Intent Decay

What is Intent Decay?

Intent decay is the phenomenon where buyer intent signals lose predictive value and urgency over time as prospects move through evaluation cycles, priorities shift, or purchase decisions resolve. This time-dependent degradation means that a prospect researching "revenue operations platforms" today represents significantly higher engagement value than the same research activity from 90 days ago, requiring GTM teams to weight signal recency heavily in prioritization models.

In B2B sales and marketing, intent signals—content downloads, web research, product comparison searches, and engagement behaviors—indicate active evaluation and buying interest. However, these signals don't maintain constant value indefinitely. A prospect who downloaded a pricing guide yesterday is likely in active evaluation, while someone who downloaded the same guide six months ago may have purchased a competitor, postponed the project, or moved to a different company. Intent decay quantifies this degradation, enabling teams to prioritize recent signal activity over stale indicators.

The concept emerged as intent data providers proliferated in the mid-2010s and early users discovered that treating all intent signals equally produced poor conversion rates. Research by Forrester on B2B buyer behavior and intent data providers found that intent signals lose approximately 50% of their predictive value within 30-45 days for typical B2B software purchases with 3-6 month sales cycles. Longer enterprise cycles show slower decay, while transactional purchases decay rapidly. Understanding and modeling decay rates for your specific market enables more accurate lead scoring, better resource allocation, and improved conversion efficiency.

Key Takeaways

• Time-Dependent Value: Intent signals lose approximately 50% of predictive value within 30-45 days for typical B2B SaaS purchases, requiring recency weighting in scoring models

• Non-Linear Decay: Signal degradation follows exponential curves rather than linear patterns—value drops rapidly in early weeks, then flattens over time

• Category Variation: Different intent signal types decay at different rates—demo requests decay slower (30% over 30 days) than content downloads (60% over 30 days)

• Cycle Alignment: Decay rates correlate with typical sales cycle length—enterprise products with 9-12 month cycles show slower decay than SMB products with 30-60 day cycles

• Reactivation Indicators: New intent signals from previously decayed prospects represent stronger buying signals than first-time signals, indicating renewed evaluation

How It Works

Intent decay operates through multiple mechanisms reflecting prospect evaluation dynamics and market realities:

Time-Based Degradation: The most fundamental decay mechanism is simple time passage. Each day that elapses between signal generation and current date reduces signal value. Most models apply exponential decay functions where recent signals (0-7 days) maintain near-full value, moderate-age signals (8-30 days) lose value progressively, and old signals (60+ days) retain minimal predictive power. The specific curve shape and decay rate depend on product category and sales cycle characteristics.

Signal Type Differentiation: Different intent signals decay at different rates based on their behavioral commitment level. High-commitment signals—demo requests, trial signups, pricing page visits—decay slowly because they represent significant evaluation steps with longer effective windows. Low-commitment signals—blog post reads, general research, competitor comparisons—decay rapidly as they may represent early exploration that doesn't lead to immediate purchase. Scoring models apply signal-specific decay rates rather than uniform degradation.

Competitive Resolution: Intent signals decay when prospects resolve their evaluation by purchasing—from you, a competitor, or deciding to build internally. Once a buying decision completes, previous research signals become historically interesting but operationally irrelevant. Market intelligence about typical deal closure timeframes informs decay assumptions—if 70% of deals close within 90 days of first contact, signals older than 120 days likely represent resolved evaluations.

Priority Shifts: Organizational priorities change due to budget cycles, leadership transitions, strategic pivots, and external market conditions. A prospect intensely researching customer data platforms in Q2 may shift focus to cost reduction in Q3 as budget freezes occur. Their earlier intent signals decay not because evaluation completed but because organizational context changed. Macro indicators like economic conditions and industry trends can accelerate decay rates during uncertain periods.

Contact Turnover: B2B contact database decay averages 30-40% annually as people change roles, companies, or leave the workforce. When the person who generated intent signals departs, those signals lose value even if they were recent. Combining intent decay with contact data decay creates compound degradation—a 90-day-old signal from a contact who changed companies has near-zero predictive value.

