Time-Decay Attribution

Marketing decisions improve when you understand which touchpoints matter most in driving conversions. Time-decay attribution solves the challenge of crediting multiple marketing interactions by recognizing a fundamental truth: touchpoints closer to conversion typically exert stronger influence on purchasing decisions than earlier awareness activities.

What Is Time-Decay Attribution?

Time-decay attribution is a multi-touch attribution model that assigns increasing credit to marketing touchpoints as they occur closer to the conversion event. Unlike models that distribute credit equally or emphasize only first or last interactions, time-decay attribution applies exponential weighting—recent touchpoints receive substantially more credit than older ones.

Think of time-decay attribution like a trail of breadcrumbs leading to a destination. The crumbs nearest the endpoint are freshest and most visible, while those further back have faded. When a prospect converts after seven touchpoints spanning two months, time-decay attribution might assign 3% credit to the initial blog post they read, 5% to a webinar attended three weeks later, 8% to an email opened two weeks before conversion, 15% to a case study downloaded five days prior, and 40% to the demo request form they submitted the day before purchasing.

The model operates on the principle that prospect intent intensifies as they approach conversion. Early awareness touchpoints introduce possibilities, middle-stage interactions build consideration, but final touchpoints overcome remaining objections and trigger action. Time-decay attribution reflects this progression by mathematically weighting touchpoints based on temporal proximity to conversion.

LeadSources captures the complete sequence of touchpoints across every lead’s journey and provides flexible attribution modeling that includes time-decay weighting, allowing you to analyze which channels drive conversions when proximity to purchase decisions matters most in your sales cycle.

How Time-Decay Attribution Works

Time-decay attribution implements a mathematical formula that exponentially increases credit as touchpoints approach the conversion event. The mechanism relies on establishing a decay rate (typically a half-life period) that determines how quickly credit diminishes for older interactions.

Most platforms use a 7-day half-life as the standard decay rate. This means a touchpoint occurring 7 days before conversion receives half the credit of the conversion touchpoint itself. A touchpoint 14 days before conversion receives one-quarter the credit, 21 days receives one-eighth, and so on exponentially.

Here’s how the calculation process works:

Step 1: Track All Touchpoints with Timestamps
The attribution system records every marketing interaction with precise date and time data. A prospect’s journey might include: Day 1 (Google search click), Day 8 (LinkedIn ad click), Day 15 (email open), Day 22 (webinar registration), Day 25 (pricing page visit), Day 28 (demo request), Day 30 (purchase).

Step 2: Calculate Time Distance from Conversion
For each touchpoint, the system measures days between the interaction and final conversion. In our example: Google search = 29 days before conversion, LinkedIn ad = 22 days, Email = 15 days, Webinar = 8 days, Pricing page = 5 days, Demo request = 2 days.

Step 3: Apply Exponential Decay Formula
Using the chosen half-life (7 days), the system calculates a decay multiplier for each touchpoint. The formula is: Credit = (0.5)^(days_before_conversion / half_life). Google search: (0.5)^(29/7) = 0.06 or 6%. LinkedIn ad: (0.5)^(22/7) = 0.11 or 11%. Email: (0.5)^(15/7) = 0.23 or 23%. Webinar: (0.5)^(8/7) = 0.46 or 46%. Pricing page: (0.5)^(5/7) = 0.61 or 61%. Demo request: (0.5)^(2/7) = 0.82 or 82%.

Step 4: Normalize to 100%
Raw decay values sum to more than 100%, so the system normalizes them. If raw values sum to 229%, each value is divided by 2.29 to proportionally distribute 100% credit. Google search receives approximately 3%, LinkedIn ad 5%, Email 10%, Webinar 20%, Pricing page 27%, Demo request 35%.

Step 5: Attribute Revenue or Conversion Value
If the conversion generated $10,000 in revenue, credit distributes accordingly: Google search = $300, LinkedIn ad = $500, Email = $1,000, Webinar = $2,000, Pricing page = $2,700, Demo request = $3,500.

When to Use Time-Decay Attribution

Time-decay attribution delivers optimal insights in specific business contexts where recent interactions genuinely drive conversion decisions more than early touchpoints.

