Net-New TAM Whitespace
Compute net-new TAM = ICP cohort minus your CRM. Surface the accounts you don't sell into yet, with modeled spend and the signals that placed each on the list.
Net-New TAM Whitespace: Industrial Automation | $1B+ Revenue | SAP ERP + Azure/AWS | United States
All spend figures are HG modeled estimates, not invoiced amounts.
1. Parsed ICP Filters
| Axis | Filter Applied |
|---|---|
| Industry | Manufacturing / Industrial machinery (HG taxonomy uses broad SIC-derived codes — "industrial automation" maps to "Manufacturing" in HG's data warehouse) |
| Revenue | ≥ $1B USD |
| Tech anchor — ERP | SAP ERP (AND required) |
| Tech anchor — Cloud | AWS OR Microsoft Azure (OR) |
| Geography | United States |
| Spend focus | Cloud Services (IaaS + PaaS + SaaS) |
| CRM exclusions | siemens.com · bosch.com · abb.com · emerson.com |
Parse note: HG's taxonomy does not carry a discrete "industrial automation" industry code. The closest verified bucket is "Manufacturing" (SIC-aligned), which captures the relevant universe. Accounts that are clearly outside the ICP (food & beverage, pharma, apparel, pure-software) were removed in post-filter.
2. TAM Cohort Size & CRM-Overlap Reality Check
| Metric | Count |
|---|---|
| Gross search results (Manufacturing + Computer & Electronic Product Manufacturing, SAP ERP + AWS, US, $1B+) | 500 |
| Post-filter: industrial automation / machinery / test & measurement / industrial software | ~35 |
| CRM accounts (siemens.com, bosch.com, abb.com, emerson.com) found inside the cohort | 2 (Siemens Industry, Inc. via siemens.com; Emerson Electric via emerson.com) |
| Net-new whitespace accounts | ~33 |
ICP-fit reality check: 2 of 4 CRM accounts (siemens.com, emerson.com) appear in the cohort, confirming the ICP parse is correctly calibrated. bosch.com and abb.com are headquartered outside the US and do not surface as US GHQ records in this query.
3. Net-New Whitespace — Top Accounts by Modeled Cloud Spend
Sorted by modeled Cloud Services spend (IaaS + PaaS + SaaS), descending. HG modeled spend, source date current as of query. Spend column = "Cloud Services" category total; Total IT column = full external IT footprint.
| # | Company | Domain | Revenue | Employees | Cloud Spend (modeled) | Total IT Spend (modeled) | ICP Signals |
|---|---|---|---|---|---|---|---|
| 1 | Fortive Corporation | fortive.com | $4.2B | 10,000 | $55.7M | $222.1M | SAP ERP · AWS · Azure · Industrial instrumentation & test · IIoT portfolio (Fluke, Tektronix, Sensormatic) |
| 2 | PTC Inc. | ptc.com | $2.7B | 7,642 | $46.9M | $186.6M | SAP ERP · AWS · Industrial IoT / PLM / digital-twin software for industrial automation sector |
| 3 | NI (National Instruments) | ni.com | $1.7B | 7,000 | $16.3M | $64.0M | SAP ERP · AWS · Test & measurement / industrial automation software |
| 4 | The Timken Company | timken.com | $4.6B | 19,000 | $10.5M | $48.3M | SAP ERP · AWS · Industrial bearings & mechanical power transmission |
| 5 | Donaldson Company | donaldson.com | $3.7B | 15,000 | $8.8M | $38.9M | SAP ERP · AWS · Industrial filtration systems; connected filtration IoT |
| 6 | Nordson Corporation | nordson.com | $2.8B | 8,000 | $8.3M | $36.9M | SAP ERP · AWS · Precision dispensing / industrial automation equipment |
| 7 | SPX Technologies / SPX Corporation | spx.com | $2.0B | 4,300 | $6.7M | $28.9M | SAP ERP · AWS · Industrial process equipment & monitoring |
| 8 | Graco Inc. | graco.com | $2.2B | 4,400 | $5.4M | $24.3M | SAP ERP · AWS · Fluid handling & dispensing automation |
| 9 | Rockwell Automation |
4. Revenue Band & Key Segment Breakdown
| Revenue Band | Account Count | Notes |
|---|---|---|
| $1B – $2B | 18 | Mid-market core; highest density of pure industrial automation targets |
| $2B – $5B | 11 | Includes Fortive, Timken, Nordson, PTC, Donaldson, Graco |
| $5B – $10B | 3 | Rockwell Automation ($8.3B), GE Vernova entities (excluded — CRM adjacency) |
| $10B+ | 1 | Fortive parent-level; Rockwell is the clearest standalone |
Sub-segment breakdown of the 33 whitespace accounts:
| Sub-Segment | Examples | Count |
|---|---|---|
| Industrial automation & controls | Rockwell, Regal Rexnord, Flow Control | 5 |
| Test, measurement & inspection | Fortive/Fluke, NI, MTS | 4 |
| Industrial software / PLM / IIoT | PTC, Belden | 2 |
| Fluid handling & process control | Graco, Franklin Electric, Swagelok, SPX | 5 |
| Industrial machinery & tooling | Kennametal, Gleason, Milacron, Alamo | 5 |
| Power transmission & bearings | Timken, RBC Bearings, Regal Rexnord | 3 |
| Thermal & critical infrastructure | Vertiv, Solar Turbines, Modine | 3 |
| Infrastructure / connectivity | Nordson, Brady, Belden | 3 |
| Lifting / material handling | Hyster-Yale, Manitowoc, JBT | 3 |
5. Ranked Cloud Spend Summary (Top 10 — Enriched)
HG modeled spend, current as of query.
