Score Decay
What is Score Decay?
Score decay is a lead scoring mechanism that automatically reduces a prospect's engagement score over time when they stop interacting with your brand, ensuring that lead prioritization reflects current buying intent rather than historical engagement. This time-based score reduction prevents leads who were once highly engaged but have since gone dormant from appearing as high-priority prospects in the sales pipeline.
Traditional lead scoring models assign points for positive actions like downloading content, attending webinars, or visiting pricing pages, but many models fail to account for recency. A prospect who downloaded five whitepapers and attended two webinars eight months ago might have the same score as someone who completed those same actions last week, despite dramatically different purchase intent. This creates a critical flaw where sales teams waste time pursuing cold leads with artificially inflated scores while missing truly hot prospects.
Score decay solves this problem by implementing time-based score reduction that gradually decreases engagement points as they age. A webinar attendance might contribute 10 points immediately but decay to 7 points after 30 days, 4 points after 60 days, and eventually 0 points after 90 days. According to Marketing Sherpa research, implementing score decay improves lead quality perception by sales teams by 41% and increases MQL-to-opportunity conversion rates by 23% by ensuring sales focuses on prospects exhibiting current buying signals rather than stale engagement.
Key Takeaways
Recency Over Volume: Score decay prioritizes recent engagement over total historical activity, reflecting that timing matters more than quantity for identifying active buyers
Prevents Score Inflation: Automatically reduces scores for inactive leads, preventing them from clogging sales pipelines with artificially high scores
Improves Lead Quality: Sales teams report 30-40% higher lead quality when decay is implemented because scores reflect current intent
Enables Re-Engagement: Identifies previously engaged prospects who have gone cold, triggering nurture campaigns to reactivate interest
Dynamic Prioritization: Creates self-adjusting lead prioritization where the hottest prospects naturally rise to the top based on current behavior
How It Works
Score decay operates through mathematical formulas that systematically reduce engagement scores based on time elapsed since each activity:
1. Activity Aging and Score Reduction
Every scored activity receives a timestamp when it occurs. The scoring system then applies decay formulas that reduce point values as time passes. Different activities may have different decay rates—for example, pricing page visits might decay faster (30 days to zero) than content downloads (90 days to zero) because pricing page views indicate stronger immediate intent that becomes less relevant quickly.
2. Decay Rate Calculation
Most scoring systems use either linear decay or exponential decay formulas. Linear decay reduces scores by a fixed percentage over time (e.g., 10% per week until reaching zero). Exponential decay applies larger reductions early and smaller reductions later, better reflecting how engagement relevance actually diminishes. A common exponential formula: Current Score = Original Score × e^(-decay_rate × days_elapsed).
3. Threshold-Based Decay Models
Some organizations implement step-function decay where scores remain stable for an initial period, then drop sharply at defined intervals. For example, an activity maintains 100% score value for 30 days, drops to 50% for days 31-60, then 25% for days 61-90, and finally 0% after 90 days. This approach is simpler to implement and explain than continuous decay formulas.
4. Activity-Specific Decay Rates
Advanced scoring models assign different decay rates to different activity types based on how quickly engagement signals lose relevance. High-intent activities like demo requests or pricing page visits decay quickly (15-30 days), medium-intent activities like webinar attendance decay at moderate rates (45-60 days), and low-intent activities like blog visits decay slowly (60-90 days).
5. Firmographic Exemptions
Most decay models only apply to behavioral/engagement scores while keeping firmographic fit scores (company size, industry, revenue) stable since these attributes don't change based on time. This ensures that well-qualified companies maintain their fit score even during periods of low engagement, making it easier to identify high-value dormant accounts worth re-engaging.
Key Features
Time-based score adjustment that automatically reduces engagement points as activities age without manual intervention
Activity-specific decay rates allowing different engagement types to decay at appropriate speeds based on intent strength
Configurable decay formulas supporting linear, exponential, or step-function decay patterns based on organizational preferences
Automated re-scoring processes that recalculate lead scores continuously or on scheduled intervals to maintain accuracy
Integration with nurture triggers identifying leads whose scores have decayed significantly for targeted re-engagement campaigns
Use Cases
Enterprise SaaS Lead Prioritization
A $75M ARR enterprise software company struggles with sales reps complaining about poor lead quality despite high lead scores. Analysis reveals that 40% of Marketing Qualified Leads haven't engaged in 60+ days but maintain high scores from historical activity. The company implements exponential decay with 30-day half-lives for high-intent activities and 60-day half-lives for content consumption. Within three months, sales acceptance rates improve from 52% to 71% as sales teams receive leads showing current buying signals. The company also creates automated nurture campaigns triggered when scores decay by 30+ points, reactivating 18% of dormant leads.
