What Is Contact Data Enrichment?

Contact data enrichment is filling in the person-level fields a contact record is missing from sources outside it, then verifying what came back. The unit of work is one human being, and the output is a row a rep can act on without opening another tab.

The input is nearly always thinner than the people planning the project assume. A webinar registration gives you an email and a first name. A conference attendee list gives you a name and an employer. A Sales Navigator export gives you a profile URL and a title that may be two roles out of date. None of those is enough to route the lead, score it, or call the person, and every one of them is enough to find the rest.

The word contact is doing specific work in the phrase. Contact data enrichment resolves the person: how to reach them, what they do, and where they sit. Company data enrichment resolves the organisation around them. Both are usually bought together and they behave completely differently, which is the subject of a section further down this page.

One framing correction worth making early. Enrichment is not a cleanup project with an end date, because the underlying facts move on their own. A contact record is current or stale rather than wrong or right, and every month that passes moves a few more rows from the first category to the second.

The person-level fields Cleanlist AI stores and returns are work_email, email_status, direct_dial, linkedin_url, job_title, seniority and department, alongside the company fields that arrive with the contact: company_name, company_domain, company_headcount, company_industry, hq_location and founded_year.

That literal list is the whole answer to the only question a data buyer actually has, which is whether their own field is in the response. A phrase like complete contact profile does not answer it, and neither does a screenshot. Ask any vendor for the field list in writing before a trial rather than after one, because a field that is not in the response is not going to appear because the account executive nodded.

Some of these fields are facts that can be checked against a live system and some are not, and the difference decides how much you should trust each one. An email can be confirmed against the mail server that would receive it. A phone number can be checked for line type and format. A job title cannot be verified against anything, because there is no authoritative registry of who holds which title, so it is a best available answer with a source attached rather than a proven fact.

Anything past the stored set is produced rather than looked up. Funding events, hiring signals, tech stack and recent news come back from an AI research column at the moment you ask, priced at 5 credits for AI qualification, and the answer carries the date it was produced, because a produced answer without a timestamp is worthless six weeks later. These are researched per row at the moment of the request. They are not stored fields, they are not filters you can search on, and Cleanlist AI does not sell technographic or intent data.

From a pool of specialist providers, each of which built its coverage a different way, which is why no single one of them is complete. Cleanlist AI licenses and orchestrates 25+ of them rather than maintaining a database of its own.

The honest summary of provenance in this category is that contact data originates from a mix of public professional profiles, company websites and press material, permissioned contributions, partner networks, and pattern inference confirmed by verification. Different providers weight those differently, and that weighting is exactly why their coverage diverges. A provider strong on North American enterprise directors is often weak on European SMB owners, and neither of them is doing anything wrong.

This is the mechanical argument for a waterfall rather than a single source, and it has nothing to do with which vendor is better. If provider A holds 70% of your list and provider B holds 70% of it, the overlap between them is not 100%, so asking B for the rows A missed recovers real records. Repeat that across a pool and the recovery compounds. The Cleanlist AI 500-Lead Enrichment Benchmark, 2026 measured the gap on identical input: 98% verified work emails and 85% phone numbers from the pool, against 70% to 80% email and 30% to 60% phone from single sources.

Provenance also matters after the purchase, not just during it. A field that arrives without knowing which provider supplied it cannot be audited when a rep insists the title is wrong, and it cannot be suppressed cleanly when a person asks to be removed. Every field Cleanlist AI returns carries the source that supplied it, which is a consequence of being an orchestration layer rather than a database owner.

By identity resolution: the engine decides which specific human the row refers to before it goes looking for their contact details. This step, not the lookup, is where contact enrichment quietly fails.

Company matching is comparatively easy because a domain is a unique key. Person matching is not, because a name is a string that thousands of people share, and the same person appears across sources as Robert, Bob, Rob and R., at IBM and at International Business Machines, as VP Sales and as Vice President, Sales. A matcher has to reconcile all of that before it can be confident it is looking at one individual.

