Time-Decay Attribution

What is Time-Decay Attribution?

Time-Decay Attribution is a multi-touch marketing attribution model that assigns progressively greater credit to customer touchpoints that occur closer in time to the final conversion event. Unlike models that distribute credit equally or focus solely on first or last touches, time-decay attribution recognizes that interactions nearer to the purchase decision typically have stronger influence on conversion outcomes.

In this model, the most recent touchpoint before conversion receives the highest attribution weight, with each preceding interaction receiving exponentially less credit as you move backward through the customer journey. For example, if a prospect converts after five touchpoints over 60 days, the final interaction might receive 40% of the credit, the second-to-last 25%, the third 15%, the fourth 12%, and the initial touchpoint just 8%. The specific decay rate—how rapidly credit diminishes over time—can be customized based on typical sales cycle length and organizational priorities.

Time-decay attribution is particularly valuable for B2B SaaS companies with moderate to long sales cycles where late-stage nurture activities, product demos, and sales engagement play crucial roles in driving conversions. It provides a more sophisticated view than last-touch attribution while acknowledging the practical reality that recent interactions often have the greatest impact on decision-making. This approach helps marketing teams understand which late-stage tactics drive conversions while still giving proportional recognition to earlier awareness and consideration activities.

Key Takeaways

• Recency Weighting: Time-decay attribution assigns exponentially higher credit to touchpoints closer to conversion, reflecting the increased influence of recent interactions on purchase decisions

• Customizable Decay Rates: Organizations can adjust the decay rate (half-life) based on their average sales cycle length, with typical half-lives ranging from 7 days for transactional sales to 30+ days for complex B2B deals

• Balanced Recognition: This model acknowledges both early-stage awareness efforts and late-stage conversion activities, providing a more nuanced view than single-touch attribution while remaining simpler than algorithmic models

• Late-Stage Insight: Time-decay attribution is particularly effective for identifying which bottom-of-funnel tactics—such as product demos, case study engagement, and retargeting campaigns—most effectively drive conversions

• Implementation Requirements: Effective time-decay attribution requires accurate timestamp tracking for all touchpoints, unified customer journey data across channels, and clear conversion event definitions aligned to business goals

How It Works

Time-decay attribution operates on a mathematical decay function that systematically reduces the credit assigned to touchpoints as their temporal distance from the conversion increases. The model begins by identifying all tracked interactions in a prospect's journey from first awareness through final conversion, capturing timestamps for each touchpoint.

The most common implementation uses an exponential decay function with a specified half-life parameter. The half-life represents the time period after which a touchpoint's attributed value decreases by 50%. For example, with a 7-day half-life, a touchpoint occurring 7 days before conversion receives half the credit of the final touchpoint, while an interaction 14 days prior receives 25% of the final touchpoint's value, and so on.

The calculation process involves several steps. First, determine the time difference in days (or hours for shorter cycles) between each touchpoint and the conversion event. Second, apply the decay function: weight = (1/2)^(days_before_conversion / half_life). Third, normalize the weights so they sum to 100% across all touchpoints. Finally, distribute the conversion value proportionally based on these normalized weights.

For instance, consider a conversion worth $10,000 with four touchpoints at 30, 20, 10, and 1 day before conversion, using a 7-day half-life. The raw weights would be calculated, then normalized to percentages, with the final touchpoint receiving approximately 45%, the 10-day prior touchpoint getting 28%, the 20-day touchpoint receiving 18%, and the initial touchpoint capturing just 9% of the attributed value.

Modern marketing analytics platforms like HubSpot, Google Analytics 360, and attribution-specific tools automate these calculations, allowing marketers to visualize the impact of different decay rates and adjust models based on their specific sales cycle characteristics and business priorities.

Key Features

• Exponential decay function that progressively reduces credit as temporal distance from conversion increases

• Configurable half-life parameters that can be adjusted based on industry, product complexity, and average sales cycle length

• Automated calculation across unlimited touchpoints without arbitrary limits on journey length

• Channel-agnostic application that evaluates all marketing interactions regardless of medium or platform

• Balanced perspective that credits both early awareness activities and late-stage conversion drivers proportionally to their timing

Use Cases

B2B SaaS Demand Generation Optimization

A marketing automation company with a 45-day average sales cycle implements time-decay attribution with a 14-day half-life to evaluate campaign performance. They discover that while initial awareness content (blog posts, social ads) generates substantial traffic, late-stage interactions—particularly product demo requests, pricing page visits, and case study downloads occurring within 10 days of conversion—receive the highest attribution weights. This insight leads them to reallocate budget toward bottom-of-funnel content and retargeting campaigns targeting prospects who've engaged with educational content but haven't yet requested demos. Within two quarters, they see a 23% increase in demo-to-close conversion rates by focusing resources on the high-impact late-stage touchpoints identified through time-decay analysis.

