Restaurant Client Stats

MarketAll

Executive overview

Sample data · Illustrative Power BI mockup · Last 90 days

A decision-ready first page: traffic, engagement, orders, conversion and online value, followed by the trend behind the headline figures.

Sessions

184.2K

Reconstructed GA4 sessions in the selected 90-day period.

vs prior +8.3%

Reconstructed GA4 sessions in the selected 90-day period.Source: Planned pbi_sessions viewTable: restaurant_reporting.pbi_sessions

Illustrative query pattern for the planned session view. Figures in this demo are synthetic sample data.

SELECT session_date, market, device_category, default_channel_group, is_engaged_session, has_purchase FROM `restaurant_reporting.pbi_sessions` WHERE session_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE()

Engaged session rate

65.1%

Share of sessions meeting the documented GA4 engaged-session rule.

vs prior +3.2%

Share of sessions meeting the documented GA4 engaged-session rule.Source: Planned pbi_sessions viewTable: restaurant_reporting.pbi_sessions

Illustrative query pattern for the planned session view. Figures in this demo are synthetic sample data.

SELECT session_date, market, device_category, default_channel_group, is_engaged_session, has_purchase FROM `restaurant_reporting.pbi_sessions` WHERE session_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE()

Orders

8.46K

Deduplicated completed orders in the selected period.

vs prior +12.7%

Deduplicated completed orders in the selected period.Source: Planned pbi_conversions viewTable: restaurant_reporting.pbi_conversions

Illustrative query pattern for the planned conversion view. Figures in this demo are synthetic sample data.

SELECT conversion_date, market, transaction_id, conversion_value, currency, customer_type, default_channel_group FROM `restaurant_reporting.pbi_conversions` WHERE conversion_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE()

Session conversion rate

4.6%

Sessions containing an approved purchase divided by sessions.

change +0.5%

Sessions containing an approved purchase divided by sessions.Source: Planned pbi_funnel_daily viewTable: restaurant_reporting.pbi_funnel_daily

Illustrative query pattern for the planned daily funnel view. Figures in this demo are synthetic sample data.

SELECT event_date, market, funnel_stage, stage_sessions, step_conversion_rate FROM `restaurant_reporting.pbi_funnel_daily` WHERE event_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE()

Online order value

$212.85K

Illustrative online order value in one reporting currency.

vs prior +14.2%

Illustrative online order value in one reporting currency.Source: Planned pbi_conversions viewTable: restaurant_reporting.pbi_conversions

Illustrative query pattern for the planned conversion view. Figures in this demo are synthetic sample data.

SELECT conversion_date, market, transaction_id, conversion_value, currency, customer_type, default_channel_group FROM `restaurant_reporting.pbi_conversions` WHERE conversion_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE()

Sessions by week

Source: Planned pbi_sessions viewTable: restaurant_reporting.pbi_sessions

Illustrative query pattern for the planned session view. Figures in this demo are synthetic sample data.

SELECT session_date, market, device_category, default_channel_group, is_engaged_session, has_purchase FROM `restaurant_reporting.pbi_sessions` WHERE session_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE()

Sessions by week data
Week	Sessions	Conversion rate
05 May	13.5K	4.2%
12 May	14.2K	4.2%
19 May	14.75K	4.2%
26 May	14.9K	4.4%
02 Jun	15.1K	4.4%
09 Jun	15.4K	4.5%
16 Jun	15.9K	4.5%
23 Jun	16.15K	4.6%
30 Jun	16.3K	4.7%
07 Jul	16K	4.9%
14 Jul	16.2K	5%
21 Jul	15.8K	5.4%

Orders by week

Source: Planned pbi_conversions viewTable: restaurant_reporting.pbi_conversions

Illustrative query pattern for the planned conversion view. Figures in this demo are synthetic sample data.

