What Is B2B Data Enrichment?

B2B data enrichment is filling in the fields a record is missing from sources outside it, then verifying what came back. It usually means taking a name and a company domain and returning a verified work email, a phone number, a job title and the firmographics of the company.

The input is almost always thinner than people expect. A form fill gives you an email and nothing else. A conference list gives you a name and a company. A Sales Navigator export gives you a profile URL. None of those is enough to route a lead, score it, or call the person, and all of them are enough to find the rest.

Enrichment is not a one-off cleanup project either, because the underlying facts move. People change jobs, companies rename, domains get retired, and a record that was correct at import is a slightly worse record every month afterwards. That is why the useful question is not whether your database was enriched but when, and against what.

Five are commonly named: contact, firmographic, technographic, intent and social. Cleanlist AI does contact and firmographic enrichment, plus AI research columns that answer a question you write. It does not sell technographic or intent data.

Saying so plainly matters more than it looks, because the five get sold as one word and they come from completely different places. Contact and firmographic data are facts about a person and a company that several providers hold and can be verified against each other. Intent data is inferred from behaviour somebody else observed, and there is no way to verify a signal against a second source the way an email can be verified against a mail server.

If a buying motion depends on intent, a platform built around it is the correct purchase, and ZoomInfo and Cognism are the two most teams end up comparing. If it depends on knowing who somebody is and how to reach them, that is the job this page describes.

Contact enrichment. The person: verified work email, phone number, LinkedIn URL, job title, seniority and department. Cleanlist AI does this.

Firmographic enrichment. The company: name, domain, industry, employee count, headquarters location and founded year. Cleanlist AI does this.

Technographic enrichment. The software a company runs. Cleanlist AI does not sell this as a stored field.

Intent enrichment. Signals that a company may be in a buying cycle, inferred from third-party behaviour. Cleanlist AI does not sell this.

Social enrichment. Public profiles and activity. Cleanlist AI returns the LinkedIn URL and stops there.

The stored fields are the ones the API returns, spelled the way it spells them: work_email, email_status, direct_dial, linkedin_url, job_title, seniority, department, company_name, company_domain, company_headcount, company_industry, hq_location and founded_year.

That list is the whole answer to the only question a data buyer really has, which is whether their own field is in the response. A phrase like complete contact profile does not answer it, so the field list is worth asking any vendor for in writing before a trial rather than after one.

Anything past that set is produced rather than stored. Funding events, hiring signals, tech stack and recent news come back from an AI research column at the moment you ask for it, priced at 5 credits for AI qualification, and the answer carries the date it was produced because a produced answer without one is worthless six weeks later.

Contact enrichment resolves a person and how to reach them. Company enrichment resolves the organisation around them. They are priced and sourced differently, and a record can succeed at one and fail at the other.

The practical consequence shows up in routing. Company fields are the ones lead scoring and territory assignment need, and they resolve for almost any record that arrives with a real domain. Contact fields are the ones outbound needs, and they depend on whether any provider in the pool holds that individual person, which is a much harder question for a 12-person company than for a 12,000-person one.

So a list that comes back with complete firmographics and patchy phone numbers is telling you something about the market you chose rather than about the enrichment. The companies are findable and the people are not yet, and the fix is usually a change to the ICP rather than a change of vendor.

Because B2B contact data decays continuously. Cognism puts the rate at about 2.1% a month and 22.5% a year, Cognism and SparkDBI put email addresses at 22.5% to 30% a year as the fastest field to go, SparkDBI puts phone numbers at about 18%, and LinkedIn's Economic Graph puts annual job changes at 10.9% of professionals.

Compound those and the shape of the problem is clear. A CRM that was accurate two years ago and has not been touched since is wrong on roughly two records in five, and the wrongness is concentrated in exactly the fields outbound depends on.

The failure is quiet, which is what makes it expensive. A stale record does not throw an error. It sends an email that bounces against your sending domain, or routes a lead to a territory owner who no longer covers it, or scores a company at last year's headcount. The cost lands in deliverability and in rep time rather than in a line item anybody reviews.

