44 Data Sources to Find Prospects in Any Niche

Finding the right prospects is the hardest part of outbound. Not the emails. Not the follow-ups. The sourcing.

We run outbound for 70+ B2B companies at ColdIQ, and the single biggest variable in campaign performance is list quality. A mediocre email to a perfect list will outperform a perfect email to a mediocre list every time.

The problem is that most teams default to one or two databases and call it a day. They search LinkedIn Sales Navigator, export a list, and wonder why reply rates sit below 1%. The best prospects often live outside the standard databases, and reaching them requires knowing which sourcing method fits which situation.

This guide walks through 44 data sources across seven sourcing methods, explains when to use each one, and shows you how to combine them into a sourcing stack that actually matches your ICP.

1. AI Sourcing Agents

Standard databases force you into predefined filters. Job title, industry, headcount, location. That works when your ICP fits neatly into those boxes, but many target audiences do not.

AI sourcing agents work differently. You describe what you are looking for in plain language, and the agent searches the web to find matches. This is the method to reach for when your ideal prospect is something like "B2B SaaS companies under 50 employees that recently launched an AI feature" or "marketing agencies that specialize in healthcare clients." Try expressing that through dropdown filters.

When to use this method: Your ICP is defined by behaviors, attributes, or combinations that standard database filters cannot capture. You need a smaller, highly targeted list rather than a massive export.

How to get started:

Start by writing a clear description of your ideal prospect. Be specific about what makes a company a fit. The more precise your prompt, the better the results. "Companies that sell software" will give you garbage. "Series A B2B SaaS companies in the US that sell to HR teams and have fewer than 100 employees" will give you a usable list.

Clay supports agentic sourcing through Claygent. Inside a Clay table, you can use Claygent to browse the web and pull structured data from unstructured sources. The workflow is: describe what you want, let Claygent research it, then enrich the results with additional Clay columns for email, phone, and firmographic data. This keeps everything in one platform.

Exa takes a different approach with Websets. You write a natural language prompt, and Exa crawls the web semantically to find companies and people that match the meaning of your description. Exa is strongest when you need to find companies based on what they do rather than what industry code they fall under.

Relevance AI lets you build multi-step agentic workflows. You can chain together research, validation, and enrichment into a single agent. This is useful when your sourcing requires several sequential steps, like finding companies, checking if they meet specific criteria, and then pulling contact data for the right people.

Perplexity and Manus AI work well for exploratory research before you build a full list. Use Perplexity to map out a market segment, identify the key players, and understand the landscape. Use Manus AI when the research task requires multiple steps, like finding companies in a niche, visiting their websites, and extracting specific data points.

Airtop handles browser-based sourcing where the agent needs to navigate websites, interact with pages, and extract data from sites that require login or multi-step navigation.

Choosing between agents: If you already use Clay, start with Claygent since your data stays in one place. If you need pure list generation from a prompt, Exa Websets is the fastest path. If your sourcing requires complex multi-step logic, Relevance AI gives you the most flexibility.

2. Standard B2B Databases

Databases are the fastest way to build a large list. If your ICP maps cleanly to standard filters like job title, location, employee count, industry, and revenue, this is where you start. You can go from zero to a list of 5,000 prospects in under an hour.

When to use this method: Your target audience is defined by standard firmographic and demographic attributes. You need volume. You want speed.

How to get started:

Pick the database with the deepest coverage for your specific ICP. Do not just default to the biggest one. A database with 275 million contacts is useless if your target segment is poorly covered.

LinkedIn Sales Navigator is still the most comprehensive source for finding people by role, seniority, and company attributes. Boolean search is powerful here. Instead of using the basic filters alone, combine them with boolean strings to narrow your results. For example, searching for titles that include "VP Sales" OR "Head of Sales" NOT "Sales Development" will give you cleaner results than selecting a broad seniority filter. Save your searches as lead lists and export them through tools like Wiza or Clay.

Apollo offers one of the largest B2B databases with over 275 million contacts. The filtering is granular, and the built-in sequencing means smaller teams can source and send from a single platform. Apollo is a good starting point if you do not have a separate enrichment and sending stack yet.

Prospeo provides accurate B2B contact data with particularly strong coverage across European markets. If you are targeting EMEA, Prospeo often finds contacts that US-centric databases miss.

