Tag: track companies

How AI Agents Use the News Events Dataset to Power Smarter Sales

September 11, 2025 /

There’s a lot of talk about AI agents right now. Some see AI agents powered by News Events dataset as futuristic assistants, others as overhyped chatbots in disguise. The truth lies somewhere in between: AI agents are becoming practical tools for sales teams, and what makes them useful isn’t just the AI itself — it’s the data feeding them.

AI agents powered by News Events dataset are utilizing the News Events dataset effectively. One dataset that’s proving especially powerful here is the News Events dataset.

Every headline hides an opportunity — the key is knowing which ones matter.

Why AI Agents Need Real-Time Signals

An AI agent without fresh data is basically a parrot. It can mimic patterns, but it won’t know when your prospect just raised a Series B, or when your competitor opened a new office in London. That’s where the PredictLeads News Events dataset steps in.

Since 2016, it has processed millions of blogs, press releases, and articles, surfacing structured signals like:

  • A company receives financing
  • A new executive hire or departure
  • A competitor launches a product
  • A business expands into a new region

Instead of raw news headlines, the dataset gives AI agents clean, categorized events they can instantly understand and act on. This makes them excellent AI agents powered by News Events dataset.

Turning Events Into Action

Here’s how it looks in practice:

  • Prospecting agent: While scanning a target account list, the agent notices that “Company X just signed a new client in your industry.” Instead of sending a generic email, it drafts a message that congratulates them and positions your product as the next logical step.
  • Account monitoring agent: Your AI checks daily for news about top accounts. It flags that a CEO has stepped down at one company, suggesting you re-engage before new leadership sets a different direction.
  • Competitive intelligence agent: While tracking your market, it picks up that a competitor “is developing” a new feature. That becomes part of your next strategy meeting, long before it makes it into glossy press releases.

The dataset doesn’t just enrich records in your CRM — it gives AI agents powered by News Events dataset the awareness they need to behave less like scripts and more like actual teammates.

Why Structure Matters

The power here isn’t only in freshness, it’s in structure. AI agents thrive on clarity. If a news article says, “Rumors suggest the company might launch a new product later this year,” the dataset captures that nuance as planning = true, rather than treating it as a confirmed launch.

That kind of detail is the difference between an AI agent that spams prospects with irrelevant updates and one that reaches out with credibility.

The Bigger Picture

AI agents powered by News Events dataset are quickly moving from novelty to necessity in sales. But what separates the helpful ones from the noise is data quality. The News Events dataset acts like a stream of real-time situational awareness, allowing AI to spot openings humans might miss — and do it at scale.

In a sense, it gives AI agents something they usually lack: context. And in sales, context is everything.

Final Thought

If the last decade was about building bigger CRMs and larger lead lists, this one will be about equipping AI agents with the right signals. The News Events dataset is one of those signals — turning headlines into structured intelligence that AI can understand, prioritize, and act on. Therefore, AI agents powered by News Events dataset are becoming indispensable tools in modern sales strategies.

Because at the end of the day, the future of sales isn’t just AI for the sake of AI. It’s AI that knows when the moment is right.

Interested in our API Docs? Feel free to find them “here“.

How AI Sales Agents Are Transforming B2B Prospecting and How PredictLeads Steps In
How PredictLeads Company Data powers modern Sales Intelligence & Data Enrichment
Company Intelligence Data in Your Sales Enablement Platform

The Billion-Dollar Clues Hiding in The Right Blend of Company Data

August 11, 2025 /

In 2012, Stripe was just a little payments API that almost nobody outside of Silicon Valley had heard of.
By 2021, it was worth $95 billion.

The uncomfortable truth is the signals that Stripe was going to be huge were visible years before the big headlines hit. Most people just weren’t looking for that crucial early-stage investment signals (or didn’t know where to look).

That’s the edge today’s smartest investors are chasing: finding billion-dollar companies before they look like billion-dollar companies. And it starts with something almost no one talks about. The right blend of News and Connections data.

The Secret’s in the Signals

At PredictLeads, we monitor more than 20 million news sources and close to 100 million companies worldwide, capturing early-stage investment signals in a company’s journey. Spaning from funding rounds and product launches to strategic partnerships, hiring surges, and market expansions.

But we don’t stop at just the news.

Our Connections dataset maps the business relationships that reveal how a company is truly positioning itself in the market – from product integrations and investor ties to vendor agreements and partnerships with industry leaders. This is done by scaning company websites for partner and customer logos, using our image recognition system to match each logo to a verified domain. We also analyze case study pages, testimonials, and “Our Customers” sections to uncover customers, partners, vendors, and investors that often go unreported in press releases or traditional news.

