In our ongoing efforts to enhance the reliability and sustainability of our services, we have implemented a significant update regarding the scraping of missing LinkedIn profiles. Effective immediately, credits will be consumed when attempting to scrape a missing LinkedIn profile (resulting in a 404 status code). Allow us to explain the rationale behind this decision.

At Datamagnet, our API People Profile endpoint is designed to crawl public LinkedIn profiles. However, not all LinkedIn profiles, even if valid, are publicly accessible. In cases where a profile is not available to the public, our LinkedIn People Profile Endpoint returns a 404 status code.

Today, we are introducing an update to our service that charges credits when an API request is made to scrape a public LinkedIn profile that is missing from the public domain. This adjustment is crucial because the resources required to scrape both valid and invalid profiles remain consistent and equally expended.

Regrettably, we have encountered instances where our services were exploited by attackers using trial API keys to spam numerous requests to random invalid profile URLs. This malicious activity resulted in a significant strain on Datamagnet's API service, ultimately leading to a denial of service.

We sincerely appreciate your understanding and cooperation as we implement these changes to ensure the sustainability and reliability of our services. These measures are essential for supporting our users who rely on Datamagnet's services to build and grow their businesses. Thank you for your continued support as we strive to provide you with the best possible experience.

Image Credits: [Image by storyset on Freepik](https://www.freepik.com/free-vector/404-error-with-landscape-concept-illustration_20602785.htm#query=404&position=5&from_view=keyword&track=sph&uuid=7f8f0b2c-a19a-4db9-8438-15fda23759e4)

Important Update: Changes to Credit Consumption for Missing LinkedIn Profiles

## Introduction

Until now, Datamagnet's API focused on people and company data. Today, we're expanding into **LinkedIn post analytics** with 4 new endpoints that let you extract everything about a LinkedIn post—from the full post content to every comment, reaction, and repost.

Whether you're building a social listening dashboard, tracking content performance for clients, or analyzing engagement patterns at scale, these endpoints give you the raw data you need.

All you need is a LinkedIn post URL. We handle extracting the activity ID, calling the right upstream APIs, and returning clean, paginated, `snake_case` JSON.

---

## What's New

Here's a quick overview of the 4 new endpoints:

| Endpoint | What It Does | Cost |
|---|---|---|
| `POST /api/v1/linkedin/post` | Full post details, author, social counts | 5 credits |
| `POST /api/v1/linkedin/post/comments` | Paginated comments with commenter profiles | 5 credits |
| `POST /api/v1/linkedin/post/reactions` | Who reacted and reaction types | 5 credits |
| `POST /api/v1/linkedin/post/reposts` | Who reposted, with commentary and reshare info | 5 credits |

All endpoints accept a simple JSON body:

```json
{
  "url": "https://www.linkedin.com/posts/username_slug-activity-7430615551086850048-XXXX"
}
```

We support every common LinkedIn post URL format—whether it's a `/posts/` share link, a `/feed/update/` link, or a `ugcPost` URL. The API auto-extracts the activity ID for you.

---

## 1. Post Details

This is your starting point. Given any LinkedIn post URL, it returns the complete post data in a single call:

- **Post content** — full text, posted timestamp, and the post's URN
- **Author info** — name, headline, profile picture, and profile link
- **Social counts** — total likes, comments, shares, and a reaction-type breakdown (e.g., 108 Likes, 10 Praise, 4 Empathy)
- **Article attachments** — title, subtitle, link, and thumbnail for shared articles
- **Repost metadata** — if the post is a repost, you get the original post header and who reposted it
- **Related URNs** — the `reactions_urn`, `comments_urn`, and `reposts_urn` you can use with the other endpoints

```python
import requests

response = requests.post(
    "https://api.datamagnet.co/api/v1/linkedin/post",
    json={"url": "https://www.linkedin.com/feed/update/urn:li:activity:7219434359085252608"},
    headers={"Authorization": "Bearer YOUR_API_KEY"}
)

data = response.json()
print(data["message"]["post_text"])
print(f"Likes: {data['message']['social_count']['num_likes']}")
```

---

## 2. Post Comments

Retrieve every comment on a post with full commenter profiles. Supports cursor-based pagination to walk through all comments.