This is also the argument for enriching on a schedule rather than once. A single cleanup restores the database to correct on the day it runs and starts decaying again the following morning.

Six steps, in this order: normalise the input, resolve the identity, cascade across providers until one returns a deliverable result, verify the answer against a live system, merge it into a single record with its provenance, and write it back where the team already works.

Normalise the input. The lookup keys decide the match, so they are worth cleaning before any credit is spent. A company written three ways across three imports is three different keys to a matcher. A domain beats a company name, and a LinkedIn URL beats both, because a URL is an identifier and a name is a string that two companies can share.

Resolve the identity. The engine decides which real person the row refers to before it looks for their contact details. This is the step that quietly fails on common names at large companies, and it is the reason a second key alongside the name, a domain or a profile URL, changes the outcome more than any vendor choice.

Cascade. A waterfall asks providers in cost order rather than asking one and giving up. The first deliverable result ends the run, so the providers after the hit are never called and never billed. This is where the coverage difference between a single source and a pool actually comes from: no single database holds every person, and the recovery from asking the next one compounds across a list.

Verify. A returned address is a candidate, not a fact, until something outside the provider confirms it. Cleanlist AI verifies at the mailbox before accepting an email, which is what lets the record carry an email_status rather than a hope. An address that survives that handshake beats an address that does not, whoever supplied it.

Merge. Several providers may answer for the same person and disagree. The reconciled record keeps the value that could be proven and the origin of each field it could not prove, which is what makes it auditable later.

Write back. Enrichment that ends in a CSV nobody imports has not changed anything. The last step is the record landing in the CRM, the list or the sequencer the team already works in, which is why the direction of a CRM integration matters as much as its existence.

Two-way sync with all three on the Pro and Enterprise plans: records come in from the CRM, get enriched in Cleanlist AI, and the filled fields are written back. Outreach, Salesloft and Lemlist receive rows one way only. Pushing a finished lead costs 0.2 credits.

Two-way is the part worth reading carefully, because most tools that say CRM integration mean one-way export. Reading from the CRM is what lets enrichment run against the records you already own rather than only against new ones, which is where the decay above is actually sitting.

The three sequencers are one-way because a sequencer only needs the finished lead. Teams that do not run one can use Sequences, included on every paid plan, which sends email from your own inbox, with call tasks, at 0 credits.

On Starter, CRM sync is not included. A Starter workspace moves data by CSV export, by the API on Pro and above, or through the MCP server, which is available from Starter upward.

Cleanlist AI charges per returned field, not per lookup attempt: 1 credit for a verified work email, 10 for a phone number, 11 for both, 1 for a company enrichment, 0.5 for validating an address, 5 for AI qualification, 0.2 to push a lead to a CRM, and 0 for a missed email or phone number. People Search and Company Search cost nothing.

The plan fee buys the credits. Free is $0 for 50 credits a month with up to 3 seats. Starter is $49 per seat a month with 750 credits per seat. Pro is $89 per seat with 1,500 credits per seat and credit levels up to 100,000 a month, and adds CRM sync, the public REST API and agents. Enterprise is custom. Annual billing takes 25% off.

Working an example through: one Starter seat's 750 credits is 750 verified work emails, or about 68 contacts with email and phone together at 11 credits each, or some mix. Filtering a market down to the 300 people worth enriching is usually where the real saving is.

Every seat adds its credits to one shared team balance, so usage moves between reps without anybody buying a second subscription. A missed email or phone number costs nothing and unused credits roll over.

Cleanlist AI returned 98% verified work emails and 85% phone numbers across 500 stratified B2B leads in the Cleanlist AI 500-Lead Enrichment Benchmark, 2026. Single sources returned 70% to 80% email and 30% to 60% phone on the identical input.

Any accuracy figure without a denominator and an input list is not a figure. A vendor quoting 95% has told you nothing until you know 95% of what: of the records it chose to return, or of the records you asked about. Those two numbers can differ by forty points on the same run, because returning fewer rows is the easiest way to raise the first one.

