---
title: "Technographic data"
description: "Technographic data from the live web: detect CRM, ecommerce, analytics and chat tools from page source, network calls, job postings and integration pages."
canonical: https://www.zenrows.com/use-cases/technographic-data
updated: 2026-10-18
---

# Technographic data

Technographic data records which technologies a company uses, such as its CRM, ecommerce platform, analytics, chat and payment tools. Zenrows fetches each company's rendered pages and the network calls they make, so you can match script hosts, tags and job-posting mentions against your fingerprint list and keep a dated, evidence-backed record of every company's stack.

## Sample output: one company's detected stack

```json
{
  "domain": "example-outdoor.com",
  "scanned_at": "2026-10-17T07:40:00Z",
  "pages_scanned": ["/", "/contact", "/careers"],
  "technologies": [
    { "name": "Shopify", "category": "ecommerce", "evidence": "script host cdn.shopify.com", "page": "/" },
    { "name": "Klaviyo", "category": "email marketing", "evidence": "script host static.klaviyo.com", "page": "/" },
    { "name": "Google Tag Manager", "category": "tag management", "evidence": "googletagmanager.com/gtm.js", "page": "/" },
    { "name": "HubSpot", "category": "crm", "evidence": "network call to js.hs-scripts.com", "page": "/contact" },
    { "name": "Zendesk", "category": "support", "evidence": "help link to example-outdoor.zendesk.com", "page": "/" },
    { "name": "Salesforce", "category": "crm", "evidence": "job post: 'experience administering Salesforce'", "page": "/careers" }
  ],
  "first_seen": { "Klaviyo": "2026-07-14" },
  "removed_since_last_scan": ["Intercom"]
}
```

An illustrative scan result; every detection carries the evidence string and page it was found on, so a rep can verify it before using it in outreach.

## What technographic data teams track

Technographics answer one question per account: what is installed, and since when. The detection method depends on the category, because a CRM rarely leaves the same trace as a checkout platform.

| Job | Data you collect | Typical sources |
|---|---|---|
| Platform targeting | Ecommerce or CMS platform, theme, plugins | Script hosts and meta generator tags on the homepage and product pages |
| Competitive displacement | Accounts running a competitor's widget, SDK or tag | Rendered page source and network calls on key pages |
| Integration fit | Tools a prospect must connect to, such as CRM, help desk or payments | Integrations and partner pages, help center links, checkout pages |
| Internal tools and back office | ERP, CRM and data stack used by staff | Job postings on Indeed, Greenhouse, Lever and careers pages |
| Adoption and churn signals | Tools added or removed since the last scan | Repeated scans of the same pages, diffed over time |
| Market sizing | Share of a segment using a given technology | Scans across a list of domains in one industry or region |

## Why tech stack detection misses tools

Most tags load after the first HTML response, through a tag manager or a consent banner. A plain HTTP request sees a fraction of them, and the sites that matter most often sit behind bot protection.

| What breaks | What it looks like | How Zenrows handles it |
|---|---|---|
| Tags injected by JavaScript | Raw HTML shows Google Tag Manager and nothing it loads | Adaptive Stealth Mode escalates to browser rendering and returns the page after scripts have run |
| Tools that only call home | A chat or CRM tool shows up as a network request, never as a script tag | Fetch captures the page's background network calls (XHR) with their URLs |
| Protected sites | The scan records a challenge page and "no technologies found" | Zenrows' stealth gets through Cloudflare, DataDome, Akamai and similar protections and returns the real page |
| Tools used only by staff | A CRM or ERP leaves no trace on the public site | Job postings name the tools candidates must know |
| One page is not the whole stack | Checkout, pricing and support pages load different vendors | Batch scans several paths per domain in one job |

## How to collect technographic data with Zenrows

1. **Write the fingerprint list.** Map each technology to the traces it leaves: script hosts, cookie or global names, network call hosts, subdomains, keywords in job posts.
2. **Scan the rendered pages.** Fetch the homepage and two or three key paths with Adaptive Stealth Mode, and match the HTML against your fingerprints.
3. **Capture network calls.** Capture the page's background network calls to see the requests it makes, which catches tools without a visible script tag.
4. **Read job posts.** Fetch careers pages as Markdown and match tool names in the requirements.
5. **Re-scan and diff.** Run the domain list through Batch monthly and store first-seen and last-seen dates per technology.

```python Python
# pip install requests
import re
import requests

FINGERPRINTS = {
    "Shopify": r"cdn\.shopify\.com",
    "HubSpot": r"js\.hs-scripts\.com|js\.hsforms\.net",
    "Klaviyo": r"static\.klaviyo\.com",
    "Google Tag Manager": r"googletagmanager\.com/gtm\.js",
    "Zendesk": r"[a-z0-9-]+\.zendesk\.com",
}

html = requests.get("https://api.zenrows.com/v1/", params={
    "apikey": "YOUR_ZENROWS_API_KEY",
    "url": "https://example-outdoor.com/",
    "mode": "auto",  # Adaptive Stealth Mode
}).text

found = [name for name, pattern in FINGERPRINTS.items() if re.search(pattern, html)]
print(found)
```

```javascript Node.js
// Node.js 18+: list the hosts a page calls while it loads
const params = new URLSearchParams({
  apikey: "YOUR_ZENROWS_API_KEY",
  url: "https://example-outdoor.com/contact",
  js_render: "true",
  json_response: "true",
});

const response = await fetch(`https://api.zenrows.com/v1/?${params}`);
const { xhr } = await response.json();
const hosts = [...new Set(xhr.map((call) => new URL(call.url).hostname))];
console.log(hosts); // match these against your fingerprint list
```

```bash CLI
npx -y @zenrows/cli init
# Rendered homepage HTML, to match against your fingerprints
zenrows fetch https://example-outdoor.com/ --out home.html
# Careers page as Markdown, to find tool names in job requirements
zenrows fetch https://example-outdoor.com/careers --output markdown
```

```text Claude Code (MCP)
# Connect once
claude mcp add zenrows -e ZENROWS_API_KEY=YOUR_ZENROWS_API_KEY -- npx -y @zenrows/mcp

# Then ask:
Use Zenrows to load the homepage and checkout page of example-outdoor.com and list every third-party script host and network call domain they load, then name the ecommerce platform, email, analytics, chat and payment tools those hosts point to.
```

