confirm, say "estimate based on N=2 peers"
- Cross-sell sequence specifies a trigger to wait for, not "as soon as possible"