The test that settles it takes an afternoon. Take 100 rows from your own CRM where you already know the answer, hold them back as a control, run them through the tool, and count three things: how many rows came back at all, how many of those matched what you knew, and how many bounced when you sent to them. Do it with your own list rather than a vendor's sample, because a vendor's sample is chosen.

Every new Cleanlist AI workspace starts with 14 days of Pro: 250 credits and 3 seats, which is enough to run that control both ways before anybody signs anything.

A golden record is the single reconciled version of a contact, assembled from every source that answered rather than from whichever answered last. Cleanlist AI verifies each candidate value before it is accepted, so a field is filled from the source that could prove it.

Merging is where most enrichment quietly goes wrong. Two providers return two different job titles for the same person, and a naive merge takes the newest write, which is only the right answer if the newest source is also the best one. Verification is what breaks that tie for the contactable fields: an address that survives an SMTP handshake beats an address that does not, whoever supplied it.

For the fields that cannot be verified against a live system, such as a title or a headcount, the honest posture is to keep the answer and its origin together rather than to average them. A merged record that cannot tell you where a field came from is a record you cannot audit when a rep says the title is wrong.

All three, for different records. Enrich on import so nothing enters the CRM empty, on a schedule so the records you already own do not rot, and on demand for the accounts a rep is working this week.

On import is the cheapest of the three because the volume is bounded by how many leads actually arrive. It is also the one that pays back fastest, since a lead that arrives already scored and routed is a lead nobody has to triage by hand.

On a schedule is the one teams skip, and it is where the 22.5% annual decay lives. The practical version skips re-enriching everything every month and re-runs the fields that decay fastest against the segment that matters most, which for most teams is open opportunities and anything a sequence is about to touch.

On demand is the smallest volume and the highest value per row, because a rep asking for a phone number is asking about a specific account on a specific day. That is the case the Chrome extension and the MCP server exist for.

Yes to both. The public REST API v2 is included on Pro and Enterprise, authenticates with clapi_ Bearer keys and OAuth scopes, and returns a signed cost quote before any credits are spent. The MCP server is available from Starter upward and lets an assistant such as Claude run search and enrichment directly.

Bulk enrichment through the API is asynchronous. A job returns a workflow_id that you poll until the results are ready, which is the right shape when a single lookup may escalate through several providers and take as long as the slowest one it had to ask.

The signed quote is the part worth building against. An integration can read the price of a job before running it and decline, which means a runaway script cannot quietly spend a month of credits in an afternoon.

Both are open during the 14-day Pro trial.

It can be, and it is not automatic. B2B enrichment in the EU and the UK is normally carried out under legitimate interest rather than consent, which is a lawful basis you have to document and defend rather than a box that is ticked for you. This section is a description of how the obligations usually fall and is not legal advice.

What legitimate interest actually asks of you is a written balancing test: the purpose, why the processing is necessary for it, and why the person's rights do not override it. Keep it with your records of processing, because the moment it matters is the moment somebody asks, and reconstructing the reasoning afterwards is not the same thing as having had it.

The operational duties are the ones that get missed. A person enriched into your database has a right to know you hold data about them and where it came from, so the notice in your first outbound message is doing real work. Deletion and objection requests have to reach every copy, which in most stacks means the CRM, the sequencer and the enrichment tool, not just the CRM. Under CCPA and CPRA the disclosure duty is similar in shape: say what categories you collected and from where, and honour deletion and opt-out requests. Under CAN-SPAM every commercial email needs a working unsubscribe honoured within ten business days.

The part that is a purchasing decision rather than a policy decision is provenance. Ask a provider where a field came from, whether they can suppress a person on request across their own sources, and what their data processing agreement actually commits them to. A vendor that cannot answer the first question cannot help you answer a regulator's. Cleanlist AI runs other people's data rather than a database of its own, which is exactly why every field it returns carries the source that supplied it.

Test each shortlisted tool against the same control list from your own CRM, and decide on cost per valid record rather than on price per lookup or on database size. Everything else on a vendor comparison is downstream of those two numbers.