SELECT conversion_date, market, transaction_id, conversion_value, currency, customer_type, default_channel_group FROM `restaurant_reporting.pbi_conversions` WHERE conversion_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE()

Orders by week data
Week	Orders	Order value
05 May	560	$13.83K
12 May	590	$14.63K
19 May	620	$15.44K
26 May	650	$16.25K
02 Jun	670	$16.82K
09 Jun	690	$17.32K
16 Jun	720	$18.14K
23 Jun	745	$18.77K
30 Jun	770	$19.48K
07 Jul	785	$19.86K
14 Jul	815	$20.70K
21 Jul	847	$21.60K

Funnel & conversion

Shows where restaurant sessions progress, where they stop, and whether conversion differs by acquisition source or customer type.

Add-to-basket rate

13.4%

Sessions reaching add_to_cart divided by all sessions.

Sessions reaching add_to_cart divided by all sessions.Source: Planned pbi_funnel_daily viewTable: restaurant_reporting.pbi_funnel_daily

Illustrative query pattern for the planned daily funnel view. Figures in this demo are synthetic sample data.

SELECT event_date, market, funnel_stage, stage_sessions, step_conversion_rate FROM `restaurant_reporting.pbi_funnel_daily` WHERE event_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE()

Checkout completion

67.3%

Completed orders divided by sessions starting checkout.

Completed orders divided by sessions starting checkout.Source: Planned pbi_funnel_daily viewTable: restaurant_reporting.pbi_funnel_daily

Illustrative query pattern for the planned daily funnel view. Figures in this demo are synthetic sample data.

SELECT event_date, market, funnel_stage, stage_sessions, step_conversion_rate FROM `restaurant_reporting.pbi_funnel_daily` WHERE event_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE()

Cart abandonment

65.8%

Sessions with add_to_cart and no purchase divided by add-to-cart sessions.

Sessions with add_to_cart and no purchase divided by add-to-cart sessions.Source: Planned pbi_funnel_daily viewTable: restaurant_reporting.pbi_funnel_daily

Illustrative query pattern for the planned daily funnel view. Figures in this demo are synthetic sample data.

SELECT event_date, market, funnel_stage, stage_sessions, step_conversion_rate FROM `restaurant_reporting.pbi_funnel_daily` WHERE event_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE()

New customer share

37%

Orders labelled New divided by orders with a known customer type.

Orders labelled New divided by orders with a known customer type.Source: Planned pbi_conversions viewTable: restaurant_reporting.pbi_conversions

Illustrative query pattern for the planned conversion view. Figures in this demo are synthetic sample data.

SELECT conversion_date, market, transaction_id, conversion_value, currency, customer_type, default_channel_group FROM `restaurant_reporting.pbi_conversions` WHERE conversion_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE()

Session funnel

Source: Planned pbi_funnel_daily viewTable: restaurant_reporting.pbi_funnel_daily

Illustrative query pattern for the planned daily funnel view. Figures in this demo are synthetic sample data.

SELECT event_date, market, funnel_stage, stage_sessions, step_conversion_rate FROM `restaurant_reporting.pbi_funnel_daily` WHERE event_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE()

Session funnel data
Stage	Sessions	Step rate
Sessions	184.2K	100%
Menu viewed	118.6K	64.4%
Item viewed	91.4K	77.1%
Added to basket	24.72K	27%
Checkout started	12.58K	50.9%
Order complete	8.46K	67.3%

Conversion rate by channel

Source: Planned pbi_funnel_daily viewTable: restaurant_reporting.pbi_funnel_daily

Illustrative query pattern for the planned daily funnel view. Figures in this demo are synthetic sample data.

SELECT event_date, market, funnel_stage, stage_sessions, step_conversion_rate FROM `restaurant_reporting.pbi_funnel_daily` WHERE event_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE()

Conversion rate by channel data
Channel	Conversion rate	Sessions	Orders
Organic Search	4.9%	54.8K	2.68K
Paid Search	5.6%	43.4K	2.43K
Direct	4.3%	38.2K	1.63K
Paid Social	3.6%	23.6K	860
Email	4.4%	14.8K	650
Referral	2.3%	9.4K	212

Orders by customer type

Source: Planned pbi_conversions viewTable: restaurant_reporting.pbi_conversions

Illustrative query pattern for the planned conversion view. Figures in this demo are synthetic sample data.

