Ocean.io
Point at your best customers and get the market segment that looks like them
Ocean.io is a B2B account segmentation and lookalike platform: it reads what a company actually does by turning its website into a vector rather than trusting industry codes, then finds and ranks the companies that resemble a customer list, sizes the resulting segment, enriches it with company and contact data from a database of around 35 million companies, and exports or syncs it to a CRM, priced on credits from about $79 a month.
Overview
Ecommerce segmentation asks which of your customers to talk to next. B2B segmentation asks which companies should have been your customers in the first place, and that question is badly served by the standard tooling. Industry codes are a taxonomy from a different era: a vertical SaaS company, a marketplace, and a services firm can share one code and share nothing else. Ocean.io's entire premise is that a company's own website describes what it does better than any code assigned to it.
The mechanism is worth understanding because it explains both the strengths and the failures. Ocean.io converts company websites into mathematical vectors, so similarity is computed on what a business actually says it does rather than on a label. Feed it a list of your best customers, or a single company URL, and it returns companies whose descriptions sit near them in that space. The team rebuilt the engine on this contextual approach around 2019 after starting with simpler keyword embeddings.
The practical use is market segmentation rather than list building, even though it can do both. Before a campaign, the question is usually how many companies genuinely look like our thirty best accounts, where are they, and how big is that market. Ocean.io answers that as a sized, filterable segment, and only then does contact enrichment turn it into something you can send email to. Teams that treat it purely as a contact database tend to be disappointed and are usually using the wrong tool for that job.
Commercially it is a credit model with a slider rather than clean tiers. Published plans start around $79 a month billed annually with a few hundred credits, with a professional tier near $299 that adds CRM integrations, and per-credit rates that fall with volume. The important detail buried in that structure is that different actions cost different numbers of credits, with phone numbers being markedly more expensive than company records, and independent reviews consistently report customers exceeding their allowance and paying overages.
The company is a real business rather than a thin wrapper: founded in Copenhagen in 2017 by Michael Heiberg, with an office in Minneapolis, more than $10 million raised, and a reported $9.6 million of revenue with around 64 staff as of 2024. Its database covers roughly 35 million companies and a large number of professional profiles, and it claims a very high CRM match rate, which is the metric that decides whether a segment can be compared against what you already own.
Best for
B2B teams that need to define and size an addressable market from their own best customers rather than from industry codes, and that want a ranked, filterable account segment they can enrich and push into a CRM without buying an enterprise data contract.
Not the right fit for
- Ecommerce businesses segmenting their own consumer customers, which is a completely different job served by the RFM tools in this category.
- Teams whose primary need is a large volume of cheap contact records, since credit costs mount quickly and dedicated data providers are better value for raw contacts.
- Buyers who want predictable flat pricing, because a slider-based credit model with variable action costs makes the monthly bill genuinely hard to forecast.
- Businesses selling to consumers or to very small local businesses, where website-based similarity has much less signal to work with.
- Companies without an existing customer base to seed from, since lookalike search is only as good as the examples it is given.
How it works
- 1
You define what good looks like, either by pointing at a single company URL or by uploading a list of your best customers as the seed for a lookalike search.
- 2
Ocean.io converts company websites into vectors describing what those businesses actually do, then finds and ranks companies that sit close to the seed set in that space.
- 3
You narrow the result with firmographic filters such as size, geography, technology, and growth signals, which turns a similarity list into a defined market segment.
- 4
The segment is sized, so the output answers how large the addressable market actually is rather than only naming companies inside it.
- 5
You spend credits to enrich the segment with company detail, contacts, and verified email addresses, with different actions consuming different credit amounts.
- 6
The segment is exported or synced into a CRM or outbound tool, where it becomes a target account list, a campaign audience, or the basis for territory planning.
Feature breakdown
21 features in 4 modulesLookalike segmentation
Similarity computed from what companies do, not how they are classified.- Contextual vector matching
- Company websites are translated into mathematical vectors so similarity reflects actual business description rather than an industry code. This is the core mechanism and the reason results differ from conventional filters.
- Seed from a customer list
- Upload your best accounts and get the companies that resemble them, which encodes an ideal customer profile as examples rather than as a set of rules somebody guessed at.
- Single-URL lookalike search
- One company address is enough to start, which makes exploratory market research fast rather than a project requiring data preparation.
- Ranked similarity
- Results are ordered by closeness rather than returned as an undifferentiated list, so a team can work down the ranking as budget allows.
- Niche discovery
- Because matching is descriptive rather than categorical, narrow segments that no industry code isolates can still be found, which is the specific case where this product beats a conventional database.
Market sizing and filtering
Turning a similarity list into a defined segment.- Segment sizing
- The platform reports how many companies match, which answers the market question rather than only supplying names, and that number is often the reason to run the search at all.