The questions worth asking in writing, before a trial rather than after one: what is the literal list of fields the response returns, how is a lookup that finds nothing billed, is the CRM integration two-way or export-only, is there a per-seat charge on top of usage, and what is the minimum commitment. Each of those has been the reason a tool that demoed well was the wrong purchase.

Database size is the least useful number in the category. A hundred million verified records beats three hundred million unverified ones on every metric a rep experiences, and nobody sells against their own coverage gaps, so the only honest measure is a control list you already know the answers to. Run the same 100 rows through every finalist on the same day.

Be honest about which category you are actually buying. If the motion depends on knowing which accounts are in market, an intent platform such as ZoomInfo or Cognism is the correct purchase and enrichment will not substitute for it. If the team needs a research surface to browse rather than a pipeline to fill, that is a database licence. If the need is a list you already have coming back complete and verified, and landing in the CRM without an import step, that is what enrichment is for.

One bias worth naming: this page is published by a vendor in the category. The test above is designed to be run against Cleanlist AI as well, and the 14-day Pro trial with 250 credits exists so that it can be.

At the contact and company record. Cleanlist AI is an orchestration layer over other people's data rather than a data vendor with a database of its own, and it does not sell intent data, technographics or an owned database to browse.

Each of those has a right answer that is not us. Intent belongs to a platform built around it. A stored database you want to explore as a research surface is a licence purchase. Sending is different. Sequences, included on every paid plan, sends email from your own inbox, with call tasks, at 0 credits, and Cleanlist AI pushes to Outreach, Salesloft and Lemlist for teams that already run one.

What is left is a narrow job: take a list, resolve who the people are, find and verify how to reach them, and put the result where the team already works. Everything on this page is a claim about that job and nothing else.

Re-verify before you send rather than re-enriching everything on a calendar. Validation is 0.5 credits an address and finding a phone number is 10, so the cheap move is to check what you hold and only re-enrich what fails.

A workable cadence for most teams: validate any segment before a campaign touches it, re-enrich records that come back invalid, and re-run contact fields on open opportunities quarterly. That spends credits where decay actually hurts instead of spreading them evenly over a database that is mostly dormant.

The second half is upstream and is a CRM administration decision rather than a data purchase. A CRM that lets reps type into the same fields enrichment writes will drift however often it is cleaned, so the enriched fields should be the ones that are not hand-edited.

There is no single best tool, and the honest split is by what you are buying: a database licence, a workflow builder, or a per-result enrichment layer. Prices re-read from each vendor's own pricing page on September 7, 2026; plan compositions from the Cleanlist AI B2B Data Pricing Index, fetched September 1, 2026 and published at /data/b2b-data-pricing-index-2026-09.csv.

Cleanlist AI: Best for teams that want every rep drawing on one shared credit balance. It runs waterfall enrichment across 25+ providers and charges 1 credit per verified work email and 10 per phone number, with a miss costing nothing, starting at $49 per seat a month.

Apollo: Best for volume outbound that also needs sequencing in the same tool. Its Basic plan grants 30,000 credits per seat per year at 1 credit per email and 8 per phone, from $49 a month billed annually.

Clay: Best for RevOps teams building custom multi-source workflows. It meters Actions and Data Credits on two separate sliders, from $167 a month billed annually on Launch.

UpLead: Best for buyers who want one credit to cover email and mobile together. Its Essentials plan is 170 credits a month from $74 a month billed annually.

ZoomInfo and Cognism: Best for teams buying intent data and enterprise breadth. Neither publishes a purchasable price, so both are a quote conversation.

One bias worth naming twice: this page is published by a vendor in the category, and the control-list test in the accuracy section above is meant to be run against Cleanlist AI alongside everything else on this list.

Twelve contact-enrichment vendors publish enough for a price to be normalised to a common unit, and across those twelve, 1,000 verified work emails on an entry annual plan cost between $14.50 and $435.29. That is a 30x spread for the same nominal unit.

