What Data Sources Drive Account Scoring?
Accurate account scores blend fit, intent, and engagement signals from multiple systems. The right data mix predicts revenue moments and routes accounts to the next best action for marketing, SDRs, and sales.
Robust scoring combines first-party behavior (web, email, events), third-party intent (topic surges, research), fit data (firmographics/technographics), and commercial context (CRM stage, open opportunities, product usage). Signals are normalized, time-decayed, and validated against pipeline, ACV, win rate, and cycle time.
Core Data Categories for Scoring
The Scoring Data Playbook
Unify, cleanse, and weight signals so scores reliably predict revenue outcomes.
Audit → Integrate → Normalize → Weight → Calibrate → Route → Govern
- Audit sources & gaps: Inventory MAP, web analytics, intent, enrichment, CRM, product analytics.
- Integrate & resolve: ETL/CDP plus identity resolution to tie people→accounts; dedupe and merge.
- Normalize events: Standardize taxonomies (UTM, campaign, content), remove bots, apply time decay.
- Weight by outcome: Boost high-signal interactions (pricing, trials) and buying-group coverage.
- Calibrate with back-tests: Optimize thresholds using lift on meeting rate, pipeline, ACV, and wins.
- Route with SLAs: Map score bands to SDR/AE actions, plays, and cadences across channels.
- Govern & document: Owners, change logs, QA checks, and monthly reviews with Sales/RevOps.
Scoring Data Capability Matrix
| Capability | From (Ad Hoc) | To (Operationalized) | Owner | Primary KPI |
|---|---|---|---|---|
| Identity Resolution | Leads not linked to accounts | People→account stitching with role tags and de-dupe | RevOps/MOPs | Coverage %, Merge Accuracy |
| First-Party Capture | Basic pageviews | Event taxonomy with high-intent actions and decay | Marketing Ops | Meeting Rate Lift |
| Intent Integration | Single vendor | Multi-source corroboration and noise suppression | Demand Gen | Tier-1 Precision |
| Enrichment (Fit/Tech) | Annual CSVs | Automated refresh with negative filters | Data Ops | Qualified Account % |
| CRM Hygiene | Stale stages | Validated stage changes & opportunity health | Sales Ops | Velocity by Score |
| Product Analytics | Anecdotal usage | Feature adoption & expansion propensity scoring | Product/CS Ops | Expansion ARR |
| Privacy & Governance | Implicit consent | Consent & purpose management, audit logs | Legal/Compliance | Audit Pass, Opt-in Rate |
Client Snapshot: Signals that Predict Pipeline
A SaaS company unified MAP, two intent providers, technographics, and product analytics. After back-testing, pricing-tool use + topic surge + buyer-group coverage became the Tier-1 trigger. Result: more meetings, faster cycles, higher ACV. Explore results: Comcast Business · Broadridge
Mature scoring pairs lead management discipline with ABM signal orchestration to prioritize revenue now and expansion later.
Frequently Asked Questions about Scoring Data
Operationalize Your Scoring Data
We’ll unify signals, apply decay and weights, and prove lift against pipeline and ACV.
Build a Data-Driven Score Operationalize ABM Signals