Each connection is a signal of strategic intent: integrations hint at ecosystem alignment, investor relationships point to future funding potential, and vendor or partner deals often precede market entry or expansion. When combined with our other datasets, these connections turn scattered updates into a clear, data-backed narrative of growth — and within that narrative is where the next unicorn often emerges.

The Pattern Every Investor Dreams Of

Picture this:
January > a startup raises a modest $8M Series A.
February > they integrate with Stripe’s API.
March > our company data shows a vendor relationship with Shopify.
April > they expand into London and start hiring engineers at double the previous rate.

If you’re only reading headlines, you’ll miss the story.
If you’re tracking news events and company connections in real time, you’ll see it months before the rest of the market and you’ll be in the room when the deal is still hot.

Why Public Headlines Are Too Late

By the time TechCrunch reports a $100M Series C, the race is already crowded and you’re not ahead of the game, you’re simply keeping pace with everyone else.

To spot opportunities earlier, you need to look where others aren’t. News data reveals unannounced or smaller funding rounds — early startup investment signals that indicates momentum gain. Connections data uncovers the strategic moves behind that momentum, from product integrations and new partnerships to key customer wins and vendor relationships.

Overlay these signals, and you will not wait for the news — you’ll see them coming. The result is an early warning system for hypergrowth, giving you a competitive edge long before the headlines hit.

The Future of Investment Intelligence

In the next five years, the biggest wins in venture won’t go to the investors with the most meetings — they’ll go to the ones who can see conviction in the data before the rest of the market believes it.

The edge won’t come from chasing every funding headline, but from quietly tracking the early indicators of momentum: a new integration with a market leader, a sudden hiring surge in engineering, an unexpected expansion into a high-growth region.

When you can spot these early-stage investment signals as they happen — and connect them into a bigger story — you stop reacting to the market and start anticipating it. Finding the next unicorn and its startup investment signals isn’t about luck; it’s about reading the signals early enough to act, while the opportunity is still invisible to everyone else.

If you’re ready to see what those whispers sound like, let’s talk.

AI Adoption and Sector Shifts Through Job Openings Data
Understanding Pension Funds’ Strategic Shift to Private Markets with PredictLeads’ Data
What Summer BBQs Can Teach Us About Reading B2B Buying Signals

What Summer BBQs Can Teach Us About Reading B2B Buying Signals

July 15, 2025 /

It’s a Saturday in mid-July and you’ve been invited to four different BBQs.

You’re walking through a quiet suburban neighborhood, sunglasses on, sandals flapping. The sun is relentless, the scent of grilled meat hangs in the air… and you’re on a mission. 🥩🧑‍🍳

The first house?
You catch a whiff of burnt tofu and hear someone ask if the kombucha is homemade.

Hard pass.

You keep moving.

A few steps down, you hear music (real music) and spot a lineup of Ford Raptors and a 96 Chefy parked out front. There’s laughter behind a wooden fence, and you catch sight of a green ceramic grill puffing steady smoke, with a line forming around the buffet table.

You don’t need to ask for a menu.
You already know:

This is the one worth joining.

You skip the silent lawns and low-energy gatherings and you:
1. Read the signals.
2. Follow the smoke.
3. Choose wisely.

🎯 In B2B Sales and Investing, the Same Rules Apply

Some companies signal quality before you even step in the door.
Their websites, partners, and public presence give off subtle (and measurable) signs:

  • Logos of well-known brands appear on their sites.
  • Integrations and partnerships get highlighted.
  • Case studies and testimonials drop recognizable names.
  • All of it is smoke – but in this case, smoke that matters.

It’s all smoke! But in this case – it means something.

In B2B such smoke isn’t always obvious. That’s why we built the Connections Dataset at PredictLeads – to read the grill smoke signals at scale.

🔍 Why Logos Matter and Why They’re Hard to Track

To gain credibility, B2B startups often put logos of companies they work with directly on their websites. These show up under sections like:

  • “Our Customers”
  • “Trusted by”
  • “Partners”
  • “Who we work with”
  • Testimonials or Case Study pages

The challenge?
Most of these logos are not backlinked. There’s no easy text trail or hyperlink to follow. A Google search won’t help. Scraping doesn’t cut it.

So we built something smarter.

Logo Recognition Meets Entity Mapping

Our system uses image recognition to detect logos on company websites. Then we match those logos to verified domain names and legal entities.

This enables us to connect:

  • Which company is claiming a relationship
  • Who the other party is (vendor, partner, customer, etc.)
  • Where and how that connection is represented

We don’t just scan the homepage. We parse through case study sections, customer lists, footers, header navs, press pages (anywhere companies hint at collaboration).

Each relationship is then categorized:

  • “vendor” → “Company A is a vendor to Company B”
  • “partner” → “Company A collaborates with Company B”
  • “integration” → “Company A integrates with Company B”
  • “investor”, “published_in”, “parent”, “rebranding” (and more)

We even timestamp when we first and last saw the connection. That means you can prioritize based on recency and relationship type.

