Tag: prospecting (Page 1 of 3)

How to Find Companies Migrating to Cloud Data Warehouses Using Technology Detection Signals

Cloud data warehouse migrations are one of the clearest signs that a company is about to spend money.

When a team moves to Snowflake, BigQuery, Redshift, Azure Synapse, or Databricks, they rarely stop there. New warehouse usually means:

  • New ETL or ELT tools
  • New BI layer
  • Data governance upgrades
  • Security reviews
  • Consulting support
  • Cloud cost optimization

In other words, budget opens up.

The problem is timing and most B2B teams find out about this a bit too late.

This guide explains how to identify companies that are migrating right now using time-based technology detection signals — and how to turn that into a repeatable targeting workflow.

Detect companies transitioning from legacy infrastructure to Snowflake, BigQuery, Databricks, or Redshift using verified technology detection signals.

Why Active Cloud Migrations Are Hard to Spot

Companies don’t announce:
“Today we started migrating our warehouse.”

Migration happens quietly.

Engineers spin up environments.
Pipelines run in parallel.
Legacy systems stay live during transition.

By the time a blog post or press release appears, the migration is often done.

Surface Signals Are Too Slow

Common approaches don’t work well:

  • Job postings show up mid-project
  • Press releases come after contracts are signed
  • Sales discovery depends on someone replying

All of these identify accounts after vendor decisions are already in motion.

If you want leverage, you need earlier evidence.


What Early Migration Signals Actually Look Like

The earliest reliable signal is simple:

A cloud data warehouse appears in a company’s tech stack for the first time.

Not three years ago.
Not “currently detected.”
But newly detected.

That timestamp matters because migration is not an event. It’s a timeline.


Why Cloud Warehouse Migration Signals Matter Commercially

Warehouse migrations don’t happen in isolation.

When a company moves from on-prem databases to Snowflake, they often re-evaluate:

  • ETL (Fivetran, Airbyte, Stitch)
  • BI (Looker, Power BI, Tableau)
  • Reverse ETL
  • Data observability
  • Governance tools

This creates a 3–6 month window where architecture decisions are still flexible.

If you engage during that window, you influence the stack.

If you engage after it closes, you compete on price.

That’s the difference.


Step-by-Step: How to Find Companies Migrating to Cloud Data Warehouses

Here’s the practical workflow.

Step 1: Define What “Migration” Means for You

Start by defining scope clearly.

Are you looking for:

  • Any new Snowflake detection?
  • Companies switching from Oracle or Teradata to cloud?
  • BigQuery adoption among mid-market SaaS?
  • Databricks expansion inside enterprise accounts?

Without a defined scope, you’ll generate noise.

Cloud data warehouse migration signals filtered by timestamp and routed into CRM and outbound targeting workflows.
Filter recent cloud data warehouse detections and route migration signals directly into CRM, outbound sequencing, and account scoring workflows.

Step 2: Identify First-Time Detections

Filter for companies where a warehouse platform appears for the first time.

Example logic:

  • Technology = Snowflake
  • first_seen_at exists
  • No prior Snowflake detection historically

This removes long-time users and isolates change events.


Step 3: Apply a Recency Window

Now narrow by time.

Filter first_seen_at within:

  • Last 30 days (aggressive targeting)
  • Last 60 days (balanced)
  • Last 90 days (broader coverage)

Why?

Because a warehouse first detected 2 years ago is not a migration signal anymore. It’s just part of the stack.

Recency separates momentum from history.


Step 4: Check for Parallel or Legacy Systems

Migration often means coexistence.

If you detect:

  • Snowflake + Oracle
  • BigQuery + on-prem SQL Server
  • Databricks + Hadoop

That overlap suggests transition.

If legacy tech disappears over time (based on last_seen_at), you likely caught a replacement cycle.

That’s stronger than a single detection.


Step 5: Segment by ICP

Now layer firmographics:

  • Company size
  • Revenue
  • Industry
  • Geography
  • Funding stage

You can also segment by data maturity:

  • Number of data tools detected
  • Presence of ETL + BI + warehouse
  • Cloud provider preference

This prevents wasting time on companies that don’t fit your model.


