A practical guide to using AI-powered scraping to extract structured contact and company data from directories, association member lists, conference pages, and any other website into a usable lead list.
What software a company uses is a better ICP signal than company size alone. Here's how to detect tech stacks at scale and use them to score, filter, and personalize outreach for higher reply rates.
Funding stage, total raised, and lead investor data are among the most reliable ICP qualification signals for B2B SaaS. Here's how to use them to prioritize accounts, time outreach, and access warm intro paths.
Company size is the most common ICP filter — but 'size' means different things depending on how you measure it. Here's how to combine revenue ranges and headcount data for precise account qualification.
Industry classification, HQ location, founding year, and website domain are the building blocks of CRM routing, territory planning, and outreach personalization. Here's how to enrich them accurately at scale.
Newly funded startups have fresh budget, growing teams, and new tooling needs. Here's why the window is short, how to monitor funding announcements automatically, and how to generate personalized outreach at scale.
Scraping Google Maps gives you business names and phones. Adding an AI website crawl gets you decision-maker names, emails, and personalized first-line copy — all in one automated pipeline from Google Sheets.
A practical guide to scraping Google Maps by city and category, then automatically enriching each business with verified emails and website health scores — all inside Google Sheets.
Your competitors' LinkedIn company pages are updated constantly with product announcements, hiring signals, and engaged prospect data. Here's how to monitor them systematically and turn that intelligence into pipeline.
LinkedIn company pages contain structured business data that most CRMs are missing — descriptions, specialties, headcount, HQ location, and founding year. Here's how to extract it at scale and what to do with it.
Already have their email? Reverse-lookup their LinkedIn profile to enable social selling workflows, CRM enrichment, and multi-channel outreach — all from a work email alone.
People actively posting about your category's problems are some of your best leads. Here's how to systematically monitor LinkedIn keywords and hashtags to find and engage them before your competitors do.
Commenting requires effort — which means commenters signal stronger intent than passive likers. Here's how to extract commenters from relevant posts, filter by ICP, and use their comment text as outreach personalization.
Why people who engage with specific LinkedIn posts are among the warmest B2B leads available — and how to extract, enrich, and reach them before your competitors do.
Turning a list of names and company names into verified LinkedIn URLs is often the missing first step in enrichment pipelines. Here's the technique, the accuracy expectations, and how to handle disambiguation.
Bulk LinkedIn profile extraction — pulling experience, skills, and contact data from hundreds of profiles — is possible with the right setup. Here's what works in practice, what rate limits look like, and how to enrich at scale without hitting walls.
Mobile number enrichment for B2B is harder than email and carries compliance considerations. Here's how to maximize coverage with a waterfall approach and use mobile numbers appropriately for SMS and direct outreach.
Most prospect lists start with just a name and company. Full profile enrichment adds verified email, phone, job title, seniority, and LinkedIn URL — the complete contact record your CRM and sequences need.
Reddit contains some of the most explicit buying intent signals in B2B — people asking for tool recommendations, describing exact pain points, and comparing competitors. Here's how to capture them systematically.
News events create natural outreach triggers — expansions, new hires, product launches, funding closes. An AI-powered RSS monitoring system captures these triggers automatically and surfaces enriched leads before your competitors see the same article.
Why no single enrichment provider covers your whole list — and how a waterfall cascade strategy consistently returns 20–35% more verified contacts than single-provider enrichment.
Direct dials are the hardest contact data to find at scale. A waterfall approach cascading through multiple phone providers consistently outperforms single-provider lookup. Here's the strategy.
An honest comparison of the top LinkedIn email finder tools in 2026 — Apollo, Hunter.io, Clay, Datagma, LeadMagic, and the LinkedIn Enricher for Google Sheets — with a decision guide for each use case.
A practical guide to turning LinkedIn profile URLs into verified work emails without leaving your spreadsheet. No coding required.
Using a single enrichment provider leaves emails on the table. Here's why dual-provider fallback consistently outperforms any single provider, and how to configure it correctly.
A complete walkthrough of the LinkedIn Enricher Google Sheets add-on — from installation to your first batch of enriched contacts, with screenshots of what to expect at each stage.
The repeatable workflow for going from LinkedIn Sales Navigator search to an outreach-ready prospect list with emails — using Google Sheets and LinkedIn enrichment.
The most common ways teams burn through Datagma and LeadMagic credits with nothing to show for it — and the specific settings and Sheets formulas that prevent each one.
We ran the same 100 LinkedIn profile URLs independently through Datagma and LeadMagic, then tested both together with fallback enabled. Here are the real hit rates, overlap, and which one wins by use case.
The math behind LinkedIn enrichment fallback logic, when the extra credit cost pays off, when it doesn't, and a decision matrix for SDRs, recruiters, and agencies.
Where your API key actually goes when you enter it in a Google Sheets add-on, how Google's PropertiesService works, what it protects against, and what it doesn't.
What happens when you send a LinkedIn URL to a data provider, why enrichment can't return 100% hit rates, and what the match rate benchmarks actually look like by profile type and industry.
How LinkedIn enrichment works, every approach from all-in-one tools to Google Sheets add-ons, when each is the right call, and the 5 most common mistakes teams make.
A real run of 500 LinkedIn profile URLs through the LinkedIn Enricher add-on with Datagma and LeadMagic fallback. Email hit rates, phone rates, cost per result, and what the not-found rows had in common.
The four types of enrichment outcomes, what the add-on writes to your sheet for each, how to read and filter results, and how to build a re-enrichment queue for not-found rows.
The legal basis for B2B contact enrichment under GDPR, how CAN-SPAM and CASL compare, what LinkedIn's terms cover, and what you're responsible for as the sender.
An honest comparison of Hunter.io alternatives for email verification. Decision table, 'Choose this if...' breakdowns, and a clear recommendation by use case.
A 7-step checklist for cleaning a lead list before cold outreach — including what to do with catch-alls, role addresses, and aged contacts. Copy and use it directly.
An honest comparison of the best free bulk email verification tools in 2026. Decision table, 'Choose this if...' breakdowns, and a clear recommendation by list size.
The clear answer to what email validation and verification actually do, which one you need for your use case, and a decision tree to pick the right approach by volume.
The actual numbers on email list decay, industry variance, and the thresholds where invalid addresses start damaging deliverability — not estimates, benchmarks.
Why standard bounce-rate advice fails at scale, the hidden factors most guides miss, and the minimum viable fix for cold outreach teams in 2026.
The complete breakdown of what invalid email addresses do to your sender reputation, deliverability, and domain — with a decision tree for what to do about it.
Why fixing subject lines and CTAs doesn't move the needle, the list quality problems most senders ignore, and the minimum viable fix.