- Firmographic filters
- Size, geography, and company attributes narrow a similarity result into a segment that matches how the sales team is actually organized.
- Technology and signal filters
- Segments can be refined by technology in use and growth indicators, which separates companies that look similar from companies that are similar in the way that matters.
- Company database coverage
- Roughly 35 million company profiles underpin the search, which is the population any segment is drawn from and therefore the ceiling on what can be found.
- Territory and market planning use
- Sized, filtered segments support planning decisions such as which markets to enter or how to split a book, not only campaign targeting.
Enrichment and activation
From segment to something the team can work.- Contact discovery
- Company segments are populated with people at those companies, which is the step that turns market research into an actionable target list.
- Verified email addresses
- Email verification is part of the credit model, so contacts can be validated before they reach a sequencer and damage sending reputation.
- Phone number enrichment
- Direct dials and mobiles are available and consume markedly more credits than other actions, which is the single most common cause of unexpected overage.
- CRM integrations
- Segments sync into CRM systems on the professional tier and above, which is where an account segment becomes an operating target list rather than a spreadsheet.
- High CRM match rate
- The vendor claims a very high match rate against existing CRM records, which matters because it determines whether a new segment can be compared cleanly against accounts you already have.
- Export to outbound tooling
- Segments leave the platform for sequencers and other GTM tools, keeping the segmentation decision independent of the sending platform.
Commercial terms
A credit slider rather than clean tiers.- Starter from about $79 a month
- Billed annually with a few hundred credits each for companies, contacts, and email verification, which suits evaluation and small, focused segments.
- Professional from about $299 a month
- Roughly two thousand credits with CRM integrations included, which is the tier where the product becomes part of an operating process.
- Slider-based credit purchasing
- Volume and billing term set the per-credit rate, so the price is chosen rather than picked from fixed packages.
- Credit rollover on subscriptions
- Unused credits carry forward for a period on subscription plans, which softens the mismatch between lumpy research work and monthly billing.
- Overage pricing
- Per-credit overage rates apply beyond plan limits, and independent reviews report most customers exceeding their allowance, so budget for more than the plan number.
Use cases
4 documentedFounder defining an ideal customer profile
The company has thirty good customers and a strong intuition about why they fit, but no way to find the rest of the market that resembles them.
The customer list seeds a lookalike search and returns a ranked, sized segment of similar companies, which converts intuition into an addressable market.
Marketer sizing a new vertical before committing
A vertical looks promising, and the decision to build campaigns for it depends on how many companies actually exist in it.
Segment sizing answers the question before the budget is spent, which is a materially different use from list building.
Sales team whose targeting is driven by industry codes
The current account list is built on classification codes that group companies sharing nothing beyond a label, and conversion is uneven as a result.
Description-based similarity replaces the code, and the resulting segment groups companies by what they actually do.
RevOps team planning territories
Books are split by geography and headcount, with no read on how much genuinely similar business exists in each patch.
Sized, filtered segments with high CRM match rates make territory design a data exercise rather than a negotiation.
Pricing
from About $79 per month billed annuallyCredit-based subscription with a slider, where volume and billing term set the per-credit rate. Different actions consume different credit amounts, and overages are billed per credit beyond the plan allowance.
| Plan | Price | Includes |
|---|---|---|
| Starter | About $79 per month billed annually |
Enough for evaluation and focused research, not for continuous list building. |
| Professional | About $299 per month billed annually |
The tier where segments become an operating process rather than a research exercise. |
| Pay as you go | About $0.08 per credit, minimum around 1,000 credits |
|
Billing notes
- Credit costs vary by action, and phone number enrichment is dramatically more expensive per record than company or contact data, which is where most budgets are actually consumed.
- Independent reviews consistently report customers exceeding plan allowances, with overages billed at roughly five to fifteen cents per credit, so budget above the plan number.
- Subscription pricing runs about a penny per credit cheaper than pay as you go, and credits roll over for a period on subscriptions.
- CRM integrations sit on the professional tier, so teams that need segments in the CRM should price at $299 rather than $79.
- The slider model means published tiers are indicative rather than fixed, and the real price comes from a configuration rather than a plan name.
- There is no published free trial, so evaluation means either the pay-as-you-go minimum or a vendor conversation.
Value assessment: For its actual job, defining and sizing a market segment from examples, Ocean.io is well priced: $79 to $299 a month is a fraction of the enterprise account intelligence platforms like Keyplay and MadKudu that start in five figures a year. For the job people often mistake it for, bulk contact acquisition, it is poor value, because credits deplete fast and dedicated data providers deliver more records per dollar. Buy it to answer who the market is and how big; do not buy it to fill a sequencer with fifty thousand contacts.