SELECT conversion_date, market, transaction_id, conversion_value, currency, customer_type, default_channel_group FROM `restaurant_reporting.pbi_conversions` WHERE conversion_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE()

Orders by customer type data
Customer type	Orders	Share
Returning	5.33K	63%
New	3.13K	37%

Pages & behaviour

Summarises content demand, exits and approximate engagement using stable business page groups rather than long raw URLs.

Page views by page group

Source: Planned pbi_pageviews viewTable: restaurant_reporting.pbi_pageviews

Illustrative query pattern for the planned page-view view. Figures in this demo are synthetic sample data.

SELECT event_date, market, page_category, is_landing_page, is_exit_page, engagement_time_ms_on_page FROM `restaurant_reporting.pbi_pageviews` WHERE event_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE()

Page views by page group data
Page group	Page views	Approx. engagement (seconds)
Menu	151K	74
Item details	122.4K	62
Home	75.2K	41
Store finder	48.6K	55
Basket	39.5K	88
Checkout	25.4K	126
Account & loyalty	6.8K	58

Exit rate by page group

Source: Planned pbi_pageviews viewTable: restaurant_reporting.pbi_pageviews

Illustrative query pattern for the planned page-view view. Figures in this demo are synthetic sample data.

SELECT event_date, market, page_category, is_landing_page, is_exit_page, engagement_time_ms_on_page FROM `restaurant_reporting.pbi_pageviews` WHERE event_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE()

Exit rate by page group data
Page group	Exit rate	Page views
Menu	22.5%	151K
Item details	31.2%	122.4K
Home	37.6%	75.2K
Store finder	18.4%	48.6K
Basket	26.4%	39.5K
Checkout	19.8%	25.4K
Account & loyalty	21.4%	6.8K

Campaigns, devices & events

Connects acquisition performance with device behaviour and the approved events that will be exposed to Power BI.

Conversion rate by device

Source: Planned pbi_sessions viewTable: restaurant_reporting.pbi_sessions

Illustrative query pattern for the planned session view. Figures in this demo are synthetic sample data.

SELECT session_date, market, device_category, default_channel_group, is_engaged_session, has_purchase FROM `restaurant_reporting.pbi_sessions` WHERE session_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE()

Conversion rate by device data
Device	Conversion rate	Session share	Orders
Mobile	4.7%	74.7%	6.48K
Desktop	4.3%	21.7%	1.7K
Tablet	4.2%	3.6%	282

Approved event volumes

Source: Planned pbi_events viewTable: restaurant_reporting.pbi_events

Illustrative query pattern for the planned approved-event view. Figures in this demo are synthetic sample data.

SELECT event_date, market, event_name, event_category, COUNT(*) AS event_count FROM `restaurant_reporting.pbi_events` WHERE event_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE() GROUP BY ALL

Approved event volumes data
Event	Events	Reporting group
page_view	468.9K	Page
view_item	91.4K	Ecommerce
store_select	28.9K	Store
add_to_cart	24.72K	Ecommerce
begin_checkout	12.58K	Ecommerce
purchase	8.46K	Conversion
login	8.1K	Account
sign_up	3.2K	Conversion

Campaign performance

Source: Planned pbi_conversions viewTable: restaurant_reporting.pbi_conversions

Illustrative query pattern for the planned conversion view. Figures in this demo are synthetic sample data.