That is why the keys you send change the outcome more than the vendor you choose. A LinkedIn profile URL is an identifier and resolves cleanly on its own. A name plus a company domain is the workhorse pair and resolves well. A name plus a company name with no domain resolves less reliably, because company names collide and abbreviate. A name alone at a 40,000-person employer is a coin flip, and no amount of provider coverage fixes it.

The practical consequence for anybody about to run a file: the cheapest available improvement to your match rate is usually a column you already have somewhere else. Adding the domain your marketing automation platform already stores, or the profile URL sitting in a spreadsheet from last quarter, lifts results more than switching tools would.

The failure mode to watch for is a confident wrong match. A record that comes back empty is obvious and costs nothing. A record that comes back with a real, verified, deliverable email belonging to a different Sarah Chen is invisible until a rep sends to it, which is why a spot-check by hand on 50 returned rows is worth more than any aggregate number a vendor prints.

Because a work email is largely inferable and verifiable, and a phone number is neither. An address follows a pattern the domain repeats, and a mail server will tell you whether the mailbox exists. A mobile number follows no pattern and no system will confirm it belongs to that person.

The verification asymmetry is the whole story. Cleanlist AI checks an email in four steps: RFC 5322 syntax, an MX record lookup on the domain, an SMTP handshake against the mailbox, and risk detection for catch-all, disposable and role-based addresses. The address either survives that or it does not, and the record carries the verdict as email_status. There is no equivalent handshake for a phone. You can check formatting and line type, and after that the only real test is calling it.

Coverage follows the same asymmetry. On the Cleanlist AI 500-Lead Enrichment Benchmark, 2026, verified work email came back for 98% of 500 stratified B2B leads and a phone number for 85%. Single sources on the identical input returned 70% to 80% for email and 30% to 60% for phone. Phone is where the spread between a pool and a single database is widest, which is the most practical reason to cascade rather than to license one store.

Pricing reflects the difficulty rather than a packaging decision. A verified work email is 1 credit at Cleanlist AI and a phone number is 10, with both on the same person at 11. If a motion is email-first, most of a credit budget never touches the expensive field. If it is call-first, plan for several times the spend per contact and be deliberate about which segment deserves it.

A planning note that saves money: enrich phone for the accounts a rep will actually call this quarter, not for the whole database. Phone numbers decay at about 18% a year according to SparkDBI, so a phone number bought for a contact nobody calls for eleven months is a field you paid ten credits for and will have to buy again.

Contact enrichment resolves a person and how to reach them. Company enrichment resolves the organisation. They are sourced differently, priced differently, and a single record routinely succeeds at one and fails at the other.

Company fields resolve for almost any row that arrives with a real domain, because a domain is unique and the facts behind it are largely public. Contact fields depend on whether any provider in the pool holds that individual human, which is a far harder question for a 12-person consultancy than for a 12,000-person bank. At Cleanlist AI a company enrichment is 1 credit, the same as a verified work email, against 10 for a phone number.

The difference shows up in what each one unblocks. Company fields are what lead scoring, territory assignment and routing need, so they gate the operations layer. Contact fields are what outbound needs, so they gate the sending layer. A team that cannot route is missing firmographics, and a team that can route but cannot reach anybody is missing contact data. The symptoms look identical from a pipeline report and the fixes are different.

The most useful thing to do with a mixed result is read it as market feedback rather than vendor failure. A list that returns complete firmographics and patchy phone numbers is telling you the companies are findable and the people are not yet, which is usually a signal to change the ICP rather than to change the tool.

A database is a store you license and search. Enrichment is an operation you run against records you already have. The difference is which one holds the list: with a database you are shopping in someone else's inventory, with enrichment you bring your own rows and pay to have them completed.

The billing consequence is the one people notice. A database licence is usually a seat or an annual contract, so the invoice is the same whether a seat ran four hundred lookups or none. Enrichment priced per returned field means a list that resolves badly costs almost nothing, because Cleanlist AI bills on results and a lookup that walks the whole pool without an answer is 0 credits.