🧾 Example: Invoicy → Salesforce

Let’s say a small fintech startup called Invoicy includes a line on their “Customers” page that says:

“Trusted by finance teams at companies like Salesforce, Rippling, and Brex.”

There are no backlinks. Just static logos and a sentence tucked beneath a testimonial.

Our system scans the page, detects the Salesforce logo, maps it to the domain salesforce.com, and parses the surrounding text.

The language >“trusted by finance teams”< suggests that Invoicy is a vendor to Salesforce, likely providing tooling for invoicing, reconciliation, or internal financial workflows.

That gets recorded as:

  • category: “vendor”
  • source_url: the exact URL of the “Customers” page
  • first_seen_at: when the connection was first detected
  • last_seen_at: when it was last confirmed

For a company like Invoicy, being able to show they’re used by a giant like Salesforce is a huge trust signal and even more so when made searchable and machine-readable.

Now sales teams, investors, and analysts can factor that credibility directly into targeting models, scoring frameworks, or due diligence … without ever scraping a webpage by hand.

🔥 What This Means for You

For GTM teams:
 Use vendor and partner relationships to qualify and prioritize leads.
If your ICP already sells to Snowflake, Notion, or Google – that’s your BBQ. Bring your best pitch.

For investors:
 Track which startups are gaining traction with known buyers.
Logos and partnerships are sometimes more honest than press releases.

For growth teams:
 Score accounts based on who trusts them.
If they’ve passed another company’s procurement process, they’re likely enterprise-ready.

🛠️ The Grill is Hot so Start Reading the Signals!

You wouldn’t walk into a BBQ blind. You look for smoke, listen for music, and trust the signs.

The same goes for B2B:

Who they work with tells you who they are.

And PredictLeads helps you see that across millions of companies in real time.

Want a quick walkthrough or test run of the Connections Dataset?
Explore the PredictLeads API

Use Data Enrichment to Filter and Prioritize Prospects
Overcome Market Challenges by Leveraging Investor Networks 🤔
How PredictLeads Company Data powers modern Sales Intelligence & Data Enrichment

How AI Sales Agents Are Transforming B2B Prospecting and How PredictLeads Steps In

July 4, 2025 /

Over the last 18 months, AI agents have gone from experimental prototypes to everyday tools transforming how go-to-market (GTM) teams work. The emergence of AI sales agents has revolutionized traditional methods. Today, AI sales agents can automate lead qualification, personalize outreach, prioritize accounts, and enrich CRMs — at a scale humans simply can’t match.

But here’s the catch: AI is only as good as the data you feed it.
Even the most advanced agent can’t create meaningful output without real-time, event-based company intelligence. AI sales agents benefit greatly from data-driven insights, and that’s exactly where PredictLeads comes in.

What Is PredictLeads?

PredictLeads is a data provider built for modern GTM, sales, marketing, and investment teams. Our infrastructure tracks 92M+ companies globally and provides dynamic signals that go far beyond static firmographics, crucial for AI sales agents.

We capture:

Instead of manually compiling lists, you can plug into our API or webhooks to enrich leads, monitor accounts, and score opportunities in real-time. This is where AI sales agents truly shine.

Why AI Agents Need Event-Based Company Data

Here’s the truth: most AI agents are bottlenecked by poor context.

Whether you’re building in LangChain, AutoGPT, OpenAgents, Pipedream, n8n, or Zapier, many agents still rely on outdated CRMs or static CSVs. That means they lack the situational awareness needed to act intelligently. AI sales agents that have access to real-time data perform best.

PredictLeads changes that. By feeding your AI with real-time hiring, funding, technology, and partnership signals, you create agents that don’t just automate tasks — they anticipate market shifts.

Example: An AI SDR Agent

Imagine this workflow:

  1. AI monitors 10,000 target accounts.
  2. Detects when a company hires a Sales Enablement Manager or adopts Outreach.io.
  3. Generates a personalized intro email mentioning the hiring signal and tech stack.
  4. Pushes the draft to an SDR’s inbox or LinkedIn sequence.

This isn’t theoretical. Teams are already building these automations with PredictLeads + AI agents, exemplifying the true potential of AI sales agents.

Top Use Cases for PredictLeads in AI Workflowsads

Use CaseDatasetAI Output
Outbound AutomationJob Openings + TechnologiesPersonalized emails or LinkedIn messages
Account ScoringNews Events + FundingDynamic ICP fit scoring
CRM EnrichmentCompanies + Website EvolutionAuto-filled account descriptions & tags
Market MappingConnections + Tech DetectionsRelationship graphs and industry maps
Timing SignalsJob ads + Product LaunchesPredictive lead routing and prioritization

Built for AI-First AI Sales Agents Workflows

Our API-first architecture gives AI agents exactly what they need:

  • JSON responses and simple endpoints
  • Daily refreshed datasets
  • Filters by title, tech, domain, industry, revenue, geography
  • Works seamlessly in Pipedream, n8n, Make.com, Zapier, Retool, Hex, or your data warehouse

No login UI. No bloated dashboards. Just raw, real-time signals delivered at scale — the way AI expects them.