The ladder, in dollars per 1,000 verified work emails at each vendor's entry annual rate: Skrapp $14.50, Dropcontact EUR 16.50 (Dropcontact prices in euros), Apollo $19.60, Snov.io $29.25, Kaspr $49.00, Cleanlist AI $49.33, Ocean.io $63.00, LeadIQ $75.00, Surfe $260.00, Amplemarket $266.67, RocketReach $274.17, UpLead $435.29. Phone numbers on the same basis, per 1,000: Apollo $156.80, Cleanlist AI $493.33, UpLead $435.29, Ocean.io $630.00, LeadIQ $750.00. Every figure is the entry plan's annual price divided by the credits it grants, with the arithmetic published per row in the Cleanlist AI B2B Data Pricing Index, fetched September 1, 2026.

The index covers 37 vendors. Eleven with a normalisable email price are held out of that ladder: three are validation-only or sending tools, and eight are Cleanlist AI's own waterfall suppliers, which this page credits rather than ranks.

Six of the 37 publish no purchasable price at all: ZoomInfo, Cognism, Seamless.AI, NeverBounce, Outreach and Salesloft. Rechecked on September 7, 2026, zoominfo.com/pricing still answers HTTP 403 to a browser request and cognism.com/pricing still carries no dollar figure. Google's AI Overview for this term quotes ZoomInfo at around $14,900 a year, and at $15,000 a year on the adjacent tools and software queries, which is a number ZoomInfo does not publish. Cleanlist AI's own plan ladder is in the cost section above rather than repeated here.

One worked row. Before: a job title and a company domain, say a VP of Engineering at a payments company whose domain you already hold. Two fields, and nothing anybody can act on.

After: the same row carrying work_email, email_status, direct_dial, linkedin_url, job_title, seniority, department, company_name, company_domain, company_headcount, company_industry, hq_location and founded_year. Thirteen fields, spelled the way the Cleanlist AI API spells them, each one recorded against the provider that supplied it.

The bill on that row is 11 credits, 1 for the verified work email and 10 for the phone number. If the waterfall had walked the whole pool of 25+ providers and none had confirmed a contactable field, the row would have come back empty and the charge would have been 0.

That single row is the unit the whole category is priced in. A vendor quoting a per-record price is quoting it, and the only fair comparison between two quotes is how many of the thirteen fields each one filled and how many of those were verified against a live system rather than inferred.

B2B data is the set of facts about a business and the people inside it that a go-to-market team uses to reach them: firmographics about the company, contact fields for the person, and behavioural or technographic signals layered on top. It is a category of record rather than a single product, which is why two vendors both selling B2B data can have almost no overlap in what they return.

The three layers behave differently once you own them. Firmographics are comparatively stable and widely held, so several providers can be checked against each other. Contact fields decay fastest and are the ones a waterfall exists to recover. Signals are inferred from behaviour somebody else observed, and there is no second source to verify a signal against the way an email can be verified against a mail server.

Cleanlist handles the first two layers and does not sell the third. It returns company and contact fields with the supplying source attached to each, and it sells no intent data, no technographics and no database to browse. Reading a vendor's literal field list is the fastest way to work out which of the three layers you are being sold, and it is worth asking for in writing before a trial rather than after one.

There is no credible free B2B contact database. What exists is free enrichment allowances attached to paid products, which is a different object: a few dozen lookups a month against the same verified pool, rather than a browsable index you can export in bulk.

The allowances that are actually published, from the Cleanlist AI B2B Data Pricing Index fetched September 1, 2026, with Clay's and Kaspr's re-read from their own pricing pages on September 7, 2026. The Cleanlist AI Free plan is 50 credits a month for up to 3 seats. A new Cleanlist AI workspace starts with 14 days of Pro: 250 credits and 3 seats. Apollo's free plan grants 75 credits per seat per month. Clay's free plan grants 6,000 actions and 1,200 data credits a year. Kaspr's free plan grants 15 B2B email credits, 5 phone credits and 5 direct email credits a month. UpLead runs a 7-day trial with 5 credits.