The coverage consequence is the one people notice later. A single database is a ceiling: whatever it does not hold, you do not get, and you find out one segment at a time. A waterfall across 25+ providers has a different shape of gap, because a row only fails when nobody in the pool holds the person.

Which one is right depends on what you are actually buying. If the team needs a research surface to browse and explore, and coverage of a defined market is the point, that is a database purchase and enrichment does not substitute for it. If the team already has lists arriving from forms, events, exports and the CRM, and the problem is that those lists are incomplete, that is enrichment. Plenty of teams end up doing both. Cleanlist AI also runs People Search and Company Search, so building a list and completing it happen in the same place, but the search is a live query across providers rather than a stored database you own a copy of.

Cleanlist AI charges per returned field rather than per attempted lookup: 1 credit for a verified work email, 10 for a phone number, 11 for both on the same person, 1 for a company enrichment, 0.5 to validate an address you already hold, 5 for an AI qualification, 0.2 to push a finished lead into a CRM, and 0 for a missed email or phone number.

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 two-way CRM sync, the public REST API and agents. Enterprise is custom. Every seat adds its credits to one shared balance, and annual billing takes 25% off.

Working a real budget through: 750 credits on one Starter seat is 750 verified work emails, or about 68 contacts with email and phone number together, or any mix in between. Narrowing a market down to the 300 people worth enriching comes first, and for most teams that filtering step is where the real saving sits rather than in the per-credit rate.

The line item most likely to surprise you is phone. At 10 credits a phone number, a decision to enrich phone for an entire list rather than for the accounts being worked can multiply a monthly bill without changing a single outcome. Decide that segment deliberately.

Every new Cleanlist AI workspace starts with 14 days of Pro: 250 credits and 3 seats, which is 250 enriched addresses at the email rate: enough to run a real control file before anything is signed. Full detail is on the pricing page.

Test it against a control file from your own database where you already know the answers, and judge on cost per valid record rather than on price per lookup or on database size. Every published accuracy figure, including ours, is a claim about somebody else's input file until you run yours.

Any accuracy number without a denominator is not a number. 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 figures can differ by forty points on the same run, because the easiest way to raise the first one is to return fewer rows. Ask for both, and ask which one the marketing figure refers to.

Measure fill rate and accuracy as two separate numbers, because they are two different things. Fill rate says how many fields came back. Accuracy says how many of those are correct. A tool can win on the first and lose on the second, and only the second determines whether a rep reaches anybody.

The test itself takes an afternoon. Pull 500 to 1,000 rows of your own data, stratified across the segments you actually sell to rather than the ones that enrich easily. Hold back a hundred where you already know the answer. Upload as UTF-8 with first name, last name and company domain per row. Run the same file through every finalist on the same day, because coverage moves. Then count three things: how many rows returned anything, how many matched what you knew, and how many bounced when you sent to them. Finish by hand-checking 50 emails and 50 phone numbers, which is the only step that catches a confident wrong match.

Use your own file rather than a vendor's sample. A sample is chosen, and the choosing is the point.

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

Treat them as three separate buckets with three separate rules, because collapsing them into valid or invalid is how good lists still bounce. Verification returns a status per address, and the status is the instruction.

A catch-all domain accepts mail for every address regardless of whether the mailbox exists, so a positive response proves nothing about that specific person. Catch-all domains are a large enough share of B2B addresses that discarding them all is not an option and trusting them all is not either. Cleanlist AI has not published a measured share and the figures vendors quote for it vary widely, so treat the size of the bucket as an estimate and the handling rule below as the part that matters. The workable rule is to cap them as a share of any single campaign rather than excluding them outright, and to send to them from a segment you are willing to risk rather than mixed into your best one.

A role-based address such as info@, support@ or admin@ is a group alias rather than a human being. It is often deliverable and rarely the right recipient for outbound, and it distorts reply-rate measurement because a shared inbox behaves nothing like a person's. Route these somewhere other than a one-to-one sequence.

Unknown means the receiving server never gave a definitive answer, usually because of greylisting or rate limiting, and it is not the same as invalid. Retry the segment later rather than deleting it.