Why This Matters in 2025

AI sales agents are getting smarter and more autonomous every month. But autonomy without context is just automation.

By pairing AI sales agents with PredictLeads’ event-based company intelligence, GTM teams gain:

  • Faster awareness of shifts in buyer behavior
  • Sharper targeting based on real-world company events
  • Smarter automation that adapts as markets move

The future isn’t about replacing sales teams with bots. It’s about enabling them with AI sales agents that understand companies as they evolve.

Final Thoughts

At PredictLeads, we believe the next wave of GTM efficiency will come from AI sales agents powered by live market signals.

If you’re building AI tools that need to know what companies are doing — not just who they are — we should talk.

What Summer BBQs Can Teach Us About Reading B2B Buying Signals
How PitchBreeze is Changing the Game in Sales Outreach – An Insider’s Perspective
💜The Untold Story of Data Analytics in Boosting B2B Marketing💜

Using PredictLeads + Polytomic to Power GTM Execution (in HubSpot and Salesforce)

June 13, 2025 /

Modern go-to-market teams rely on timely data to prioritize accounts, launch targeted campaigns, and coordinate sales and marketing outreach. Yet too often, valuable buying signals get buried in spreadsheets or trapped in data warehouses out of reach for the teams who need them most.

That’s why we’re excited to share how teams can now use Polytomic to ingest PredictLeads data and sync it directly into CRMs like HubSpot and Salesforce which enables faster, more data-driven GTM execution.

Why is this worth checking out? 

PredictLeads provides structured datasets that reveal what companies are doing today and not just who they are. One of the most actionable sources is the Jobs dataset, which includes job openings published by companies across regions, industries, and roles.

This data becomes even more valuable when combined with Polytomic’s no-code integration and sync capabilities. Companies can now ingest and filter PredictLeads datasets inside Polytomic and push enriched company profiles directly into downstream systems such as Salesforce or HubSpot.

The result? GTM teams can identify the right accounts earlier and take action faster + without waiting for engineering teams to build pipelines or sync logic (read – lower cost overall).

Some Examples

Below are specific ways companies are already leveraging PredictLeads + Polytomic to accelerate sales and marketing efforts:

1. Identify Companies Expanding Their Marketing Teams

A B2B marketing automation company can use PredictLeads to track companies hiring for roles like “Head of Demand Generation” or “Growth Marketing Manager” across North America.

Using Polytomic, they can filter the dataset to include only companies hiring in target regions or industries and sync those records to Salesforce with enriched fields like job title, location, and department.

This gives SDRs a live list of companies expanding marketing efforts which often leads to indicators of new technology investment.

2. Prioritize Sales Outreach Based on Engineering Hires

A DevOps platform provider can monitor companies hiring for “DevOps Engineers” or “Platform Engineers.”

When PredictLeads detects these job openings, Polytomic can automatically add these companies to a HubSpot static list, assign them to specific reps, or trigger sequences.

This ensures the sales team is focusing on companies building out the exact functions their product supports.

3. Regional Expansion Tracking

A SaaS company entering the DACH market can use PredictLeads to identify existing accounts or net-new prospects that are hiring in Germany, Austria, or Switzerland & even if the companies are headquartered elsewhere.

Polytomic enables dynamic filtering by job location and continuous syncing of these expansion signals into the CRM.

This allows the GTM team to prioritize outreach to accounts actively expanding into target regions.

4. Surface High-Intent Accounts in Product Categories

A cybersecurity firm can monitor job descriptions for keywords like “SOC2,” “Zero Trust,” or “compliance.”

With PredictLeads, these keyword-based filters can be applied at the job posting level. Polytomic can then transform this insight into CRM data fields and automatically assign these companies to tailored marketing or outbound workflows.

How It Works

  1. Ingest PredictLeads data into Polytomic: Use Polytomic’s UI or API to import PredictLeads datasets, including Jobs, Technologies, News Events, or other signals.
  2. Filter and enrich: Apply filters based on department, location, job title, or keywords. Combine with internal firmographic or historical data.
  3. Sync to your CRM or tool stack: Polytomic allows you to push data to HubSpot, Salesforce, Google Sheets, and many other tools (no code required.)
  4. Activate GTM workflows: Enable automated lead scoring, list assignment, alerts, or outbound triggers based on fresh buying signals.

Bottom Line?

