Everything You Need to Know About the Time-Decay Attribution Model

Imagine tracking your marketing strategy performance like footprints in the sand—and you realize the tide is washing half of the data away. You can see the final steps before conversion, but the earlier steps are faded or completely missing.

When your attribution model doesn’t account for complex or long sales cycles, your insights won’t be accurate. First-touch attribution can give too much credit to initial awareness, while last-touch ignores the journey there—forcing you to assume what else influences customer behavior. 

To compound these issues, digital marketing data often remains siloed across different tools and black box platforms, making it difficult to attribute actual revenue figures to touchpoints and prove impact.

Time-decay attribution offers a different approach since it’s designed to give credit to each touchpoint, but assign more weight to recent interactions. 

However, the real value comes from customizing the decay curve to match your sales cycle and visualizing attribution alongside cost and performance data—giving you a more reliable view of your marketing efforts. 

What is Time-Decay Attribution?

Time-decay attribution is a multi-touch and multi-channel attribution model that’s designed to increase the amount of credit or weight closer to the final touchpoints or conversion event, as mentioned above. In other words, the older the touchpoint interaction, the less important that interaction becomes.

Here’s an illustrative example of what a time-decay attribution model looks like:

In longer sales cycles, such as B2B attribution scenarios, this multi-touch approach helps marketers figure out the most impactful efforts and allocate resources accordingly. It’s also a complementary approach to revenue attribution—where you can tie real revenue figures to each weighted touchpoint.

Time-Decay vs. Other Attribution Models

You can use several other marketing attribution models besides time-decay. You may already be familiar with some of them:

• First-touch attribution model: This model prioritizes the first interaction with your brand. But it potentially overlooks nurturing steps along the customer journey in longer cycles. However, this model is useful if the first touch is also designed to be the last—for example, newsletter signups.
• Last-touch attribution model: This model focuses on the conversion event. It is useful for figuring out the best conversion message or method, but it often undercredits important top and mid-funnel marketing activity. This model is most useful for channels focusing solely on conversions—typically for B2C attribution.
• Linear attribution model: This option is also a multi-touch attribution model but distributes equal credit across every touchpoint—but it can lack nuance for recency and can oversimplify the customer journey.
• Position-based attribution model: This model emphasizes both the first touchpoint and the last touchpoint. It assigns weight to both ends of the journey—illustrating a “straightforward” A–B structure. However, this model can undervalue mid-funnel interactions where a lot of influential activity can happen.

When comparing time-decay to other models, time-decay is often the most appropriate attribution model for longer sales cycles—but it heavily depends on your business’s unique needs.

When and Why to Use Time-Decay Attribution

The time-decay model helps businesses using multiple marketing channels understand how each channel contributes. It shows which channels have the most immediate impact, while still valuing all customer interactions. This information can help convince stakeholders to support several marketing channels.

However, time-decay isn’t the “best” attribution model available for every business, though it can be the best option for some. Here are the main business models where time-decay attribution can be the most useful:

Long B2B sales cycles

As we’ve already mentioned, B2B sales cycles tend to have a lot of touchpoints. Data from DemandGen suggests 71% of B2B buyers downloaded multiple content assets to help with their decision-making process. Therefore, each touchpoint contributes towards a final decision.

Since B2B purchases tend to require multiple stakeholders’ approval, the sales cycle can last weeks to months. With lengthy sales cycles, time-decay attribution can monitor these interactions over time and illustrate the customer journey more accurately.

Account-based marketing (ABM)

When building relationships with your client accounts, the influence of each interaction tends to increase over time.

This scenario can be a perfect candidate for time-decay attribution, but there is a caveat. If your ABM approach uses short funnels or few interactions, time-decay may not be the best option since it’s designed to measure the opposite.

How to Set Up Time-Decay Attribution Properly

Many of the attribution models are quite simple to set up and track. For example, first and last touch is available by default in Google Analytics (first and last interaction, or first-click and last-click). However, time-decay attribution takes a little more effort upfront to make it as accurate as possible.