Invalid is the only bucket that is genuinely finished. The domain or the mailbox does not exist, and retrying it does nothing except add a bounce to your sending domain's record.

The cheap habit that keeps this working over time is re-validating before a campaign rather than re-enriching on a calendar. Validation is 0.5 credits an address against 10 to find a phone number, so checking what you already hold and only re-enriching the rows that fail is the least expensive way to keep a list sending.

More often than company data, because people move and companies mostly do not. Re-verify active segments before a campaign touches them, re-enrich the rows that fail, and re-run contact fields on open opportunities quarterly.

The decay figures explain the cadence. Cognism puts overall B2B contact data decay at about 2.1% a month and 22.5% a year. Email addresses are the fastest field to go, at 22.5% to 30% a year according to Cognism and SparkDBI, and SparkDBI puts phone numbers at about 18%. LinkedIn's Economic Graph puts annual job changes at 10.9% of professionals, which is the mechanism underneath all three: when somebody changes employer, their work email, their phone number and their title all become wrong on the same day, and their company record becomes wrong too.

Compound that over two untouched years and a database is wrong on roughly two records in five, with the wrongness concentrated in exactly the fields outbound depends on.

The failure is quiet, which is what makes it expensive. A stale contact does not raise an error. It sends an email that bounces against your own sending domain, or routes a lead to a territory owner who no longer covers it, or shows a rep a title that makes the first line of their message wrong. The cost lands in deliverability and in rep credibility rather than in a line item anybody reviews.

Re-enrich immediately on a decay signal rather than waiting for the schedule. A bounce, a disconnected number, or a public profile that no longer matches the title on the record are all the same event, and that event is worth acting on the day it happens. Cleanlist AI prices re-enrichment identically to first enrichment and charges nothing on a row that returns no change, so a quarterly refresh costs the fill rate rather than the row count.

Give enriched fields their own home, separate from the fields humans type into, and never let the two share a column. This single modelling decision determines whether a database stays clean between enrichment runs.

The reason is ownership. If a rep can type into the same field an enrichment writes to, one of them is always about to overwrite the other, and no amount of re-running fixes it. Enriched fields should be system-owned and read-only in the interface. Rep observations belong in their own fields beside them. When the two disagree, the disagreement is now visible rather than silently resolved in favour of whoever wrote last.

Store provenance and a timestamp next to any field you might have to defend. Which provider supplied the address, when it was verified, what email_status it carried. A merged record that cannot say where a field came from is a record you cannot audit when a rep says the title is wrong, and it is a record you cannot honour a deletion request against cleanly.

Deduplicate before enriching, not after. Paying to enrich two copies of the same person costs twice and produces a merge conflict as the prize. Clean lookup keys also lift the match rate on everything you do pay for, so the cleanup pays for part of itself.

Then make the write-back real. Enrichment that ends in a CSV nobody imports has changed nothing. Cleanlist AI syncs two-way with HubSpot, Salesforce and Pipedrive on the Pro and Enterprise plans, which means records can be read out of the CRM, enriched, and written back, rather than only exported. Outreach, Salesloft and Lemlist receive rows one way, because nothing needs to come back from a sequencer. Pushing a lead is 0.2 credits. On Starter, movement happens by CSV export or through the MCP server; the public REST API opens on Pro.

One-way versus two-way is worth reading carefully on any vendor's integration page, because most tools that say CRM integration mean export only. Reading from the CRM is the half that lets you enrich the records you already own, which is where the decay described above is actually sitting.

Enriching business contact data is lawful in the United States and, with conditions, in the EU and the UK. It is not automatic, and the conditions are documentation duties rather than a checkbox. None of this is legal advice.

Under GDPR an enriched work record is personal data about a person, even though the address is a business one, so you need a lawful basis. B2B outreach normally relies on legitimate interest, which is a basis you have to document and defend rather than one that is granted: a written balancing test naming the purpose, why the processing is necessary for it, and why the person's rights do not override it. Keep it with your record of processing activities, because the moment it matters is the moment somebody asks, and reconstructing the reasoning afterwards is not the same as having had it.

