Ocean.io Pitch Deck (2022): 18-Slide Series A Deck

Ocean.io’s Series A deck is a masterclass in identifying a specific market failure—the inaccuracy of traditional industry categorization—and positioning a product as the surgical solution. By contrasting 'Traditional Categorization' with their own AI-driven 'Context Vector' approach, they move beyond simple lead lists into strategic account-based marketing (ABM). The deck is light on dense financial tables but heavy on product utility, showcasing features like AI lookalike search and TAM calculators. With $60K MRR and 150 customers at the time of the deck, the narrative focuses on the transit…

Key takeaways

• The deck identifies a massive shift in marketing, noting that teams running ABM grew from 49% in 2016 to 95% in 2020 (Slide 1).
• Ocean.io differentiates itself by challenging traditional industry codes, showing how Zoom is misclassified as 'IT & Services' by competitors but correctly as 'Video conferencing' by Ocean (Slide 5).
• The platform's data scale is significant, claiming data collection from 300M+ web pages and support for nine languages (Slide 6).
• Product functionality is divided into four key pillars: Targeting, Personas, Intelligence, and Integrations (Slide 7).
• The 'Intelligence' feature includes a TAM calculator and revenue projections to take the 'guesswork' out of ICP (Slide 10).
• Traction is clearly stated as $60K MRR, which was a 3x increase in 2021, with 150 customers as of August 2021 (Slide 15).
• The competitive landscape slide positions Ocean.io as the only player with 'Context Vector' industry categories and 2-way CRM integration (Slide 16).
• The deck lacks a specific 'Team' slide featuring founder bios or a detailed 'Use of Funds' slide for the $7M raise.

Frequently asked questions

What is the primary problem Ocean.io solves?

According to Slides 2 and 3, the primary problem is that companies are doing Account-Based Marketing (ABM) wrong by targeting the 'right job titles in the wrong companies.' This happens because traditional industry categorization is too broad, leading to high marketing spend and low lead quality.

How does Ocean.io's data differ from competitors like ZoomInfo or LinkedIn?

Slide 16 highlights that while competitors use 'Traditional' industry categories and focus on 'Leads,' Ocean.io uses 'Context Vectors' and focuses on 'Companies.' They also claim to be the only provider in their competitive set offering 2-way CRM integration and global coverage simultaneously.

What are the key metrics shared in the deck?

Slide 15 provides the core traction: 53 employees across 3 offices, $60K MRR (3x growth in 2021), 100% Net Revenue Retention (NRR), and 150 customers as of August 2021. The data engine is powered by 300M+ web pages (Slide 6).

What integrations does the platform support?

As shown on Slide 11, Ocean.io integrates with major CRM and sales tools including Pipedrive, Salesforce, HubSpot, LinkedIn, and Zapier. These integrations are described as 'seamless connections' to the existing tech stack.

What is the target market for Ocean.io?

Slide 17 defines the target as B2B companies with 10+ people in sales and marketing globally. Their immediate focus starting in Q3 2021 was on Enterprise companies in Western Europe that use HubSpot or Salesforce.

Ocean.io pitch deck: the facts

Company

Ocean.io

Year

2022

Stage

Series A

Slides

18

Sector

Software

Ocean.io pitch deck PDF

The full Ocean.io deck is embedded on this page and can be read slide by slide in the browser — no download or account required. Each slide is covered in the breakdown above.

What the Ocean.io pitch deck was used for

This is Ocean.io’s 18‑slide Series A pitch deck used in early 2022 to raise around $7M for its AI-driven B2B prospecting and account-based marketing data platform. The company positions itself against traditional lead-based strategies, emphasizing account-based data, persona search, and lookalike targeting to align sales and marketing and improve conversion quality, as seen in the OCR text from slides 4, 7, and 9. The stated use of funds in contemporaneous coverage was U.S. and broader international expansion, including hiring a U.S. team and strengthening go‑to‑market and thought leadership. The teardown focuses on how this deck uses product-led storytelling and vertical targeting to differentiate in a crowded sales intelligence and martech market.

Business model: Ocean.io provides a B2B prospecting and account-based marketing (ABM) data platform that uses AI-powered lookalike search and advanced filters to help sales and marketing teams find and target ideal accounts and contacts.

Lead investor

Peak (Peak Capital)

Investors

Peak (Peak Capital), Lottrup & Co., Rulebreaker Ventures

Founded

2017

Founders

Michael Heiberg

Headquarters

Copenhagen, Denmark, with presence in San Francisco, United States.