Wiza pulls contact data directly from LinkedIn profiles, which means the data tends to be more current than databases that rely on older records. It integrates natively with Sales Navigator, so the workflow is: build your list in Sales Nav, export through Wiza, and you have verified emails attached.

DiscoLike takes a different approach. Instead of searching by filters, you input your best existing customers and it finds lookalike companies. This is useful when you know your ICP by example but struggle to define it through standard attributes.

For niche segments, specialized databases fill gaps that the generalists miss:

Openmart is purpose-built for SMBs and local businesses. If your ICP includes restaurants, retail stores, clinics, or other local service providers, Openmart surfaces data that LinkedIn and Apollo simply do not have. Most B2B databases are blind to businesses without a strong online presence.

Influencers.club focuses specifically on creator and influencer data. If your product targets content creators or you are building influencer partnerships, this is the only database purpose-built for that audience.

Decision framework: Start with LinkedIn Sales Navigator for account-based outreach where you need precision. Use Apollo when you need volume and speed from a single platform. Add Prospeo for European coverage. Use Openmart for SMB and local business targeting. Use DiscoLike when you have great customers but cannot articulate why they are great through filters.

Using a combination of these databases and a bit of AI, we built a tool that finds the right people at target companies. You can use it to identify decision-makers at any company, for free:

People Finder Tool

3. Technology Targeting

If your product replaces, integrates with, or complements a specific technology, technographic data is one of the highest-signal prospecting approaches available. A company already using a competitor or complementary tool tells you two things: they have budget allocated for this category, and they understand the problem you solve. That eliminates the two biggest objections in outbound.

When to use this method: You sell a product that relates to other software. You want to target companies using a competitor, a complementary tool, or a specific technology stack. You need a concrete reason to reach out that is not generic.

How to get started:

First, map out your technology ecosystem. Write down three lists: direct competitors your product replaces, complementary tools your product integrates with, and technologies that signal a company is in your buying category. Each of these lists becomes a separate prospecting campaign with different messaging.

PredictLeads provides technographic data through an API that integrates directly into Clay workflows. What makes it particularly useful is the adoption and churn data. You can find companies that recently adopted a technology (they are investing in this category) or recently dropped one (they are actively looking for a replacement). A company that just churned from a competitor is one of the warmest prospects you will ever find.

TheirStack takes a different angle by monitoring job postings for technology mentions. If a company is hiring a "Salesforce admin" or a "HubSpot specialist," that tells you exactly which tools they use and where they are investing. Job postings often reveal technology decisions before they show up in any crawling database. This is an underused signal that most teams overlook.

BuiltWith scans websites to detect the technologies running behind them. This covers analytics tools, CMS platforms, marketing automation, payment processors, and more. It is strongest for identifying web-facing technologies. If you sell something that appears on a website (live chat, analytics, A/B testing, CMS), BuiltWith is your primary source.

BuyerCaddy combines multiple data signals to identify companies using specific software, making it easier to build targeted lists based on tech adoption patterns.

Practical workflow: Use BuiltWith to find companies using a competitor's web-facing technology. Pull the list into Clay. Enrich with PredictLeads to check for recent adoption or churn signals. Use TheirStack to identify companies hiring for roles related to the technology category. Cross-reference the lists to find companies showing up in multiple sources. Those are your highest-priority targets.

We built a free tool based on these technographic data sources. If you want to see what technologies a company is running before you reach out, you can check here:

Tech Stack Finder Tool

4. Buying Intent Signals

Technographic data tells you what a company uses. Intent data tells you when a company is actively looking to buy. The difference matters because timing is often the gap between a reply and silence. A company using a competitor has budget. A company actively evaluating alternatives has urgency.

When to use this method: You want to prioritize outreach to companies that are most likely to buy right now. You have a large addressable market and need to focus your team's effort on the accounts with the highest probability of converting.

How to think about intent signals:

Not all signals carry equal weight. Here is how to rank them from strongest to weakest:

Technology changes are the strongest signal. A company that just dropped a competitor or adopted a complementary tool is in active buying mode. They have already made a decision to change, and you are reaching them during the transition window.

Funding events are the next strongest. A company that just raised a round has capital to deploy and pressure from investors to scale. The window between funding announcement and tool purchase is typically 30 to 90 days. Reach out within the first two weeks for the best results.