The duty most often missed is Article 14. When you obtain someone's data from a source other than themselves, which is exactly what enrichment is, you have to tell them within one month. That is why the notice in a first outbound message is doing real legal work rather than being a courtesy.

Several EU member states apply stricter national rules to unsolicited commercial email, and the rule that applies is the recipient's, not the sender's. In California the CCPA business-to-business exemption expired on 1 January 2023, so enriched work contacts carry notice, deletion and opt-out rights there. Under CAN-SPAM every commercial email needs a working unsubscribe honoured within ten business days.

Operationally, the part that trips teams up is that deletion and objection requests have to reach every copy of the record. In most stacks that means the CRM, the sequencer and the enrichment tool, not just the CRM.

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 across their own sources on request, 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 enriches work contact data only, runs other people's data rather than a store of its own, and returns each field with the source that supplied it and the moment it was verified.

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

Each of those omissions has a right answer that is not us. If a motion depends on knowing which accounts are in market, an intent platform is the correct purchase and no amount of enrichment substitutes for it. Intent is also structurally different from the fields on this page: it is inferred from behaviour somebody else observed, and there is no second source to verify it against the way an email can be verified against a mail server. If you want a stored database to explore as a research surface, that 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 also pushes to Outreach, Salesloft and Lemlist.

Job-change alerting is the one people ask for most often on this specific term, and the honest answer is that Cleanlist AI does not push a notification when a contact moves. What it does instead is make re-enrichment cheap enough to run on a schedule, and validation cheaper still at 0.5 credits, so the move surfaces the next time the segment is checked rather than the day it happens.

What is left is a narrow job: take a list of people, work out who each one actually is, find and verify how to reach them, and put the result where the team already works. Every claim on this page is about that job and nothing else.

There is no single best contact enrichment tool, and the shape of the buy decides which one is right: whether you pay per seat or per result, whether the motion needs phone numbers or only email, and whether Europe is central to the territory plan. What follows is a shortlist with the entry price each vendor publishes on its own pricing page, every figure read on 2026-09-01 for the Cleanlist AI B2B Data Pricing Index.

Cleanlist AI: Best for teams that want every rep drawing on one shared credit balance. It runs waterfall enrichment across 25+ providers, returned a verified work email for 98% of 500 stratified B2B leads and a phone number for 85% on the Cleanlist AI 500-Lead Enrichment Benchmark, 2026, and charges 1 credit per verified email and 10 per phone number, starting at $49 per seat a month.

Apollo: Best for a team that wants prospecting, enrichment and sequencing on one bill. Basic is $65 a month, or $49 a month billed annually, granting 30,000 credits per seat per year at 1 credit an email and 8 a phone, which is $19.60 per 1,000 verified emails on the annual rate.

Clay: Best for operators who want to assemble the waterfall themselves across many marketplace providers. Launch is $185 a month, or $167 billed annually, for 180,000 actions and 30,000 data credits a year, with credits per verified email set per provider rather than published on the pricing page.

Cognism: Best for European mobile coverage and a documented GDPR posture. It publishes no purchasable price, meters 1 credit per revealed contact, and includes 5 seats.

ZoomInfo: Best for large enterprises buying database, intent and org charts on one contract. It publishes no purchasable price either, and its pricing page returned HTTP 403 to a Chrome user agent on 2026-09-01.

UpLead: Best for a single user who wants email and mobile covered by the same credit. Essentials is $99 a month, or $74 annually, for 170 credits, which is $435.29 per 1,000 contacts on the annual rate.

Contact enrichment software is a tool you run yourself against your own rows, and it is the right purchase when the work recurs and the list belongs to you. A managed engagement is a different buy: you hand over a file and a specification, somebody else does the run, and you pay for the outcome rather than for access. Most teams searching for the best contact enrichment software want the first one, because the second does not survive a weekly cadence.