Industry

B2B marketing technology (martech) / sales intelligence / business productivity software.

Round: Series A (though some sources categorize it as seed or venture unspecified).[]

Year: 2022, with funding announced around January 14–20, 2022.

Raising: Series A / early VC round focused on U.S. and international expansion.

Raised: Approximately $7M (with some third-party databases reporting $6.8M–$6.9M).[]

Total funding: Approximately $7M total funding as of the January 2022 round, with some sources indicating more than $10M by 2024.

Use of funds as presented: Accelerate U.S. expansion by hiring about 25 people in the U.S. by end of 2022, strengthen thought leadership, and broaden reach across Europe and the U.S. through enhanced go‑to‑market activities.

What happened after the Ocean.io deck

Following its early 2022 round of roughly $7M, Ocean.io expanded its account-based data platform, grew revenue significantly, and scaled its customer base, remaining an active martech and sales intelligence company with multi-million ARR by 2024.

• By early 2022, Ocean.io reported a sevenfold increase in revenue over 2021 with a revenue run rate of about $6M and hund
• Case studies published on Ocean.io’s site report specific customer outcomes such as doubling demo booking rates through
• A 2026 review notes that Ocean.io had raised more than $10M in total funding and reached approximately $9.6M ARR with ar

What the Ocean.io deck got right

• Clear articulation of familiar B2B go‑to‑market pains. The deck’s narrative around the pains of traditional lead-based strategies—misaligned sales and marketing, low-quality leads, poor personalization, and difficulty measuring ROI—directly connects Ocean.io’s solution to widely understood B2B challenges, making the problem-solution fit intuitive.
• Comprehensive, integrated product story. Product slides emphasize AI-driven account-based data, persona targeting, TAM calculations, extensive filters, validated emails, and integrations with major CRMs and LinkedIn, showcasing a robust, workflow-integrated platform rather than a point solution.
• Positioning around buying committees and personas. The persona and buying committee framing ("more than just contacts" and search by job title, role, seniority, and department) positions Ocean.io as helping users map complex B2B buying centers, which is a key requirement for modern account-based strategies.
• Compelling early traction and customer validation. External coverage of the same round highlights strong traction—7x revenue growth in 2021, $6M run rate, and hundreds of customers including recognizable logos—which reinforces the credibility of the claims likely presented in the deck’s traction slides.
• Focused geographic expansion use-of-funds narrative. The deck is explicitly tied to a concrete expansion plan, using funds to enter the U.S. market and hire a local team, which gives investors a clear sense of how capital will be deployed to capture a larger market.
• Differentiation from traditional scraped-data providers. Ocean.io’s differentiation against "databases that just scrape LinkedIn and outdated registries" centers the story on proprietary data collection, analysis, and normalization, which can be compelling to investors looking for defensible data products.

What could have been stronger

• Limited quantified impact of solving stated pain points. The OCR text shows strong qualitative descriptions of pains in traditional lead-based strategies (misaligned teams, poor personalization, high spend/low lead quality) but does not surface specific quantitative metrics for improvements (e.g., uplift in conversion rates or CAC reductions), which could make it harder for investors to gauge impact rigorously from the slides alone.
• Feature list is dense without clear hierarchy of differentiation. While Slide 7 lists a rich feature set—lookalike search, TAM calculations, 48 filters, validated emails, CRM deduplication, and integrations—the OCR suggests many bullet points in dense text, which may overwhelm readers without a clear prioritization of the most differentiated capabilities versus table-stakes sales intelligence features.
• Integration claims lack visible technical depth. The deck excerpt emphasizes integrations with Salesforce, HubSpot, Pipedrive, and LinkedIn export, but does not, in the available text, indicate depth of integration (e.g., real-time sync, bi-directional data, deployment complexity), potentially leaving technical and implementation questions unanswered for more technical investors.
• Buying committee differentiation not fully evidenced. Persona Search is presented as "more than just contacts" to find the buying committee, but the OCR shows only high-level filters (job title, role, seniority, department) without examples of how Ocean.io uniquely identifies multi-stakeholder buying groups beyond conventional contact-enrichment tools.
• Defensibility and competitive moat not clearly articulated in visible text. The OCR snippets focus on pains and product capabilities but do not show explicit competitive positioning or why Ocean.io’s data collection and AI models are durably better than traditional data providers, which may be a gap for investors scrutinizing defensibility in a crowded martech and sales intelligence space.