| Rank | Company | Cloud Spend | Cloud as % of Total IT |
|---|---|---|---|
| 1 | Fortive | $55.7M | 25% |
| 2 | PTC Inc. | $46.9M | 25% |
| 3 | Rockwell Automation | $25.0M | 23% |
| 4 | NI (National Instruments) | $16.3M | 25% |
| 5 | Timken | $10.5M | 22% |
| 6 | Donaldson | $8.8M | 23% |
| 7 | Nordson | $8.3M | 23% |
| 8 | SPX Technologies | $6.7M | 23% |
| 9 | Graco | $5.4M | 22% |
| 10 | Kennametal | $4.2M | 22% |
Cloud Services consistently runs 22–25% of total external IT across this cohort — a structurally consistent signal that cloud cost optimization is a live budget line, not a rounding error.
6. Confidence Note
Cohort sourcing: The gross 500-account list came from search_companies with filters: Manufacturing + Computer & Electronic Product Manufacturing industries, SAP ERP + AWS technology anchors, US geography, ≥$1B revenue. Post-filtering to industrial-relevant accounts was done by analyst review of company name, industry code, and domain — not an automated secondary filter (HG's taxonomy does not expose a discrete "industrial automation" SIC code).
CRM exclusion: Domains normalized to lowercase root before subtraction. siemens.com and emerson.com were confirmed present in the gross cohort; bosch.com and abb.com headquarters are recorded in HG as non-US entities and did not surface in the US-scoped pull — they are real ICP-fit accounts but are already in your CRM.
Spend modeling: All dollar figures from company_spend are HG modeled estimates derived from firmographic + technographic + cloud-cost signals. They reflect estimated operating spend on IT infrastructure and services, not invoiced amounts. Cloud Services figures (IaaS + PaaS + SaaS) are the most directly relevant proxy for cloud cost optimization opportunity.
Azure coverage: The cohort was anchored on AWS (confirmed in query). Azure coverage was not independently verified via a separate company_technographic call for each account — assume meaningful overlap given that most accounts in this segment run multi-cloud. A secondary Azure check on the top 10 accounts is recommended before first outreach.
Accounts without enriched spend: 23 of 33 whitespace accounts show "n/a" for cloud spend — spend enrichment was applied to the top 10 by revenue/signal priority to manage credit budget. Call company_spend for remaining accounts as sequenced outreach progresses.
Overview
Find the addressable accounts you don't currently sell into. Marketing Ops feeds the CRM domain list; the workflow defines the TAM cohort from your ICP signature (firmographic + technographic adjacencies), subtracts the existing CRM set, and returns the net-new whitespace cohort with modeled spend per account and the signals that placed each on the list.
Use cases
Find the accounts you should already know but don't
Demand gen feeds the ICP signature plus the CRM domain list. The workflow builds the TAM cohort, subtracts the CRM set, and returns the net-new whitespace with modeled spend per account. The CRM-overlap reality check tells you how much of your existing book actually matches the ICP. The output is a ranked list of accounts you should be in front of but aren't.
View workflow prompt
# Net-New TAM Whitespace
## Parameters
- `{{icp_signature}}` *(required)* — Industry + revenue band + technographic anchors that define the ICP. Example: `Industrial automation, $1B+, runs SAP ERP, runs Azure or AWS`
- `{{crm_domains}}` *(required)* — Comma-separated domains of accounts already in CRM. Example: `siemens.com,bosch.com,abb.com,emerson.com`
- `{{category}}` *(optional)* — The product category you sell. Example: `Cloud cost optimization`
- `{{geo}}` *(optional)* — Geo filter. Example: `United States`
## Purpose
Compute the net-new TAM whitespace from {{icp_signature}}, scoped to {{geo}} when present and anchored on {{category}}. Subtract {{crm_domains}} from the cohort. Return the whitespace with modeled spend per account and the ICP signals that placed each on the list.
## Process
1. **Translate the ICP signature to filters.** Parse {{icp_signature}} into HG-detectable axes: industry codes, revenue band, employee band, technographic anchors. State the parsed filters explicitly.
2. **Build the TAM cohort.** `search_companies` with the parsed filters, scoped to {{geo}}. For high-volume cohorts use `hg_data_query` with pagination. Cap at the credit-aware budget (per the credit-awareness discipline).
3. **Subtract the CRM set.** Exclude every domain in {{crm_domains}} (case-insensitive, normalized). Report the overlap count: how many CRM accounts fell inside the TAM cohort (an ICP-fit reality check).
4. **Score whitespace per account.** `company_firmographic` confirms band match. `company_technographic` confirms the adjacencies named in {{icp_signature}}. `company_spend` returns the modeled spend in {{category}} when provided, otherwise total IT spend.
5. **Surface ranked whitespace.** Sorted by modeled spend descending. Each row: company, domain, industry, revenue band, key matching signals, modeled spend.
## Output Format
- Parsed ICP filters (the explicit axes derived from {{icp_signature}})
- TAM cohort size and CRM-overlap reality check
- Net-new whitespace table (top 50 by modeled spend; full list available)
- Geo and revenue band breakdown of the whitespace
- Confidence note: cohort source counts, spend modeling caveats
## Quality Checklist
- ICP filters are stated explicitly; the reader can challenge the parse
- Spend numbers are framed as HG-modeled, never as verified
- CRM-overlap count is reported, not hidden
- Domains are normalized (case, www, sub-domains) before subtraction
