Attribution is a method of assigning credit for sales and conversions to touchpoints in a customer’s lifecycle throughout your website or app. Attribution is available across most Kissmetrics reports and something we do very well!
An attribution model is the rule that figures out how credit for sales and conversions is assigned to touchpoints in conversion paths. Each model groups customers into buckets based on their interactions with your site or app, and how you ask to view the data. If a number in a Kissmetrics report looks off to you, check your attribution model - you may be expecting a different answer than the question you are asking.
For example, the Last Touch model in Kissmetrics assigns 100% credit to the final touchpoint/click that immediately precedes a sale or conversion. In contrast, the First Ever model assigns 100% credit to the touchpoint that brought the customer in when we first saw them come to the site or app as an anonymous user. If you are expecting Last Touch data, but running a First Ever query, your data will undoubtedly look off to you.
Will group your users according to the first property/touchpoint value they ever received (can be outside the date range).
What was the first campaign this purchaser ever received?
Will group your users according to the last property/touchpoint value they ever received (can be outside of date range).
What was the last campaign this purchaser received?
Will group your users according to the first property value they received within the date range.
What was the first campaign this purchaser received starting 1/1/2016?
Will group your users according to the last property value they received within the date range.
What was the last campaign this purchaser received in 2015?
Will group your users according to the property value they had at the time of performing the metric’s event. This is the only attribution model that makes it possible for one user to be counted across multiple property values or touchpoints. For example: If I purchase from both San Francisco and Los Angeles, it would show that one person purchased in San Francisco and one person purchased in Los Angeles even though I'm the same person.
For the metrics “Total Value for Property” and “Average Value for Property”, we’ll first need to distinguish between the segmented property value and the property value we are looking to calculate. We will refer to the segmented property value as property value and to the calculated value as amount. In this grouping, we’ll attribute the amount to the property value on the person at the time we received the amount.
Lets walk through an example to show the output for each type of attribution model. In our example, let’s create a metric for “Total Value for Property” where our calculated property will be revenue. In our example, Bob will receive the following events and properties on the following dates:
*Visited Site* *Referrer*: linkedin.com
*Visited Site* *Referrer*: facebook.com *Purchased* *Revenue*: 50
*Visited Site* *Referrer*: google.com *Purchased* *Revenue*: 100
*Visited Site* *Referrer*: pinterest.com
Here is the output for each type of grouping for the date range of May, segmented by referrer:
|Attribution by referrer||Total value returned|
|Current Value |
|First in Date Range |
|Last in Date Range |
|Last Touch |
For the metrics “Conversion Rate” and “Average Time Between Event” – to be grouped in any bucket using “Last Touch”, the user must have the same value at the time of both events. Otherwise, they are excluded from calculation.
Updated about 1 year ago