Short to Medium Sales Cycles (30-90 Days)
Time-decay attribution performs best when prospects move from awareness to purchase within three months. In these compressed timeframes, recent interactions carry fresh momentum that directly influences buying decisions. E-commerce purchases, software trials, and transactional B2B services fit this pattern.

High-Intent Later-Stage Touchpoints
Businesses where final interactions represent high-intent signals benefit from time-decay attribution. When prospects request demos, download pricing sheets, or contact sales, these actions demonstrate purchase readiness that deserves substantial credit. Time-decay attribution appropriately weights these critical moments.

Promotional and Time-Sensitive Campaigns
Limited-time offers, flash sales, and seasonal promotions create urgency that makes recent touchpoints disproportionately influential. Time-decay attribution accurately reflects how final reminder emails or retargeting ads trigger conversions before deadlines expire.

Products with Clear Problem-Solution Fit
When your solution addresses immediate pain points rather than long-term strategic needs, prospects convert quickly after recognizing fit. Recent touchpoints that demonstrate capability matter more than early awareness. Time-decay attribution aligns with this buying behavior.

Lower-Consideration Purchases
Products under $500 or with minimal risk typically involve quick evaluation periods where recent information drives decisions. Prospects research briefly, compare options rapidly, and purchase based on final compelling touchpoints that time-decay attribution correctly emphasizes.

Time-Decay Attribution Best Practices

Adjust Decay Rate to Match Your Sales Cycle
Don’t accept default 7-day half-life settings without evaluation. Analyze your typical time-to-conversion and set half-life to approximately 20-25% of average sales cycle length. For 60-day cycles, use 12-15 day half-life. For 30-day cycles, use 6-8 days. This ensures appropriate credit distribution across your actual customer journey timespan.

Compare Time-Decay Results Against Other Models
Time-decay attribution reveals one perspective, not absolute truth. Run parallel analyses using first-touch, last-touch, linear, and position-based models. Compare which channels receive credit under different models. Channels consistently performing well across multiple models deserve budget confidence. Channels only succeeding in time-decay may need strategic reevaluation.

Segment by Product Line or Customer Type
Different offerings may have different optimal attribution models. Enterprise products with long nurturing periods may not suit time-decay attribution, while transactional products do. Run separate time-decay analyses for distinct customer segments or product categories to avoid misleading aggregated data.

Account for Multi-Session Journeys
Ensure your attribution system tracks complete journeys across multiple sessions and devices. If tracking only captures single sessions, time-decay attribution will systematically overvalue whatever touchpoint happens to occur in the conversion session, missing critical earlier interactions that built consideration.

Monitor Early-Stage Channel Performance Separately
Time-decay attribution will consistently undervalue top-of-funnel channels like content marketing, social media, and brand awareness campaigns. Track these channels with first-touch or linear attribution alongside time-decay. Don’t cut brand-building budgets just because time-decay shows low credit—they enable later high-credit touchpoints.

Set Minimum Touchpoint Thresholds
For journeys with many touchpoints (10+), extremely early interactions receive negligible credit that adds complexity without insight. Consider setting a floor where touchpoints beyond a certain age (90 days, for example) receive zero credit to simplify analysis and focus on relevant recent interactions.

Test Different Half-Life Periods Quarterly
Market conditions, competitive pressure, and product maturity affect buying behavior over time. What worked six months ago may not reflect current customer journey patterns. Quarterly testing of different decay rates against actual revenue outcomes validates your attribution accuracy.

Common Challenges with Time-Decay Attribution

Challenge: Systematically Undervaluing Brand Awareness
Early touchpoints that introduce prospects to your brand receive minimal credit even though they’re essential for eventual conversion. Without initial awareness, later high-credit touchpoints never occur.

Solution: Use time-decay attribution for optimization decisions about later-stage tactics (retargeting, email nurture sequences, demo processes) but maintain separate metrics for brand awareness channels. Track brand lift, share of voice, and aided recall for top-of-funnel investments rather than relying solely on time-decay attribution credit.