Step 6: Prioritize Based on Stack Complexity

Not all migrations are equal.

High-priority accounts often show:

  • Recent warehouse first_seen_at
  • Multiple data tools
  • Legacy tech still present
  • Active hiring for data roles

That combination usually means real architectural change.


How Technology Detection Data Makes This Possible

You cannot do this manually.

Technology detection datasets track which tools are used by which companies — and when those tools were first and last seen.

Two fields matter most:

  • first_seen_at
  • last_seen_at

If Snowflake first appears 45 days ago and is still detected, that’s likely active rollout.

If Teradata detection disappears shortly after, that suggests replacement.

This timeline view turns static tech stacks into motion data.

That’s the difference between “uses Snowflake” and “just started using Snowflake.”


Multi-Signal Analysis Reduces False Positives

One detection can mean many things.

But multiple coordinated detections strengthen the signal.

For example:

  • New Snowflake detection
  • New Fivetran detection
  • BigQuery API endpoints detected
  • Tableau usage declining

That cluster suggests intentional transformation.

Single-point snapshots miss this.

Longitudinal tech data reveals it.


Common Mistakes Teams Make

Mistake 1: Treating “Uses Snowflake” as Intent

Usage does not equal migration.

Without first_seen_at analysis, you’re targeting stable accounts.

Mistake 2: Ignoring Time

Migration is a process.
Static lists don’t capture direction.

Mistake 3: Not Connecting Signals to GTM

If migration data sits in a spreadsheet, it’s useless.

It should trigger:

  • CRM enrichment
  • Outbound sequences
  • Account scoring
  • Partner alerts

Speed matters. A 90-day window closes fast.


Turning Migration Signals Into Revenue

Cloud warehouse migrations create rare moments of openness.

During that window, teams are:

  • Re-architecting
  • Reviewing vendors
  • Allocating budget
  • Rewriting workflows

If you align outreach to that moment, relevance increases immediately.

Instead of:

“Just checking if this is relevant…”

You can say:

“Saw you recently adopted Snowflake. We help teams optimize ELT pipelines during warehouse transitions.”

Now you’re turing a cold pitch into context.


Final Thought and a Quick Word About PredictLeads

PredictLeads helps B2B teams identify companies migrating to cloud data warehouses by tracking technology detections over time.

Instead of static tech stack snapshots, you get access to:

  • First-time detections of Snowflake, BigQuery, Redshift, Databricks, and more
  • first_seen_at and last_seen_at timestamps
  • Company-level technology change signals
  • API access for automated targeting
  • And much much more

By monitoring when a cloud data warehouse is first detected, you can identify companies actively migrating and not those who adopted years ago.

If you want to find companies moving to Snowflake or BigQuery before the rest of the market notices, PredictLeads provides the underlying technology detection data to make that possible.

PredictLeads data provider showing real-time company technology detection and cloud migration signals with book a demo button.
Use PredictLeads to monitor real-time technology changes and identify companies migrating their data infrastructure.

How to Find Companies Hiring Product Managers Using Job Openings Data

Most B2B teams discover new opportunities too late and usually after budgets are already approved, vendors are shortlisted, and internal decisions are already locked in. At that point, you’re competing on price and familiarity instead of relevance and timing.

One of the earliest and most reliable buying signals appears much sooner: when a company starts hiring Product Managers.

Product management hiring often precedes major product initiatives, tooling decisions, and external partnerships. If you can identify these signals early, you can engage accounts before buying decisions are finalized.

This guide explains how to find companies hiring Product Managers using job openings data — and how to turn that insight into actionable account prioritization for sales, marketing, and research teams.

Illustration showing Product Manager hiring as an early-stage business signal beneath the surface, preceding roadmap planning, team formation, budget allocation, and vendor shortlisting.
Hiring Product Managers is one of the earliest signals of upcoming product investments, team expansion, and vendor selection.

Why hiring Product Managers is a high-intent business signal

Hiring decisions reflect strategic intent. When companies invest in product leadership, they’re signaling change — whether that’s launching new products, scaling existing platforms, or professionalizing internal processes.

