Experience customer breakdown export

This is for:


In this article, we’ll introduce you to our experience customer breakdown export and how you can use it to get an insight into the transactions attributed to your experiences.

Reporting detail

This export type provides insight into the details of each logged-in user that was served an experience.

As with all Qubit exports, you can specify the level of reporting detail to get a much greater understanding of reportables and focus on the details you’re really interested in.

For this export type, you can select:

  • Which experiences to export

  • Which segments to export

  • A date range to include only data covering a defined period of time

  • Exclusion dates to exclude particular days or a period of time from the data

  • Dimensions, including device type and location, which allow you to decide how to break down the data

  • Metrics that allow you to choose which metrics to include in the data


All selected segments are run through the stats model in order to calculate the uplift.


We extract the details of all the logged-in users that were served the selected experiences within the defined date range. We then look at the session and user events received for those users to extract the device, location, user Id, and email. Finally, we look at all transactions for those users to calculate their lifetime value.

The following logic is applied:

  • When applying the date range filter, we take the date when the experience was seen

  • The date the experience was seen is taken from meta_recordDate according to the configured timezone

  • Dimensions such as location and device information are taken from session data. Where there are duplicate events for the same session, we take the first value in ascending alphabetical order

  • To report users correctly, a user event must be emitted for each pageview while the user is logged in

  • User email and Id are taken from the user event (for example, ecUser, egUser, trUser). If email and Id are emitted in view, events, such as ecView, we can also get the data from this event

  • Only logged-in users with a non-null email address or Id are included

  • User email and Id data is only extracted from those events in the specified date range

  • In the unlikely event that multiple emails or user Ids are associated with the same logged-in user the first time the experience was seen in the date range, we take the first values in ascending alphabetical order

  • If segments are selected in the export, we only include logged-in users that were part of that segment when they saw the experience


Duplicate user and session events account for less than 1% of all user and session events.

Dimensions, metrics, and output


Name Description Output

User ID

Unique Id for the signed-in user viewing the experience


User Email

Email of the signed-in user viewing the experience


Returning Visitor

Whether the signed-in user is new or returningTRUE for returning, FALSE for new


Device Type

The device type used when the experience was seen


Browser Version

The browser and browser version used when the experience was seen


Operating System

The operating system used when the experience was seen



The country the signed-in user was in when the experience was seen


Metro Area

The metro area the signed-in user was in when the experience was seen



The region the signed-in user was in when the experience was seen



The city the signed-in user was in when the experience was seen



Name Description Output

Lifetime Conversions

Total number of conversions carried out by the signed-in user across their lifetime


Lifetime Total Order Value (Conversions)

Sum of all conversion amounts for the signed-in user across their lifetimeReported with currency field



Additional fields not included in the dimensions and metrics tables above:

Field Description


Name of experience


Name of experience iteration


Experience variation name


Whether experience variation is control group


Name of segment