Challenge: Difficulty Comparing Across Sales Cycle Lengths
Time-decay attribution produces incomparable results when applied to products with dramatically different sales cycles. Your 30-day product and 180-day product can’t be analyzed with the same decay rate.

Solution: Segment attribution analysis by product line or deal size category. Apply appropriate decay rates for each segment. A 5-day half-life for transactional products, 10-day for mid-market, 20-day for enterprise. This prevents misleading aggregation across incompatible customer journeys.

Challenge: Conversion Timing Luck Affecting Credit
If a prospect happens to convert immediately after one touchpoint but slowly after another identical touchpoint, the second receives far more credit purely due to timing coincidence rather than actual influence.

Solution: Analyze attribution results at aggregate channel level rather than individual conversion level. Pattern emerges across hundreds of conversions that smooth out individual timing variations. Focus on directional trends rather than precise percentages for strategic decisions.

Challenge: Cross-Device Journey Tracking Gaps
When prospects research on mobile but convert on desktop (or vice versa), broken tracking creates incomplete touchpoint histories that distort time-decay calculations. Missing early mobile touchpoints makes later desktop interactions appear more influential than they actually are.

Solution: Implement cross-device tracking through authenticated user identification when prospects log into accounts, or use probabilistic device matching from your attribution platform. For un-trackable gaps, acknowledge the limitation and focus time-decay insights on known complete journeys rather than claiming comprehensive coverage.

Challenge: Offline Touchpoint Integration
Phone calls, in-person events, direct mail, and sales conversations don’t naturally integrate into digital time-decay attribution systems, yet they often represent critical high-value touchpoints occurring close to conversion.

Solution: Manually log offline interactions with timestamps into your CRM. Use unique tracking numbers for phone calls, event registration URLs for conferences, and QR codes for direct mail. When offline touchpoints are timestamped properly, they integrate into time-decay calculations alongside digital interactions for complete journey visibility.

Frequently Asked Questions

When should I use time-decay attribution instead of other models?

Time-decay attribution works best when your sales cycle is relatively short (30-90 days) and you believe recent interactions have stronger influence on purchase decisions. It’s ideal for e-commerce, SaaS trials with quick decision cycles, and promotional campaigns where the final touchpoints directly trigger conversions. Avoid it if your sales cycle is long (6+ months) or if early awareness touchpoints play equally critical roles in eventual conversion.

How does time-decay attribution differ from linear attribution?

Linear attribution distributes credit equally across all touchpoints regardless of timing. If a prospect had five interactions, each receives 20% credit. Time-decay attribution weights touchpoints based on recency—the first interaction might receive 5% credit while the last receives 40%, with middle touchpoints receiving graduated amounts. Time-decay assumes recent touchpoints matter more; linear assumes all touchpoints contribute equally.

What decay rate should I use for time-decay attribution?

Most attribution platforms use a 7-day half-life as the standard decay rate, meaning touchpoints lose half their value every 7 days before conversion. However, you should adjust based on your sales cycle length. For 30-day cycles, try a 5-day half-life. For 90-day cycles, use 10-14 days. B2B companies with longer consideration periods may need 21-day half-lives. Test different rates and compare results against actual closed revenue to find your optimal setting.

Can time-decay attribution undervalue brand awareness efforts?

Yes, time-decay attribution systematically undervalues early-stage touchpoints like brand awareness campaigns, social media impressions, and educational content that introduce prospects to your solution. While these touchpoints may not directly trigger conversions, they build essential foundation for later consideration. To address this, use time-decay alongside other models like first-touch or position-based attribution to gain complete perspective on your customer journey.

How do I implement time-decay attribution in my marketing stack?

Most marketing attribution platforms and analytics tools offer time-decay as a built-in model option. In Google Analytics, navigate to Conversions > Attribution > Model Comparison Tool and select Time Decay. For custom implementation, you’ll need to track all touchpoint timestamps, calculate the time difference between each touchpoint and conversion, apply exponential decay formula based on your chosen half-life, normalize the weighted values so they sum to 100%, and distribute credit accordingly. Many CRM platforms like HubSpot and Salesforce also support time-decay through their attribution reporting features.