Each comment includes:

- Comment text and permalink
- Commenter name, headline, and profile picture
- Whether the comment is pinned or edited
- Reaction breakdown on the comment itself
- Reply count and top replies

```python
page = 1
all_comments = []

while page is not None:
    response = requests.post(
        "https://api.datamagnet.co/api/v1/linkedin/post/comments",
        json={
            "url": "https://www.linkedin.com/posts/...",
            "page": page
        },
        headers={"Authorization": "Bearer YOUR_API_KEY"}
    )

    data = response.json()
    all_comments.extend(data["message"]["comments"])
    page = data.get("cursor")

print(f"Total comments: {len(all_comments)}")
```

---

## 3. Post Reactions

See exactly who reacted to a post and what type of reaction they gave. LinkedIn supports 6 reaction types:

- 👍 **Like**
- 👏 **Praise** (Celebrate)
- ❤️ **Empathy** (Love)
- 💡 **Interest** (Insightful)
- 🙏 **Appreciation** (Support)
- 😄 **Entertainment** (Funny)

Each reaction record includes the person's name, headline, profile URL, profile picture, and the specific reaction type. Paginated the same way as comments.

```python
response = requests.post(
    "https://api.datamagnet.co/api/v1/linkedin/post/reactions",
    json={
        "url": "https://www.linkedin.com/posts/...",
        "page": 1
    },
    headers={"Authorization": "Bearer YOUR_API_KEY"}
)

data = response.json()
for reaction in data["message"]["reactions"]:
    print(f"{reaction['title']} — {reaction['reaction_type']}")
```

---

## 4. Post Reposts

Track how content spreads across LinkedIn. This endpoint returns everyone who reposted a specific post, including:

- **Direct reposts** — the reposter's name, headline, profile picture, and any commentary they added
- **Reshares** — when someone reposted a repost, you get a nested header showing the chain
- **Company reposts** — when a company page reshares, you get the company name, logo, and follower count

```python
response = requests.post(
    "https://api.datamagnet.co/api/v1/linkedin/post/reposts",
    json={
        "url": "https://www.linkedin.com/posts/...",
        "page": 1
    },
    headers={"Authorization": "Bearer YOUR_API_KEY"}
)
```

---

## Design Decisions

**Snake case everywhere.** All response keys are in `snake_case`, even though LinkedIn's internal API uses camelCase. We convert everything automatically so your Python and Ruby code looks idiomatic without post-processing.

**Consistent pagination.** Every paginated endpoint uses the same pattern: pass `page` in the request body, read `cursor` from the response. When `cursor` is `null`, you're done. No offset math required.

**Any URL format.** We auto-detect and extract activity IDs from multiple LinkedIn URL formats — `/posts/` share links, `/feed/update/` links, `ugcPost` URLs, and more. Just paste whatever URL you have.

**Flat 5 credits per call.** Predictable pricing across all 4 endpoints, whether you're fetching a post with 2 reactions or 2,000.

---

## Getting Started

1. **Get your API key** at [datamagnet.co/app/api-keys](https://datamagnet.co/app/api-keys)
2. **Read the docs** at [docs.datamagnet.co](https://docs.datamagnet.co) — each endpoint has full request/response examples
3. **Start with Post Details** — use it to get the post overview and social counts, then drill into comments, reactions, or reposts as needed

If you have questions or run into issues, reach out to us at support@datamagnet.co.

Happy building! 🚀

Introducing 4 New LinkedIn Post Endpoints: Full Post Analytics via API

Speed and precision aren't nice-to-haves in B2B sales anymore — they're the baseline. Sales teams that operate on incomplete or outdated prospect data lose outreach opportunities before they even start.

B2B contact data decays up to 70% per year. Real-time enrichment APIs fix that — compare top providers on accuracy, speed, and CRM fit.

7 Best Real-Time B2B People Enrichment APIs for Sales Teams (2026)

B2B Data and Sales Intelligence Research Team

Datamagnet Team

Datamagnet

Home

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Sales Intelligence

A real-time B2B people enrichment API instantly appends missing contact and company data to a prospect record the moment you request it. Submit an email, name, or LinkedIn URL and receive a full profile: verified email, phone, job title, company size, and more. Top APIs respond in under 500ms. Contact data decays at up to 70% annually (Landbase, 2026), making real-time enrichment essential for accurate outreach.

What is a real-time B2B people enrichment API?

Accuracy varies by provider. Top-tier APIs hit 90-98% email deliverability accuracy, with Cleanlist citing 98% as the best-in-class benchmark (Cleanlist, 2026). Apollo.io's actual email accuracy runs closer to 65-70%, which drives up bounce rates on high-volume campaigns. Always test accuracy on a sample of your specific target market before committing to a provider.