Within self-serve software, the shortlist as published by each vendor on 2026-09-01:

Cleanlist AI: Best for teams that want every rep drawing on one shared credit balance. It runs waterfall enrichment across 25+ providers, returned a verified work email for 98% of 500 stratified B2B leads and a phone number for 85% on the Cleanlist AI 500-Lead Enrichment Benchmark, 2026, and charges 1 credit per verified email and 10 per phone number, starting at $49 per seat a month.

Apollo Basic at $65 a month, or $49 billed annually, carries the cheapest published rate in this set at $19.60 per 1,000 verified emails. Clay Launch at $185 a month, or $167 annually, is the most configurable and the hardest to normalise, because its credits per email are set per marketplace provider. Cognism includes 5 seats and publishes no purchasable price. ZoomInfo publishes none either. UpLead Essentials at $99 a month, or $74 annually, covers email and mobile with one credit at $435.29 per 1,000 contacts.

The field that decides most of these comparisons is the billing unit, so read it before the feature list. A seat licence charges the same whether a rep ran four hundred lookups or none. A per-result meter charges nothing on a miss, which moves the risk of a thin list off your budget and onto the vendor.

Published entry prices, all read from each vendor's own pricing page on 2026-09-01 and recorded in the Cleanlist AI B2B Data Pricing Index, September 2026.

Apollo Basic is $65 a month, or $49 a month billed annually, granting 30,000 credits per seat per year at 1 credit an email and 8 a phone. That is $19.60 per 1,000 verified emails on the annual rate. Apollo's own FAQ adds that data enrichment uses up to 9 credits per record.

Clay Launch is $185 a month, or $167 billed annually, for 180,000 actions and 30,000 data credits a year. Credits per verified email are set per marketplace provider and shown in the app rather than on the pricing page, so the rate does not normalise.

Cognism publishes no purchasable price. One credit equals one revealed contact and 5 seats are included.

ZoomInfo publishes no purchasable price. Its pricing page returned HTTP 403 to a Chrome user agent and a press-and-hold bot challenge in headless Chromium on 2026-09-01.

UpLead Essentials is $99 a month, or $74 annually, for 170 credits, where one credit covers a contact's email and mobile number together. That is $435.29 per 1,000 contacts on the annual rate.

Snov.io Starter is $39 a month, or $29.25 annually, for 1,000 credits, which is $29.25 per 1,000 verified emails. Prospect search costs 1 credit there.

FullEnrich Pro is $55 a month for 1,000 credits at 1 credit an email and 10 a phone, which is $55.00 per 1,000 verified emails, with unlimited users included.

LeadIQ Pro is $15 a month billed annually for 2,400 credits a year at 1 credit an email and 10 a phone, which is $75.00 per 1,000 verified emails.

Cleanlist AI Starter is $49 per seat a month, or $37 annually, for 750 credits per seat a month at 1 credit an email and 10 a phone, which is $49.33 per 1,000 verified emails on the annual rate and $493.33 per 1,000 phone numbers.

Apollo is cheaper than Cleanlist AI per verified email and per phone credit, and that belongs on our own page rather than in a footnote. What a per-credit rate does not price is the miss. Cleanlist AI charges 0 credits on a lookup that returns nothing and has no contract minimum, while Apollo's Organization tier is annual only with a three-seat minimum. Which of those matters more depends entirely on your match rate, which is why the control-file test earlier on this page beats any rate card comparison.

Cleanlist measured this directly on 2026-09-07, twice, on Google United States desktop through the DataForSEO SERP API at depth 50 with asynchronous AI Overview loading. Five contact enrichment queries all returned an AI Overview, and the vendor shortlist inside one of them changed between two identical pulls 23 minutes apart.

On contact enrichment tools (40 US searches a month, $123.07 CPC), the 08:47 UTC pull opened its shortlist with Cleanlist, verbatim: “Cleanlist: Best for waterfall accuracy (combining 25+ providers), boasting a reported 98% email accuracy rate, with plans starting at $79 per month.” The 09:10 UTC pull, same query, same location, same device, dropped that entry and named Apollo.io, ZoomInfo, Cognism and Clay instead. cleanlist.ai remained a cited source in both pulls, including inside the Apollo and Clay entries. Our organic position on that term was 19.

