This article helps you:
Understand how specific touch points are contributing to your marketing outcomes
It can be challenging to attribute success of marketing activities without being able to clearly pinpoint which activities led your users to the desired outcome. For example, let's say a user visited your website after exposure to a Google ad, then interacting with a Facebook post, and finally watching a TikTok video. There are many ways you can attribute credit to one or more of the activities that led to the user's visit to your website. Attributing success to various property values, often referred to as multi-touch attribution, can provide more context for and drive the future of your marketing plans.
This feature is available to users on all Amplitude plans. See our pricing page for more details.
This feature is available to users on all plans. See the pricing page for more details.
Amplitude includes common, pre-built attribution models that you can configure on your metric.
First Touch and Last Touch are the only pre-built attribution models for which the event totals for all attribution groups add up to match the total event total count (as shown in the Overall
row).
For example, the Last Touch model attributes 100% of the credit to a single property value—the last one. For this reason, it makes sense to expect the resulting attribution group totals to sum to 100%. But the Participation model attributes 100% credit to multiple property values. As a result, you should expect the resulting attribution group totals to sum to more than 100%.
Also, when measuring unique users, none of these models generate attribution group totals that sum to the count in the Overall
row. This is because each unique user can appear in multiple attribution groups.
First Touch: Gives all credit for the selected metric to the first property value within the selected lookback window relative to the date the metric occurred.
Last Touch: Gives all credit for the selected metric to the last property value within the selected lookback window relative to the date the metric occurred.
Linear: Credit for the selected metric is equally distributed for all property values within the selected lookback window relative to the date the metric occurred. For example, with two properties each would receive 50% credit, and with three properties each would receive 33.3%.
Participation: Credit for the selected metric is fully allocated to all property values within the selected lookback window relative to the date the metric occurred. For example, with two properties each would receive 100% credit, and with three properties each would receive 100%.
U-Shaped: Credit for the selected metrics biases credit to the first and last values for the selected property. With two touch points, the middle 20% is equally added to the first and middle touch points (50%, 50%). With four touch points, the middle two touch points would share the 20% (40%, 10%, 10%, 40%).
J-Shaped: Distributes credit for the selected metrics in a way that biases credit to the more recent values from the selected property. With two touch points, the first 20% is equally added to the last and middle touch points (30%, 70%). With four touch points, the final two touch points would share the 20% (10%, 10%, 20%, 60%).
Inverse J-Shaped: Distributes credit for the selected metrics in a way that biases credit to the first values from the selected property. With two touch points, the last 20% is equally added to the first and middle touch points (70%, 30%). With four touch points, the last two touch points would share the 20% (60%, 20%, 10%, 10%).
Data Driven: With this model, Amplitude Analytics relies on a probabilistic algorithm based on first-order Markov chains. Every customer journey—defined here as a sequence of channels or touch points—is represented as a chain in a directed Markov graph, where each node is a possible state (either a channel or a touch point), and the edges represent the probability of transition between states. Next, Amplitude Analytics removes the nodes one by one and estimates the impact of removing nodes on the conversion rate. Each channel gets credit in proportion to its removal effect. In general, you should use this model with properties that don't have a large number of unique values (those with 50 or fewer work best). Learn more about the algorithm here.
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values.
Inside a data table, you can configure an attribution model on each metric column by following these steps:
If the pre-built attribution models don't meet your needs, you can also create a custom model. You must be an Admin or Manager to create a custom attribution model. To do so, follow these steps:
Amplitude recommends that all weights should equal 100%, although it's not required.
Each metric type supports a specific set of attribution types:
Uniques
Conversion
Event totals
property sum, revenue total, and formula (clauses: uniques, total, propsum)
The attribution model applies only to the property in the outermost group by level. Properties in inner group by levels don't undergo attribution modeling. Instead, they inherit their values from the attributed event.
In this example, the attribution model applies only to the Channel
, as its the outermost group by property.
utm_source
, as an inner group by property derives its value from the attributed events, rather than applying attribution separately.
Here is a brief example to highlight the differences between attribution models and lookback windows.
In Amplitude Analytics, attribution queries have a scope of one day.
Suppose a user has three touch points before the Sign Up
event, each with a different UTM source:
UTM source | Date | Event |
---|---|---|
2022-05-01 | Viewed Home Page | |
2022-05-07 | Viewed Blog Post | |
TikTok | 2022-05-10 | Viewed Promotion Page |
2022-05-10 | Sign Up |
Here are some example combinations of the attribution model and lookback window and the resulting attribution of credit to each UTM source.
Attribution Model | Lookback Window | Credit | Explanation |
---|---|---|---|
First Touch | 30 Days | Google: 100% | All credit goes to the first touch within the last 30 days, which is Google on 2022-05-01. |
First Touch | 7 Days | Facebook: 100% | All credit goes to the first touch within the last 30 days, which is Facebook on 2022-05-07. |
Last Touch | 7 Days | TikTok: 100% | All credit goes to the last touch within the last 7 days, which is TikTok on 2022-05-10. |
Linear | 30 Days | Google: 33% Facebook: 33% TikTok: 33% |
Divides evenly between all three touch points in the last 30 days. |
Linear | 7 Days | Facebook: 50% TikTok: 50% |
Divides evenly between the two touch points in the last 7 days. |
J-Shaped | 30 Days | Google: 20% Facebook: 20% TikTok: 60% |
In the last 30 days, the first touch gets 20%, middle touches 20%, and last touch 60%. |
J-Shaped | 7 Days | Facebook: 30% TikTok: 70% |
There is no middle touch, so the 20% gets split across the first and last touches. |
Custom 5% - 20% - 75% |
30 Days | Google: 5% Facebook: 20% TikTok: 75% |
In the last 30 days, the first touch gets 5%, middle touches 20%, and last touch 75%. |
January 30th, 2025
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