Enterprise Sales Pipeline Attribution

An enterprise software company with 90-120 day sales cycles uses time-decay attribution with a 21-day half-life to understand marketing's role in their complex buyer journeys. Their analysis reveals that while initial trade show interactions and thought leadership content start most journeys, the critical drivers of closed-won deals are solution briefings, executive webinars, and ROI calculator engagement in the final 30 days before contract signature. The sales team uses these insights to coordinate with marketing on highly targeted campaigns for late-stage opportunities, while marketing maintains broader awareness efforts knowing they'll receive proportional credit for initiating journeys even as late-stage activities receive higher weights.

Multi-Channel Campaign Assessment

A SaaS company running integrated campaigns across paid search, display advertising, email nurture, webinars, and content syndication implements time-decay attribution to understand true channel contribution beyond last-click metrics. They discover that while paid search consistently appears as the last touchpoint (receiving highest weight in time-decay), email nurture sequences and webinar attendance 10-20 days before conversion are strong secondary contributors. This prevents them from over-investing in paid search at the expense of mid-funnel activities that significantly influence conversions. They optimize their media mix accordingly, maintaining paid search for demand capture while expanding investment in nurture and educational programs that receive meaningful attribution credit under the time-decay model.

Implementation Example

Time-Decay Attribution Calculation Framework

Standard Exponential Decay Formula:

Touchpoint Weight = (1/2)^(Days Before Conversion ÷ Half-Life)

Normalized Weight = (Touchpoint Weight ÷ Sum of All Weights) × 100

Sample Attribution Scenario:

Consider a $50,000 ACV opportunity with the following journey and a 7-day half-life:

Visual Representation:

Time-Decay Attribution Weight Distribution
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Conversion Day (0)
    ↓
    Sales Email Click ████████████████████ 29.3%
    ↓ (1 day gap)
    Demo Request █████████████████ 21.1%
    ↓ (7 days)
    Pricing Page ███████████ 18.0%
    ↓ (6 days)
    Case Study ███████ 14.2%
    ↓ (10 days)
    Email Click ██ 4.4%
    ↓ (10 days)
    Blog Visit █ 2.8%
    ↓ (7 days)
    Webinar ▌ 10.2%
    ↓ (45 days from conversion)

Half-Life Selection Guidelines:

Implementation Steps:

1. Define conversion events: Identify primary conversion actions (demo requests, trial signups, closed-won deals)

2. Establish lookback window: Determine how far back to consider touchpoints (typically 60-180 days for B2B SaaS)

3. Select half-life parameter: Choose based on average sales cycle length using guidelines above

4. Configure tracking: Ensure all touchpoints capture accurate timestamps and user identifiers

5. Calculate attribution: Apply decay formula to all touchpoints within the lookback window

6. Analyze and optimize: Review which channels and campaigns receive highest attribution at different journey stages

7. Iterate decay rate: Test different half-life parameters to find optimal balance for your business

Related Terms

• Multi-Touch Attribution: The practice of assigning credit to multiple customer touchpoints across the buyer journey, of which time-decay is one methodological approach

• First-Touch Attribution: A single-touch model that assigns 100% credit to the initial interaction, opposite to time-decay's recency weighting

• Last-Touch Attribution: A model giving all credit to the final touchpoint before conversion, representing an extreme version of recency bias

• U-Shaped Attribution: A model emphasizing first and last touches while distributing remaining credit across middle interactions, offering different weighting than time-decay

• Marketing Attribution: The overall process of identifying which marketing activities contribute to conversions and business outcomes

• Data-Driven Attribution: An algorithmic approach using machine learning to determine optimal credit distribution based on actual conversion patterns

• Customer Journey Mapping: The process of documenting all touchpoints and interactions prospects experience, providing the foundation for attribution analysis

Frequently Asked Questions

What is Time-Decay Attribution?

Quick Answer: Time-Decay Attribution is a marketing attribution model that assigns progressively more credit to customer touchpoints that occur closer to the conversion event, using an exponential decay function to weight interactions based on their proximity to the final purchase decision.

This approach reflects the reality that recent interactions often have greater influence on conversion decisions than earlier touchpoints. The model uses a configurable decay rate (half-life) that determines how rapidly attribution credit decreases for older touchpoints. Unlike single-touch models that ignore most of the journey or equal-weight models that don't account for timing, time-decay provides a balanced view that recognizes both early and late-stage activities while emphasizing recency.

How do you calculate the half-life for time-decay attribution?

