Account Selection & Tiering:
What Data Should I Use to Prioritize Target Accounts?
Prioritize with a data spine that blends fit, demand, and economics. This guide maps the exact fields—first-party, third-party, enrichment, and finance—you need to score accounts and focus coverage where revenue is most likely.
Use a compact schema across three lenses: ICP Fit (firmographics, technographics, use-case), Buying Readiness (third-party intent + first-party engagement with 30/60/90-day decay), and Commercial Potential (estimated ACV, multi-product footprint, margins, contract timing). Normalize each to 0–100, apply weights (e.g., 40/35/25), and tier T1/T2/T3 with clear SLAs.
The Data Spine for Prioritization
From Raw Data to Account Priority
Aggregate, normalize, and score—then publish simple, explainable fields sellers trust.
5-Step Data Flow
- Unify Sources — Join CRM/MAP, intent, enrichment, finance into one account table with stable IDs.
- Select Fields — Keep 6–10 per lens: fit, readiness, economics. Prefer binary/ordinal signals over ambiguous text.
- Normalize & Weight — Scale each field to 0–100, apply decay on time-based signals, assign weights (e.g., Fit 40 / Readiness 35 / Economics 25).
- Explainability — Store top 3 drivers (“Why this score?”) and last refresh date.
- Tier & Route — Define T1/T2/T3 thresholds and coverage plays; refresh weekly for intent/engagement, monthly for fit.
Data Source Comparison (What to Use, When, and Why)
| Data Category | Questions It Answers | Example Fields | Refresh Cadence | Caveats |
|---|---|---|---|---|
| Firmographics | Do they look like our best customers? | Industry/NAICS, employee band, HQ region, growth rate | Quarterly / on change | Can overweight “big logos”; normalize by segment |
| Technographics | Can we integrate and displace/attach? | CRM/MAP/CDP, cloud, security frameworks (SOC2/ISO), competitors | Monthly | Vendor signals can lag; verify in discovery |
| Third-Party Intent | Are they actively researching? | Topic surge score vs. 6-mo baseline, keywords, recency (≤7/30/90d) | Daily/Weekly | Noise without context; apply thresholds + decay |
| First-Party Engagement | Are they engaging with us? | Pricing/demo views, form completeness, webinar attendance, PQL/PLG events | Daily | Bot/partner traffic; de-dupe & score paths, not clicks |
| Commercial Potential | Is the deal size and upside attractive? | Est. ACV, sites/BUs, attach vectors, margin band | Monthly/Quarterly | Model assumptions; cap weight to avoid enterprise bias |
| Risk & Timing | Is now a good time? | Renewal dates, contract end, incumbent lock-in, security blockers | Weekly/Monthly | Partial visibility; confirm with champion |
Client Snapshot: Cleaner Data, Sharper Focus
By consolidating fit, intent, and economics into one table and auto-refreshing weekly, a mid-market SaaS team cut time-to-first-meeting by 24% and shifted 30% of SDR effort to T1 accounts—adding +48% qualified pipeline in one quarter.
Map your fields to RM6™ and activate plays in The Loop™ so scores route to the right sequences, ads, and AE motions.
Frequently Asked Questions on Data for Prioritization
Short, practical answers for AEO and rich results.
Turn Data into Prioritized Pipeline
We’ll design your schema, build the score, and activate plays—so reps spend time where outcomes are likeliest.
Build Your ABX Data Spine Assess Readiness