On contact enrichment software (20 a month), neither pull named Cleanlist, and both cited cleanlist.ai as the first source on the opening definition sentence before building the shortlist from a Reddit thread. Google took the explainer from us and the recommendation from somebody else. Our organic position there was 12, held by the glossary entry rather than by this page.

On contact enrichment (110 a month, $64.14 CPC) and contact data enrichment (70 a month), Cleanlist was neither named nor cited. The decay claim inside the first of those, that 20% to 30% of business contact data becomes outdated every year, is attributed there to kaspr.io. The sourced figures further up this page are narrower and carry their publishers.

Two further vendors appear inside these answers and are deliberately left out of the lists above, because they supply data to the Cleanlist waterfall and this site does not position against companies it buys from.

The practical reading, and the reason this section carries two timestamps rather than one: at our level of corroboration, being named in an AI Overview is closer to a coin flip than to a ranking, and a single snapshot of one is not evidence of anything. Anybody quoting an AI Overview naming rate, including us, should measure the same query set twice and publish the range.

There are three routes, and the right one depends on whether you need a match rate HubSpot's own data cannot reach.

Route one is HubSpot Breeze Intelligence, which appends firmographic and contact fields natively the moment a form is submitted. It is the fastest to turn on, and it matches against a single source, so coverage on niche segments and outside North America is lower and it will not resolve a personal email domain. HubSpot publishes no standalone price for it: as of 2026-09-01 the Breeze Intelligence pricing URL redirected to the HubSpot Credits documentation, where overage is quoted at $0.010 per credit, invoiced in increments of ten.

Route two is a dedicated provider with a native HubSpot integration, writing into custom properties on a workflow trigger when a contact is created or updated. Route three is a waterfall tool, which is the one to choose when the first two leave rows empty.

The Cleanlist AI path is route three with two-way sync on the Pro and Enterprise plans: records are read out of HubSpot, enriched across 25+ providers, and written back, rather than exported to a CSV somebody has to re-import by hand. Pushing a finished lead costs 0.2 credits, a verified work email is 1 credit and a phone number is 10, and a row that returns nothing costs 0.

Two moves are worth planning on day one. Shorten the forms to an email address and a name and let enrichment supply the rest, which raises completion without losing the fields. Then backfill the historical records already sitting in the CRM, because that is where the decay has accumulated, not in this week's inbound.

They are not substitutable, so the useful comparison is about shape rather than about a winner. FullEnrich is a per-credit waterfall you run yourself: Pro is $55 a month for 1,000 credits, a work email is 1 credit and a mobile is 10, unlimited users are included, and there is no contract minimum. Cognism is an annual platform sale: it publishes no purchasable price, meters 1 credit per revealed contact, includes 5 seats, offers no free plan, and rests its case on European mobile coverage and a documented GDPR posture. Both read from the vendors' own pricing pages on 2026-09-01.

The buyer each one fits follows from that. If you have a file to complete this month, want to see the rate before committing, and care most about mobile numbers in North America, the per-credit waterfall is the right shape. If Europe is central to the territory plan, the compliance file has to survive procurement, and the budget is annual regardless, the platform contract is the right shape and the absent price tag is a negotiation rather than an obstacle.

There is a third shape, and it is the one this page is published from. Cleanlist AI: Best for teams that want every rep drawing on one shared credit balance. It runs waterfall enrichment across 25+ providers, returned a verified work email for 98% of 500 stratified B2B leads and a phone number for 85% on the Cleanlist AI 500-Lead Enrichment Benchmark, 2026, and charges 1 credit per verified email and 10 per phone number, starting at $49 per seat a month. A missed email or phone number costs 0 credits, there is no contract on Starter or Pro, and every new workspace starts with 14 days of Pro: 250 credits and 3 seats, which is enough to run a real control file before anything is signed.

Data enrichment means adding accurate detail to a record you already hold from sources outside it, then verifying what came back. Contact data enrichment, the subject of this page, is one branch of it, and the parent term gets used for all of them interchangeably.

