Matchbook AI, which offers External Data and Data Hygiene solutions to enterprise clients, has announced a $3 million seed extension.  It previously received $1.7 million in seed and friends and family funding.

Matchbook was founded in 2018 but operated as a garage project for a couple of years before being incorporated.  CEO Rushabh Mehta had the idea for the Matchbook Data Hub while an industry evangelist and initially built the solution with Dun & Bradstreet data.  The Data Hub provides a configurable, hierarchical matching service that matches and enriches records with a single API call.  Both batch and real-time matching are supported, with cascading and waterfall matching processes.  In addition, a rules engine allows customers to construct bespoke data cleansing and filtering rules specific to various business units and use cases.  The Data Hub will be powered by Snowflake.

The system can use other identifiers such as domains or emails if the account cannot be matched by name and address.  In addition, the system manages deduplication and prevents creating duplicate records when onboarding accounts. The solution can also support more complex matching scenarios to allow for verification checks and multi-attribute matching.

“I can immediately see if I already have an existing relationship with that account,” Mehta explained to GZ Consulting.  “Just because I keyed in the name incorrectly or a previous account had a different address for that same company, I should still be able to identify and say, ‘Hey, no, it’s the same company to the same account.’”

The Data Hub also manages information for enterprise clients with multiple CRMs, helping “provide that visibility across CRMs, across ERPs, or CRM and ERP.”  Thus, Matchbook can identify whether “there is already an existing relationship within the organization with that particular entity” through third-party identification.  Furthermore, matching identifies parent-sub relationships tied together by D-U-N-S numbers.

According to Mehta, customers want controlled updates to their CRM or ERP, not real-time updates.  They also want to control which fields are updated with the data hub keeping “everything mastered in one place” with the intelligence accessible to the organization.

Along with Dun & Bradstreet’s data sets (e.g., companies, contacts, hierarchies, beneficial ownership, D-U-N-S identifiers, technographics, competitors, company news, and credit and supplier risk profiles), Matchbook AI provides third-party reference data from ZoomInfo, Demandbase, Moody’s, Experian, Melissa, and Google.  The service also includes sanctions and terrorist watchlist data for compliance use cases.

The Data Hub operates as a centralized external data repository for maintaining data quality and standardizing data across platforms, including Salesforce, Snowflake, SAP, Informatica, Microsoft, Oracle, Certa, and Reltio.  The Data Hub supports a broad set of processes and departments, with sales, marketing, finance, IT, logistics, compliance, legal, and supplier management use cases. It also plays a critical role in MDM use cases through integrations with Reltio and others.

Matchbook claims implementations between two days and two months, significantly faster than its competitors.  Furthermore, as a DaaS solution, it is 90% less expensive than in-house solutions.  It also claims a 75% savings on expenses related to maintaining a data stewardship team due to “improved data quality and automated management.”

VP of Sales & Marketing Wesley Billingslea described a recent dinner with a Fortune 500 CIO who described Matchbook AI as “quite strategic and pervasive because we go across departments,” whereas “most MDM projects sit in the IT organization.”  This approach “empowers” teams across the organization with superior data and a plug-and-play solution.

Matchbook focuses on enterprise clients, with 59% of its customers in the Fortune 500.  It takes a land-and-expand approach that proves itself in one department or on one platform and then extends to others.  Contracts are usually written for a single year and then converted to multi-year contracts a year later.  The strategy has resulted in a 118% net retention rate and a projected ARR increase of 220% this year.

“As we gear up our sales and marketing efforts, we are confident that we will soon achieve $3.5 million in annual recurring revenue (ARR) with Current ARR of $1.85 million,” said Mehta.  “Data should be trusted, enriched, and always ready.  I feel very confident in our approach as we take these next steps and help companies understand their data DNA in this age of intelligent business.”

Matchbook claims an impressive LTV/CAC ratio greater than 12, an important indicator of stickiness, value, and an efficient go-to-market approach.

Mehta noted that it is just entering a large market with a rapidly expanding TAM.  According to MarketsandMarkets, data cleansing and global master data management were an $11.3 billion market in 2020, growing to $27.9 billion by 2025 (19.8% CAGR).

“Our value proposition to an MDM implementation can mean the difference between success and failure,” said Mehta.

Pricing is based on a records-under-management model, providing a predictable budget line to companies.  Implementations range from 50,000 to 100 million records. Matchbook has grown to 51 employees in the Americas and Asia.  The bulk of its R&D is conducted in Asia.