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Conversions and attribution models explained

Marfeel enables publishers to set up trackable conversions, making it easy to measure how users take key actions on their site, such as subscribing. To provide deeper insights, Marfeel offers multiple attribution models that determine how credit for a conversion is distributed across touchpoints in the user journey. These models help publishers identify which content, channels, or interactions play the biggest role in driving business goals like subscriptions.

Marfeel tracks two types of conversions: attributed and non-attributed.

Non-attributed conversions are those for which Marfeel does not compute attribution data using the models described below. They are best used to track events such as button clicks, pop-up closes, or the user reaching a certain scroll depth in an article.

Attributed conversions are those for which Marfeel computes attribution data using the models described below. They are best used to track revenue-generating actions such as subscriptions, where the preceding user journey is important for analysis and optimization purposes.

Marfeel allows publishers to configure custom conversions according to the needs of their business. Any action a user takes on your site can be set up as a conversion, such as becoming a subscriber, signing up for a newsletter, accepting push notifications, watching a video, or any other behavior that the publisher deems relevant. Visit our documentation to find out how to set up custom conversions.

A measurement of the number of conversions per article you have published.

A measurement of the number of conversions per user that visits your site.

A measurement of the number of conversions per pageview that your site receives.

All credit for the conversion goes to the last page the user visited, the page from which the conversion took place.

Diagram showing Last Hit attribution where credit goes to the final page visited|690x431

All credit for the conversion goes to the page preceding the one from which the conversion took place. This is useful in scenarios where the conversion happens after a redirection.

For example, if the user’s journey is Article > Subscription page > Conversion, the conversion will be credited to the Editorial page.

Diagram showing Second Last Hit attribution where credit goes to the page before the conversion page|690x431

All credit for the conversion goes to the first page viewed by the user in their journey. This model is useful for publishers evaluating top-of-funnel acquisition channels.

Diagram showing First Hit attribution where credit goes to the first page in the user journey|690x431

Credit for the conversion is distributed equally across all pages in the user’s journey towards the conversion.

Diagram showing Linear attribution where all pages receive equal credit|690x431

Pages viewed closer to the end of the user’s journey receive more credit, using a 4-view half-life. This model helps identify which articles or sources directly generate conversions.

Diagram showing Decay attribution where credit decreases from the last page backward|690x431

Pages viewed at the beginning of the user’s journey receive more credit, using a 4-view half-life. This model helps identify content that acquires users who later convert.

Diagram showing Inverse Decay attribution where credit decreases from the first page forward|690x431

40% of credit goes to both the first and last pages, with the remaining 20% spread equally across the other pages in the user’s journey.

Diagram showing Position-based attribution where first and last pages receive 40% each|690x431

All credit for the conversion goes to the first page viewed by the user in the last session, when the conversion took place. This model helps identify what initiated the session that resulted in a desired goal, such as becoming a subscriber: was it an email, an ad, Google Discover, or a specific type of content?

Diagram showing First Hit Last Session attribution where credit goes to the first page of the converting session|690x431

Credit for the conversion is distributed equally across all pages visited by the user in the last session, when the conversion took place. This model reveals what content the user consumed in the session that preceded the conversion, which is important because conversions often come after periods of concentrated consumption and activity.

Diagram showing Linear Last Session attribution where all session pages receive equal credit|690x431

Credit for the conversion is distributed equally across all pages visited by the user in the 24 hours preceding the conversion. This model reveals what content the user consumed in the period leading up to the conversion, which is important because conversions often follow periods of concentrated activity.

Diagram showing Linear Last 24h attribution where all pages in the prior 24 hours receive equal credit|690x431

All credit for the conversion goes to the last viewed page with publish time. This model focuses specifically on editorial content, filtering out non-article pages from the attribution.

Diagram showing Last Editorial Hit attribution where credit goes to the last editorial page visited|690x431



To view a comprehensive list of all of Marfeel’s Metrics and Dimensions, visit our Glossary.

What is the difference between attributed and non-attributed conversions?

Non-attributed conversions track simple events like button clicks or scroll depth without computing attribution data. Attributed conversions track revenue-generating actions like subscriptions and include full attribution data showing which pages in the user journey contributed to the conversion.

Which attribution model should I use to identify top-of-funnel content?

Use the First Hit attribution model, which gives all credit to the first page viewed in the user journey. This helps publishers evaluate which acquisition channels and content pieces bring users into the funnel.

How does the Position-based attribution model distribute credit?

The Position-based model gives 40% of credit to both the first and last pages in the user journey, with the remaining 20% spread equally across all pages in between.