Quick Answer: The half-life for time-decay attribution should typically be set to 30-50% of your average sales cycle length, meaning a 30-day sales cycle would use a 10-15 day half-life, while a 90-day cycle might use a 30-45 day half-life parameter.

The half-life determines how quickly attribution credit decays over time. A shorter half-life creates steeper decay, heavily favoring recent touchpoints, while a longer half-life distributes credit more evenly across the journey. Start with 30-50% of your average sales cycle, then test variations to see which provides the most actionable insights for your business. B2B SaaS companies with 45-60 day sales cycles commonly use 14-21 day half-lives. You can also analyze your closed-won deals to identify when the most influential touchpoints typically occur and set your half-life accordingly. Most marketing analytics platforms like Google Analytics 360, HubSpot, and Salesforce allow you to adjust this parameter and compare results across different decay rates.

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

Quick Answer: Time-decay attribution is most effective for businesses with moderate to long sales cycles (30-120 days) where late-stage nurture activities significantly influence conversions, and you want to balance recognition of early awareness efforts with emphasis on bottom-of-funnel tactics that directly drive decisions.

Use time-decay when you have a reasonably consistent sales cycle length, multiple touchpoints across the buyer journey, and a need to understand both early and late-stage marketing impact. It's particularly valuable when last-touch attribution over-credits final interactions (like brand search or direct traffic) at the expense of earlier demand generation efforts, but you still want to acknowledge that recent touchpoints matter more than distant ones. Avoid time-decay if you have very short sales cycles (where last-touch may suffice), highly variable cycle lengths that make choosing a half-life difficult, or if you have the data sophistication to implement data-driven attribution models that use machine learning to determine optimal weighting.

What are the limitations of time-decay attribution?

Time-decay attribution has several important limitations. First, it assumes that recency is the primary factor in touchpoint importance, which may not accurately reflect your customer's decision-making process—some early touchpoints may be more influential than the timing-based model suggests. Second, it requires an arbitrarily chosen half-life parameter that can significantly impact results; different decay rates can lead to substantially different channel performance assessments. Third, like all rule-based models, it doesn't account for the actual influence of specific touchpoints or channels based on historical conversion data. Fourth, it can still under-credit early awareness activities compared to late-stage conversions, potentially leading to underinvestment in top-of-funnel efforts. Finally, it requires comprehensive tracking across all channels with accurate timestamps, which can be technically challenging to implement consistently.

How does time-decay attribution differ from position-based attribution?

Time-decay attribution uses a mathematical decay function that continuously reduces credit based on temporal distance from conversion, while position-based attribution (also called U-shaped) assigns fixed percentages to specific positions in the journey—typically 40% to first touch, 40% to last touch, and 20% distributed among middle touchpoints. Time-decay is time-sensitive and automatically adjusts credit based on actual timing, whereas position-based attribution focuses on journey position regardless of when touchpoints occurred. Time-decay provides more granular credit distribution across all touchpoints proportional to recency, while position-based emphasizes the endpoints of the journey. Choose time-decay when timing matters more than position, and select position-based when you want to specifically emphasize journey initiation and conversion completion regardless of sales cycle length. Many organizations test both approaches to see which provides more actionable insights for their specific GTM strategy and channel mix.

Conclusion

Time-decay attribution represents a sophisticated middle ground between overly simplistic single-touch models and complex algorithmic approaches, making it an accessible yet powerful tool for B2B SaaS marketing teams seeking to understand true campaign contribution. By systematically weighting touchpoints based on their proximity to conversion, this model provides actionable insights into which late-stage tactics most effectively drive deals while still acknowledging the cumulative impact of earlier awareness and nurture activities.

For demand generation teams, time-decay attribution illuminates the relative value of bottom-of-funnel content, retargeting campaigns, and sales enablement assets compared to top-of-funnel activities. Revenue operations professionals use these insights to optimize budget allocation across channels, ensuring investment flows to tactics that demonstrably influence conversions rather than merely appearing in the journey. Sales teams benefit from understanding which marketing touchpoints most reliably precede closed-won opportunities, enabling better qualification and prioritization of prospects showing similar engagement patterns.

The strategic value of time-decay attribution extends beyond campaign measurement to fundamental GTM optimization. By revealing which activities drive conversions at different journey stages, organizations can design more effective multi-touch campaigns, refine lead scoring models to weight recent engagement appropriately, and build cohesive journeys that guide prospects toward conversion through strategically sequenced touchpoints. As B2B buying processes become increasingly complex with multiple stakeholders and extended evaluation periods, time-decay attribution provides the nuanced visibility required to optimize marketing performance in today's sophisticated marketing attribution landscape.

Last Updated: January 18, 2026