Explore: Performance Trends

Description

This explore rolls metrics such as sessions, sales, CVR and AOV up to the daily level, normalizing them so that they can all be viewed accurately together. Use this explore if you wish to see top-level performance of key metrics, or broken down by channel.

Common Use Cases

  1. Compare common metrics (Sales, Traffic, AOV, CVR, etc.) at the daily level. Because not all data is automatically aggregated to a daily view, these data sets have been transformed and normalized to provide daily results with no duplicates.

Fields

Performance Trends: performance_trends
Filters, Dimensions and Measures Category Description
Performance Trends: Created Date dimension Calendar date
Performance Trends: Created Day of Week dimension The day of the week
Performance Trends: Created Day of Week Index dimension A number representing the day of the week, with Monday = 0 And Sunday = 6
Performance Trends: Created Month dimension The calendar month in YYYY-MM format
Performance Trends: Created Month Name dimension The calendar month name
Performance Trends: Created Month Num dimension The month number from 1 to 12
Performance Trends: Created Quarter dimension The quarter number in YYYY-Q# form
Performance Trends: Created Week dimension The calendar week in YYYY-MM-DD form
Performance Trends: Created Year dimension The calendar year in YYYY form
Performance Trends: GA Last Click Channel dimension Modified Last Click Google Analytics Channel Based on Your Custom Logic
Performance Trends: Marketing Attribution Channel dimension The Marketing Channel Based on Custom Sources - EX: discount code, ga, surveys, and etc.
Performance Trends: Store Country dimension Country entered in the Daasity UI for the Integration
Performance Trends: Store Integration Name dimension The Name from the Location otherwise the name from the Daasity UI
Performance Trends: Store Name dimension Location Name otherwise the Store Type from the Daasity UI (eCommerce, Wholesale, Retail, Amazon, Marketplace, Manual Orders)
Performance Trends: Store Type dimension Type selected in the Daasity UI for the Integration
Performance Trends: UTM Medium dimension UTM Medium from GA
Performance Trends: UTM Source dimension UTM Source from GA
Performance Trends: $/Visit measure Units per Transaction = Total Units/Number of Orders
Performance Trends: $/Visit measure Average Units Revenue = Total Sales/Number of Units
Performance Trends: AOV measure Average Order Value = Total Sales/Number of Orders
Performance Trends: AOV measure Average Order Value = Total Sales/Number of Orders
Performance Trends: AUR measure Average Units Revenue = Total Sales/Number of Units
Performance Trends: AUR measure Average Units Revenue = Total Sales/Number of Units
Performance Trends: Conversion measure Total Orders/Total Sessions
Performance Trends: Conversion measure Total Orders/Total Sessions
Performance Trends: Orders measure Total number of orders attributed to last click channel
Performance Trends: Orders measure Total number of orders in attribution channel
Performance Trends: Sales measure Total amount of sales attributed to last click channel
Performance Trends: Sales measure Total amount of sales in attribution channel
Performance Trends: Sessions measure Total number of session attributed to last click channel
Performance Trends: Sessions measure Total number of session in attribution channel
Performance Trends: UPT measure Units per Transaction = Total Units/Number of Orders
Performance Trends: UPT measure Units per Transaction = Total Units/Number of Orders
Performance Trends: Units measure Total number of units attributed to last click channel
Performance Trends: Units measure Total number of units in attribution channel

 

Calendar: retail_calendar
Filters, Dimensions and Measures Category Description
Calendar: 1 - Pivot by Yesterday dimension Pivot Dataset by Yesterday: TY vs LY. To Use, Pivot This Field and Add a Filter for 'Dimension is Not Null'
Calendar: 2 - Pivot by Week dimension Pivot Dataset by Current Week: TY, LY, & LW. To Use, Pivot This Field and Add a Filter for 'Dimension is Not Null'
Calendar: 3 - Pivot by Month dimension Pivot Dataset by Current Month: TY, LY, & LM. To Use, Pivot This Field and Add a Filter for 'Dimension is Not Null'
Calendar: 4 - Pivot by Quarter dimension Pivot Dataset by Current Quarter: TY vs LY. To Use, Pivot This Field and Add a Filter for 'Dimension Is Not Null'
Calendar: 5 - Pivot by Year dimension Pivot Dataset by Current Year: TY vs LY. To Use, Pivot This Field and Add a Filter for 'Dimension is Not Null'
Calendar: 6 - Pivot by Previous Period Selected dimension The reporting period as selected by the Previous Period Filter and Add a Filter for 'Dimension is Not Null'
Calendar: Calendar Date dimension Calendar date
Calendar: Calendar Day of Week dimension The day of the week
Calendar: Calendar Day of Week Index dimension A number representing the day of the week, with Monday = 0 And Sunday = 6
Calendar: Calendar Month dimension The calendar month in YYYY-MM format
Calendar: Calendar Month Name dimension The calendar month name
Calendar: Calendar Month Num dimension The month number from 1 to 12
Calendar: Calendar Quarter dimension The quarter number in YYYY-Q# form
Calendar: Calendar Time dimension The full calendar timestamp
Calendar: Calendar Week dimension The calendar week in YYYY-MM-DD form
Calendar: Calendar Week of Year dimension The numbered calendar week from 1-52
Calendar: Calendar Year dimension The calendar year in YYYY form
Calendar: Retail Day of Week dimension Day of Week of Selected Date Based on Sunday Week Start (NRF Calendar)
Calendar: Retail Week dimension Retail Week Based on the NRF Calendar
Calendar: Retail Week Day Number dimension Retail Day of Week Based on the NRF Calendar
Calendar: Retail Year dimension Retail Year Based on the NRF Calendar
Calendar: Previous Period Filter filter Use this filter for period analysis - i.e. select This Year vs. Last Year for the Period using Pivot #6

Using Explores

Exploring data can be insightful and fun! Before you start your exploration, make sure to go through our Using Explores Bootcamp

Views
 

View Cardinality Relationship Join
performance_trends - self -
retail_calendar left_outer one_to_one ${performance_trends.created_date} = ${retail_calendar.calendar_date}

Flow

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