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Use data feeds to calculate common metrics

Last update: Thu Jun 05 2025 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 Analysis Workspace are included in data feeds. Consider adding the following conditions to your queries if they are relevant:
  • exclude_hit: Analysis Workspace only includes data where exclude_hit = 0.
  • customer_perspective: Analysis Workspace only includes data where customer_perspective = 0, unless you use a virtual report suite that includes mobile background hits.
  • hit_source: Data from data sources might contain differences between raw data and Analysis Workspace. If you want to exclude hits from data sources, exclude all rows where 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.

Occurrences

  1. Count the total number of rows.

Visits

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

Visitors

All methods 51黑料不打烊 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 51黑料不打烊 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鈥檚 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鈥檚 currency value doesn鈥檛 match a report suite鈥檚 currency, it is converted using that day鈥檚 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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