How accurate are real-time people enrichment APIs?

Costs range from roughly $0.01 per email lookup at the low end to $0.50 per full contact record for premium providers, depending on data depth and volume tier (Prospeo, 2026). Most providers use credit-based pricing that scales with usage. Setting conditional enrichment rules typically cuts costs by 40-60% without affecting lead quality.

How much does real-time people enrichment cost per contact?

Under 500ms is the baseline for a usable real-time experience. Under 300ms is strong. Top providers like PDL and Clearbit hit roughly 200ms at median latency (Autobound, 2026). Response time matters most for interactive use cases where a rep is waiting for data during a live workflow. For batch jobs, latency matters less than throughput and accuracy.

How fast should a real-time enrichment API respond?

Most reputable providers maintain GDPR and CCPA compliance frameworks, but compliance is your responsibility too. GDPR's legitimate interest basis applies to B2B enrichment but requires a documented balancing test (Derrick App, 2026). Verify your provider's data sourcing documentation, opt-out mechanisms, and audit trail capabilities before assuming compliance.

Are people enrichment APIs GDPR and CCPA compliant?

Real-time enrichment is better for inbound lead handling and live outbound prospecting. Batch enrichment is better for periodic CRM cleanup and large-scale re-enrichment. The most efficient approach combines both. Inbound leads reached within 5 minutes of form submission are 9x more likely to convert than those reached later (Utmost Agency, 2026).

Is real-time enrichment better than batch enrichment for sales?

Set conditional enrichment rules that only trigger when specific fields are empty or below a confidence threshold. Don't enrich every record unconditionally. Focus on leads that meet minimum qualification criteria and use event-driven enrichment triggered by specific CRM actions rather than scheduled bulk runs. Audit enrichment usage monthly and adjust rules based on actual field completion rates.

How do I prevent over-enriching and wasting API credits?

Standard fields include verified email, direct dial and mobile phone, current job title, company name and domain, industry, company size, seniority level, and LinkedIn profile URL. Premium providers also return recent job changes, company funding status, technographic data, and social media handles. Coverage varies by provider and market segment.

What data fields can I get from a people enrichment API?

Most APIs connect via REST endpoints. For Salesforce and HubSpot, use native connectors, Zapier, or Make for no-code setups, or build custom middleware for more control. Follow the RACE Framework: identify enrichment triggers, configure authentication, map fields with conditional logic, and set up fallback waterfall rules so incomplete results don't overwrite higher-confidence existing records.

How do I integrate a people enrichment API into my CRM?

Waterfall enrichment cascades a lookup request across multiple data sources in sequence. If the first source returns an incomplete result, the next source is queried automatically. This architecture produces significantly more complete profiles than single-source lookups, especially for contacts outside North America where individual data sources have thinner coverage. It's the key architectural difference between top-tier enrichment APIs and single-vendor solutions.

What is waterfall enrichment and why does it matter?

Compare real-time B2B people enrichment APIs on accuracy, speed, and compliance. Learn how to implement enrichment in your sales stack and pick the right provider.

Real-Time B2B People Enrichment APIs (2026)

Identifying job changes in real time is a critical advantage for B2B sales and recruiting teams. These transitions create a short window of opportunity. Executives typically evaluate new vendors within their first 90 days in a role, making timing the single largest variable in whether your outreach lands or disappears.

Compare the 7 best real-time job change APIs for B2B sales and recruiting. Ranked by detection speed, coverage, and CRM integration. Updated May 2026.

7 Best Real-Time Intent Signal APIs for Job Changes

B2B Data & Sales Intelligence Research Team

A real-time job change API monitors professional profiles across public sources to detect career transitions as they occur. It uses automated scraping and record-matching to identify when a contact's job title or employer changes, then delivers that signal via webhook or API response. New executives are among the most active technology buyers, with purchasing decisions concentrated in the first 90-120 days of a role (Gartner, 2024).

What is a real-time job change API and how does it work?

Detection latency ranges from sub-hour for providers using live scraping, to 24-48 hours for those relying on LinkedIn's update feed, to weekly or monthly for batch-oriented providers. Sub-hour detection lets your team reach a new hire in their first week, before competitors. Batch detection means the same prospect may already be in three competitor pipelines by the time you reach out.

