Calculate conversion credit using time-decay attribution. Give more credit to touchpoints closer to the conversion using exponential decay weighting.
Time-decay attribution gives more credit to marketing touchpoints that occur closer to the conversion event. Using an exponential decay function with a configurable half-life, this model recognizes that recent interactions typically have more influence on the purchase decision than earlier ones.
The half-life parameter determines how quickly credit decays: a 7-day half-life means a touchpoint 7 days before conversion receives half the weight of the last touchpoint in the final day. Touchpoints 14 days out receive one-quarter the weight, and so on. This creates a smooth gradient of credit that still recognizes earlier interactions.
Time-decay is particularly effective for businesses with considered purchase cycles where customers research over days or weeks before buying. It balances the need to credit closing interactions more heavily while still acknowledging the awareness and consideration stages of the journey.
Understanding this metric in precise terms allows marketing professionals to set realistic goals, track progress effectively, and refine their approach based on real performance data.
When recency drives conversion probability, time-decay attribution provides the most accurate credit distribution. It's ideal for considered purchases where customers research over time and the most recent interactions have the most influence on the final decision. Regular monitoring of this value helps marketing teams detect shifts in audience behavior early and adapt strategies before competitive advantages are lost in the marketplace.
Weightᵢ = 2^((tᵢ − t₀) / half-life) Normalized Weightᵢ = Weightᵢ / ΣWeights Creditᵢ = Normalized Weightᵢ × Conversion Value
Result: Touch 1: $26.67 | Touch 2: $53.33 | Touch 3: $106.67 | Touch 4: $213.33
With a 7-day half-life and 4 touchpoints at 21, 14, 7, and 1 day(s) before conversion, the weights are approximately 1, 2, 4, and 8. After normalization, the last touch gets 53.3% of credit and the first touch gets 6.7%, reflecting the exponential decay pattern.
Time-decay uses an exponential function to weight touchpoints by their proximity to conversion. The formula 2^(t/half-life) creates a smooth curve where credit doubles for every half-life period closer to conversion. This mathematical elegance makes it both intuitive and rigorous.
The half-life is the most important parameter in time-decay attribution. Too short and you essentially replicate last-click attribution. Too long and you approach linear attribution. The ideal half-life reflects your actual customer journey dynamics — use conversion lag data to inform this choice.
Time-decay works best when combined with sufficient tracking data. You need timestamp information for each touchpoint to calculate accurate weights. Ensure your analytics platform captures interaction times, not just the channel. Cross-device tracking gaps can introduce errors in the timing data.
Time-decay attribution is a multi-touch model that assigns exponentially more credit to touchpoints closer to the conversion. It uses a half-life parameter to control the decay rate, ensuring recent interactions receive substantially more credit than earlier ones.
The half-life determines how quickly credit decays over time. With a 7-day half-life, a touchpoint 7 days before conversion gets half the weight of the most recent touchpoint. Shorter half-lives concentrate credit on recent touches; longer ones distribute it more evenly.
Use time-decay for considered purchases with multi-day research cycles, such as SaaS, automotive, financial services, and B2B products. It's particularly effective when your data shows that recent interactions are more correlated with conversion than earlier ones.
Time-decay emphasizes recency uniformly. Position-based specifically credits first and last touches 40% each. Time-decay may undervalue the first awareness touch, while position-based guarantees it 40% credit. Choose based on whether recency or funnel position matters more.
Start with 7 days for e-commerce, 14 days for high-consideration B2C, and 21–30 days for B2B. Then validate against your data: compare time-decay channel valuations with different half-lives to see which best matches observed conversion patterns.
Google Analytics 4's default data-driven attribution incorporates time-decay elements. The legacy Universal Analytics offered an explicit time-decay model. You can also calculate time-decay attribution using exported path data in a spreadsheet or data warehouse.