A genuinely free scraped database is a liability rather than a bargain. Under the legitimate-interest basis described in the compliance section above you have to be able to say where a field came from and suppress a person on request across every copy, and a dataset with no provenance answers neither question.

A data provider owns and licenses records. An orchestration layer buys from several providers and resolves between them. Cleanlist AI is the second, which is why every field it returns carries the source that supplied it and why it publishes no database size of its own.

The category sorts into three shapes rather than one ranked list. API-first providers license records to be queried programmatically, and People Data Labs, Coresignal and Explorium are the ones most often built against. Database licences sell a seat on a stored index with a research surface attached, which is what ZoomInfo, Cognism and Seamless.AI are. Platforms sit above both and combine sources with workflow, which is where Clay and Apollo sit, and where Cleanlist AI sits.

Which shape you want follows from the job. A product that enriches its own signups wants an API-first provider or an orchestration layer with a REST API. A team that wants to browse a market and build lists from scratch wants a database licence. A team holding a list that needs to come back complete, verified and landed in the CRM wants the third shape, and paying a licence for that is the expensive route to it.

Yes for the produced fields, and no for the stored ones. A model can research a company and write an answer to a question you asked. It cannot know a person's phone number, and an address a model generates is a guess until something outside the model confirms it.

In Cleanlist AI that boundary is priced. AI research columns cost 5 credits and answer a question you write, such as what a company sells or whether it has announced a funding round, and the answer carries the date it was produced because a produced answer without one is worthless six weeks later. The stored fields go the other way: an email candidate returned by any of the 25+ providers is verified at the mailbox before it is accepted, which is what lets the record carry an email_status instead of a hope.

The conversational surface is the MCP server, available from Starter upward, which lets an assistant such as Claude run search and enrichment directly against the account. The public REST API on Pro and Enterprise takes structured filters instead, so an integration passes explicit criteria rather than a sentence.

For Salesforce specifically the first question is whether the integration reads from the CRM or only writes to it. An export-only tool enriches lists you assemble somewhere else and pushes the result in. A two-way integration pulls the records you already own, enriches them, and writes the filled fields back, which is where the 22.5% annual decay described above is actually sitting.

Cleanlist syncs two-way with Salesforce, HubSpot and Pipedrive on the Pro and Enterprise plans at 0.2 credits per lead pushed, and Starter has no CRM sync at all. Outreach, Salesloft and Lemlist receive rows one way only.

The alternatives sort by where they live. The enterprise suites sell a Salesforce-native package with the licence, at a price neither of them publishes. HubSpot Breeze Intelligence, the former Clearbit, is HubSpot-only and has no standalone price: its pricing URL redirects to the knowledge base, checked September 1, 2026. Whichever you shortlist, ask in writing whether the sync reads or only writes, because a demo shows the write direction either way.

Measured on September 7, 2026, Google's AI Overview for the query b2b data enrichment names three vendors, cites eleven sources, and includes Cleanlist AI in neither list. We ran the census ourselves against Google US desktop through the DataForSEO SERP API, and the detail is worth printing because it shows what the engine is reading when it writes a shortlist.

The definition is taken from commonroom.io, the four-type taxonomy of firmographics, technographics, contact data and intent from bombora.com, and the benefits list from demandbase.com, whose short FAQ page also holds organic position 1. Positions 2 and 3 are smarte.pro and autobound.ai. The answer then prints a Popular Tools block: ZoomInfo at around $14,900 a year, Apollo.io at $49 a month, and Demandbase. On the adjacent queries b2b data enrichment tools and b2b data enrichment software the named set widens to ZoomInfo, Apollo, Clay at $149 to $167 a month, Cognism and HubSpot Breeze Intelligence, and on both of those cleanlist.ai is one of the cited sources.

One pattern runs through all three answers. Every vendor Google names arrives attached to a price, and every price it prints was lifted from a third-party article rather than from the vendor's own page, including the ZoomInfo figure that ZoomInfo does not publish. That is the reason the cost section above carries the ladder with its arithmetic and its fetch date attached.