Job openings are a reliable mid-tier signal. A company hiring three SDRs probably needs sales tools. A company posting for a "Head of Data" might need enrichment solutions. The key is matching the role to your product category. A single job posting is weak. Three related postings at the same company is strong.

Social engagement is the weakest signal on its own but becomes powerful when combined with others. When a prospect comments on a competitor's LinkedIn post or engages with content about your category, they are signaling awareness. Pair this with a funding event or a job posting and you have a multi-signal prospect worth prioritizing.

Tools for capturing intent:

PredictLeads aggregates multiple intent signals including job postings, technology changes, and company events into a single API. It is one of the most comprehensive sources for third-party intent data, and it plugs directly into Clay workflows.

Common Room aggregates buying signals across community engagement, product usage, social activity, and content consumption. It scores contacts and accounts based on signal strength. This is particularly useful for product-led growth companies where community engagement is a leading indicator of purchase intent.

Trigify.io monitors social signals on LinkedIn and tracks ICP-relevant engagement patterns. When someone at a target account posts about challenges your product solves, Trigify surfaces that signal so you can reach out while the topic is fresh.

How to operationalize intent: Set up a monitoring workflow in Clay that pulls signals from PredictLeads and Common Room on a weekly cadence. Score each account based on signal type and recency. Companies showing two or more signals in the past 30 days go into your priority outreach queue. Single-signal accounts go into a nurture sequence. This way your team spends its time on the accounts most likely to convert instead of working through a static list from top to bottom.

Based on these intent data providers, we built a free tool to track buying signals. If you want to see which companies are actively researching solutions in your space right now, you can check for free here:

Intent Signals Tool

5. Custom Data Extraction

Sometimes the data you need does not live in any database. It sits on industry directories, review sites, government registries, social platforms, or niche websites that no B2B data provider has indexed. This is common when you sell to verticals like healthcare, legal, construction, or local services where the prospects live on industry-specific platforms rather than LinkedIn.

When to use this method: Your target prospects are listed on a specific website, directory, or platform that no standard database covers. You need structured data from an unstructured source. Your ICP is defined by presence on a particular platform (e.g., companies listed on G2, businesses on Yelp, startups on Product Hunt).

How to get started:

Step one is identifying where your prospects already exist online. Think about where they list themselves, where they get reviewed, where they congregate. A dentist is on Google Maps and Healthgrades. A SaaS startup is on Product Hunt and G2. A restaurant is on Yelp and TripAdvisor. A contractor is on Angi and state licensing boards. The platform is your list.

Step two is choosing the right extraction tool for that platform.

Apify is the first place to check. It offers hundreds of pre-built scrapers (called Actors) for popular platforms including Google Maps, Yelp, TripAdvisor, Crunchbase, Product Hunt, Reddit, and dozens of others. Search the Apify store for your target platform. If an Actor exists, you can run it in minutes without writing any code. For sites without an existing Actor, Apify lets you build custom scrapers.

Instant Data Scraper is a browser extension that detects tabular data on any webpage and exports it as CSV. It requires zero setup and works well on directory pages, search results, and listing sites. This is the fastest option for a quick one-time extraction when you do not need automation.

Firecrawl handles web scraping with AI-powered extraction. It navigates complex sites, handles pagination, and extracts structured data from pages that would break simpler scrapers. Reach for this when the site has dynamic content, JavaScript rendering, or unusual page structures.

ZenRows provides scraping infrastructure that handles anti-bot protection, JavaScript rendering, and proxy rotation. This is the tool you need when sites actively block scrapers with CAPTCHAs, rate limiting, or bot detection.

Airtop combines browser automation with AI agents for extraction tasks that require multi-step navigation. When you need to log into a site, navigate through filters, and extract data across multiple pages, Airtop handles the entire workflow.

Practical workflow: Identify your target platform. Check Apify for a pre-built Actor. If one exists, run it and export the data. If not, try Instant Data Scraper for simple pages or Firecrawl for complex ones. Import the extracted data into Clay, then enrich with email and phone data using the waterfall method described in section 6. You now have a list that no competitor using standard databases will ever build.

6. Finding Contact Data: Point Solutions and Waterfalls

Once you have your list of target companies and people from any of the methods above, the next step is finding their email addresses and phone numbers. This is where most teams leave coverage on the table by relying on a single provider.