Signal Refresh: When prospects generate new intent signals, previous decayed signals regain relevance through aggregation. A prospect who researched your category 6 months ago (heavily decayed) but just downloaded a new whitepaper represents higher intent than either signal alone. Models track both signal recency and signal density over time windows to identify reactivating prospects.

Key Features

• Exponential Decay Curves: Mathematical models that apply accelerating value reduction over time, reflecting real-world evaluation pattern data

• Signal-Specific Rates: Differentiated decay speeds for various intent types based on commitment level and typical evaluation progression

• Half-Life Calculations: Measurement of time required for signals to lose 50% of predictive value, enabling cross-category comparison

• Recency Weighting: Scoring algorithms that multiply intent values by time-based coefficients, emphasizing recent activity

• Reactivation Boost: Enhanced scoring for prospects showing renewed intent after decay periods, indicating reconsidered evaluations

Use Cases

Lead Scoring Optimization

Marketing operations teams apply intent decay models to improve lead scoring accuracy and sales prioritization. Instead of treating a product comparison research event from 60 days ago equally to yesterday's pricing page visit, the scoring model applies decay factors: yesterday's visit retains 100% value (10 points), while the 60-day-old research retains 25% value (2.5 points). This recency weighting dramatically improves lead score correlation with conversion rates. According to HubSpot's research on lead scoring, teams that implement time-weighted intent scoring see 20-30% improvements in qualified lead conversion according to demand generation research.

Campaign Timing Optimization

Demand generation teams use decay curves to optimize retargeting campaign windows and budget allocation. Analysis shows that prospects who visited pricing pages 0-7 days ago convert at 8%, 8-30 days ago at 4%, and 31-60 days ago at 1.5%. Based on these decay-informed conversion rates, the team allocates 50% of retargeting budget to 0-7 day window, 35% to 8-30 day window, and 15% to 31-60 day window, maximizing ROI by concentrating spend when intent value is highest. Campaigns also adjust creative urgency—recent visitors see "schedule your demo this week" messaging while older visitors receive softer "still exploring?" re-engagement content.

Sales Territory Assignment

Revenue operations teams incorporate intent decay into territory routing and account assignment logic. When multiple accounts show intent signals, accounts with recent, high-value signals are prioritized for immediate sales assignment and high-touch outreach, while accounts with older, decayed signals are routed to lower-touch nurture sequences or inside sales teams. An enterprise account with intent signals from 7 days ago gets assigned to a senior AE with immediate follow-up SLA, while a similar-sized account with 90-day-old signals enters automated nurture until new signals appear. This ensures expensive sales resources focus on timely opportunities.

Implementation Example

Here's a comprehensive framework for modeling and applying intent decay in GTM operations:

Intent Signal Decay Rates by Type

Calculation: Value Retention % = 100% × 0.5^(Days Elapsed / Half-Life)

Time-Weighted Intent Scoring Model

Intent Score Calculation with Decay
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
<p>Base Score × Decay Factor = Weighted Score</p>
<p>Example: Product Comparison Research<br>Base Value: 25 points<br>Signal Age: 45 days<br>Half-Life: 35 days</p>
<p>Decay Factor = 0.5^(45/35) = 0.5^1.29 = 0.41</p>
<p>Weighted Score = 25 × 0.41 = 10.25 points</p>
<p>Compare to Recent Signal:<br>Signal Age: 5 days<br>Decay Factor = 0.5^(5/35) = 0.5^0.14 = 0.91<br>Weighted Score = 25 × 0.91 = 22.75 points</p>