How fast can job change APIs detect when someone switches roles?

The best fit depends on your outreach motion. Outbound SDR teams should prioritize sub-hour detection and webhook support — Datamagnet and ZoomInfo Enterprise both deliver this. Relationship-led or expansion teams benefit most from UserGems' champion tracking. All-in-one platform users often find Apollo.io sufficient. Map your API's detection latency to your sequence cadence before committing to a vendor.

Which job change API is best for B2B sales teams?

Entry-level platforms start at $99/month (Apollo.io) or $98/month for 350 API credits (People Data Labs). Enterprise platforms like ZoomInfo require annual contracts typically in the five-figure range. Custom-priced APIs like Datamagnet, Clearbit, and UserGems scale by volume based on profiles monitored and webhooks fired per month.

How much does a job change intent signal API cost?

Yes. UserGems and ZoomInfo offer deeply embedded native Salesforce integrations with automated task creation and sequence enrollment. Datamagnet delivers events via webhook, connecting to Salesforce and HubSpot through Zapier, Make, or custom middleware. Apollo.io handles integration natively. People Data Labs requires the most custom engineering work and has no native CRM connector.

Can job change APIs integrate with Salesforce and HubSpot?

Compare detection speed, coverage, and integrations for the top 7 job change intent signal APIs. Find the right fit for your sales or recruiting workflow.

7 Best Real-Time Job Change APIs for B2B Sales Teams (2026)

Account Research Infrastructure

Account Executive Productivity

B2B buyers complete 60-70% of their evaluation before they ever talk to a salesperson. That means your AE walks into most conversations already behind. If they're piecing together account context on the fly, they're not catching up — they're losing.

AEs waste up to 70% of their week on non-selling tasks. A 4-layer account research infrastructure fixes that — here's how to build it.

Account Research Infrastructure for AEs: A 4-Layer Guide

The Datamagnet team builds real-time people and company data APIs for B2B sales and recruiting teams. We publish research on sales intelligence, data enrichment, and pipeline efficiency.

Account research infrastructure is the integrated system of data sources, tools, workflows, and processes that equips AEs with consistent, high-quality intelligence about target accounts. It transforms research from an ad-hoc burden into a repeatable, systematic process. AEs currently spend up to 70% of their week on non-selling activities, and structured research infrastructure directly reclaims that time (Prospeo, 2026).

What is account research infrastructure for sales teams?

Companies using sales intelligence platforms report up to a 5:1 revenue-to-cost ROI within 90 days (Revnew, 2026). Teams that consolidated research tools saw 42% higher sales velocity and 35% better win rates (Salesmotion, 2026). The ROI case is strongest when infrastructure reduces AE research time while improving conversion at each funnel stage.

What is the ROI of investing in account research infrastructure?

The three highest-value triggers are funding rounds, executive hires, and tech stack changes. Funding triggers generate 400% higher conversion rates when acted on within 48 hours. Leadership change signals produce 14% response rates versus 1.2% for cold outreach. Tech stack shifts signal displacement opportunities or expansion needs (Autobound, 2026).

What triggers should AEs monitor for account research?

Active deals need real-time or near-real-time data. Target accounts in your pipeline should refresh weekly. Total addressable market lists can refresh monthly. Data staleness is one of the most common causes of bounced sequences and irrelevant outreach (Default, 2026).

How often should account research data be refreshed?

For net-new accounts, 20-30 minutes of structured research is typically enough to prepare relevant, informed outreach. Warm leads or active opportunities may only need 10-15 minutes of pre-call refresh. Top-performing AEs spend 82% more time on pre-call research than average reps, but efficient infrastructure makes that time go further (Prospeo, 2026).

How much time should AEs spend researching each account?

https://www.datamagnet.co/images/blog/account-research-infrastructure-for-aes-hero.png

An account executive reviewing a structured account research dashboard with company signals, org charts, and trigger alerts on a large monitor

AEs waste 70% of their week on non-selling tasks. Build the 4-layer research stack — Data Foundation, People Intel, Triggers, Competitive Context.

Pratik Dani

Important Update: Changes to Credit Consumption for Missing LinkedIn Profiles

Introducing 4 New LinkedIn Post Endpoints: Full Post Analytics via API

7 Best Real-Time B2B People Enrichment APIs for Sales Teams (2026)

7 Best Real-Time Intent Signal APIs for Job Changes

Account Research Infrastructure for AEs: A 4-Layer Guide