Account Interest Rating (AIR)
A 0–100 per-account score that combines intent signals, first-party behaviour, and role-level engagement into a single actionable number — the operational heart of Phase 2 (Sense) in the Revenue Motion Framework™.
What is the Account Interest Rating?
The Account Interest Rating, or AIR, is a per-account score between 0 and 100 that quantifies how likely an account is to be actively in-market. It’s calculated by combining weighted intent signals, first-party behavioural data, and role-level engagement patterns across the entire account — not the individual — and applying recency decay so recent activity counts more than historical noise. The AIR is the output every downstream operational decision in the Revenue Motion Framework™ depends on: which accounts the Marketing Account Developer works this week, which spike patterns trigger a development play, which accounts sales sees as heating up.
Why AIR exists
Most B2B revenue teams have too much signal data and no way to prioritise it. A typical intent-data vendor surfaces hundreds or thousands of “in-market” accounts each month. Sales has bandwidth for a fraction. Marketing tries to serve everyone. The team drowns in dashboards.
AIR solves this by collapsing the noise into a single number per account. Instead of a list of 2,000 accounts flagged as “showing intent” with no ranking, teams get a rank-ordered list where the top 20–40 accounts have visibly higher scores. The scoring model is transparent, the thresholds are agreed in advance, and everyone in marketing and sales works from the same view.
Without AIR (or an equivalent score), signal data becomes reporting theatre — impressive-looking dashboards that don’t drive action. With AIR, the dashboard produces a decision.
How AIR works under the hood
AIR uses four inputs, blended into one score:
- Weighted signal taxonomy. Not all signals carry the same weight. Buying signals — pricing-page repeat visits, demo requests, competitor comparison reads, RFP-language searches — score highest, typically 5× the weight of learning signals. Evaluation signals (analyst-report reads, peer-review activity, “vs” searches) sit in the middle at roughly 3× weight. Learning signals (top-of-funnel blog reads, category education, intro webinars) score lowest at 1× weight. Background signals (job changes, funding events, tech-stack shifts) don’t score on their own — they act as multipliers when combined with anything else.
- Recency decay. A signal that fired today is more predictive than one from six months ago. AIR applies exponential decay to each signal with a configurable half-life, typically 30 days. Old signals fade automatically. New signals push the score up sharply.
- Role concentration. Three different roles from the buying group engaging with your properties in the same week scores much higher than the same person engaging three times. AIR rewards diversity of engagement across the buying group, which correlates strongly with real buying committee activity.
- Signal-type combination. Multiple signal types firing in the same window score higher than the same signal type repeating. An account with a pricing-page visit + a competitor comparison read + a peer-review click scores higher than the same account with ten pricing-page visits alone.
The output is a single 0–100 number that updates nightly as new signals arrive and old ones decay.
AIR thresholds and dashboard views
Three thresholds structure the daily use of AIR:
- 70+ Spiking — act now. This is the Marketing Account Developer’s queue. Accounts crossing this threshold trigger a development play immediately.
- 40–70 Warming — watch. Light engagement is appropriate; the account isn’t ready for dedicated development yet, but it’s worth keeping in the peripheral view.
- Below 40 Background — don’t interrupt. Continue standard nurture; don’t allocate dedicated resource.
The AIR is surfaced through a unified dashboard with three role-specific views:
- Account Manager view: target-account list ranked by AIR, presented as insight — context on which of the AM’s accounts are heating up. Not a task list.
- Business Developer view: forward-look showing which spiking accounts are likely to produce a Qualified Opportunity packet for review in the coming weeks.
- Marketing Account Developer view: working queue of accounts to develop this week, with the topic driving each spike and the roles most active.
Same data, three views, three different next moves.
AIR in context
- Phase 2 — Sense — where AIR is designed and produced. See the Sense phase page for the full operating model.
- Marketing Account Developer — the role that works AIR-spiking accounts into Qualified Opportunities. (Glossary entry coming soon.)
- Four-stage operating model — AIR is what triggers the Target → Spotted transition. From Spotted to Qualified to Accepted is the human work above the score. (Glossary entry coming soon.)
- Persona × maturity matrix (Phase 4) — informs which messaging to use when the AIR spike triggers a development play.
- AI Search and the Dark Funnel — how AIR adapts to the era of AI-mediated buyer research and fewer first-party signals.
FAQ
No. Intent data is one input into AIR. AIR combines third-party intent, first-party behavioural data, and role-level engagement patterns into a single weighted, decayed, combination-aware score. Intent data on its own is noisy; AIR is the discipline of valuing that data.
No. AIR is a model, not a vendor. It can be implemented on top of most modern intent-data platforms (6sense, Demandbase, Bombora) combined with first-party analytics and CRM data. The important thing is the model — the weighting logic, the decay rule, the role-concentration reward — not the tool.
Absolute numbers are less meaningful than the distribution and the thresholds. In a well-tuned model, 5–10% of your target accounts should be in the Spiking (70+) band at any given time, 15–25% in the Warming (40–70) band, and the rest in Background. If everything is spiking, the weights need recalibration. If nothing is, the same.
Quarterly is the minimum. Signal predictiveness shifts as your market shifts. The weights that worked last year rarely work this year. The Revenue Motion Framework’s Phase 5 (Optimize) is where the recalibration happens — driven by which signals actually correlated with won deals over the previous quarter.
This is the emerging challenge for any signal-based model. First-party behavioural data is scarcer as buyers research inside AI conversations before visiting your properties. The AIR model adapts by weighting the remaining first-party signals more heavily and by adding AI-mediated signals (mentions of your brand in AI answers) as a new fourth source category. See AI Search and the Dark Funnel for the strategic context.