If you need further assistance, could you please send me a screenshot or a code snippet of the Experiment Assignment Query you're using, along with a screenshot of the columns/table in BigQuery to match?

Here is the one of the assignment query that we are using:

WITH
  __rawExperiment AS (
    SELECT
      user_pseudo_id as anonymous_id,
      TIMESTAMP_MICROS(event_timestamp) as timestamp,
      experiment_id_param.value.string_value AS experiment_id,
      variation_id_param.value.int_value AS variation_id,
      geo.country as country,
      traffic_source.source as source,
      traffic_source.medium as medium,
      device.category as device,
      device.web_info.browser as browser,
      device.operating_system as os
    FROM
      `weezietowelsdaton`.`analytics_279996039`.`events_*`,
      UNNEST (event_params) AS experiment_id_param,
      UNNEST (event_params) AS variation_id_param
    WHERE
      (
        (_TABLE_SUFFIX BETWEEN '20231130' AND '20240118')
        OR (
          _TABLE_SUFFIX BETWEEN 'intraday_20231130' AND 'intraday_20240118'
        )
      )
      AND event_name = 'experiment_viewed'
      AND experiment_id_param.key = 'experiment_id'
      AND variation_id_param.key = 'variation_id'
      AND user_pseudo_id is not null
  ),
  __experimentExposures AS (
    -- Viewed Experiment
    SELECT
      e.anonymous_id as anonymous_id,
      cast(e.variation_id as string) as variation,
      CAST(e.timestamp as DATETIME) as timestamp
    FROM
      __rawExperiment e
    WHERE
      e.experiment_id = 'ab-quiz-test'
      AND e.timestamp >= '2023-11-30 15:57:00'
      AND e.timestamp <= '2024-01-18 19:02:14'
  ),
  __experimentUnits AS (
    -- One row per user
    SELECT
      e.anonymous_id AS anonymous_id,
      (
        CASE
          WHEN count(distinct e.variation) > 1 THEN '__multiple__'
          ELSE max(e.variation)
        END
      ) AS variation,
      MIN(e.timestamp) AS first_exposure_timestamp
    FROM
      __experimentExposures e
    GROUP BY
      e.anonymous_id
  ),
  __distinctUsers AS (
    SELECT
      anonymous_id,
      cast('All' as string) AS dimension,
      variation,
      first_exposure_timestamp AS timestamp,
      date_trunc(first_exposure_timestamp, DAY) AS first_exposure_date
    FROM
      __experimentUnits
  ),
  __metric as ( -- Metric ([Octane AI] Quiz ROUND ONE - Question Wh)
    SELECT
      anonymous_id as anonymous_id,
      1 as value,
      CAST(m.timestamp as DATETIME) as timestamp
    FROM
      (
        SELECT
          user_pseudo_id as anonymous_id,
          TIMESTAMP_MICROS(event_timestamp) as timestamp
        FROM
          `weezietowelsdaton.analytics_279996039.events_*`
        WHERE
          (
            (_TABLE_SUFFIX BETWEEN '20231130' AND '20240121')
            OR (
              _TABLE_SUFFIX BETWEEN 'intraday_20231130' AND 'intraday_20240121'
            )
          )
          AND event_name = '[Octane AI] Quiz ROUND ONE - Question Wh'
      ) m
    WHERE
      m.timestamp >= '2023-11-30 15:57:00'
      AND m.timestamp <= '2024-01-21 19:02:14'
  ),
  __userMetricJoin as (
    SELECT
      d.variation AS variation,
      d.dimension AS dimension,
      d.anonymous_id AS anonymous_id,
      (
        CASE
          WHEN m.timestamp >= d.timestamp
          AND m.timestamp <= DATETIME_ADD(d.timestamp, INTERVAL 72 HOUR) THEN m.value
          ELSE NULL
        END
      ) as value
    FROM
      __distinctUsers d
      LEFT JOIN __metric m ON (m.anonymous_id = d.anonymous_id)
  ),
  __userMetricAgg as (
    -- Add in the aggregate metric value for each user
    SELECT
      variation,
      dimension,
      anonymous_id,
      MAX(COALESCE(value, 0)) as value
    FROM
      __userMetricJoin
    GROUP BY
      variation,
      dimension,
      anonymous_id
  )
  -- One row per variation/dimension with aggregations
SELECT
  m.variation AS variation,
  m.dimension AS dimension,
  COUNT(*) AS users,
  SUM(COALESCE(m.value, 0)) AS main_sum,
  SUM(POWER(COALESCE(m.value, 0), 2)) AS main_sum_squares
FROM
  __userMetricAgg m
GROUP BY
  m.variation,
  m.dimension

@brief-honey-45610 In the initial Common Table Expression (CTE) of this assignment query, an examination of the WHERE clause reveals the inclusion of filtering conditions for event_name, experiment_id_param.key, and variation_id_param.key:

event_name = 'experiment_viewed'
AND experiment_id_param.key = 'experiment_id'
AND variation_id_param.key = 'variation_id'

It should be noted that there is no occurrence of an event_name equal to 'experimat_viewed'. Is the query intended to be executed in its current state?

These are all the events name captured in the data source: event_name add_payment_info add_to_cart Wunderkind Submission MiniCartUpsellClick event scroll user_engagement first_visit begin_checkout social_click HolidayDrawerClicks footer_tracking view_item error_tracking Wunderkind Impression SearchIconClick [Octane AI] Quiz ROUND ONE - Question Wh page_view session_start purchase Quiz ROUND ONE Started Quiz ROUND ONE Completed view_item_list button_click select_item newsletter_click remove_from_cart Wunderkind Click [Octane AI] Quiz ROUND ONE Viewed [Octane AI] Quiz Enjoy the Stay - Questi link_click add_shipping_info view_cart view_search_results page_variant view

Hi @brief-honey-45610 just following up here in case you missed my message.

@brief-honey-45610 just following up again. I'd really like to know how to proceed 🙂

Hi @brief-honey-45610 I'm confused because we're on shopify and I don't see any info in these docs https://docs.growthbook.io/integrations/shopify about a datalayer push.