Early-stage startups typically rely on founders or engineers to manage product decisions. When a company begins hiring dedicated Product Managers, it often indicates a growing or increasingly complex product surface, new product lines or feature expansion, and a shift from ad-hoc development to structured roadmapping.

For B2B vendors, this usually means upcoming investments in analytics, infrastructure, UX, experimentation, and customer feedback tools.


What Product Manager roles reveal about roadmaps and tooling

Not all Product Manager roles are the same. Job descriptions often reveal far more than just headcount growth.

They can indicate specific focus areas such as growth, platform, AI, payments, or enterprise use cases. They also reveal how product teams collaborate with design, data, and engineering, and which tools or workflows are critical to success.

Mentions of analytics platforms, experimentation frameworks, research tooling, or CI/CD processes provide strong clues about upcoming vendor needs and partnership opportunities.


Why timing matters more than targeting alone

Engaging a company while it’s still assembling its product team puts you upstream of buying decisions. At this stage, teams are defining workflows, selecting tools, and choosing long-term vendors.

Once the product organization is fully staffed and processes are set, most purchasing decisions are already locked in. Timing, in this case, becomes just as important as targeting.


A step-by-step workflow to find companies hiring Product Managers

Turning job postings into a reliable buying signal requires structure.

Start by defining which Product Manager roles actually match your ideal customer profile. Focus on relevant titles such as Product Manager, Senior PM, Group PM, or Head of Product, along with specializations like Growth, Platform, Technical, or AI. Narrowing by company type — for example, B2B SaaS versus B2C — helps eliminate noise early.

Next, refine your dataset by filtering for Product or Engineering departments, seniority levels that indicate decision-making authority, and locations aligned with your go-to-market coverage. This removes internships, duplicate postings, and roles outside your sales region.

From there, look beyond individual job listings and focus on hiring velocity. Multiple PM roles opened in a short timeframe, re-posted or expanded listings, and sudden increases in product headcount all signal urgency and internal momentum.

Context matters as well. Segment companies by growth stage and hiring pattern. Startups hiring their first Product Manager are typically formalizing product strategy, while scale-ups building multi-layered product teams are preparing for rapid growth. Enterprises hiring senior product leadership often signal new product lines or major transformations.

Finally, prioritize accounts based on role seniority and team structure. Senior hires such as Principal Product Managers or Heads of Product usually correlate with strategic initiatives and budget ownership. Companies building entire product teams at once should rank higher than those filling a single replacement role.

Visual representation of Product Manager hiring detected across multiple companies, highlighting how job openings data can be used to enrich CRM lists, trigger outbound campaigns, and support market research.
Detect Product Manager hiring across companies and turn job openings into actionable go-to-market signals.

Aligning product hiring signals with your go-to-market motion

Once identified, product hiring signals should be mapped directly to your GTM strategy.

Sales teams can time outreach around active hiring windows. Marketing teams can tailor messaging to product expansion, platform maturity, or scaling challenges. Research teams can use hiring data to track emerging product trends across industries.

The key is treating hiring data as a trigger, not just a filter.


Using job openings data with PredictLeads

Job openings data becomes far more powerful when it’s structured, historical, and enriched.

PredictLeads tracks job postings across millions of companies, making it possible to identify organizations actively hiring Product Managers without manual job board scraping. Instead of static snapshots, hiring activity can be tracked over time, allowing teams to distinguish between one-off roles and sustained investment in product teams.

When product hiring data is combined with other company-level signals — such as headcount growth, funding events, or geographic expansion — it becomes a reliable indicator of upcoming spend and operational change.

This data can be used to enrich CRM and ABM account lists, trigger outbound sequences based on hiring activity, and support market research and competitive analysis.


Common mistakes when using Product Manager hiring data

Despite its value, job openings data is often misinterpreted.

Treating all Product Manager roles as equal is a common mistake. A junior replacement hire does not carry the same weight as a new product leadership role.

Relying on single job posts without considering velocity can also lead to false signals. One listing may be outdated, paused, or experimental. Momentum is what signals real intent.