Prospect Intent Timeline with Decay

Prospect: Jane Smith, VP Marketing @ Acme Corp
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
<p>Day 0   │ Downloaded whitepaper (10 pts base)<br>│ Current value: 10.0 pts (100% retention)</p>
<p>Day 15  │ Attended webinar (15 pts base)<br>│ Current value: 15.0 pts (100% retention)<br>│ Whitepaper value: 6.7 pts (67% retention)<br>│ Total Active Intent: 21.7 pts</p>
<p>Day 30  │ Visited pricing page (25 pts base)<br>│ Current value: 25.0 pts (100% retention)<br>│ Webinar value: 10.6 pts (71% retention)<br>│ Whitepaper value: 2.9 pts (29% retention)<br>│ Total Active Intent: 38.5 pts → Crosses MQL threshold</p>
<p>Day 60  │ No new activity<br>│ Pricing visit value: 13.5 pts (54% retention)<br>│ Webinar value: 5.3 pts (35% retention)<br>│ Whitepaper value: 0.8 pts (8% retention)<br>│ Total Active Intent: 19.6 pts → Below MQL, re-enter nurture</p>

Decay-Informed Campaign Strategy

Immediate Response Window (0-7 Days)
- Decay Rate: Minimal (95-100% value retention)
- Strategy: High-urgency, high-touch, personalized outreach
- Channels: Sales call, personalized email, LinkedIn message
- Messaging: "I noticed you were just exploring [topic]..."
- Budget Allocation: 50% of intent-based campaign spend

Active Consideration Window (8-30 Days)
- Decay Rate: Moderate (50-95% value retention)
- Strategy: Educational nurture with clear CTAs
- Channels: Email sequences, retargeting ads, content recommendations
- Messaging: "As you continue evaluating [category]..."
- Budget Allocation: 35% of intent-based campaign spend

Cooling Interest Window (31-60 Days)
- Decay Rate: Significant (20-50% value retention)
- Strategy: Re-engagement and soft nurture
- Channels: Lower-frequency email, display advertising
- Messaging: "Still exploring options for [use case]?"
- Budget Allocation: 12% of intent-based campaign spend

Dormant Prospects (61+ Days)
- Decay Rate: Severe (<20% value retention)
- Strategy: Broad nurture, signal monitoring, pause high-cost channels
- Channels: Monthly newsletter, new content alerts
- Messaging: General thought leadership, no pressure
- Budget Allocation: 3% of intent-based campaign spend (or pause)

Segment-Specific Decay Adjustments

Enterprise Segment (9-12 month sales cycles)
- Apply 0.7× decay rate multiplier (slower decay)
- Pricing page half-life: 45 → 64 days
- Demo request half-life: 60 → 86 days

Mid-Market Segment (3-6 month sales cycles)
- Apply 1.0× standard decay rates
- Use baseline half-life values

SMB Segment (30-60 day sales cycles)
- Apply 1.5× decay rate multiplier (faster decay)
- Pricing page half-life: 45 → 30 days
- Demo request half-life: 60 → 40 days

Related Terms

• Buyer Intent Signals: The behavioral indicators whose value degrades over time through intent decay processes

• Lead Scoring: Prioritization methodology that incorporates intent decay through recency weighting and time-based adjustments

• Engagement Signals: Interaction data that experiences decay similar to intent signals but often at different rates

• Account-Level Intent: Organization-wide research patterns that may show different decay characteristics than individual contact intent

• Behavioral Signals: Broader category of customer actions that includes intent signals and also experiences time-based degradation

• Content Consumption Signals: Specific intent indicators known for relatively rapid decay due to low commitment level

• Digital Body Language: Observable online behaviors that require interpretation in temporal context accounting for decay

• Composite Signal Score: Aggregated metrics that combine multiple signals with decay-adjusted weighting

Frequently Asked Questions

What is intent decay?

Quick Answer: Intent decay is the time-dependent reduction in predictive value and urgency of buyer intent signals as prospects progress through evaluations, priorities change, or purchase decisions resolve, requiring recency-weighted scoring approaches.

Intent signals indicate active buying interest at the moment they occur, but this interest doesn't remain constant over time. Prospects may purchase from competitors, postpone projects, change roles, or shift priorities—all of which reduce the relevance of earlier research signals. Intent decay quantifies this degradation, typically following exponential curves where signals lose 50% of value within 30-60 days depending on signal type and product category. GTM teams apply decay models to weight recent signals more heavily than old signals in prioritization and scoring systems.