Define the Lookback Window

The “lookback window” refers to the amount of time a conversion event should “look back” for touchpoints. There are different types of lookback windows, such as:

• Visit lookback window: This looks up the beginning of a visit/touchpoint where conversion happened. Naturally, this is a pretty narrow window.
• Visitor lookback window: This looks up all visits/touchpoints from a predefined date range (typically monthly). This window is longer and suitable for slightly shorter sales cycles.
• Custom lookback window: This option looks up visits/touchpoints up to a customized range. This one is better for longer sales cycles.

These windows help narrow the scope of measurable data.

Customize the Half-Life Based on the Length of the Sales Cycle

Half-life refers to the amount of time that should pass before the credit should half the original value, otherwise known as the decay rate. In other words, with a half-life of the default seven days, if an interaction occurred seven days prior to the conversion, it gets half of the conversion credit.

The standard formula we use for this calculation is:

y = 2^(-x/half-life)

Where “x” is the number of days before conversion when the interaction happened. With this formula, you assign less credit to older interactions, and the most credit the last touchpoint driving conversions.

For longer sales cycles, you may need to customize the default seven-day half-life to a longer one. For example, if your typical sales cycle is two months or more, you might set it to 30 or 45 days. This is especially important since a shorter half-life can heavily discount upper-funnel channels on longer sales cycles.

However, customizing the half-life to a shorter period (such as three days) can make the time-decay model more relevant for shorter B2C business models—finding valuable insights on customer decisions.

Validate Proper Data Tracking and Data Collection

Nailing your data sources and tracking is one of the most important aspects of any attribution model. Without proper tracking, your model will produce inaccurate insights.

If your business is active on Meta social media platforms, for example Facebook ads, you’ll want to set up a Meta pixel to track activity between social media ads and your website.

You should also be using UTM parameters for other marketing campaigns (such as Google ads or other paid search ads), which you can do with Google Analytics 4 (GA4). Here’s what that looks like in a URL:

Integrate Your CRM and Ad Platforms

The purpose of a time-decay attribution model is to attribute multi-channel touchpoints over time. To finish the time-decay setup, you’ll need attribution software to help you integrate your CRM and ad platforms.

As a result, we recommend making sure you compare the list of available integrations with your current (or desired) tech and ad stack when choosing an attribution software.

Turning Insights From Time-Decay Attribution Into Strategy

Time-decay attribution is particularly valuable for uncovering mid-funnel marketing efforts that usually get overlooked, unlike other attribution models. That means campaigns and channels, like webinars, email nurturing, and organic content, get the proper attribution they deserve.

With insights from time-decay, you can identify which mid-funnel interactions have a greater influence on conversions. This will help you double down in the right places.

For example, in Attribution, you can view conversion paths down to individual customers:

As well as compare Return on Ad Spend (ROAS) across channels:

When you’re presenting time-decay insights to executives or clients, you should focus on business impact rather than the model’s technical details.

Report on key metrics like conversions influenced by mid-funnel channels, changes in cost-per-acquisition (CPA) with a redistributed budget, and pipeline contribution from sources you may have previously undervalued. It can also be helpful to include visualizations like weighted journey maps or comparative attribution models that show the difference in channel weighting.

Time-Decay Attribution in the Attribution Platform

While it’s possible to develop a bespoke time-decay attribution model for your business, it’s often more cost-effective to adopt an existing solution.

The Attribution platform offers unmatched functionality for marketing strategy optimization, including out-of-the-box time-decay attribution modeling. The default half-life is seven days, but it’s configurable for different funnel lengths.

Attribution also offers a single dashboard that seamlessly compares time-decay alongside first-touch, last-touch, linear, and machine-learning driven models:

The Attribution dashboard also displays advanced reporting and visualization features such as:

• Automatic channel grouping
• Cost data aggregation
• Accurate Return On Investment (ROI) metrics

The time-decay model, in particular, also offers real-time updates for accurate mid–late funnel interaction tracking across different touchpoints.