Marketing Automation Platform Lead Nurturing
A fast-growing marketing automation vendor generates 2,000+ leads monthly through content marketing, creating a database of 50,000+ contacts with varying engagement recency. Without decay, their scoring model becomes increasingly unreliable as historical engagements accumulate. They implement a step-function decay model where activities maintain full value for 21 days, drop to 50% for days 22-45, then 0% after 45 days. This aggressive decay reflects their rapid sales cycle where prospects typically buy within 30-60 days of active engagement. The new model surfaces 200+ re-engaged prospects monthly who had gone cold but returned showing new buying signals, generating $800K in additional pipeline quarterly.
Account-Based Marketing Score Management
A cybersecurity company running targeted account-based marketing campaigns for 500 named accounts uses score decay to manage account-level engagement. Their account engagement score aggregates activity across all contacts at each account but implements decay to prevent accounts from maintaining high scores based solely on past campaigns. When key accounts show declining engagement (score decay of 40+ points over 60 days), the system triggers alerts to account executives prompting proactive outreach. This early warning system helps prevent deals from stalling, reducing average sales cycle length by 17% by catching engagement drops before prospects go completely cold.
Implementation Example
Here's a comprehensive framework for implementing score decay in your lead scoring model:
Score Decay Implementation Architecture
Decay Rate Configuration Table
Activity Type | Initial Points | Decay Method | Decay Schedule | Rationale |
|---|---|---|---|---|
Demo Request | 50 | Exponential | 50% @ 15d, 0% @ 30d | Highest intent, loses relevance quickly |
Pricing Page Visit | 30 | Exponential | 50% @ 20d, 0% @ 40d | Strong intent but slightly longer relevance |
Webinar Attendance | 25 | Linear | -25% per 20d, 0% @ 80d | Moderate intent with longer tail value |
Content Download | 15 | Linear | -20% per 30d, 0% @ 150d | Lower intent but educational value persists |
Email Click | 10 | Step Function | 100% → 50% @ 30d → 0% @ 60d | Simple engagement, moderate relevance window |
Website Visit | 5 | Step Function | 100% → 0% @ 45d | Lowest intent, short relevance period |
Decay Formula Examples
Exponential Decay (Recommended for High-Intent Activities):
Linear Decay (Good for Mid-Intent Activities):
Step Function Decay (Simplest to Implement):
Lead Scoring with Decay - Complete Example
Prospect A - Active Engagement:
| Activity | Date | Original Points | Days Ago | Decayed Points |
|----------|------|-----------------|----------|----------------|
| Demo Request | 5 days ago | 50 | 5 | 46 |
| Pricing Page Visit | 10 days ago | 30 | 10 | 26 |
| Webinar Attendance | 15 days ago | 25 | 15 | 21 |
| Total Engagement Score | | | | 93 points |
| Firmographic Fit Score | | | | 30 points |
| TOTAL LEAD SCORE | | | | 123 points ✅ HOT |
Prospect B - Stale Engagement:
| Activity | Date | Original Points | Days Ago | Decayed Points |
|----------|------|-----------------|----------|----------------|
| Webinar Attendance | 75 days ago | 25 | 75 | 3 |
| Content Download | 90 days ago | 15 | 90 | 6 |
| Email Click | 120 days ago | 10 | 120 | 0 |
| Total Engagement Score | | | | 9 points |
| Firmographic Fit Score | | | | 30 points |
| TOTAL LEAD SCORE | | | | 39 points ❄️ COLD |
Decay Monitoring Dashboard
Metric | Current | Target | Trend |
|---|---|---|---|
Avg Engagement Score Age | 28 days | <35 days | ✅ Healthy |
Leads with Decayed Scores (30+ days no activity) | 3,200 (32%) | <40% | ✅ Good |
MQL Score at Handoff (avg) | 87 points | >75 points | ✅ Exceeding |
Sales Acceptance Rate | 68% | >65% | ✅ On Target |
Reactivated Leads (from decay nurture) | 140/month | 100+/month | ✅ Exceeding |
Related Terms
Lead Scoring: The broader methodology for ranking prospects that score decay enhances with time-based adjustments
Behavioral Lead Scoring: Scoring based on prospect actions and engagement that decay addresses
Intent Decay: Related concept of how buying intent signals lose relevance over time
Lead Recency: The importance of recent engagement in prioritizing leads
Lead Scoring Analytics: Analysis of scoring model effectiveness that helps optimize decay rates
Marketing Qualified Lead (MQL): Leads identified through scoring models that incorporate decay
Dynamic Lead Scoring: Advanced scoring approaches that automatically adjust based on various factors including time
Frequently Asked Questions
What is score decay?
