Challenge

Operating at Fortune 500 scale means data problems are also Fortune 500 scale problems. This software company's marketing team was uploading and enriching 800 lead lists every month — a volume that made manual processing not just inefficient but genuinely unsustainable at the quality level the business required. Each list had to be cross-referenced against account records in the Corporate MDM, a database of 2.5 million account golden records, for which the Corporate IT team had no automated process to maintain or enrich. The MDM was the authoritative source — but without automation, keeping it current with technographics and intent data required manual effort that did not scale. On the enrichment side, single-vendor enrichment was producing an average match rate of only 50%. That meant half of the records the team was working with were missing the company-level data needed for accurate SMB segmentation, targeting, and matching. Every campaign and scoring model built on that data was working from an incomplete foundation — and the team knew it.

Solution

The company deployed Openprise to address both problems simultaneously. List loading was automated end-to-end — all 800 monthly lists now flow through an Openprise pipeline that handles upload, enrichment, and MDM cross-referencing without manual intervention. The matching process was integrated directly with the Corporate MDM, pulling in updated technographics and intent data as part of the automated flow. What had required manual effort at every step — uploading, enriching, cross-referencing — became a pipeline that ran without the team having to touch it.

The Multi-Vendor Enrichment implementation was the data quality breakthrough. Rather than relying on a single vendor to achieve 50% match rates, Openprise's MVE waterfall sequenced multiple providers, with D&B as the anchor vendor for SMB account data. The result was a match rate improvement from 50% to 88% — a 38-point lift that fundamentally changed the completeness of the data available for segmentation, targeting, and scoring. Secure file and API connections were built into the pipeline to provide MVE-on-demand capabilities, ensuring the enrichment process could be applied across the full data operation at scale.

"We're now believers in MVE."

— Ops of Fortune 500 software company

Impact

The $250k annual savings are the most direct financial measure of what list loading automation delivered. 800 lead lists per month, previously requiring manual upload and enrichment effort, are now processed automatically. The cost of that manual work — in time, in agency resources, in the operational overhead of maintaining a manual process at that volume — is gone. And the automation does not just save money: it removes the variability introduced by manual processing, producing consistent output quality across every list, regardless of when it runs or who submits it.

The match rate improvement from 50% to 88% changes the quality of data available for every GTM initiative that depends on it. A 50% match rate means campaigns are built on half-complete account data. An 88% match rate means the data is substantively trustworthy — which means segmentation is more precise, targeting is more accurate, and the scoring models built on that data are more reliable. The 38-point lift came from moving from single-vendor enrichment to multi-vendor enrichment (MVE)     with D&B as anchor — a structural change in how the enrichment pipeline works, not a one-time cleanup.

The Corporate MDM integration brought updated technographics and intent data into an asset that had previously been maintained without automated enrichment. A 2.5 million record database of account golden records is only as useful as the data it contains — and keeping that data current at that scale requires automation that no manual process can sustainably provide. With Openprise integrated into the MDM pipeline, the golden records stay enriched, the technographic and intent signals stay current, and the data foundation that every downstream GTM activity depends on stays accurate.