Case study
Core Account Profile
The program behind the framework. Core Account Profile defined what a GTM-ready account record looks like, scored approximately 100,000 records against that standard, benchmarked the enrichment vendors writing to those fields, automated the hierarchy layer underneath them, and wrote readiness back into Salesforce where GTM teams actually work.
Overview
Account data is the foundation every GTM motion runs on. Most orgs do not yet have a shared definition of what counts as good.
Segmentation, territory planning, ICP modeling, routing, reporting, and increasingly AI workflows all run on account data. In the org where this program ran, like many fast-scaling GTM orgs, account quality varied by creation date, creating team, and creation source. A shared operational definition of a GTM-ready account, and a single accountable owner for account data quality, did not yet exist.
The Core Account Profile set that definition and built the measurement around it. It defines which fields matter, how account quality is scored, which vendors to trust for which fields, and where in the process quality breaks down. It scored the full account base, roughly 100,000 records, against that standard.
A field having a value is not the same as a field you can run a GTM motion on.
The problem
Five recurring patterns, compounding.
Account data quality was not one issue. It was five common patterns, and each one amplified the others.
- 01
Definitions of ready varied by team.
Without a documented minimum field standard, each team worked from its own assumption about what "good" looked like.
- 02
Distributed ownership without a single accountable owner.
Account data was written and updated by multiple functions, through different tools and cadences, without one operational owner accountable for its quality end to end.
- 03
Inconsistent records at the door.
Accounts entered from more than thirty sources. Some produced clean, structured records. Long-tail sources introduced inconsistent values that quietly weakened the account base.
- 04
Standardization gaps amplified downstream.
Inconsistent country, industry, vertical, and address values quietly degraded territory assignment, segmentation, reporting, and automation.
- 05
Accuracy lacked a validation backbone.
Multiple sources wrote to the same key fields without a defined source hierarchy, and values were rarely revalidated once populated.
Field population was being treated as field quality. They are not the same measurement.
The framework
Gated readiness: measure three dimensions, and refuse to average them away.
The Core Account Profile scores every account on three dimensions, separately:
Is the field populated.
Is the value in a consistent, usable format.
Is the value actually correct, tested against cross-field logic, multiple vendor sources, and recency.
Fields are tiered first. Core fields carry the most weight, Supporting fields carry moderate weight, and Supplemental fields carry little or none, so nice-to-have metadata cannot make a strategically unusable account look ready.
The three dimension scores roll up into one Core Account Readiness result. The rule that keeps the result honest is the gate: if any dimension falls below its floor, the record drops to a failing readiness class, no matter how strong the average looks. A composite score lets one strong dimension carry two weak ones. The gate does not.
An account is only as ready as its weakest dimension. The score should say so out loud.
What I built
Six builds, one operating system for account readiness.
The scoring model.
A weighted, tiered model that scores Completeness, Standardization, and Accuracy for each account, applies the gate, and returns a readiness class. Run against the full account base, approximately 100,000 records, to establish a baseline.
Diagnostic slices.
The same scores cut by creation timeframe, creating team, and creation source, so data quality stopped being one number and became a map of where in the process quality was breaking down.
The Vendor Report Card.
A benchmark of enrichment and prospecting vendors against a 200-company reference set, with ground-truth values scrubbed directly from each company's own website, graded by field instead of by vendor reputation. The output was a Platinum, Gold, Silver, and Bronze hierarchy and a Best Source by Field model, which replaced assumption-based source-of-record decisions with evidence.
Read the full case studyParent Account auto-population.
An n8n workflow that populated account hierarchy from corporate-parent and global-headquarters identifiers, with self-parenting prevention, overwrite protection so existing values were never clobbered, and a master-record selection waterfall for picking which parent a child should nest under when more than one candidate existed.
Read the full case studyReadiness, written back to Salesforce.
The score and grade for each dimension and for overall readiness were operationalized as account-level Salesforce fields, refreshed automatically on a schedule. Readiness stopped living in a workbook and started living in the CRM, where it could drive list views, reporting, routing, and prioritization directly.
An AI-assisted Command Center.
Continuous monitoring shipped as a product, not a one-off dashboard. A joined Salesforce and Snowflake data layer let revenue teams filter by team, source, or timeframe and pull the cohort of records in any given readiness state, surfaced as GTM-readable signals on top of the raw scores. The operator design, what to surface, how to filter, which decisions to support, came from me. Claude Code and an internal enterprise AI assistant accelerated the build, compressing what would have been a multi-month engineering backlog item into something I could ship, observe, and iterate on directly.
Outcomes
What changed.
- A baseline readiness score across the full account base, usable for prioritization, remediation, and book selection.
- A vendor hierarchy grounded in benchmarked evidence, replacing "this vendor is just better" with "this vendor is better for this field."
- Automated hierarchy population, improving roll-up reporting and enterprise account visibility.
- Readiness scores lived in Salesforce and in the monitoring layer, making account quality visible to the teams that created the data and the teams that depended on it.
The goal was never a cleaner spreadsheet. It was account data the business could make decisions on.
The portable POV
What carries to any company.
The Core Account Profile was built for one Salesforce org, but the thinking is not specific to it.
Composite scores flatter you.
A single blended number hides the records that are quietly broken.
Gated readiness is the honest alternative.
Measure each dimension, and do not let a strong average buy back a fatal weakness.
Data quality is a behavioral problem dressed up as a tooling problem.
The fix is shared definitions, clear ownership, and measurement, not another vendor.
Robots can run the motion once the data underneath them is trustworthy. They cannot define ready for you.
Want to see the framework before the case study?
The Scorecard is a generalized, public version of what Core Account Profile does at scale. It scores a synthetic dataset against the same three dimensions, tiers, and gate, right in your browser. The framework is not GTM-specific. The same field-level scoring and gating apply to any data product where customers depend on the answer being trustworthy.
Redaction note
This case study is generalized from real GTM systems work. Specific company names, customer data, internal field mappings, exact operational telemetry, and proprietary implementation details have been removed or abstracted. Any demo data shown on this site is synthetic.