Clay Tutorial: From Setup to Situation-Based Targeting in 2026

By Mitchell Keller, Founder & CEO, LeadGrow. Managed 3,626+ cold email campaigns. 6.74% average reply rate. 2,230+ meetings booked in 2025.

Why Clay is the backbone of modern outbound

Before Clay, building a properly enriched prospect list meant logging into 5 different tools, exporting CSVs, matching records in spreadsheets, and hoping the data aligned. A process that took hours and still produced mediocre coverage.

Clay puts 75+ data sources in one spreadsheet-like interface. You import contacts, enrich them with data from multiple providers, run AI agents to research them, score them based on custom logic, and export campaign-ready segments. All in one table.

We use Clay on every campaign at LeadGrow. It's the single tool that's had the biggest impact on our list quality. Our reply rates average 6.74% across 3,626+ campaigns while most teams sit below 3%. The list quality that comes from Clay is the primary reason.

This tutorial walks through exactly how we set it up, from account creation to launching situation-based campaigns.

Account setup and workspace structure

Creating your account

Sign up at clay.com. The free tier gives you limited credits to experiment, but any real outbound work requires a paid plan. The Pro plan at $134/month includes enough credits for most teams running 2 to 5 campaigns simultaneously.

Create a workspace for each client or project. This keeps data separated and credit usage trackable. We run a separate workspace per client so credit spend maps cleanly to client billing.

Understanding credits

Clay charges credits for enrichment actions. Different providers cost different credit amounts. Apollo email lookup might cost 1 credit. Claygent research might cost 5 to 10 credits per row depending on prompt complexity. Understanding credit costs per action is essential before you start building tables, otherwise you'll burn through your monthly allocation in a single table.

Check Clay's pricing page for current credit costs per provider. These change periodically. As of early 2026, most email enrichments cost 1 to 2 credits and Claygent costs 3 to 10 credits per row.

Your first table

Clay works like a spreadsheet on steroids. Each row is a contact or company. Each column is a data point. The magic is that columns can pull data automatically from enrichment providers, run AI prompts, or execute custom logic.

You can import data from:

• CSV upload (most common starting point)
• Direct integrations (Apollo, LinkedIn Sales Nav, HubSpot, Salesforce)
• Clay's built-in people and company search
• Webhooks from other tools (n8n, Zapier, Make)

Start with a CSV import of your base list. Minimum fields: company name, company domain, contact name, contact title, and contact email (if you have it). You'll enrich everything else inside Clay.

The color-coding system

This is the organization method that keeps complex Clay tables from turning into chaos. When a table has 30+ columns (which happens fast), you need a visual system to navigate it.

We color code every column based on its function:

Green: input columns

Your source data. The information you imported or that came from your initial data source. Company name, domain, contact name, title, email, LinkedIn URL. Green means "this data existed before Clay touched it." Never modify green columns with enrichment. They're your ground truth.

Blue: processing columns

Enrichment and data transformation columns. When you run an enrichment provider to get company revenue, that goes in blue. When you use a formula to clean a job title into a standardized format, that's blue. Blue means "Clay did something to produce this data." These are intermediate steps that feed into your final outputs.

Yellow: customization columns

Custom logic, AI prompts, and manual adjustments. Claygent research results, custom scoring formulas, personalization snippets generated by AI, and manual tags all go in yellow. Yellow means "human judgment or AI interpretation was applied." These are the highest value columns because they contain intelligence, not just data.

Violet: output columns

The final columns that get exported to your campaign tool. Cleaned, enriched, scored, personalized data that's ready for outreach. Contact email (verified), personalized first line, segment tag, priority score. Violet means "this is going out the door." Only violet columns should appear in your exports.

Building enrichment waterfalls

An enrichment waterfall is a cascading sequence of data providers that tries each one in order until it gets a result. This is how you maximize data coverage while minimizing credit spend.

Why waterfalls matter

No single data provider has 100% coverage. Apollo might have emails for 60% of your list. Hunter covers a different 55%. Dropcontact covers another 50%. But combined in a waterfall, you get 85 to 95% coverage.

Without a waterfall, you either accept gaps in your data or pay for multiple providers running on every row. With a waterfall, you run the cheapest provider first, then only escalate to more expensive providers for rows where the first provider returned nothing.

Email waterfall (our standard setup)

Here's the email enrichment waterfall we run for most campaigns:

• Step 1: Apollo (cheapest). Enriches the email. If found, stop. If not, continue.
• Step 2: Hunter.io. Domain search for the email. If found, stop. If not, continue.
• Step 3: Dropcontact. Finds and verifies. If found, stop. If not, continue.
• Step 4: Prospeo or FindyMail. Final attempt with a different methodology.

This waterfall achieves 85 to 95% email coverage. Running just one provider gets you 55 to 65%. The waterfall saves 40 to 60% on credits versus running all providers on every row because most emails are found in steps 1 or 2.

How to build it in Clay

• Click "Add Enrichment" and select your first provider (Apollo)
• Click "Add Fallback" to add the next provider in sequence (Hunter)
• Set conditions for when to try the next provider (typically "if previous result is empty")
• Repeat for each provider in your cascade

Clay handles the logic automatically. It runs provider 1 first, checks if the result is empty, and only moves to provider 2 for rows that didn't return a result. Credits are only consumed when a provider actually returns data.

