# UTMs vs Surveys: How Attribution Really Works in Amplitude Source: https://www.youtube.com/watch?v=Iuz6Y4ZgKlk Recap page: https://rapidrecap.app/video/Iuz6Y4ZgKlk Generated: 2026-01-26T15:04:48.632+00:00 --- ## Quick Overview Amplitude's prebuilt attribution models can be used with mixed sources like UTM data and self-reported survey data by setting up custom channel groupings that define how traffic is classified, and then layering these different data sources for comparison, although implementing this requires careful configuration, especially when integrating data from tools like Segment. **Key Points:** - Prebuilt attribution models in Amplitude can incorporate mixed sources, including UTM data and self-reported survey responses, by defining custom channel groupings. - The recommended approach involves starting with definitive data sources like UTMs and user behavior data (collected via the Amplitude SDK) and then layering the less definitive, self-reported survey data on top for comparison. - Users should first set up their channels within Amplitude's Channel Classifier, as the system pulls attribution data from these definitions. - The Standard Attribution Channels model provides a baseline using properties like `utm_medium` and `referring_domain`, which can be customized for specific UTM parameters and subdomains. - Self-reported data, such as 'How did you find us?' survey responses, is captured as a user property and can be used alongside definitive data sources. - Segment data piped into Amplitude can work, but it requires some configuration (tinkering) to ensure the user trait properties align correctly with Amplitude's destination settings. ![Screenshot at 00:05: The screen displays the initial question on a Miro board: "Can you walk through how to use Amplitude's prebuilt attribution models with mixed sources," setting the context for the subsequent demonstration of channel configuration.](https://ss.rapidrecap.app/screens/Iuz6Y4ZgKlk/00-00-05.jpg) **Context:** This video addresses a user question about how to effectively use Amplitude's prebuilt attribution models when dealing with multiple, potentially conflicting, data sources, specifically comparing definitive data derived from UTM parameters against qualitative data gathered through user surveys. The discussion centers on configuring the Channel Classifier within Amplitude to accurately categorize traffic sources and layer these different inputs for a comprehensive view of user journeys, particularly for users who purchase subscriptions. ## Detailed Analysis The discussion confirms that Amplitude's prebuilt attribution models can handle mixed sources like UTM data and survey responses by creating custom channel groupings. The recommended strategy is to start by defining solid, definitive channels based on UTM data (Source/Medium, Referring Website) and direct user behavior data collected through the Amplitude SDK. This definitive data should be established first in the Channel Classifier setup. Users can then layer the self-reported survey data on top of this baseline. The speaker notes that self-reported data is inherently 'a little sus' because users might click the first option or move on quickly, meaning it should be used to calibrate against the definitive data. If Segment is used to pipe data into Amplitude, it can work, but it needs configuration adjustments ('tinkering') to align the Segment SDK information with Amplitude's destination settings. The goal is to make channel groupings as definitive as possible, allowing for comparisons like J-curve or U-shaped attribution models, as Amplitude keeps track of the user's history over time once the data collection and definitions are correctly established. ### Attribution Strategy - Start with definitive data (UTMs, user behavior) and layer self-reported data on top - Create custom channel groupings in the Channel Classifier to define how traffic is classified - Compare definitive vs. self-reported attribution sources. ### Channel Configuration in Amplitude - Users must set up channels first; the system pulls attribution from these definitions (00:33). The Standard Attribution Channels list shows definitions based on `utm_medium` and `referring_domain` (00:44). ### Handling Survey Data - Surveys provide a possible self-reported source that can be compared against UTM data (01:14). Survey responses are captured as a user trait property (02:05). ### Attribution Modeling Options - Users can configure settings to pick U-shaped or Last-Touch attribution models (03:21), and Amplitude tracks user history over time (04:03). ### Data Integration with Segment - Segment data piped into Amplitude can work, but it requires some 'tinkering' to align properties correctly (04:43). ![Screenshot at 00:04: Title slide: "Using Prebuilt Attribution Models with Mixed Sources", introducing the core topic.](https://ss.rapidrecap.app/screens/Iuz6Y4ZgKlk/00-00-04.jpg) ![Screenshot at 00:05: A Miro board showing user questions, one of which prompts a walkthrough on using Amplitude's prebuilt attribution models with mixed sources.](https://ss.rapidrecap.app/screens/Iuz6Y4ZgKlk/00-00-05.jpg) ![Screenshot at 00:40: The Amplitude Channels tab showing existing 'Standard Attribution Channels' definitions, including 'Direct' and 'Social'.](https://ss.rapidrecap.app/screens/Iuz6Y4ZgKlk/00-00-40.jpg) ![Screenshot at 01:01: The Amplitude Properties screen showing the 'Website Content' external assumption table being reviewed.](https://ss.rapidrecap.app/screens/Iuz6Y4ZgKlk/00-01-01.jpg) ![Screenshot at 02:24: The Amplitude Channels screen where the speaker is pointing out that channel groupings can be layered on top of each other because user-reported data is always a little suspect.](https://ss.rapidrecap.app/screens/Iuz6Y4ZgKlk/00-02-24.jpg)