Competitor Identification

Competitor Identification

Overview

How to find and validate the nearest competitors for a company, starting from existing data assets and expanding through manufacturer partner networks, free APIs, and large dataset screening. Produces a ranked Top 10 competitor list with product/service coverage matrices, brand overlap analysis, and geographic mapping.

Developed and validated on BROGAV Solutions LLC (June 2026) — a ~$5-7M data center equipment VAR. The methodology screened 4,797 S&P Global companies, queried SimilarWeb API for 12 domains, and searched manufacturer partner directories to produce a validated Top 10.

When to use this skill

• You need to find the 5-15 closest competitors for a company
• You have an existing intelligence dossier with supplier/partner data to work from
• You've been given a bulk competitor dataset (S&P Global, Dun & Bradstreet, etc.) and need to extract the relevant companies
• You need to compare competitors on specific dimensions (products, brands carried, services, geography, size)

Critical principle: Think before you search

The biggest mistake in competitor identification is jumping straight to web searches. The right sequence is:

1. Define the target profile precisely — What makes a company a "competitor"? Write it down before searching.
2. Mine existing data first — Read every file in the dossier. The partner list, supplier line card, event/association data, and any existing competitor datasets contain more signal than a web search.
3. Search outward from the supplier line card — If you know which brands the target carries, find who else carries those brands. Those are the real competitors.
4. Validate with quantitative data — Use SimilarWeb API, company profile databases, and financial data to confirm size/relevance.
5. Build comparison matrices — Product coverage, brand overlap, geographic overlap. Numbers, not narratives.

Phase 1: Define the target profile (15 minutes)

Write a crisp profile table before doing any research:

| Dimension | Target Profile | Competitor Must Be |
|-----------|---------------|-------------------|
| Business model | [e.g., VAR + installation] | [Same type, not manufacturer or mega-distributor] |
| Revenue | [e.g., ~$5-7M] | [Under $50M, or whatever ceiling makes sense] |
| Products | [List specific categories] | [Must overlap on core categories] |
| Services | [e.g., sourcing, installation, maintenance] | [Must offer services, not just ship boxes] |
| Geography | [HQ + sales territories] | [US-based, or whatever scope] |
| Brands carried | [List key manufacturer partners] | [Must carry 1+ of the same brands] |

Exclusion list

Equally important — define what does NOT qualify:

Phase 2: Mine existing data (30 minutes)

Read EVERYTHING in the dossier before doing a single web search:

Files to read and what to extract

Processing large competitor datasets

If you have a bulk dataset (S&P Global, D&B, etc.), don't eyeball it. Process it programmatically:

import csv

# Step 1: Filter by revenue
under_threshold = [r for r in rows if float(r['revenue_m']) < 50]

# Step 2: Keyword match on descriptions
# Build keywords from the target's actual products/services
product_keywords = ['ups', 'pdu', 'precision cooling', 'containment', 
                    'busway', 'critical power', 'server rack', 'cabinet',
                    'leak detection', 'data center']

brand_keywords = ['eaton', 'schneider', 'vertiv', 'panduit', 'legrand',
                  'starline', 'chatsworth', 'generac']

for row in under_threshold:
    desc = row.get('description', '').lower()
    product_hits = [kw for kw in product_keywords if kw in desc]
    brand_hits = [kw for kw in brand_keywords if kw in desc]
    if len(product_hits) >= 1 or len(brand_hits) >= 1:
        matches.append(row)

# Step 3: Rank by relevance score
matches.sort(key=lambda x: -(len(x['product_hits']) + len(x['brand_hits']) * 2))

Key lesson from BROGAV analysis: A 4,797-company dataset from S&P Global yielded only 8 genuine competitors after filtering. >99% were irrelevant (generic tech distributors, consumer electronics, software companies miscategorized under hardware SIC codes). Don't skip the programmatic screening.

Phase 3: Search outward from the supplier line card (30 minutes)

For each of the target company's top 5-10 manufacturer brands, find who else resells them:

Manufacturer partner directories

Tip: Most directories require ZIP code or are gated. Web search for "[brand] authorized partner" OR "authorized reseller" OR "representative" [region] often surfaces names the directory doesn't easily expose.

Industry association and event networks

Phase 4: Validate with quantitative data (20 minutes)

For each candidate competitor, pull:

SimilarWeb Free API

https://data.similarweb.com/api/v1/data?domain=COMPETITOR.com

Extract: monthly visits, global rank, bounce rate, pages/visit, traffic sources, top keywords with volumes + CPC.