This integration bridges the gap between rich external data and actionable CRM workflows. With PredictLeads and Polytomic, go-to-market teams can:

  • Shorten the time from signal to action
  • Prioritize accounts based on real-time hiring intent
  • Reduce reliance on internal engineering resources
  • Improve campaign targeting and SDR productivity

If your team is already using PredictLeads (or considering it) and wants to enable more automated, intelligent GTM workflows, integrating via Polytomic is a fast and scalable option.

To learn more about setting up the integration, reach out to our team at PredictLeads or visit polytomic.com.

How AI Sales Agents Are Transforming B2B Prospecting and How PredictLeads Steps In
PredictLeads Hackathon: Creating Space for Innovation
Use Data Enrichment to Filter and Prioritize Prospects

US-China Tariffs and Shopify Adoption: Signals to Watch

April 9, 2025 /

Trade tensions between the US and China are once again front and center — and this time, the numbers are steep, affecting hiring signals in various sectors.

  • China’s finance ministry has announced an 84% tariff on all goods imported from the US.
  • In response, the US has implemented a 104% tariff on all Chinese goods, which officially took effect today, Wednesday, April 9.

While it remains to be seen whether a last-minute deal will be struck, if these tariffs go into effect as planned, they are expected to introduce significant friction into global ecommerce, logistics, and retail operations, influencing hiring signals in these industries.

At PredictLeads, we’re looking into how this situation might influence two key areas where strategic shifts often show up first:

  • Hiring signals across ecommerce and logistics
  • Technology adoption patterns, particularly around Shopify

Shopify: A platform exposed to global flows

Shopify plays a central role in enabling international ecommerce expansion. It’s widely used by brands that rely on cross-border fulfillment and Chinese manufacturing, making it particularly exposed to the effects of rising tariffs, which also affects hiring signals for roles related to Shopify and ecommerce.

If the new trade restrictions take hold:

  • Some sellers may pause or delay global expansion efforts.
  • Others might shift their infrastructure strategy toward more localized platforms or hybrid solutions.
  • We may see slowed adoption of Shopify among brands operating from or targeting heavily affected markets.

Together with our partners in the market intelligence space, we’re keeping a close eye on the data — particularly around Shopify adoption trends and ecommerce tech stack changes — to better understand how and where these shifts might emerge.

It’s still early, but this is the moment to start watching for new hiring signals.

Hiring signals: A directional early warning

Job data has historically been one of the earliest and most reliable indicators of how companies react to market disruption, often seen in hiring signals.

Over the next several weeks, we’ll be tracking:

  • New job postings that mention Shopify, global logistics, or cross-border ecommerce
  • Changes in hiring behavior tied to international expansion roles
  • Increased focus on domestic operations, regional warehousing and job creations, and supply chain resilience

These subtle shifts in hiring priorities can offer a first glimpse into how companies are adjusting their ecommerce strategies in response to the tariffs.

For market intelligence teams: where to focus

Whether you’re analyzing ecommerce growth, tracking tech adoption, or assessing exposure to global supply chain risk, now is the time to monitor alternative data sources more closely for new signals related to hiring.

We recommend focusing on:

  • Tech stack detections — to identify the adoption slowdown at platforms like Shopify
  • Hiring data — to spot where expansion plans are being paused or redirected due to new hiring signals
  • Regional trends — to see whether companies begin shifting focus toward LATAM, Southeast Asia, or domestic-only models

These early indicators can inform broader trend analysis well before public earnings or analyst reports reveal the full picture.

Stay ahead of the shift

As of April 9, the tariffs are now in effect — and unless there’s a breakthrough soon, the ripple effects across global trade could intensify, signaling new hiring patterns.

If you’re preparing internal research, building trend reports, or want a deeper look into Shopify adoption and ecommerce hiring trends in this context, feel free to reach out. We’re happy to share additional cuts of the data or collaborate on deeper analysis.

This is a developing story, and the signals are just starting to surface.

How Hiring & Tech B2B Sales Signals Help Close More B2B Deals?
AI Adoption and Sector Shifts Through Job Openings Data
Leveraging Hiring Intent

How Job Data Reveals Supply Chain Shifts: Insights from Volkswagen, EVs, and Global Labor Trends

January 6, 2025 /

Supply chains are under great strain, and no… this is not another COVID post🤷. However, we can gain valuable supply chain insights from job data to better understand current challenges.

Welcome to the “bright” present, where Lizard people and Illuminati decide that global labor shortages, geopolitical disruptions, and the rapid push for sustainability are something that businesses worldwide must adapt to.

Traditional data sources like financial reports and production metrics have long been the “go-to” of supply chain analysis. However, an often-overlooked resource – job openings – offers unique and interesting insights into a company’s operational strategies and supply chain shifts.

