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Use data feeds to calculate common metrics

Last update: Thu Sep 01 2022 00:00:00 GMT+0000 (Coordinated Universal Time)

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Describes how to calculate common metrics using data feeds.

NOTE
Hits normally excluded from Adobe Analytics are included in data feeds. Use exclude_hit = 0 to remove excluded hits from queries on raw data. Data sourced data are also included in data feeds. If you want to exclude data sources, exclude all rows with hit_source = 5,7,8,9.

Page views

  1. Count the number of rows where a value is in post_pagename or post_page_url.

Visits

  1. Concatenate post_visid_high, post_visid_low, visit_num, and visit_start_time_gmt.
  2. Count the unique number of values.
NOTE
Internet irregularities, system irregularities, or the use of custom visitor IDs can rarely use the same visit_num values for different visits. Use visit_start_time_gmt when counting visits to make sure that these visits are counted.

Visitors

All methods Adobe uses to identify unique visitors (custom visitor ID, Experience Cloud ID service, etc.) are all ultimately calculated as a value in post_visid_high and post_visid_low. The concatenation of these two columns can be used as the standard of identifying unique visitors regardless of how they were identified as a unique visitor. If you would like to understand which method Adobe used to identify a unique visitor, use the column post_visid_type.

  1. Concatenate post_visid_high and post_visid_low.
  2. Count the unique number of values.

  1. Count the number of rows where:

    • post_page_event = 100 for custom links
    • post_page_event = 101 for download links
    • post_page_event = 102 for exit links

Custom events

All metrics are counted in the post_event_list column as comma-delimited integers. Use event.tsv to match numeric values with the desired event. For example, post_event_list = 1,200 indicates that the hit contained a purchase event and custom event 1.

  1. Count the number of times the event lookup value appears in post_event_list.

Time spent

Hits must first be grouped by visit, then ordered according to the hit number within the visit.

  1. Concatenate post_visid_high, post_visid_low, visit_num, and visit_start_time_gmt.
  2. Sort by this concatenated value, then apply a secondary sort by visit_page_num.
  3. If a hit is not the last one in a visit, subtract the post_cust_hit_time value from the subsequent hit’s post_cust_hit_time value.
  4. This number is the amount of time spent (in seconds) for the hit. Filters can be applied to focus on dimension items or events.

Orders, units, and revenue

If a hit’s currency value doesn’t match a report suite’s currency, it is converted using that day’s conversion rate. The column post_product_list uses the converted currency value, so all hits use the same currency in this column.

  1. Exclude all rows where duplicate_purchase = 1.

  2. Include only rows where event_list contains the purchase event.

  3. Parse the post_product_list column to extract all price data. The post_product_list column is formatted the same as the s.products variable.

  4. Calculate the desired metric:

    • Count the number of rows to calculate Orders
    • Sum the number of quantity in the product string to calculate Units
    • Sum the number of price in the product string to calculate revenue
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