Strengths & limitations
Strengths
- Similarity computed from what companies actually do rather than from industry classification codes, which is the whole reason it finds segments conventional filters miss.
- Seeding from your own customer list turns an ideal customer profile into examples rather than a set of guessed rules.
- Segment sizing answers the market question, not only the list question, which supports planning decisions before budget is committed.
- A large company database, around 35 million profiles, as the population segments are drawn from.
- A claimed very high CRM match rate, which is what makes a new segment comparable against accounts already owned.
- Self-serve pricing from about $79 a month, against five-figure annual contracts at the enterprise account intelligence vendors.
- A real operating company since 2017 with more than $10 million raised and reported revenue near $9.6 million.
- Credit rollover on subscriptions, which fits the lumpy nature of market research work.
Limitations
- Credit pricing is hard to forecast, and independent reviews report most customers exceeding their plan allowance.
- Phone number enrichment consumes credits at a much higher rate than other actions, which surprises buyers repeatedly.
- Contact data volume is expensive compared with dedicated B2B data providers, so it is the wrong tool for bulk list building.
- CRM integrations require the professional tier, so the effective entry price for operational use is closer to $299 than $79.
- Website-based similarity works poorly for businesses with thin web presence, which weakens results for very small and local companies.
- No published free trial, unlike much of the segmentation category.
- Lookalike quality depends entirely on the seed list, so a company without a clear set of good customers gets weak results.
- Serves B2B only, with nothing to offer consumer or ecommerce segmentation.
Head-to-head comparisons
3 alternativesOcean.io vs Clay
from Free plan; paid from $149/moDifferent layers of the same workflow. Clay is the orchestration surface that combines many data providers, runs enrichment waterfalls, and lets a team build custom scoring logic. Ocean.io is a single opinionated engine for finding companies that resemble your customers. Teams already running Clay often use it to enrich and score, and reach for Ocean.io when the question is discovery of a segment rather than enrichment of a known list.
Full Ocean.io vs Clay comparisonOcean.io vs Apollo.io
from Free plan; paid from $49/user/moVolume against precision. Apollo is a large contact database with a sequencer attached, priced for filling pipelines with people. Ocean.io is a segmentation engine that finds companies resembling your best accounts and sizes that market. If you need many contacts cheaply, Apollo. If you need to know which companies are worth contacting at all, Ocean.io, and many teams use it to define the segment that Apollo then populates.
Full Ocean.io vs Apollo.io comparisonOcean.io vs Metrilo
from $199 per month, about $165 billed annuallyThe two ends of segmentation on this site. Metrilo segments the consumers who already bought from an ecommerce brand, using behavior and purchase history. Ocean.io segments the B2B companies that have not bought yet, using similarity to those who did. They share a category and almost nothing else, and no buyer is realistically choosing between them.
Full Ocean.io vs Metrilo comparisonImplementation & onboarding
- Setup time
- A lookalike search runs in minutes from a URL or a customer list, so the first segment exists the same day. CRM syncing and a repeatable process around it take longer, typically a week of configuration and agreement on definitions.
- Learning curve
- Low to use, moderate to use well. Running a search is simple; the skill is choosing a seed list that represents genuinely good customers rather than merely large ones, and managing credit spend so that enrichment goes to the accounts worth pursuing rather than the whole segment.
- Onboarding
- Self-serve subscription with published starting prices, though the slider model means most buyers end up in a configuration conversation. No published free trial, so evaluation usually starts with the pay-as-you-go minimum.
- Migration notes
- Segments are exported or synced to a CRM, so the account lists you build remain usable after leaving. What does not travel is the similarity model itself, which means rebuilding a segment elsewhere requires a different method rather than a different vendor doing the same thing.
Platform, API & security
- Platforms
- Web appCRM integrationsExport to outbound tooling
- API
- Programmatic access and CRM integrations are available on higher tiers rather than universally, so confirm what your plan includes before designing an automated workflow around it.
- Compliance
- Supplies B2B company and contact data, so lawful basis for processing contact records remains the customer's responsibilityEuropean headquarters, which is relevant to GDPR posture for teams prospecting into the EUEmail verification is offered as a paid action, which supports list hygiene obligations before sending
- Data residency
- Not published as a selectable option. The company is headquartered in Denmark with a United States office.
- SSO
- Not advertised on published plans.
- Security notes
- The platform processes uploaded customer lists as seeds for lookalike search, which means your own customer roster is shared with the vendor. That is normal for this product class and worth a moment of thought if the customer list itself is commercially sensitive.
Support & resources
- Channels
- Email supportIn-app supportAccount support on higher tiers
- Documentation
- Product documentation plus published material on ideal customer profile definition, lookalike search, and account-based targeting.