How an investor would read this deck

What draws attention

• Rapid revenue growth (7x in 2021) and a $6M run rate by early 2022, combined with hundreds of customers including well-known brands such as Sony, UserTesting, Brandwatch, and JGL, signals strong early traction in a sizable market.
• Clear articulation of pain points in traditional lead-based strategies—misaligned sales and marketing, poor personalization, high spend on low-quality leads, low conversion rates, and difficulty measuring ROI—directly aligns with enterprise go‑to‑market challenges investors recognize.
• Product-led story emphasizing AI-driven lookalike search, persona-based targeting, TAM calculations, and CRM/LinkedIn integrations suggests a scalable, software-first solution to account-based marketing and sales intelligence.
• The focus on U.S. expansion and hiring a local team points to a clear use of funds with potential to significantly expand Ocean.io’s addressable market beyond Europe.

Risks that stand out

• The martech and sales intelligence space is competitive, with numerous vendors offering intent data, firmographic data, and contact enrichment; without a strong moat, Ocean.io may face pricing pressure and customer churn.
• Dependence on accurate, fresh data and third-party signals (e.g., web sources, registries) introduces quality and compliance risks; if data quality degrades, value for account-based strategies could quickly erode.
• International expansion, particularly into the U.S., requires significant investment in sales, marketing, and support; failure to localize go‑to‑market or hire effectively could dilute returns on the raised capital.
• Some external databases show discrepancies in reported funding amounts and round classifications (e.g., seed vs. Series A), suggesting possible ambiguity in financing structure or reporting that investors may wish to clarify directly with the company.

Questions this deck invites

• What are the precise mechanics and performance characteristics of Ocean.io’s AI models for lookalike search (e.g., data sources, update cadence, model evaluation, and customer-visible accuracy metrics)?
• How do Ocean.io’s unit economics look by segment (SMB vs. mid-market vs. enterprise), including CAC, payback period, gross margin, and expansion revenue, especially in newly targeted U.S. accounts?
• What is Ocean.io’s competitive landscape versus established sales intelligence and ABM data providers, and what structural advantages (data uniqueness, proprietary models, workflow lock‑in) support long-term defensibility?
• How sticky is the product once integrated into Salesforce, HubSpot, or Pipedrive—what are historical churn, net revenue retention, and typical reasons for customer expansion or downsell?
• What were the concrete outcomes of the planned U.S. expansion (e.g., number of hires, U.S. ARR, key lighthouse customers) relative to the expectations set at the time of the 2022 round?

What founders can take from the Ocean.io deck

• Anchoring the deck in a vivid description of pains caused by traditional lead-based strategies (misalignment, poor personalization, low lead quality, slow market expansion) makes the problem space immediately recognizable to B2B investors and operators.
• Detailing product capabilities like AI-powered lookalike search, persona-based targeting, TAM calculations, and CRM/LinkedIn integrations shows how the solution operationalizes account-based strategies rather than staying at a conceptual level.
• Positioning the platform as "more than just contacts" and emphasizing buying committee coverage through persona filters helps distinguish Ocean.io from basic contact-enrichment tools in the sales intelligence market.
• Combining product-led storytelling with specific logos and traction data (7x revenue growth, $6M run rate, hundreds of clients including well-known brands) gives investors both qualitative and quantitative reasons to believe.
• Explicitly tying the use of funds to geographic expansion (U.S. market entry, hiring 25 local staff) and go‑to‑market scaling provides a clear narrative about how the round will translate into growth rather than being purely about runway.
• Highlighting integrations with core CRM tools (Salesforce, HubSpot, Pipedrive) and workflow exports to LinkedIn aligns the product with existing sales and marketing stacks, which can reassure investors about adoption friction and implementation risk.

Ocean.io pitch deck: common questions

What does Ocean.io do?

Ocean.io is a B2B prospecting and account-based marketing data platform that uses AI-powered lookalike search and advanced filters to help sales and marketing teams identify and target their ideal customer accounts and buying committees, rather than relying on generic lead lists.

Which Ocean.io pitch deck is this, and what round was it for?

Ocean.io’s pitch deck on BestPitchDeck.com is the Series A deck used in 2022 to raise about $7M, highlighting its AI-driven account-based data platform, revenue growth, and case studies with customers like Sony, UserTesting, Brandwatch, and JGL. The OCR snippets show slides explaining pains of traditional lead-based strategies and features like persona search, TAM calculations, and CRM/LinkedIn integrations.

