Multi-vendor enrichment waterfall (MVE waterfall) is Openprise's waterfall enrichment model. Instead of sending every record to a single enrichment vendor and accepting whatever match rate you get, Openprise routes each record through a sequence of providers. Your primary vendor runs first. Where it can't match, a secondary vendor runs. A tertiary vendor fills what remains. You configure the order, select the vendors, and set the rules for which provider's data takes precedence. Openprise handles the orchestration, deduplication of enrichment results, and write-back to your CRM, MAP, and data warehouse. The result is dramatically higher coverage from the same vendors you're already paying for.

ZoomInfo is sufficient in certain segments and geographies, especially mid-market US companies. It has well-documented coverage gaps in EMEA, small businesses, and specific verticals. MVE doesn’t have to completely rip and replace your existing enrichment vendor (although you can if you wish!) It's the layer that fills the gaps your primary vendor leaves behind, so your data coverage reflects your actual TAM rather than your vendor's coverage map.

You set the rules. Openprise's no-code enrichment recipe builder lets your team define which vendor's data takes precedence for each field type: company data, contact data, technographic data, intent data, and more. Openprise also includes a staging area where your team can review enrichment results before they're written to your CRM or MAP. You can audit pre- and post-import, catch outliers, and adjust the recipe before anything touches your production data.

Yes, and this is one of the most underappreciated advantages of running enrichment through Openprise. Match rates are heavily influenced by the quality of the data you send to the vendor. If your phone numbers have formatting errors, your company names have abbreviations, or your domain fields are blank, your primary vendor's match rate drops before it ever starts. Openprise standardizes and cleanses your records first, then sends clean data to your enrichment vendors.

Openprise includes built-in enrichment reporting dashboards that show match rates by vendor, by field, by geography, and by record type. Your team can see exactly which vendor is delivering value on which segment of your database, where the gaps remain, and how coverage changes over time. When contract renewal time comes, your team has the data to negotiate from a position of knowledge rather than guesswork. You see what you're paying for, field by field.

A single enrichment vendor writes its match results to your CRM and stops there. If your MAP is syncing from that CRM, it inherits whatever gaps the vendor left behind: blank industry fields, missing titles, empty phone numbers. Your segmentation logic, scoring models, and campaign targeting all run on those incomplete records. Openprise MVE runs enrichment before data is written to either system, filling gaps across multiple providers so the records that land in Salesforce and sync to Marketo are as complete as possible from the start. For teams that also feed enriched data into a data warehouse for analytics or AI model training, MVE ensures the data warehouse receives the same high-quality enriched records, not the partial output from a single vendor's coverage map.

A single enrichment vendor matches records against its own proprietary database and fills the fields it has coverage for. When it can't match a record, that record stays blank. The average single-vendor company match rate sits around 49%, and contact match rate around 56%, meaning roughly half your database doesn't get enriched at all. A multi-vendor enrichment waterfall, like Openprise MVE, routes each unmatched record to a secondary vendor, then a tertiary vendor, sequentially filling gaps that no single provider could cover alone. Openprise's MVE benchmarks show company match rates reaching 94% and contact match rates reaching 83% when multiple vendors are combined in a waterfall. The difference between 49% and 94% match coverage isn't a marginal improvement. It's the difference between a scoring model running on half your database and one running on nearly all of it.