Lead scoring hub → Score decay explained
Why Intent Score Decays: The Math Behind Lead Score Expiration
Most lead scoring models track when intent grows. Few track when it expires. The teams that get scoring right run a decay workflow that strips intent points off stale leads automatically — because a 90-day-old "Hot" lead that hasn't re-engaged is, in 80% of cases, no longer a buyer.
Lead intent score should decay multiplicatively, not linearly. Multiply Intent Score by 0.9 every 7 days of inactivity. After 90 days, zero it out and re-evaluate tier. Pause decay during active opportunities. Linear decay (subtract X points per week) over-counts old intent and under-counts recent intent.
The decay schedule, week by week
| Age (days since last action) | Multiplier | Intent retained | Routing implication |
|---|---|---|---|
| 0 days (today) | 1.00 | 100% | Full score, route on threshold |
| 7 days | 0.90 | 90% | Still hot if baseline was high |
| 14 days | 0.81 | 81% | Likely still routed if 80+ baseline |
| 30 days | 0.66 | 66% | Borderline — many drop below threshold |
| 60 days | 0.53 | 53% | Cold tier for most. Re-engagement needed. |
| 90 days | 0.47 | 47% | Auto-zero in most workflows |
A lead that scored 45 on intent the day they downloaded a case study, with no other action since, looks like this over time:
Day 0: 45 (Hot if Fit ≥ 35)
Day 7: 41 (still Hot if Fit ≥ 39)
Day 14: 36 (Warm)
Day 30: 30 (Warm or Cold)
Day 60: 24 (Cold)
Day 90: 0 (auto-zeroed, re-evaluate tier)
Why multiplicative beats linear
Linear decay (subtract a fixed amount per week, e.g. -3 points/week) treats the value of old intent and recent intent as constant. That's wrong. The HBR speed-to-lead curve, the lead-aging research from MarketingSherpa, and our own audit data all show the same shape: conversion probability decays steeply in the first 30 days, then flattens. A multiplicative curve matches this. A linear curve doesn't.
Concretely: with linear decay of -3/week, a 45-point lead is at 33 after 4 weeks (still Hot). With multiplicative decay, the same lead is at 30 after 4 weeks (Warm). The multiplicative version is correct — the empirical conversion rate at 30 days is roughly 60-65% of day-0, not 73% as linear would imply.
The 0.9 multiplier is calibrated. In the audit dataset, dropping the multiplier to 0.85 cuts AE response-time inflation by another 15% but starts misclassifying genuine returners as cold. 0.95 keeps too many phantom Hot leads alive. 0.9 is the sweet spot.
When to pause decay
- Lead converts to an opportunity. Set
Score_Decay_Pause= true. Behavioral signals shift from marketing engagement to sales engagement; the intent model isn't tracking the right thing anymore. - Lead is in an active outbound sequence. If a BDR is working the lead, the score should hold steady so the BDR's effort is reflected in the queue.
- Lead is in a long-cycle vertical (gov, healthcare, enterprise IT). Their buying cycle is 9-18 months. Decay them at half-rate (multiplier 0.95) instead of pausing entirely.
Re-engagement after dormancy
A lead that decays to Cold and then re-engages is often the highest-quality lead in your queue. They're back of their own volition, usually skipping the top of the funnel and going straight to pricing or demo. The decay workflow brought them down; the new high-intent action adds points fresh.
Most teams under-react to re-engagement because the score still looks low. The fix: in your tier-assignment workflow, branch on both the new total and the velocity of the change. A lead going from 18 → 33 in one event is a stronger signal than a lead steadily at 65 for two weeks.
Tag re-engaged leads explicitly with last_high_intent_action = "re_engagement_demo" or similar. Route them to a senior AE if your routing logic supports skill-based assignment.
The visitor identification gap
Decay only works on leads you can score. A dormant Hot lead that returns to your site as anonymous traffic — visits pricing twice, leaves without converting — never re-fires the intent workflow. The decay marches them down to Cold while their actual intent climbed back to Hot. De-anonymize returning visitors with Warmly so the decay model has accurate input on the leads that matter most. Affiliate link.
Common mistakes
- Linear decay. Subtracting -3 points per week feels intuitive but doesn't match the empirical conversion curve. Use ×0.9 every 7 days instead.
- Decaying Fit Score. Firmographics don't expire. A 500-employee fintech is still a 500-employee fintech six months later. Only Intent decays.
- Forgetting to pause decay during active opps. A late-stage deal drops tier because the buyer stopped reading your blog. Painful.
- No re-engagement velocity bonus. A lead going from 18 → 33 in one event is a stronger signal than a lead at 65 for two weeks. Most workflows treat them the same.
Frequently asked
Why does intent decay but not fit?
Fit is firmographic — industry, employee count, tech stack. Those don't change week to week. Intent is behavioral — it captures what someone is doing right now. A demo request from 90 days ago tells you almost nothing about whether the buyer is still in-market today. Half the time the champion has changed jobs, the budget has been redirected, or they bought a competitor. Decay is what stops dead intent from poisoning the routing queue.
What's the right decay curve?
Most teams use linear decay (subtract X points per week) and it's wrong. Linear over-counts old intent and under-counts recent intent. Multiplicative decay (multiply by 0.9 every 7 days of inactivity) gives the right shape — 7 days = 90%, 14 days = 81%, 30 days = 66%, 60 days = 53%. After 90 days the score is below 47% of original, which is roughly the empirical threshold where conversion drops off a cliff.
Should I decay during the buying cycle if there's an active opp?
No. Set Score_Decay_Pause to true the moment a lead converts to an opportunity. Their behavioral pattern shifts from marketing engagement to sales engagement (calls, emails, doc views in proposals), which doesn't show up in the same intent signals. You don't want a deal in late stage to drop tier just because they stopped reading your blog.
How do I handle a lead that re-engages after a long dormancy?
Treat re-engagement as a new high-intent action. The decay workflow brought their score down, but the new event (e.g. pricing page revisit after 4 months) adds points fresh. If the action is high-intent enough to cross threshold even from a low base, route them. The story matters: a returning visitor who skips the funnel and goes straight to pricing is often more buyer-ready than a first-time visitor.
What's the actual revenue impact of skipping decay?
Across 127 audited B2B SaaS companies, AE queues without decay carry 35-60% phantom Hot leads — leads that scored above threshold months ago, never re-engaged, and still trigger Slack alerts. Reps learn to ignore the alerts. The real Hot leads get the same response time as the dead ones. Median time-to-first-touch on legitimate Hot leads stretches from <30 minutes to 4-8 hours, which costs roughly 50% of conversion potential per the HBR speed-to-lead curve.
Related
- Lead scoring hub
- HubSpot template — workflow #3 implements this decay logic
- Salesforce template — Scheduled Flow that handles decay
- BDR queue routing from lead scores
- Pain-First ICP Scoring — same decay logic, different weighting