How much did Ocean.io raise with this deck and who invested?

Multiple sources report that Ocean.io raised roughly $7M in a funding round announced in mid‑January 2022, with Peak Capital (often stylized Peak) as the main investor alongside existing backers such as Lottrup & Co. and Rulebreaker Ventures. Some databases list slightly different amounts (e.g., $6.8M or $6.9M) and categorize the round as seed or venture rather than Series A.

What was Ocean.io raising money for in this deck?

Ocean.io’s Series A funds were earmarked to expand into the U.S. market and accelerate international growth, including hiring around 25 people in the U.S. by the end of 2022, strengthening thought leadership, and scaling its go‑to‑market efforts in Europe and the U.S.

What traction did Ocean.io show around the time of this Series A deck?

According to VentureBeat and the BestPitchDeck teardown, Ocean.io reported a sevenfold revenue increase in 2021, reaching a revenue run rate of around $6M by early 2022, while serving hundreds of European and U.S. clients including Sony, UserTesting, Brandwatch, and JGL. Later reviews indicate the company had reached about $9.6M ARR with roughly 64 employees by 2024.

Sources

Funding and outcome facts on this page were researched on 2026-08-22 from the pages below.

• Deck That Martech Startup Ocean.io Used to Raise $7 Million in...
• Ocean.io sets sights on Atlantic expansion with $7m investment
• Ocean.io | Valuation, Funding Rounds & Stock Price - Caplight
• B2B data platform Ocean.io raises $7M to help enterprises sell more
• Ocean.io – Funding, Valuation, Investors, News
• Ocean.io: B2B prospecting and lookalike search
• Ocean.io Pitch Deck
• Ocean.io | EU-Startups

Ocean.io pitch deck slides

What each slide of the Ocean.io pitch deck says

Slide 1

The future is Account Based (... and we think you'll agree) 49% EN 95% 2016 2020 Marketing teams running ABM The State of ABM Report, 2020 - Terminus @& ocean

Slide 2

Most companies are doing Account Based Marketing completely wrong Find me ( Sales Directors Q_ ) inthe ( Computer Software Q_) industry & ocean

Slide 3

Ending up with the right job titles in the wrong companies Find me ( Sales Directors Q_ ) inthe ( Computer Software Q_) industry Targeting leads, not accounts Too broad to personalize effectively & oceano

Slide 4

This causes all the same pains as regular lead-based strategies Sales and Marketing teams are misaligned (and maybe even at warl) Personalization is an afterthought Marketing Spend is high and lead quality is low Low conversion rates Struggling to measure the ROI of your activities )'f\ Growth into new markets is slow and painful & oceano

Slide 5

More effective ABM through specific vertical targeting zoom Traditional Categorization Ocean.io Categorization IT & Services Video conferencing, SaaS software | | Similar according to them Similar according to us accenture VISA BOSCH [a ow webex (Q ClckMeeting +1: GoToMeeting RingContral = Pod sh Bluejeans

Slide 6

Data done differently Scalable data Data collected from 300M+ web b- 53 — ~ pages, company registries, rp > — and public databases ? 2 - Nine Languages fg BY rl fig SOS SLs Ocean.io speaks English, Spanish, OS « "aed Lily French, German, Italian, Dutch, ZIE og ws Norwegian, Swedish, Danish 0 Fo = i, ho A =e \ Unparalleled Coverage Q WF Benelux, DACH, France, The LX ¥ Nordics, UKI, Spain, ANZ, South aft Africa, and Singapore 1 7 & ocean

Slide 7

Ocean.io fuels your Account Based Strategy with Account Based Data @, Targeting [E' Personas % Intelligence AlLookalike Search « Search for your personas « TAM Calculations o 48 powertul fiters « Vaidated emai addresses o Top Customer Segments « Zoro duplicates « Search by seniortty « Rovenue Projections Search inside your CAM « 43M+ contacts o Market Analysis * Build targot sts « Per-Rep performance jt a Integrations. « Seamiessy intograte with Salesforce, Hubspot. Pipedrive « Export new target accounts and contacts to Linkedin « Dedupiicate your CRM # oceano

Slide 9

More than just contacts Find more of the buying committee with Persona Search © By job title and role. « By seniorty level « By department

Slide text above is read directly from the Ocean.io deck PDF embedded on this page.

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