- Community
- No public forum. Reviews appear across the main software marketplaces, generally positive on segment quality and critical on credit consumption.
Company
- Founded
- 2017
- Headquarters
- Copenhagen, Denmark, with an office in Minneapolis, United States
- Ownership
- Privately held, venture backed
- Founders
- Michael Heiberg
- Employees
- Around 64 (reported 2024)
- Funding
- More than $10 million raised. The company reported revenue of about $9.6 million with roughly 64 employees as of 2024, and lists the United States as a stated expansion priority.
Timeline
- 2017Ocean.io is founded in Copenhagen by Michael Heiberg to find companies by what they do rather than by industry classification.
- 2019The matching engine is rebuilt on contextual vectors, translating company websites into mathematical representations instead of relying on keyword embeddings.
- 2022Lookalike search from an uploaded customer list becomes the central workflow, alongside firmographic filtering and segment sizing.
- 2023Contact discovery, email verification, and CRM integrations extend the product from market research into operational target list building.
- 2024The company reports about $9.6 million in revenue with roughly 64 staff, and opens a Minneapolis office as part of a United States expansion.
Integrations
- CRM systems on higher tiers
- CSV export
- Outbound and sequencing tools
- Enrichment workflows
Frequently asked questions
10 questionsWhat does Ocean.io actually do?
It finds and sizes B2B market segments by similarity. You give it a company URL or a list of your best customers, and it returns a ranked set of companies that resemble them, based on what their websites say they do rather than on industry codes. You then filter that into a segment, enrich it with contacts, and export or sync it to a CRM.
How is this different from a B2B database like Apollo?
The question each answers is different. Apollo is built to supply large volumes of contacts cheaply from filters you specify. Ocean.io is built to discover which companies belong in a segment in the first place, using similarity to your existing customers. Many teams use Ocean.io to define the segment and a volume provider to populate it.
How much does it cost?
Published plans start around $79 a month billed annually with a few hundred credits, with a professional tier near $299 adding CRM integrations and around two thousand credits. Pay as you go runs about eight cents a credit with a minimum purchase. The slider model means the real price is a configuration rather than a fixed plan.
Why do people run out of credits?
Because actions cost different amounts and phone numbers are expensive. Retrieving direct dials consumes many times the credits a company record does, so a plan sized for company research empties quickly once a team starts pulling mobile numbers. Independent reviews report most customers exceeding their allowance, so budget above the plan figure.
What is contextual vector matching, in plain terms?
The platform reads company websites and converts what they say into numbers, so companies that describe similar businesses end up close together mathematically. It matters because industry codes group companies that share a label and nothing else, while this approach can isolate a niche that no code describes.
How good does my seed list need to be?
Very. Lookalike search reflects the examples it is given, so a seed list of your largest customers finds large companies, while a seed list of your most profitable and fastest-onboarding customers finds something much more useful. Most disappointing results trace back to a lazily chosen seed rather than the engine.
Does it work for small or local businesses?
Less well. The matching depends on a company describing itself on a website, so businesses with a thin or generic web presence carry little signal. It works best on B2B companies with substantive sites, which is also where its customers tend to sell.
Can it segment my existing customers rather than prospects?
It works on companies rather than on individual purchase behavior, so it can group and compare accounts, and the high claimed CRM match rate makes it possible to compare a discovered segment against accounts you already own. For behavioral segmentation of consumers who bought from you, the ecommerce tools in this category are the right ones.
Is there a free trial?
No published free trial, which is out of step with most of this category. Evaluation usually means either the pay-as-you-go minimum purchase or a conversation with the vendor, so plan a specific question to answer before spending rather than exploring open-endedly on credits.
How does it compare with enterprise account intelligence platforms?
Favorably on price and unfavorably on breadth. Keyplay and MadKudu, the platforms usually mentioned alongside it, start in the five-figure annual range and add scoring models, signals, and workflow depth. Ocean.io does the discovery and sizing piece self-serve from $79 a month, which is why it fits businesses those platforms are not priced for.
Editorial verdict
Ocean.io is the B2B answer to the question this category asks, and it earns its place by attacking the weakest part of most targeting work: the assumption that industry codes describe what a company does. Seeding a search with your own best customers and getting back a ranked, sized segment is a fundamentally better method than filtering a database by a classification somebody assigned years ago, and at $79 to $299 a month it is self-serve priced against enterprise platforms that start in five figures. Two things to get right before buying. The seed list determines everything, so choose it for customer quality rather than customer size. And treat the credit model with respect, particularly phone enrichment, which is where budgets disappear. Use it to decide who the market is; use something cheaper to fill the list once you know.
Written by the SaaSTracker editorial team. Awards, when shown, are judged against the published criteria in our methodology.