Company data waterfall

Same concept applies to company data. Revenue, employee count, industry classification, and other firmographic data can all be waterfall enriched:

• Step 1: Clay's built-in company enrichment (included in plan, no extra credits)
• Step 2: Apollo company data
• Step 3: Clearbit/Breeze

For technographic data (what tools a company uses), BuiltWith and Wappalyzer are the primary sources. These don't waterfall as cleanly because they use different detection methods, so we sometimes run both and merge results.

Using Claygent for situational research

Claygent is Clay's built-in AI agent. Give it a URL or company name, tell it what to find, and it browses the web, reads pages, and returns structured data. This is where Clay goes from "enrichment tool" to "research team."

What Claygent does well

• Reading company websites to extract positioning, products, and target audience
• Pulling specific information from about pages, pricing pages, and job listings
• Summarizing what a company does in plain language
• Identifying buying signals based on public information
• Checking if a company matches criteria that structured data providers don't cover

Real workflow: situation extraction

Here's an actual Claygent prompt we use:

"Visit {company_url}. Look at their homepage, about page, and any recent news or blog posts. Answer these questions: 1) What is their primary product or service? 2) Who is their target customer? 3) Are there signs of recent growth, expansion, or new product launches? 4) Do they mention challenges or priorities that relate to [our client's solution area]? Return answers in a structured format."

For a 1,000 row table, this replaces 40+ hours of manual research with about $15 in Clay credits and 30 minutes of processing time.

Credit optimization for Claygent

• Run Claygent only on rows that passed your initial filters. Don't research companies you've already disqualified.
• Be specific in your prompts. Vague prompts cause Claygent to browse more pages and consume more credits.
• Use the "limit pages" setting to cap how many pages Claygent reads per company. 2 to 3 is usually enough.
• Cache results. If you're running the same query on the same companies across tables, export and reimport instead of re-running Claygent.

Pulling from multiple data sources

One of Clay's biggest advantages is that it's source agnostic. You can combine data from completely different providers in the same table. Here's how we typically stack data sources:

Source 1: Apollo (contact and company data)

Apollo is our primary starting point. We pull initial contact lists from Apollo based on firmographic filters (industry, company size, title, geography). This gives us the base layer: names, emails, company data. Apollo's coverage is strong in North America and decent globally.

Source 2: LinkedIn Sales Navigator (org chart and activity)

We enrich Apollo contacts with Sales Nav data to verify titles, check reporting structure, and see recent activity. Someone who posted on LinkedIn in the last 30 days is more likely to be active and responsive. We use the Sales Nav integration in Clay to pull this data automatically.

Source 3: BuiltWith/Wappalyzer (technographics)

Tech stack data tells us what tools a company uses, which reveals their maturity level and potential gaps. If a target company uses Salesforce but has no sales engagement tool, they have a gap that sales engagement products can fill. We run both BuiltWith and Wappalyzer because each detects different technologies.

Source 4: Crunchbase/PitchBook (funding and growth)

Funding data is a timing signal. Companies that raised in the last 90 days have budget to spend. We pull funding history and recent rounds to identify companies in spending mode.

Source 5: Job boards (hiring signals)

Hiring patterns reveal strategic priorities. Clay can pull job posting data from LinkedIn, Indeed, and other sources. If a company is hiring 3 SDRs, they're investing in outbound. If they're hiring a VP of Marketing, they're about to spend on marketing tools.

Combined, these 5 sources give us a 360 degree view of each prospect. The enrichment waterfall across 5 to 10 sources is how we achieve 85 to 95% data coverage versus the 60 to 70% most teams get from a single source.

Using AI for classification and scoring

Raw enrichment data is noise until you classify and score it. Clay's AI columns let you convert unstructured data into actionable categories.

Classification prompts

After Claygent pulls research on a company, use an AI column to classify the results:

"Based on this company description: {claygent_output}. Classify this company into ONE of these categories: (A) Actively growing and likely evaluating new tools, (B) Stable operations with low change likelihood, (C) Early stage with limited budget, (D) Enterprise with complex procurement. Return only the letter."

This turns a paragraph of research into a single letter you can filter and sort on. Category A companies go to your priority segment. Category B goes to a nurture sequence. Category C and D get excluded or deprioritized.

Building a prospect score

Combine individual signals into a composite score. Simple scoring works:

• Recent funding (last 90 days): +30 points
• Hiring in relevant function: +25 points
• New leader in role (last 90 days): +25 points
• Tech stack gap (uses adjacent tools but not competitor): +20 points
• Active on LinkedIn (posted in last 30 days): +10 points
• Firmographic fit (right size, industry, geo): +10 points

Companies scoring 50+ are your top priority. 30 to 49 are standard. Below 30, reconsider whether they belong in the campaign.

Building situation-based segments

This is where everything comes together. You've enriched the data. You've classified and scored. Now you group prospects into segments based on their buying situation. This is the situations beat markets philosophy in practice.