Let’s be honest -> the market is volatile (to say the least), and consulting firms are looking into multiple data sources to understand what’s happening.
Enter job data, a real-time indicator of a company’s priorities, challenges, and strategic moves. For supply chain professionals, analyzing job openings can provide early warnings of risks, identify opportunities, and gain competitive intelligence to stay ahead.

The Shifting Landscape of Supply Chains

Let’s examine some of the biggest forces shaking up global supply chains today:

  • Geopolitical Instability
    The U.S. CHIPS Act and Europe’s push for local manufacturing are changing trade dynamics. Companies are moving operations closer to key markets, creating both challenges and new opportunities across industries.
  • Labor Shortages
    The U.S. faces a shortfall of 80,000 truck drivers, pushing up costs and causing delivery delays. Globally, competition for talent in critical sectors like warehousing and logistics is intensifying.
  • The EV Transformation
    The automotive industry is racing to electrify, but not everyone is winning. For instance, Volkswagen (VW), Europe’s largest carmaker, faces a 64% profit slump and plans to close plants and cut tens of thousands of jobs due to struggles with EV adoption.
  • Automation and AI Integration
    Companies are heavily investing in robotics and AI to streamline operations. According to recent reports, the global warehouse automation market is projected to reach $35 billion by 2025, expanding at a compound annual growth rate (CAGR) of 12% from 2021 to 2024.

Traditional metrics like quarterly reports lag behind these changes. In contrast, job postings provide unfiltered, real-time insights into how companies are addressing these challenges.

The Volkswagen Crisis and its Supply Chain Wake-Up Call ⏰

The recent Volkswagen (VW) crisis exemplifies how job data can reveal deeper supply chain issues. Facing stiff competition from Tesla and Chinese EV makers, VW’s inability to keep up with market demands has led to plans for plant closures and job cuts (worthy mentions are also EU and its bureaucrats). The automaker’s net profits plummeted by 64% in Q3 2024 compared to the previous year.

The crisis highlights broader challenges:

  • Labor Shifts
    VW subsidiary Audi plans to halt EV production at its Belgium plant, affecting 3,000 jobs. German automakers have collectively shed 46,000 jobs since 2019, with more to come.
  • Technology Gaps
    As competitors like Tesla dominate EV sales globally, VW’s slower adoption of cutting-edge EV technologies has been costly.

Analyzing job data could have provided early warnings, such as fewer postings for high-tech roles or shifts in hiring priorities away from EV development.

Supply Chain Insights with Job Data

Now lets focus on the main event! PredictLeads’ Job Openings Dataset (woop woop 🎊). 

Here is where we have uncovered over 192 million job postings across 1.7 million websites since 2018. Here’s how this data can help out.

  1. Spot Emerging Trends
    A surge in logistics job postings in Mexico aligns with North America’s reshoring efforts.  Fun Fact => Mexico is now the largest importer to the U.S. ($43.7 billion) ahead of China ($39.9 billion). 🌮
  2. Identify Bottlenecks Early
    Aggressive hiring for similar roles in specific regions often signals labor shortages. Logistics companies scrambling to recruit truck drivers last year foreshadowed higher transportation costs and delays.
  3. Evaluate Supplier Resilience
    Job postings reveal supplier priorities. Companies hiring sustainability officers likely align with green initiatives, while those focused on automation are investing in efficiency.

Example: Semiconductor Shortages

The global chip shortage offers another compelling case. Months before the crisis peaked, companies like TSMC and Intel ramped up hiring for “Supply Planning Professionals” and “Procurement Specialists.” Tracking these trends could have allowed businesses to diversify suppliers or stockpile inventory before shortages disrupted industries.

The Advantage

PredictLeads’ Job Openings Dataset offers insights for supply chain professionals:

  • Job Titles and Categories: Understand where companies are investing resources.
  • Salary and Seniority Data: Understand labor market competition and hiring priorities.
  • Geographic Trends: Map hiring hotspots to identify growth or risk areas.
  • Technology Mentions: Spot the adoption of ERP systems, robotics, or AI.

With 7 million active job openings and 53 million new postings detected last year, this dataset provides a real-time pulse on global hiring trends.

Conclusion: Turning Job Data into Strategy

The Volkswagen crisis, semiconductor shortages, and the EV transformation remind us that supply chains are in constant flux (fancy talk for “nobody really knows what’s going on”). Job data offers a proactive, actionable lens into these shifts, enabling businesses to anticipate risks and seize opportunities before competitors.

As global supply chains will continue evolving in 2025, leveraging real-time job data could be the key to staying resilient and competitive.