## Which Zenrows product fits

| If you need to | Use | Why |
|---|---|---|
| Scan a domain's rendered HTML and network calls | [Fetch](/products/fetch) | Browser rendering and captured network calls show what loads after the first response |
| Scan 200,000 domains every month | [Batch](/products/batch) | One job per scan cycle, results by webhook or download |
| Spot-check a prospect's stack from the terminal | [CLI](/cli) | One command returns the page or its Markdown for a quick look |

## What success looks like

| Measure | What good looks like | How to check it |
|---|---|---|
| Detection precision | Detected tools are actually in use when a rep checks | Verify a random sample of detections against the page by hand each month |
| Scan completeness | Pages that loaded fully, not challenge pages, make up nearly every scan | Count scans whose HTML is shorter than a threshold or has no title |
| Category coverage | Every category in your scoring model has fingerprints that fire | Share of domains with at least one detection per category |
| Change latency | Installs and removals appear within one scan cycle | Compare first-seen dates with the dates companies announce a switch |
| Evidence on record | Every detection stores the trace and page it came from | Count detections with an empty evidence field |

## Technographic data FAQ

### What is technographic data?

Technographic data is information about the technologies a company uses: software, platforms, tools and infrastructure. Sales and marketing teams use it to target accounts that run a compatible or competing product, to tailor messaging to a prospect's stack, and to time outreach around a tool being added or removed.

### What are examples of technographic data?

"Runs Shopify with Klaviyo for email", "uses HubSpot forms on the contact page", "support site hosted on Zendesk", "hiring a Salesforce administrator" and "removed Intercom in September" are all technographic data points. Good records add the date first seen and the evidence behind each detection.

### How do you detect a company's tech stack from its website?

Load the page in a browser, then look for traces each tool leaves: script and stylesheet hosts, meta generator tags, global JavaScript objects, cookies, network calls to vendor domains and links to vendor-hosted subdomains. With Zenrows, browser rendering gives you the rendered HTML and capturing the page's background network calls lists every request, which you match against your own fingerprint list.

### What is the difference between technographic and firmographic data?

Firmographic data describes the company: industry, size, location, ownership. Technographic data describes its technology. A typical ideal customer profile uses both, such as "ecommerce brands with 50 to 500 employees running Shopify Plus".

### How do I use technographic data for segmentation?

Group accounts by the tools that decide fit for your product: the platform you integrate with, the competitor you replace, or a gap your product fills. Score each account on those detections plus their dates, so a tool added last month ranks higher than one seen two years ago. Re-scan monthly and move accounts between segments when a detection appears or disappears.

### Can technographic data show tools a company uses internally?

Tools used only by staff, such as an ERP or an internal CRM, usually leave no trace on the public site. Job postings are the best public evidence: requirements like "three years with NetSuite" or "experience with dbt and Snowflake" name the stack directly. Scrape careers pages and job boards, and record the posting date with each mention.

### How accurate is technographic data from web scraping?

Detections from page source and network calls are evidence that a tool loaded on that page on that date, and the stored trace lets anyone verify it. Accuracy drops when fingerprints are loose (a generic CDN host) or when a tag remains after a company stops paying for the tool. Tight fingerprints, multiple pages per domain and regular re-scans keep false positives low.

## Related use cases

- [Firmographic data](https://www.zenrows.com/use-cases/firmographic-data.md): Industry, headcount, HQ, locations and funding per company, built from registries, company sites and directories.
- [Lead enrichment](https://www.zenrows.com/use-cases/lead-enrichment.md): A domain in, an account record out: pricing, hiring, product and review signals from the company's own web.
- [Competitive intelligence](https://www.zenrows.com/use-cases/competitive-intelligence.md): Pricing, hiring, reviews, launches and partners across your market, structured for battlecards and strategy.
- [Job scraping](https://www.zenrows.com/use-cases/job-scraping.md): Job postings, salary ranges and hiring signals from job boards and ATS career pages, as one feed.

Start free: https://app.zenrows.com/register. Docs: https://docs.zenrows.com