The Benchmarks table aggregates industry-wide data to provide a comparative analysis of performance across various metrics such as average order value, conversion rates, and advertising efficiency. Querying this table lets you compare your business's performance with industry standards, identify improvement opportunities, and strategize for competitive advantage.
Note
event_dateis a required field for queries on this table.Benchmarks are available on a 1-day delay, so you should not include today's date in queries on this table.
Industry benchmarks are based on aggregated ad channel data collected by Triple Whale. To preserve anonymity, benchmark data is only shown when there are at least 20 ad channel sources that match your filter criteria.
Dimensions
Dimensions are immutable properties that can be used for grouping data.
| Name | Type | Description |
|---|---|---|
event_date | date | The date the ad was run. Based on the time zone of the ad platform at the moment of publishing. |
event_date.day | date | The day the ad was run. Derived from event_date. |
event_date.week | date | The Sunday of the week during which the ad was run. Derived from event_date. |
event_date.month | date | The month during which the ad was run. Derived from event_date. |
event_date.quarter | date | The first month of the quarter during which the ad was run. Derived from event_date.e nearest quarter. |
event_date.year | date | The year during which the ad was run. Derived from event_date. |
industry | string | The industry category of the store as set by the seller in Triple Whale admin from a pre-defined list of industries. |
aov_segment | string | Segmentation of orders based on the average order value. |
gmv_segment | string | Segmentation of orders based on the gross merchandise value. |
channel | string | The marketing or sales channel through which the order was acquired (e.g. Facebook, TikTok, Google). |
Derived
Derived fields are metrics that are pre-calculated using multiple measures or advanced formulas.
| Name | Type | Description |
|---|---|---|
channel_reported_cpa | formula | The median Cost Per Acquisition as reported by the marketing channel. Formula: approx_quantiles(channel_reported_cpa, 100)[offset(50)] AS channel_reported_cpa_median |
channel_reported_cpc | formula | The median Cost Per Click as reported by the marketing channel. Formula: approx_quantiles(channel_reported_cpc, 100)[offset(50)] AS channel_reported_cpc_median |
channel_reported_cpm | formula | The median Cost Per Mille (Cost per 1000 impressions) as reported by the marketing channel. Formula: approx_quantiles(channel_reported_cpm, 100)[offset(50)] AS channel_reported_cpm_median |
channel_reported_cvr | formula | The median Conversion Rate as reported by the marketing channel. Formula: approx_quantiles(channel_reported_cvr, 100)[offset(50)] AS channel_reported_cvr_median |
channel_reported_ctr | formula | The median Click-Through Rate as reported by the marketing channel. Formula: approx_quantiles(channel_reported_ctr, 100)[offset(50)] AS channel_reported_ctr_median |
channel_reported_mer | formula | The median Marketing Efficiency Ratio as reported by the marketing channel. Formula: approx_quantiles(channel_reported_mer, 100)[offset(50)] AS channel_reported_mer_median |
channel_reported_roas | formula | The median Return On Ad Spend as reported by the marketing channel. Formula: approx_quantiles(channel_reported_roas, 100)[offset(50)] AS channel_reported_roas_median |
channel_reported_aov | formula | The median Average Order Value as reported by the marketing channel. Formula: approx_quantiles(channel_reported_aov, 100)[offset(50)] AS channel_reported_aov_median |