Questions? PredictLeads is here to help! 🙂

(+ This is our first blog for 2025 → thank you so much for reading 🙏)

AI Adoption and Sector Shifts Through Job Openings Data
Understanding Pension Funds’ Strategic Shift to Private Markets with PredictLeads’ Data
Use Data Enrichment to Filter and Prioritize Prospects

Introducing PredictLeads’ New Technology Detection API Endpoint

August 21, 2024 /

We are excited to announce a new API endpoint from PredictLeads designed to help you discover which companies are utilizing specific technologies. Whether you’re tracking the adoption of CRM systems, cloud computing platforms, enterprise resource planning tools and more, this API offers a powerful way to gather and analyze technology usage data across the web.

How It Works

Our new endpoint allows you to ping a specific Technology ID and receive a detailed list of companies and websites utilizing that technology. This data can be invaluable for market research, sales prospecting, competitive analysis and more.

Example API Endpoint

You can use the following endpoint to start exploring technology detections:

https://predictleads.com/api/v3/discover/technologies/[TECHNOLOGY ID]/technology_detections?api_token=[API_token]&api_key=[API_Key]

Here are some Technology IDs you can use to test the API:

What You Get

When you query this endpoint, the API returns data about where the technology has been detected, including:

  • Company Information: Details about the company using the technology.
  • Subpage Detections: Specific subpages where the technology has been found.
  • Technology Details: Information about the technology, such as its name, description, and category.

Sample cURL Request

Here’s an example of how you can make a request using cURL:

curl –request GET \

  –url https://predictleads.com/api/v3/discover/technologies/70392f20-b531-53cc-afa6-26416c0d582d/technology_detections \

  –header ‘X-Api-Key: {your_api_key}’ \

  –header ‘X-Api-Token: {your_api_token}’

Additional information can be found in our docs “here”. 

Interested in Trying It Out?

We’re offering 100 free API calls to anyone who wants to test this new endpoint. Sign up at PredictLeads and start exploring + Feel free to let us know if there are any specific technologies or IDs you’d like to check the coverage of.

Note on Development

Please note that we are continually improving this endpoint, and your feedback is essential. If you encounter any issues or have suggestions, feel free to reach out to our support team.

Technology Data Snapshot

  • Technologies Tracked: ~15,000
  • Technology Adoptions Detected Since 2018: ~636 million
  • Websites Tracked: ~47 million
  • Technology Identifications Last Month: ~18 million
  • Technology Identifications Last Year: ~193 million

We look forward to seeing how you use this new feature to enhance your business intelligence and decision-making processes!

AI Adoption and Sector Shifts Through Job Openings Data

July 9, 2024 /

Artificial intelligence is changing the job market, prompting significant shifts in workforce needs across various sectors. By analyzing job postings, investment companies can gain insights into which industries are reducing their hiring for roles likely to be automated. This helps them understand potential revenue impacts and growth opportunities.

Detecting AI Adoption Trends
AI tools are increasingly integrated into business functions, ranging from data analysis to customer service and legal assistance. For example, paralegals, traditionally performing research and document review, are being replaced by AI systems that can quickly and accurately handle these tasks. This trend is highlighted in Nexford University’s article “How Will Artificial Intelligence Affect Jobs 2024-2030,” which underscores the growing use of AI in roles previously performed by humans. Monitoring job postings can reveal decreases in hiring for such roles, indicating a shift towards AI-driven solutions.

Strategic Insights for Investment
Investment companies must stay ahead of market changes to make informed decisions. A decline in job openings for traditional roles, such as customer service representatives or paralegals, in sectors like customer service, sales, and legal services can signal a move towards AI automation. This information is crucial for identifying industries at risk of revenue loss due to a lack of automation foresight, helping investors focus on more promising areas.

For example, companies like Google and Duolingo are already replacing human roles with AI technologies. Google has integrated AI into its customer care and ad sales processes, while Duolingo uses AI for content translation, reducing the need for human contractors.

Economic Impact of AI
The economic implications of AI are substantial. A McKinsey report predicts that AI could add $13 trillion to global economic activity by 2030, primarily through labor substitution and increased innovation. However, this growth comes with job displacement. Monitoring job opening trends helps investment firms gauge which companies and sectors are reducing their workforce due to AI, identifying potential risks and opportunities.

Recent examples include:

Understanding AI adoption through job postings allows investment companies to anticipate market shifts and focus on high-growth sectors. Sectors such as AI development, advanced manufacturing, and healthcare innovation are likely to attract more investment due to their proactive adoption of AI technologies. This foresight helps investors mitigate risks and capitalize on new growth opportunities.

Additional Data from the ADP National Employment Report
The ADP National Employment Report for June 2024 provides a comprehensive overview of job trends. According to the report, private employers added 150,000 jobs in June, marking a slowdown in job creation for the third straight month. “Job growth has been solid, but not broad-based. Had it not been for a rebound in hiring in leisure and hospitality, June would have been a downbeat month,” said Nela Richardson, Chief Economist at ADP​ (ADP Media Center)​.

This data underscores the importance of monitoring employment trends to understand the broader economic impact of AI and inform strategic investment decisions.