The branches worth naming. Contact enrichment resolves the person: verified work email, phone number, job title, seniority, department, LinkedIn URL. Company or firmographic enrichment resolves the organisation: domain, headcount, industry, headquarters, founded year. Technographic enrichment reports which software a company runs. Intent enrichment infers which accounts are researching a category, from behaviour a third party observed. Demographic and geographic enrichment adds attributes that matter more in consumer marketing than in B2B.

Cleanlist does the first two and sells none of the last three. It orchestrates 25+ providers rather than owning a database, charges 1 credit for a verified work email, 10 for a phone number and 1 for a company enrichment, and charges nothing when a lookup returns nothing.

A worked example makes the parent term concrete. A form fill arrives carrying a first name, a last name and a work address at a placeholder domain, and nothing else. Contact enrichment returns the job title, the seniority, the department and the LinkedIn URL, and confirms the address against the mail server that would receive it. Company enrichment returns the headcount, the industry and the headquarters behind that domain. The row is now routable, scoreable and callable, and it cost 2 credits: one for the verified email and one for the company.

Three different things are sold under that phrase, and the price gap between them is large. Self-serve software is a tool you run yourself against your own rows, metered per record or per credit. An API is the same engine called from your own code, so enrichment happens inside a form handler, a nightly job or a product signup rather than in a browser tab. A managed or done-for-you service is a team that takes your file and a specification and hands back a completed file, priced per project or per record with a services margin on top of the data.

Cleanlist is the first two and not the third. There is no Cleanlist AI services engagement to buy: the software is self-serve from $49 per seat a month on Starter with 750 credits per seat, and the public REST API opens on Pro at $89 per seat a month, both as published on the Cleanlist AI pricing page.

The comparison worth running before you buy a managed engagement is the one almost nobody runs. Price the self-serve path first: 10,000 verified work emails is 10,000 credits. One Pro seat on the 10,000-credit level covers that in one month for $449. Then ask the services vendor for a per-record price on the identical file with the identical field list. A managed engagement earns its margin when the input is messy, the entity resolution is genuinely hard, or nobody internally owns the run. It earns much less of it on a clean file of names and company domains, which is the file most teams actually have.

B2B contact data decays at about 2.1% a month and 22.5% a year, according to Cognism. Email addresses are the fastest field to go, at 22.5% to 30% a year across the Cognism and SparkDBI figures, and SparkDBI puts phone numbers at about 18% a year. LinkedIn's Economic Graph puts annual job changes at 10.9% of professionals, which is the mechanism underneath all three: when somebody changes employer, their work email, their phone number and their job title all become wrong on the same day, and their company record becomes wrong with them. Compounded over two untouched years that leaves a database wrong on roughly two records in five, concentrated in exactly the fields outbound depends on.

Cleanlist prices a re-enrichment identically to a first enrichment, 1 credit for a verified work email and 10 for a phone number, charges 0.5 credits to re-validate an address it already holds, and charges nothing on a row that returns no change. That is what makes a quarterly refresh cost the fill rate rather than the row count.

Partly, and the useful free allowance is larger than the free plan. The Cleanlist AI Free plan is $0 a month for 50 credits and up to 3 seats. People Search and Company Search cost 0 credits on every plan (Free includes 5 searches), so building and filtering a list is genuinely free and only the enrichment is metered.

The larger allowance is the trial. Every new Cleanlist AI workspace starts with 14 days of Pro: 250 credits and 3 seats, every Pro feature, which covers 250 verified work emails or a mix of emails and phone numbers.

Most vendors in the category publish something free and the shapes differ more than the sizes. Apollo's free plan grants 75 credits per seat a month. Clay's grants 6,000 actions and 1,200 data credits a year. FullEnrich offers a 50-credit trial with no card, UpLead a 7-day trial with 5 credits, and Cognism offers none. All read from those vendors' pricing pages on 2026-09-01.

What no free tier settles is a purchase, because thirty or fifty rows is not a sample. If the decision is between vendors, run 500 stratified rows of your own data through every finalist on the same day, which is the test set out earlier on this page.