Segment 1: "Just Hired, No Tool"

Companies that recently posted a job for a role your product supports AND don't currently use a competing tool. They have budget (they're hiring), urgency (they need the role productive fast), and a gap (no existing solution).

Clay filter: Hiring signal = True AND Competitor tool = None AND Employee count > 20.

Segment 2: "Growing Fast, Scaling Pains"

Companies with 30%+ headcount growth in the last 12 months. Growth creates problems that your product might solve. More employees means more coordination, more tools, more process needs.

Clay filter: Growth rate > 30% AND Revenue > $2M AND Industry matches target.

Segment 3: "New Leader with Mandate"

Someone entered a leadership role in the last 90 days. They're evaluating their team's tools and processes. This is the window where purchasing decisions get made.

Clay filter: Contact start date < 90 days AND Title level = VP or Director AND AI classification = Category A.

Segment 4: "Post-Funding, Ready to Spend"

Companies that raised a round in the last 30 to 90 days. They have capital and investor pressure to deploy it on growth. Fresh budget plus growth pressure equals openness to new vendors.

Clay filter: Funding date within last 90 days AND Round type = Seed through Series C AND Employee count > 10.

Why segment-specific messaging matters

Each segment gets its own email sequence because the buying situation is different. The "Just Hired" segment gets messaging about equipping their new hire. The "Growing Fast" segment gets messaging about maintaining quality while scaling. The "New Leader" segment gets messaging about making an impact in the first 90 days.

This is why situation-based targeting outperforms demographic targeting by 2 to 3x. You're sending the right message to each group based on what's actually happening at their company right now. Our 34 prospect list filters guide covers every filter category you can apply during this process.

End-to-end workflow: real example

Here's the actual workflow we ran for a data infrastructure client:

• Import: 3,200 companies from Apollo (filtered: SaaS, 50 to 500 employees, US based).
• Firmographic enrichment (blue): Company revenue, employee growth rate, funding history via waterfall.
• Filter: Remove companies under $2M revenue. 2,400 remaining.
• Technographic enrichment (blue): BuiltWith + Wappalyzer for tech stack. Waterfall email enrichment for contacts.
• Situation extraction (yellow): Claygent research on top 800 companies (scored by firmographic fit). Hiring signal check via LinkedIn Jobs enrichment.
• Scoring (yellow): Combined score from all signals. Segmented into 3 groups.
• Personalization (yellow): AI-generated first lines based on Claygent research output.
• Export (violet): 3 segment-specific CSVs into our sending platform. Each segment mapped to a different email sequence.

Results: 9.2% overall reply rate. Top segment ("hiring + tech match") hit 14.1%. Total Clay credit cost for this build: approximately $45. Time from import to campaign-ready: about 4 hours including setup and QA. For a comparison of Clay against other data tools, see our Clay vs Apollo analysis.

For comparison, the same client's previous agency exported a generic Apollo list with 3 filters and got 1.8% reply rate on better copy. The list was the difference.

Export and automation

What to export

Only export violet (output) columns to your sending platform:

• Contact email (verified)
• First name, last name
• Company name
• Personalized first line or personalization snippet
• Segment tag (for routing to the correct sequence)
• Any custom variables your email templates reference

What NOT to export

• Processing columns (blue). Your campaign tool doesn't need enrichment waterfall results.
• Raw Claygent output. Summarize the intelligence into a usable snippet.
• Scoring columns. The score determined segment assignment. The campaign tool just needs the segment tag.

Automating the pipeline

For ongoing campaigns, set up automated exports. Clay supports webhooks that trigger when new rows are added or when enrichment completes. Connect these to n8n or Make to automatically push enriched, scored, and segmented contacts into your sending tool.

We run automated Clay pipelines for several clients. New contacts enter Clay daily from our prospecting sources, get enriched overnight, and appear in campaign queues the next morning. Zero manual steps after initial setup. This fits into the broader outbound sales tech stack we recommend for 2026.

Credit optimization (2026 pricing)

Tip 1: Filter before you enrich

Don't enrich your entire import. Apply firmographic filters first (minimal or no credits), then only enrich the companies that pass. If 40% of your import gets filtered out at the firmographic stage, you just saved 40% on enrichment credits.

Tip 2: Use waterfalls for everything

Every enrichment with multiple available providers should be a waterfall. Single-provider enrichments waste credits on rows where a cheaper provider would have worked. This is not optional. We treat waterfall configuration as step one of any new table build.

Tip 3: Batch Claygent by industry

Companies in the same industry have similar website structures. Batch by industry so you can refine your prompts once and apply them consistently. This produces better results and uses fewer credits than vague prompts that cause unnecessary page browsing.

Tip 4: Cache and reuse across tables

If you enriched company data last month and need it for a new campaign, export the enriched data and reimport instead of re-running enrichments. Clay doesn't automatically cache across tables. Managing this manually saves significant credits over time.

Tip 5: Monitor credit usage per column

Clay shows credit consumption per enrichment column. If one column eats 40% of your credits and only marginally improves list quality, remove it from your workflow. The goal is the best possible data at the lowest credit cost per contact.