Point Solutions

Point solutions are standalone tools that specialize in finding contact data. You send in a name and company, and they return an email address or phone number. Each provider has different strengths, and the right choice depends on your target geography and the type of data you need.

Prospeo delivers strong accuracy on work emails, particularly for European contacts where other providers struggle. If your outbound targets EMEA, Prospeo should be your first lookup.

Wiza pulls emails directly from LinkedIn data, which tends to be more current than databases that rely on older records. The accuracy is consistently high because the data ties directly to active LinkedIn profiles. Best for contacts you sourced from LinkedIn Sales Navigator.

LeadMagic provides email and phone data with a credit-based model that keeps costs low for high-volume prospecting. It is a solid addition to any enrichment stack, especially as a secondary or tertiary provider in a waterfall.

Data Waterfalls

Here is the math that matters: a single enrichment provider typically finds contact data for 40 to 60 percent of your list. That means 40 to 60 percent of the prospects you carefully sourced have no usable contact information. Data waterfalls fix this.

A waterfall runs multiple providers in sequence. When the first provider comes up empty, the second takes over. Then the third. Coverage climbs to 70, 80, sometimes 90 percent. The order matters because you want your most accurate provider first and your broadest provider last.

FullEnrich runs a built-in waterfall across 15+ data providers in a single request. You do not need to configure the sequence or manage multiple subscriptions. Send in your list, and FullEnrich handles the cascading lookups automatically. This is the simplest way to run a waterfall if you do not want to build one yourself.

Clay lets you build custom waterfalls with full control over provider order, conditional logic, and fallback behavior. You decide which provider runs first, which runs only if the first fails, and which runs only for specific segments (e.g., use Prospeo first for European contacts, Wiza first for US contacts). This flexibility is why Clay is the platform we use for enrichment across all client accounts.

Enginy (formerly Genesy) and Outbond also offer waterfall enrichment capabilities, each with different provider mixes and pricing models.

Recommended approach: Start with a point solution you trust for your primary market. Prospeo for Europe, Wiza for US contacts sourced from LinkedIn. Then run unfound contacts through FullEnrich or a Clay waterfall. This two-pass approach gives you the accuracy of a dedicated provider for the majority of your list, plus the coverage gains of a multi-provider waterfall for the harder-to-find contacts.

With a combination of these contact finding tools, we built a free tool that finds verified email addresses. You can use it to find emails for your prospects here, for free:

Email Finder Tool

7. Putting It All Together

The 44 data sources above are not meant to be used simultaneously. The right combination depends on your ICP, your budget, and how targeted your outreach needs to be. Here are four sourcing stacks we use at ColdIQ for different situations:

Stack 1: Broad B2B outreach

Best for: ICP fits standard filters. You need volume quickly.

Source from Apollo or LinkedIn Sales Navigator. Export through Wiza. Enrich emails through a Clay waterfall. You can be running a campaign within a day.

Stack 2: Niche vertical targeting

Best for: Your prospects live outside standard databases.

Use Exa Websets or Clay's Claygent to find companies that match your description. Supplement with Apify to extract data from industry-specific directories. Enrich through FullEnrich for maximum coverage. This stack takes more setup but gives you lists that competitors cannot replicate.

Stack 3: Competitor displacement

Best for: You sell a product that replaces existing software.

Pull technographic data from BuiltWith and PredictLeads. Focus on companies that recently churned from a competitor or adopted a complementary tool. Layer TheirStack job posting data to confirm they are actively investing in the category. Enrich contacts through Prospeo or Wiza.

Stack 4: Intent-driven prioritization

Best for: Large addressable market where you need to focus effort.

Start with a broad list from any database. Layer intent signals from PredictLeads and Common Room. Score accounts based on signal type and recency. Route high-intent accounts to your best reps with personalized outreach. Route low-intent accounts to automated sequences. This stack maximizes conversion from the same number of outbound touches.

The teams generating the most pipeline combine at least two of these stacks. They source from databases, enrich through waterfalls, and prioritize outreach based on intent. Each layer adds signal quality. Each signal improves response rates.

The data sourcing is the foundation. Everything else, the copywriting, the sequencing, the follow-ups, builds on top of it.