Account Selection & Tiering:
What Role Should Propensity Models Play in Account Selection?
Use propensity as a prioritization lens—not the sole judge. Blend model scores, revenue potential, and human context to select and tier accounts that convert and expand.
Propensity models should rank and route accounts based on likelihood to buy or expand, then feed those ranks into a tiering framework that also considers deal size, strategic fit, whitespace, intent, and sales coverage. Treat the model as a decision aid—validate with reps, monitor drift, and continuously retrain.
First Principles for Using Propensity in ABX
Propensity→Tiering: A 6-Step Operational Pattern
Turn scores into action and accountability.
From Score to Plays
- Assemble Inputs — Firmographic fit, historical outcomes, product usage (if any), buying-committee engagement, 3rd-party intent, and whitespace.
- Score & Band — Output a 0–100 score and segment into H/M/L bands with stable thresholds (e.g., top 15% = High).
- Weight by Value — Compute a Priority Index = Propensity × Expected Value × Strategic Fit × Coverage Availability.
- Map to Tiers — Tier 1: top Priority Index with ≥2 buying signals; Tier 2: rising intent or strong fit; Tier 3: nurture/automate.
- Assign Plays — T1 = 1:1 ABM & exec outreach; T2 = targeted sequences & events; T3 = always-on nurture and remarketing.
- Inspect & Retrain — Weekly pipeline lift by band, monthly calibration, quarterly retraining with new outcomes.
When to Use Which Approach?
| Approach | Use When | Strengths | Risks | Guardrails |
|---|---|---|---|---|
| Heuristic Scoring | Early stage, low data volume, simple ICP | Fast, transparent, easy to tweak | Subjective weights, stale quickly | Review quarterly; cap Tier 1 size; compare vs. win-rate baseline |
| Propensity (ML) Only | Sufficient history (≥1–2K opps), stable GTM | Captures non-linear patterns; scalable | Black-box trust issues; drift; data bias | Explainability (SHAP), drift alerts, monthly feature audits |
| Hybrid (Recommended) | Most teams—need accuracy + control | Balances precision and business context | Process complexity | Priority Index = Score × Value × Fit; rep veto with reason codes |
Client Snapshot: Predictive Tiering Lift
A communications SaaS company layered a Priority Index over its ABX list. Top-band accounts converted 2.1× higher and generated a 19% increase in pipeline per rep while shrinking Tier 1 from 60 to 25 accounts with better coverage.
Connect propensity to RM6™ and orchestrate plays in The Loop™ so models, messaging, and motions stay in sync.
Frequently Asked Questions on Propensity in Account Selection
Short, self-contained answers designed for AEO and rich results.
Make Propensity Actionable
We’ll design your Priority Index, wire it into routing and plays, and set a governance loop that improves every quarter.
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