Quick Answer: Score decay is a lead scoring mechanism that automatically reduces engagement points over time as activities age, ensuring lead prioritization reflects current buying intent rather than outdated historical engagement.
Score decay prevents leads with high historical activity but no recent engagement from appearing as high-priority prospects. By applying time-based reduction formulas to behavioral scores, decay ensures that a prospect who engaged heavily six months ago but hasn't interacted since doesn't receive the same prioritization as someone showing current buying signals. This keeps lead queues clean, improves sales efficiency by focusing reps on active prospects, and creates more accurate pipeline forecasting based on leads who are actually in-market now.
How fast should scores decay?
Quick Answer: High-intent activities like demo requests should decay to zero within 30-45 days, while lower-intent activities like content downloads can maintain value for 60-90+ days based on typical sales cycle length.
Optimal decay rates depend on your sales cycle, buying journey, and activity significance. Companies with 30-day sales cycles need aggressive decay where most activities lose all value within 60 days. Organizations with 6-month enterprise sales cycles can implement slower decay allowing activities to maintain value for 90-120 days. Start by analyzing conversion data to understand how long between final engagement and purchase—activities that occur more than 2x your average sales cycle length ago should decay to near zero. Test different rates and measure impact on sales acceptance rates and conversion metrics.
Should firmographic scores decay?
Quick Answer: No, firmographic fit scores (company size, industry, revenue) should not decay because these attributes don't change over time and represent enduring qualification criteria rather than temporary buying signals.
Score decay applies specifically to behavioral/engagement scores reflecting prospect activity because engagement relevance diminishes with time. A company's size, industry, or revenue band remains constant regardless of when you last engaged with them, so these fit scores should remain stable. This separation is important—decay only engagement scores while preserving firmographic scores. Some organizations implement separate "fit score" and "engagement score" dimensions, applying decay only to engagement while fit remains constant. This approach makes it easier to identify high-fit accounts that have gone cold but remain excellent candidates for targeted re-engagement campaigns.
How do you implement score decay in marketing automation platforms?
Implementation approaches vary by platform. Most modern marketing automation systems like HubSpot, Marketo, or Pardot support score decay natively through configuration settings where you specify decay rates and schedules. For platforms without native decay, you can implement it through scheduled workflows that run daily or weekly to recalculate scores based on activity timestamps. Some organizations export scoring data to data warehouses, apply decay formulas using SQL or Python scripts, and push updated scores back to the CRM. The key technical requirements are storing activity timestamps, applying decay formulas based on elapsed time, and updating scores before leads are reviewed by sales teams.
What happens to leads whose scores decay significantly?
Leads experiencing significant score decay (e.g., dropping 40+ points over 60 days) represent previously engaged prospects who have gone cold, creating reactivation opportunities. Most organizations implement automated workflows triggered when scores cross decay thresholds. These might include specialized nurture campaigns with compelling offers, alerts to sales development reps prompting proactive outreach, or removal from active sales queues to prevent wasted follow-up effort. According to research from Forrester, 15-20% of decayed leads can be successfully reactivated through targeted campaigns, often with higher conversion rates than net-new leads since they already have brand familiarity. Track decayed leads as a distinct segment and test different reactivation strategies to maximize recovery.
Conclusion
Score decay represents a critical evolution in lead scoring methodology, transforming static scoring models that accumulate points indefinitely into dynamic systems that reflect the temporal nature of buying intent. For B2B SaaS organizations facing increasingly competitive markets and longer sales cycles, implementing decay ensures that lead prioritization remains accurate and sales resources focus on prospects showing current buying signals.
Marketing teams benefit from decay through improved lead quality metrics and higher sales acceptance rates, as the leads they pass to sales genuinely reflect active interest rather than historical engagement. Sales development representatives waste less time on cold leads with inflated scores and achieve better connection rates by focusing on recently engaged prospects. Revenue operations teams gain more accurate pipeline forecasting since scores reflect current buyer intent rather than accumulated historical activity.
As go-to-market motions become more sophisticated with multi-channel engagement and complex buyer journeys, score decay will become table stakes for effective lead scoring rather than an advanced optimization. Start by implementing simple step-function decay for your highest-intent activities, measure the impact on sales acceptance and conversion rates, then progressively add sophistication through activity-specific decay rates and advanced formulas. Combine decay with real-time signals from platforms like Saber that surface current company and contact signals to create a comprehensive view of prospect engagement that balances historical patterns with current intent.
Last Updated: January 18, 2026
