Clay is a data enrichment and research platform for B2B go-to-market teams. It works like a spreadsheet where each column can pull data from a different provider, run a piece of logic or ask an AI to research something. The result is prospect lists that are far more specific and personalised than a single database can produce.
This guide explains how Clay works and how B2B teams use it. If you are comparing options, see our Clay review and Clay alternatives.
Key takeaways
- Clay is a data enrichment and research workspace, not a single database
- Waterfall enrichment checks several providers to improve coverage
- AI research columns answer specific questions about each company
- Clay sends finished records to outreach tools and CRMs
- Filter early to keep credit use under control
Why Clay has become popular
Outbound has become crowded, and generic messages to generic lists produce fewer replies than they used to. Teams that still get good results tend to target narrower audiences at the right moment and say something genuinely relevant. Clay makes that practical at scale by combining data sources, timing signals and AI research in one place, which is why it has become a common part of modern outbound stacks.
What problem Clay solves
Most prospect databases give you the same filters everyone else uses: industry, company size, job title. Your competitors are pulling the same lists. Clay lets you go further by combining many data sources and adding your own research, so you can find accounts defined by triggers and context rather than generic filters.
How Clay works
- Start a table with companies or people, from a CSV, a CRM, a search or another source
- Add enrichment columns that pull data such as emails, phone numbers, tech stack or funding
- Use waterfall enrichment so Clay tries several providers until one returns a result
- Add AI research columns that read websites or other sources and answer specific questions
- Add formulas and conditions to score, filter and segment rows
- Send the finished records to your CRM or outreach tool
Key concepts
Waterfall enrichment
Instead of relying on one data provider, Clay checks one source and, if it finds nothing, moves to the next. This often finds more valid contacts than any single provider, and you only use credits on the lookups you need.
AI research
Clay can use AI to visit a company's website or search for information and answer a question such as "Does this company sell to enterprise customers?" or "What is their main product?". That makes qualification and personalisation possible at a scale no human team could manage manually.
Credits
Most enrichment and AI actions use credits. Designing tables carefully, for example by filtering out poor-fit rows before running expensive steps, keeps costs under control.
How B2B teams use Clay
- Building account lists from triggers such as hiring, funding or new locations
- Finding and verifying the right decision-makers at each account
- Scoring accounts against an ideal customer profile
- Writing personalised first lines based on real research
- Enriching inbound leads before they reach sales
- Keeping CRM records complete and up to date
A simple Clay workflow example
Imagine you sell to B2B software companies that are building outbound teams. A Clay table might work like this:
- Start with companies in your target industry and size range
- Add a column that checks whether each company is hiring sales development roles
- Filter to companies that are hiring, so later steps only run on good-fit rows
- Find the head of sales or revenue at each company using a waterfall of providers
- Verify each email address
- Use an AI column to summarise what the company sells in one sentence
- Write a first line that connects their hiring to the problem you solve
- Send the finished rows to your outreach tool and CRM
The order matters. Filtering early means expensive enrichment and AI steps only run on accounts worth contacting, which keeps credit use under control.
Clay for inbound, not just outbound
Clay is often discussed as an outbound tool, but it is just as useful for inbound. When a lead fills in a form with only an email address, Clay can enrich the company, identify the role, check fit against your ICP and score the lead before it reaches sales. That turns a bare form fill into a lead your team can act on immediately, and it feeds the lead scoring model your CRM uses.
Where Clay fits in the stack
Clay sits between data sources and action. It pulls from providers, including Apollo, and pushes finished records into a sending tool such as Instantly, a LinkedIn tool such as HeyReach, and your CRM. It is also often used alongside automation tools; see our Zapier vs Make vs n8n guide.
Common Clay use cases by team
- Outbound: building trigger-based lists with verified contacts and personalised first lines
- Marketing: enriching webinar and content leads before they enter nurture
- RevOps: filling missing CRM fields and keeping records current
- ABM: researching target accounts and mapping buying committees
- Agencies: running repeatable list-building workflows for many clients
Writing good AI prompts in Clay
AI columns are only as good as the questions you ask. Be specific, ask for one thing at a time and define the format of the answer. "Does this company sell to other businesses? Answer yes or no" works better than "Tell me about this company". Test prompts on a handful of rows, check the answers manually, then run them on the full table.
What a good Clay output looks like
A finished Clay row should be ready to act on without further research. For outbound, that usually means the company name and domain, a fit score, the reason it qualifies, the right contact with a verified email, their role and a short personalised line connecting their situation to your offer. For inbound, it means the lead enriched with company size, industry, role and a score that determines routing. If reps still have to open LinkedIn and the company website before contacting someone, the table needs another column or a better prompt.
Who Clay suits
- Outbound teams who want precision over volume
- Agencies building lists and personalisation for many clients
- RevOps teams enriching inbound leads and CRM data
- Teams comfortable with spreadsheet logic, or willing to learn it
Clay terms explained
- Table: the spreadsheet-like workspace where each row is a company or person
- Enrichment: adding data to a row, such as an email, phone number or company size
- Waterfall: trying several providers in order until one returns a result
- Credits: the units consumed by enrichment and AI actions
- AI research column: a column that asks AI a question about each row
- Integration: a connection that sends rows to or from another tool, such as a CRM
Is Clay worth learning?
For teams where list quality limits results, Clay is often one of the highest-leverage tools to learn. It turns hours of manual research into repeatable workflows and makes genuine personalisation possible at scale. For teams whose targeting fits standard filters and who do not have time to build workflows, a simpler database may deliver more value sooner. The honest answer depends on your market and your team's capacity.
Next steps
Read our Clay review for strengths and limitations, or Clay vs Apollo to see how it compares with a traditional database. If you want enriched, verified lists without building them yourself, our B2B lead generation service builds them for your ICP.