The chart titled “ADP Employment: Establishment Size Year-over-Year Percent Change” tracks the year-over-year percentage change in employment across different establishment sizes from 2011 to 2024. 

Here are some key points:

  • Trend Analysis: The chart illustrates fluctuations in employment growth across different establishment sizes over the years. A notable drop is observed around 2020, corresponding with the COVID-19 pandemic’s impact on employment. Post-2020, there is a marked recovery, with larger establishments (500+ employees) showing a more robust recovery compared to smaller establishments.
  • Recent Trends: As of June 2024, the growth rates have stabilized, though smaller establishments (1-19 employees) show slower growth compared to larger establishments. This indicates that larger companies are recovering and possibly investing more in automation and AI technologies, while smaller businesses are facing more challenges.

This chart helps visualize the employment dynamics and how different-sized businesses have been affected over the years, providing valuable context for understanding the broader economic landscape and the impact of AI on employment.

For more detailed insights and statistics, the full ADP Employment Report is available here.

Conclusion

By analyzing job openings data, investment companies can gain valuable insights into AI adoption trends and their impact on various sectors. This approach helps identify industries reducing traditional roles due to AI, enabling better-informed investment decisions. Utilizing datasets like those from PredictLeads can provide the detailed, real-time insights needed to stay ahead of market shifts, mitigate risks, and seize growth opportunities in an AI-driven economy.

  • Job Openings Data: Since 2018, there have been 166 million job openings detected.
  • Data Availability: Job openings data is available for 1.6 million websites.
  • Recent Trends: Last month, there were 5 million job openings, and over the past year, approximately 50 million job openings were recorded globally.
  • Active Job Openings: Currently, there are about 7 million active job openings uncovered by PredictLeads.

These statistics underscore the vast amount of data available to track AI adoption and its effects on the job market, providing investment firms with the necessary tools to make informed decisions.

Case Study: InReach Ventures & PredictLeads

October 12, 2021 /

InReach Ventures uses technology to help scale venture capital and make
investments in early stage startups throughout Europe. They built their own
proprietary software and developed a new model of investing to discover and invest in the most promising startups.

There’s a few major data challenges VC’s often face including data quality and the
time, effort and cost it takes to acquire or crawl data.

Here is a short interview with Ben Smith the Co-Founder / Partner / CTO of InReach Ventures and how PredictLeads company intelligence data helps InReach Ventures discover new companies and track growth signals for companies of interest.

How do you identify growing companies?

“InReach combines data from lots of different data sources. Some of that is around signals on how a company is performing like PredictLeads data, which helps us to find startups from all over Europe. This data, along with other types, allows us to look at how companies are growing, whether they’re growing their team, if they’re getting new customers or new business connections or partnering with different companies. “

PredictLeads

Are there any specifics on how PredictLeads data is being used?

“With job postings in particular, outside the general idea that a company is growing positively, it gives us an idea whether there is real substance behind a company. Seeing that a company has a product and engineering DNA and are looking to invest more in it is a positive.”

What challenges were you able to overcome with PredictLeads data?

“It’s all about how best we leverage our own product and engineering resources. Having the InReach team focus on what we’re good at and working with partners that are better than us in areas is an important point of leverage.”

Why did you decide to subscribe to PredictLeads data?

“PredictLeads helped us by doing some of the work that we had always planned but never been able to prioritise. Finding news events around a particular company and identifying company customers through logos/connections is really interesting for us and also it’s something that takes significant time and effort to get right.”

What’s your view on the VC industry using data and what are the biggest challenges on the horizon in the industry?

“The value of data, machine learning and a data driven approach to capital is an ever growing trend. The point of venture capital is to fund innovation and how much innovation is happening in venture capital in the past 10 years is very limited. I think there is a change now where data and software is being seen as a way for venture firms to innovate their model. The issue that traditional VC firms first face is that culturally at their core, they are not a technology firm but a professional services organisation. Where we think we have an advantage is that we started as a technology, product and engineering organisation, taking a very data driven approach to venture capital. That’s where we think we will long term hold the advantage because we started doing this earlier. Traditional venture capital will start to utilise data over time, but at their core they are not tech or engineering organisations. Short term, data and tech will play a broader role in terms of the whole industry using it as it’s becoming more and more of a buzz and as data is becoming more demanded.”

What are some of the trends in Venture Capital?

“My co-founder and Investment Partner Roberto layed out the the data trend in VC well in his blog post: The Full Stack Venture Capitalist“

How do you see PredictLeads to help you achieve your long term goals?

“Two things PredictLeads does and will continue to do is that it helps us discover that a startup exists in the first place and then tells us whether there’s something interesting happening that we might want to talk to them about.”

© 2025 PredictLeads Blog

Theme by Anders Noren — Up ↑