SELECT conversion_date, market, transaction_id, conversion_value, currency, customer_type, default_channel_group FROM `restaurant_reporting.pbi_conversions` WHERE conversion_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE()

Campaign performance
Campaign	Channel	Sessions	Orders	Conversion rate	Order value
Brand Search	Paid Search	18.2K	1.2K	6.6%	$30.92K
Local Menu Search	Paid Search	14.9K	792	5.3%	$19.81K
Weekend Social	Paid Social	9.8K	402	4.1%	$9.74K
Loyalty Reminder	Email	6.4K	348	5.4%	$8.82K
New Menu Launch	Paid Social	7.2K	238	3.3%	$5.75K

Core Web Vitals

Separate field-performance source. LCP, INP and CLS are not joined to individual GA4 users or sessions. They are reported by date, page group and device.

LCP (p75)

2.35 s

75th percentile Largest Contentful Paint. Good is 2.5 seconds or less.

75th percentile Largest Contentful Paint. Good is 2.5 seconds or less.Source: Separate Core Web Vitals feedTable: restaurant_reporting.cwv_page_daily

Illustrative CrUX-style site-speed query. This source remains separate from GA4 session and event data.

SELECT observation_date, market, page_group, device_category, p75_lcp_ms, p75_inp_ms, p75_cls, passing_url_share FROM `restaurant_reporting.cwv_page_daily` WHERE observation_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE()

INP (p75)

180 ms

75th percentile Interaction to Next Paint. Good is 200 ms or less.

75th percentile Interaction to Next Paint. Good is 200 ms or less.Source: Separate Core Web Vitals feedTable: restaurant_reporting.cwv_page_daily

Illustrative CrUX-style site-speed query. This source remains separate from GA4 session and event data.

SELECT observation_date, market, page_group, device_category, p75_lcp_ms, p75_inp_ms, p75_cls, passing_url_share FROM `restaurant_reporting.cwv_page_daily` WHERE observation_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE()

CLS (p75)

0.09

75th percentile Cumulative Layout Shift. Good is 0.1 or less.

75th percentile Cumulative Layout Shift. Good is 0.1 or less.Source: Separate Core Web Vitals feedTable: restaurant_reporting.cwv_page_daily

Illustrative CrUX-style site-speed query. This source remains separate from GA4 session and event data.

SELECT observation_date, market, page_group, device_category, p75_lcp_ms, p75_inp_ms, p75_cls, passing_url_share FROM `restaurant_reporting.cwv_page_daily` WHERE observation_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE()

URLs passing CWV

82%

Share of monitored URLs passing all available Core Web Vitals.

Share of monitored URLs passing all available Core Web Vitals.Source: Separate Core Web Vitals feedTable: restaurant_reporting.cwv_page_daily

Illustrative CrUX-style site-speed query. This source remains separate from GA4 session and event data.

SELECT observation_date, market, page_group, device_category, p75_lcp_ms, p75_inp_ms, p75_cls, passing_url_share FROM `restaurant_reporting.cwv_page_daily` WHERE observation_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE()

LCP (p75) by week

Source: Separate Core Web Vitals feedTable: restaurant_reporting.cwv_page_daily

Illustrative CrUX-style site-speed query. This source remains separate from GA4 session and event data.

SELECT observation_date, market, page_group, device_category, p75_lcp_ms, p75_inp_ms, p75_cls, passing_url_share FROM `restaurant_reporting.cwv_page_daily` WHERE observation_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE()

LCP (p75) by week data
Week	LCP (ms)
05 May	2,680ms
12 May	2,610ms
19 May	2,580ms
26 May	2,520ms
02 Jun	2,490ms
09 Jun	2,460ms
16 Jun	2,420ms
23 Jun	2,390ms
30 Jun	2,370ms
07 Jul	2,340ms
14 Jul	2,320ms
21 Jul	2,350ms

INP (p75) by week

Source: Separate Core Web Vitals feedTable: restaurant_reporting.cwv_page_daily

Illustrative CrUX-style site-speed query. This source remains separate from GA4 session and event data.