Rate limiting: SimilarWeb throttles after ~5-10 queries. Space them out or accept 504 errors on some.

Company profile databases (web search)

For each candidate, search: "Company Name" employees revenue size

Sources that surface: RocketReach, ZoomInfo, LeadIQ, LinkedIn, Inc.com, Craft.co, Prospeo, Crunchbase, Glassdoor.

Revenue and employee data varies wildly across sources. Use ranges, not point estimates.

Phase 5: Build comparison matrices (30 minutes)

Product/service coverage matrix

List every capability the target company has. Score each competitor:

| Capability | Target | Comp 1 | Comp 2 | Comp 3 | ... |
|------------|--------|--------|--------|--------|-----|
| Cabinets/Racks | Yes | Yes | — | Yes | |
| PDUs | Yes | Yes | Yes | — | |
| UPS | Yes | — | Yes | Yes | |
| Cooling | Yes | Yes | Yes | — | |
| Containment | Yes | — | — | — | |
| Leak Detection | Yes | — | — | — | |
| Turnkey Installation | Yes | Yes | — | Yes | |
| Refurbished/Used | Yes | Yes | — | — | |
| Rental | Yes | — | — | — | |
| Coverage breadth | 9/9 | 5/9 | 3/9 | 3/9 | |

This matrix is the deliverable. It immediately shows where the target wins (unique capabilities) and where competitors crowd (commodity categories).

Shared manufacturer brand matrix

| Brand | Target | Comp 1 | Comp 2 | Comp 3 |
|-------|--------|--------|--------|--------|
| Eaton | Yes | — | Yes | — |
| Schneider | Yes | — | — | Yes |
| Starline | Yes | Yes | — | Yes |
| ... | | | | |

Geographic overlap map

| Territory | Target | Comp 1 | Comp 2 | Comp 3 |
|-----------|--------|--------|--------|--------|
| State A (HQ) | HQ | — | Yes (acquired local firm) | — |
| State B | Field sales | HQ | — | Yes |
| National | Yes | — | Yes | — |

Phase 6: Write the report (20 minutes)

Report structure

# Competitive Landscape — [Company] Top 10 Nearest Competitors

## Selection Criteria
[Target profile table + exclusion list with reasons]

## Dataset Screening (if applicable)
[How many companies screened, filtering steps, how many survived]

## The Top 10
[For each: name, HQ, revenue, employees, products, services, territory, 
brand overlap, similarity score X/10, why they match]

## Product/Service Coverage Matrix
[Full table]

## Shared Manufacturer Brand Matrix
[Full table]

## Geographic Overlap Map
[Full table]

## Strategic Takeaways
[5-7 key findings: unique advantages, biggest threats, uncontested niches]

## Data Sources
[Every source cited]

Competitor profile template

### #N. Company Name

| Field | Detail |
|-------|--------|
| Website | example.com |
| HQ | City, State |
| Founded | YYYY |
| Employees | ~XX |
| Revenue | ~$X.XM |
| Products | [List] |
| Services | [List] |
| Territory | [States/regions] |
| Brand overlap | [Which brands they share with target] |
| Similarity score | X/10 — [Explanation of why] |

Common pitfalls

1. Confusing partners with competitors. Manufacturers the target resells for are suppliers, not competitors. Flag them explicitly.
2. Including companies that are 100x the target's size. A $5M company does not compete with a $500M company. Set a revenue ceiling.
3. Trusting SIC codes. SIC 5045 "Computers and Peripherals" includes everything from smartphone distributors to data center VARs. >95% of a SIC-based list will be irrelevant.
4. Not mining existing data first. The best competitor intelligence is already in the dossier's supplier, partner, and association files. Web searches should fill gaps, not replace local research.
5. Judging competitors by web traffic. A company with 25,000 monthly website visits but no product overlap is not a competitor. A company with 850 visits/mo that carries the same brands and serves the same customers IS.
6. Not building the matrices. A narrative list of 10 companies is mediocre. Product coverage matrices, brand overlap matrices, and geographic maps are what make the analysis actionable.

Combining with other skills

• intelligence-dossier — Populates the 04_Market_and_Customers/ and 05_Competitive_Landscape/ sections
• open-source-traffic-analysis — Use SimilarWeb API to benchmark competitor traffic and extract their keyword data
• deep-research — If you need to go deeper on a specific competitor, use the 7-phase pipeline
• company-legal-reputation-research — Run on top competitors to find legal/compliance risks