Ignoring firmographic context — such as company size, stage, and geography — makes it easy to overestimate deal potential or misread urgency.

Finally, many teams act too late. The highest-intent window is during active hiring, not months after onboarding is complete.


Turning Product Manager hiring signals into action

Companies hiring Product Managers are telling you something important: they’re investing in product.

By systematically analyzing job openings data, teams can surface high-intent accounts earlier, personalize outreach more effectively, and align go-to-market efforts with real business momentum.

When used correctly, Product Manager hiring data isn’t just a recruiting signal — it’s a strategic advantage.

About PredictLeads

PredictLeads is a company intelligence data provider used by B2B teams to detect early buying signals across millions of companies. We help sales, marketing, and research teams act on hiring, growth, and expansion data to engage accounts at the moment intent is forming.

PredictLeads helps visual promoting real-time company data to identify hiring, expansions, funding events, and partnerships, with a call to book a demo.
Use real-time company signals to act earlier and engage accounts when buying intent is forming.

5 AI Agents you can connect with PredictLeads to automate smarter (and skip the boring stuff)

Most automation tools are only as good as the data you feed them. PredictLeads focuses on providing that missing piece – clean, structured company data that can actually make automations useful. The integration with AI automation tools offered by PredictLeads allows you to surface things like job openings, tech stacks, funding events, and company news, so your workflows can react to what’s happening in real-time. Whether you’re using APIs or no-code integrations, PredictLeads helps you gain valuable insights.

You can connect PredictLeads to your favorite AI agents and automation tools such as Activepieces, n8n, Make.com, Zapier, and Bardeen.ai to make your workflows actually smart, not just automated.

Example of an automated workflow combining PredictLeads data with OpenAI and Google Sheets through Activepieces.

1. Activepieces

If you haven’t tried Activepieces, think of it as open-source Zapier that’s simple and powerful.

The new PredictLeads integration lets you pull company insights and trigger actions across hundreds of apps. You can:

  • Enrich CRM records when a new company domain shows up.
  • Post in Slack when one of your tracked companies adds several new job openings.
  • Notify your sales team when PredictLeads detects a new funding event using PredictLeads integration with AI automation tools.

Available PredictLeads actions:

  • List Companies
  • List Job Openings
  • Get Company by Domain
  • Retrieve Companies by Technology
  • Get News Event
  • List Company News Events
  • List Technologies by Domain
  • List Connections
  • Make Custom API Calls

You can start experimenting with it directly on Activepieces. No code, no setup pain.


2. n8n

n8n is great when you want more logic and control in your automations.

This tool allows for PredictLeads integration with AI automation features to blend seamlessly with CRMs, Slack, Google Sheets, or your custom systems.

Example ideas:

  • Automatically find companies hiring for “AI Engineers” and send them to your CRM.
  • Get alerts when portfolio startups start scaling their teams.
  • Filter PredictLeads data to show only companies that match your target tech stack.

n8n is for those who like to see the inner workings of their automation instead of just hitting “run.”


3. Make.com

Make.com (formerly Integromat) is perfect if you prefer visual workflows.

By connecting PredictLeads, you can:

  • Pull new job openings, check if they fit your ICP, and push them into your CRM.
  • Watch for technology changes like new marketing tools detected on company websites.
  • Create a live dashboard that tracks companies hiring for data roles in your target region through PredictLeads integration with AI automation tools.

Make.com turns PredictLeads data into visual, flowing automations that are easy to understand.


4. Zapier

Zapier might be the old classic, but it’s still the easiest starting point for most.

You can set up simple PredictLeads automations such as:

  • Adding new job openings to Google Sheets.
  • Sending outbound leads to Notion when they meet specific filters.
  • Getting Slack notifications when a company is mentioned in PredictLeads News Events with the advantages of PredictLeads integration with AI automation tools.

Zapier works great when you want to get started quickly and don’t need complex logic.


5. Bardeen.ai

Bardeen.ai is an AI agent that automates your browser.

Combine it with PredictLeads data and you can:

  • Scrape company lists from the web and enrich them instantly.
  • Build prospect lists based on who’s hiring and send them into your CRM.
  • Write personalized outreach messages using PredictLeads company data integrated with AI automation tools.