SELECT observation_date, market, page_group, device_category, p75_lcp_ms, p75_inp_ms, p75_cls, passing_url_share FROM `restaurant_reporting.cwv_page_daily` WHERE observation_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE()

INP (p75) by week data
Week	INP (ms)
05 May	218ms
12 May	211ms
19 May	205ms
26 May	199ms
02 Jun	196ms
09 Jun	192ms
16 Jun	188ms
23 Jun	185ms
30 Jun	183ms
07 Jul	179ms
14 Jul	177ms
21 Jul	180ms

CLS (p75) by week

Source: Separate Core Web Vitals feedTable: restaurant_reporting.cwv_page_daily

Illustrative CrUX-style site-speed query. This source remains separate from GA4 session and event data.

SELECT observation_date, market, page_group, device_category, p75_lcp_ms, p75_inp_ms, p75_cls, passing_url_share FROM `restaurant_reporting.cwv_page_daily` WHERE observation_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE()

CLS (p75) by week data
Week	CLS
05 May	0.12
12 May	0.11
19 May	0.11
26 May	0.1
02 Jun	0.1
09 Jun	0.1
16 Jun	0.1
23 Jun	0.09
30 Jun	0.09
07 Jul	0.09
14 Jul	0.09
21 Jul	0.09

Core Web Vitals by device

Source: Separate Core Web Vitals feedTable: restaurant_reporting.cwv_page_daily

Illustrative CrUX-style site-speed query. This source remains separate from GA4 session and event data.

SELECT observation_date, market, page_group, device_category, p75_lcp_ms, p75_inp_ms, p75_cls, passing_url_share FROM `restaurant_reporting.cwv_page_daily` WHERE observation_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE()

Core Web Vitals by device
Device	LCP p75 (ms)	INP p75 (ms)	CLS p75	URLs passing	Status
Mobile	2,460	190	0.1	78%	Good
Tablet	2,340	180	0.08	84%	Good
Desktop	1,880	120	0.06	93%	Good

Core Web Vitals by page group

Source: Separate Core Web Vitals feedTable: restaurant_reporting.cwv_page_daily

Illustrative CrUX-style site-speed query. This source remains separate from GA4 session and event data.

SELECT observation_date, market, page_group, device_category, p75_lcp_ms, p75_inp_ms, p75_cls, passing_url_share FROM `restaurant_reporting.cwv_page_daily` WHERE observation_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE()

Core Web Vitals by page group
Page group	LCP p75 (ms)	INP p75 (ms)	CLS p75	URLs passing	Status
Item details	2,680	205	0.11	69%	Needs improvement
Checkout	2,550	195	0.09	75%	Needs improvement
Basket	2,380	180	0.08	82%	Good
Menu	2,420	185	0.08	84%	Good
Store finder	2,250	170	0.07	87%	Good
Home	2,100	150	0.06	91%	Good

Data available to Power BI

The first five rows are the proposed BigQuery delivery. Core Web Vitals is deliberately a sixth, separate feed. This keeps the initial data model honest and easy to maintain.

What Power BI will receive

Source: Proposed reporting view inventoryTable: UNNEST

The proposed Power BI-facing inventory: five prepared GA4 BigQuery views and one separate site-speed feed.

SELECT * FROM UNNEST([STRUCT('pbi_sessions' AS view_name, 'One row per session' AS grain, 'Acquisition, device, landing/exit and funnel flags' AS purpose, 'GA4 BigQuery' AS source_system), STRUCT('pbi_pageviews', 'One row per page view', 'Page journeys, entrances, exits and engagement', 'GA4 BigQuery'), STRUCT('pbi_events', 'One row per approved event', 'Event volumes and reporting context', 'GA4 BigQuery'), STRUCT('pbi_funnel_daily', 'Daily summary', 'Funnel stages, abandonment and segments', 'GA4 BigQuery'), STRUCT('pbi_conversions', 'One row per conversion', 'Orders, value, customer type and attribution', 'GA4 BigQuery'), STRUCT('cwv_page_daily', 'Page group × device × day', 'LCP, INP, CLS and pass rates', 'Separate Core Web Vitals feed')])