How do you calculate intent decay rates?

Quick Answer: Intent decay rates are calculated using exponential decay formulas based on signal half-life (time to lose 50% of value), derived from historical analysis correlating signal age with conversion rates and win rates across your customer data.

Start by analyzing conversion data: segment opportunities by time between first intent signal and conversion, then calculate win rates for each time bucket (0-15 days, 16-30 days, 31-60 days, etc.). Plot these win rates over time to visualize the decay curve. Calculate half-life as the time where win rate drops to 50% of peak value. Apply exponential decay formula: Retained Value = Base Value × 0.5^(Days Elapsed / Half-Life). Validate by comparing time-weighted scores to actual outcomes and adjust half-life parameters to optimize predictive accuracy.

Do all intent signals decay at the same rate?

Quick Answer: No—high-commitment signals like demo requests and trial signups decay slowly (60-90 day half-lives), while low-commitment signals like blog reads and general research decay rapidly (14-30 day half-lives) reflecting different evaluation stages.

Decay rates correlate with behavioral commitment level and evaluation stage proximity. Signals requiring significant investment—scheduling demos, completing trial forms, attending live events—indicate advanced evaluation stages with longer persistence. These prospects have invested time and revealed themselves, creating longer action windows. Conversely, passive research—reading blog posts, viewing general content, early-stage competitor comparisons—represents exploratory behavior that may not lead to near-term purchase, creating rapid decay. Differentiate decay rates by signal type to reflect these realities.

How does intent decay differ by company size or industry?

Company size and industry affect decay primarily through their impact on sales cycle length and decision complexity. Enterprise companies with complex evaluation processes, multiple stakeholders, and lengthy approval workflows show slower intent decay—signals may retain value for 90-120 days as evaluation processes unfold gradually. SMB companies with faster decision cycles and fewer stakeholders show rapid decay—signals often lose most value within 30-45 days. Industries with long implementation timelines (regulated industries, complex infrastructure) show slower decay, while industries with quick deployment (simple SaaS tools) show faster decay. Adjust base decay rates by 0.7-0.8× for enterprise/complex, 1.5-2.0× for SMB/simple.

Can intent signals regain value after decay?

Yes—new intent signals from previously decayed prospects often represent stronger buying indicators than first-time signals, warranting reactivation bonuses in scoring models. A prospect who researched your category 6 months ago (signal fully decayed) but just downloaded new content demonstrates renewed evaluation that may be more informed and urgent than initial exploration. Apply "reactivation multipliers" (1.2-1.5×) to new signals when prospect history shows previous decayed intent, as this pattern indicates they're returning to a problem they've considered before, often with higher purchase readiness. Track signal density over extended time periods to identify these reactivation patterns.

Conclusion

Intent decay represents a critical temporal dimension in modern B2B sales and marketing intelligence. Understanding that buyer intent signals lose predictive value over time—following exponential curves influenced by signal type, sales cycle length, and market conditions—enables dramatically more accurate lead scoring, resource allocation, and campaign timing than static approaches that treat all signals equally regardless of age.

The most sophisticated revenue organizations build intent decay into every aspect of their GTM operations. Marketing automation applies time-weighted scoring that emphasizes recent signals while gradually reducing older signal values. Sales development prioritizes accounts showing recent intent spikes over accounts with stale signals. Customer success monitors reactivation patterns in existing customers to identify expansion timing. This temporal intelligence transforms intent data from binary (present/absent) to dimensional (strength/age/trajectory).

As buyer intent signals proliferate and signal sources multiply, decay modeling will become increasingly sophisticated. Gartner's research on marketing technology shows that machine learning approaches will learn segment-specific and signal-specific decay rates automatically from outcome data, dynamically adjusting as market conditions change. Organizations that master intent decay today—combining it with behavioral signals, engagement signals, and lead scoring methodologies—will maintain decisive advantages in targeting efficiency, conversion rates, and sales productivity by focusing resources where intent is strongest and timing is optimal.

Last Updated: January 18, 2026