It’s the easiest way to use PredictLeads data directly from your browser while staying in flow.


TL;DR

PredictLeads gives you the data.
Activepieces, n8n, Make, Zapier, and Bardeen give you the automation.

Put them together and you can:

  • Build lead lists automatically.
  • Track hiring trends across your ICP.
  • Get alerts before competitors do.
  • Automate the parts of prospecting that nobody enjoys.

If you want to test it out, check the PredictLeads integration on Activepieces or dive into the full API docs at docs.predictleads.com/v3

What Summer BBQs Can Teach Us About Reading B2B Buying Signals

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

How 🟣PredictLeads + Pipedream🟢 Help Founders Score and Reach Out to Leads with Relevant, Timely Signals

Most companies already have a massive list of leads covering some tens of thousands of domains, contacts, or accounts collected from various sources. So sometimes – the problem isn’t finding leads – is knowing when and why to reach out. 

That’s where PredictLeads comes in

Our API lets you ping domains in your lead list and enrich each company with fresh, real-time signals like hiring activity, new partnership, funding rounds, or changes in their tech stack. These signals help you score leads and create meaningful, personalized outreach triggers so your sales team can contact prospects at the exact right moment. 

By integrating PredictLeads data with Pipedream, your engineering team can now build fully automated workflows that: 

  • Enrich leads as they come in
  • Automatically score and prioritize based on recent business events 
  • Trigger personalized email sequences or alerts to sales reps 
  • Do all this without costly manual data processing or building complex pipelines
PredictLeads logo above a headline asking 'What do you want to automate with PredictLeads?' followed by a subtext describing AI agent deployment with over 2,500 connected apps

Why This Matters 🤔

Many founders tell us: “We have a huge lead list, but our outreach is not working. We don t know who to call first or what message to send.” 

The truth is that generic outreach is too old or irrelevant and doing that leads to wasted time and budget. But if you can layer in contextual, timely data like “This company just raised $5M“, or “They started hiring SDRs last week” suddenly your outreach becomes relevant, compelling and timely.

How the PredictLeads + Pipedream Workflow Works

  1. Upload or connect your lead list of thousands of company domain names. 
  2. Use PredictLeads API via Pipedream to ping each domain and enrich it with signals such as recent funding, hiring, new partnerhips or tech adoption.
  3. Pipedream picks up this data and runs workflows to:
    • Score each lead based on your criteria
    • Create or update records in your CRM 
    • Send tailored outreach messages via email or LinkedIn 
    • Alert sales reps in Slack or other tools 
  4. Sales teams receive prioritized leads with a strong reason to reach out making outreach timely, efficient, and high impact.

Example Use Case: Outreach After Funding or Partnership & Hiring Events

Your company has a list of 30,000 leads collected from marketing and old data vendors but it’s unclear which leads are hot right now.

You set up PredictLeads API to check these domains daily and flag those who: 

  • Raised a funding round in the past month 
  • Recently hired sales or marketing roles 
  • Started using new technologies relevant to your product 
  • Formed a partnership with a F500 company (signaling buying power)

Whenever a lead matches your triggers, Pipedream runs your workflow scoring that lead higher, sending a personalized email sequence referencing their recent event, and notifying your sales team to act fast.

List of popular PredictLeads API actions with options to get technologies, look up companies by domain, retrieve companies by technology, and access news events by domain each with links to documentation and a 'Try It' button

Why Use PredictLeads + Pipedream Together?

  • Low cost & no heavy engineering to get automated
  • Real-time enrichment without building your own data pipelines. 
  • Personalize outreach at scale to reach to the right people with the right message at the right time. 
  • Create flexible workflows integrated with any CRM, marketing platform, or communication tool via Pipedream extensive connectors.

If you already have a lead database stop wondering who to call next, know when to call & get started with PredictLeads + Pipedream now, and unlock 100 free API credits to experiment.

PredictLeads and Pipedream logos above an illustration of two stylized hands forming a handshake, symbolizing integration and partnership between the platforms
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