What Power BI will receive
Prepared view	Grain	What it supports	Source
cwv_page_daily	Page group × device × day	LCP, INP, CLS and pass rates	Separate Core Web Vitals feed
pbi_conversions	One row per conversion	Orders, value, customer type and attribution	GA4 BigQuery
pbi_events	One row per approved event	Event volumes and reporting context	GA4 BigQuery
pbi_funnel_daily	Daily summary	Funnel stages, abandonment and segments	GA4 BigQuery
pbi_pageviews	One row per page view	Page journeys, entrances, exits and engagement	GA4 BigQuery
pbi_sessions	One row per session	Acquisition, device, landing/exit and funnel flags	GA4 BigQuery

Important

Every number, campaign name and market label in this mockup is synthetic. The live dashboard would use the same layout and definitions, populated from the prepared BigQuery views and the chosen Core Web Vitals source.

Sources

Planned pbi_sessions viewbigquery

Illustrative query pattern for the planned session view. Figures in this demo are synthetic sample data.

SQL query

SELECT session_date, market, device_category, default_channel_group, is_engaged_session, has_purchase FROM `restaurant_reporting.pbi_sessions` WHERE session_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE()

Planned pbi_funnel_daily viewbigquery

Illustrative query pattern for the planned daily funnel view. Figures in this demo are synthetic sample data.

SQL query

SELECT event_date, market, funnel_stage, stage_sessions, step_conversion_rate FROM `restaurant_reporting.pbi_funnel_daily` WHERE event_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE()

Planned pbi_pageviews viewbigquery

Illustrative query pattern for the planned page-view view. Figures in this demo are synthetic sample data.

SQL query

SELECT event_date, market, page_category, is_landing_page, is_exit_page, engagement_time_ms_on_page FROM `restaurant_reporting.pbi_pageviews` WHERE event_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE()

Planned pbi_conversions viewbigquery

Illustrative query pattern for the planned conversion view. Figures in this demo are synthetic sample data.

SQL query

SELECT conversion_date, market, transaction_id, conversion_value, currency, customer_type, default_channel_group FROM `restaurant_reporting.pbi_conversions` WHERE conversion_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE()

Planned pbi_events viewbigquery

Illustrative query pattern for the planned approved-event view. Figures in this demo are synthetic sample data.

SQL query

SELECT event_date, market, event_name, event_category, COUNT(*) AS event_count FROM `restaurant_reporting.pbi_events` WHERE event_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE() GROUP BY ALL

Separate Core Web Vitals feedbigquery

Illustrative CrUX-style site-speed query. This source remains separate from GA4 session and event data.

SQL query

SELECT observation_date, market, page_group, device_category, p75_lcp_ms, p75_inp_ms, p75_cls, passing_url_share FROM `restaurant_reporting.cwv_page_daily` WHERE observation_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE()

Proposed reporting view inventorybigquery

The proposed Power BI-facing inventory: five prepared GA4 BigQuery views and one separate site-speed feed.

SQL query

SELECT * FROM UNNEST([STRUCT('pbi_sessions' AS view_name, 'One row per session' AS grain, 'Acquisition, device, landing/exit and funnel flags' AS purpose, 'GA4 BigQuery' AS source_system), STRUCT('pbi_pageviews', 'One row per page view', 'Page journeys, entrances, exits and engagement', 'GA4 BigQuery'), STRUCT('pbi_events', 'One row per approved event', 'Event volumes and reporting context', 'GA4 BigQuery'), STRUCT('pbi_funnel_daily', 'Daily summary', 'Funnel stages, abandonment and segments', 'GA4 BigQuery'), STRUCT('pbi_conversions', 'One row per conversion', 'Orders, value, customer type and attribution', 'GA4 BigQuery'), STRUCT('cwv_page_daily', 'Page group × device × day', 'LCP, INP, CLS and pass rates', 'Separate Core Web Vitals feed')])