Follow up on the latest improvements and updates.

RSS

April 7, 2025

improved

Visualization

Adding dynamic date field to Calendar V2

Calendar V2 now gives the ability to use a dynamic date dimension and control whether that dimension displays the date, week, month, or year. This makes it easy to quickly toggle back and forth between different types of trend charts in your dashboards.

April 1, 2025

improved

Visualization

Enterprise Only

Adding field descriptions

We have added field descriptions to 16 Looker views to provide additional context when interacting with reports.

March 24, 2025

fixed

Transformation

Fix for Other Orders currency conversion

When adding orders to the

Other Orders

BSD with a non-default currency, our transformation code was not properly converting the currency amounts into the default currency. We have updated our code to fix that.

March 20, 2025

fixed

Integrations

Transformation

Updating sync keys in Amazon Ads Sponsored Brand Reports

We have updated how we extract Amazon Ads Sponsored Brands Reports data so that campaign name and ad group name are no longer part of the sync key. This was causing duplicate data issues when a campaign or ad group name was updated.

The effect of this change is that your Amazon Ads reporting in Daasity will align more closely with what you see in your Amazon Ads admin.

We have updated our Amazon Ads Sponsored Brands transform script to include a section that will update historical sync keys and remove any duplicate historical data.

For reference, the following is the code that we are running to update historical sync key data:

-- Clear out test table if it already exists
DROP TABLE IF EXISTS amazon_ads.sponsored_brands_reports_test
;
-- Clone the brands report table into a test table
CREATE TABLE IF NOT EXISTS amazon_ads.sponsored_brands_reports_test
CLONE amazon_ads.sponsored_brands_reports
;
-- Update the sync keys for different report types
-- campaigns
UPDATE amazon_ads.sponsored_brands_reports_test
SET __sync_key = MD5(ARRAY_TO_STRING(ARRAY_CONSTRUCT(
      'campaigns'
      ,COALESCE(campaign_id::STRING, '')
      ,report_date::DATE
      ,profile_id::STRING
    ), ':'))
WHERE 1=1
  AND report_type = 'campaigns'
;
-- ad_groups
UPDATE amazon_ads.sponsored_brands_reports_test
SET __sync_key = MD5(ARRAY_TO_STRING(ARRAY_CONSTRUCT(
      'ad_groups'
      ,COALESCE(campaign_id::STRING, '')
      ,COALESCE(ad_group_id::STRING, '')
      ,report_date::DATE
      ,profile_id::STRING
    ), ':'))
WHERE 1=1
  AND report_type = 'ad_groups'
;
-- keywords
UPDATE amazon_ads.sponsored_brands_reports_test
SET __sync_key = MD5(ARRAY_TO_STRING(ARRAY_CONSTRUCT(
      'keywords'
      ,COALESCE(campaign_id::STRING, '')
      ,COALESCE(ad_group_id::STRING, '')
      ,COALESCE(keyword_id::STRING, '')
      ,COALESCE(keyword_text::STRING, '')
      ,COALESCE(match_type::STRING, '')
      ,report_date::DATE
      ,profile_id::STRING
    ), ':'))
WHERE 1=1
  AND report_type = 'keywords'
;
-- Delete duplicate sync keys from test table
DELETE FROM amazon_ads.sponsored_brands_reports_test
WHERE
 (__sync_key||__synced_at) IN (
    SELECT derived_key FROM (
        SELECT
            __sync_key||__synced_at AS derived_key
            ,row_number() OVER (PARTITION BY __sync_key ORDER BY __synced_at DESC) as rn
            ,*
        FROM amazon_ads.sponsored_brands_reports_test
        ORDER BY rn DESC
    )
    WHERE rn > 1
)
;
-- Clone the brands report table into a backup table
CREATE TABLE IF NOT EXISTS amazon_ads.sponsored_brands_reports_backup
CLONE amazon_ads.sponsored_brands_reports
;
-- Remove all records from production table
DELETE FROM amazon_ads.sponsored_brands_reports
WHERE 1=1
;
-- Insert the clean data from the test table into the production table
INSERT INTO amazon_ads.sponsored_brands_reports
SELECT *
FROM amazon_ads.sponsored_brands_reports_test
;

March 19, 2025

improved

Transformation

Added shipping_cost and fulfillment_cost to uos.sales_report table

We have added 2 new columns to the uos.sales_report table:

shipping_costs

and

fulfillment_costs

This is an update to the structure of the table, so any Enterprise merchants with custom scripts that push data into the uos.sales_report table will need to be updated to use the new table structure.

After the update, this will be the structure of uos.sales_report:

    transaction_id
  , store_transaction_id
  , transaction_date
  , transaction_type
  , transaction_detail_type
  , store_name
  , store_type
  , store_country
  , store_integration_name
  , business_unit
  , business_channel
  , customer_id
  , store_customer_id
  , email_address
  , order_id
  , store_order_id
  , order_date
  , refund_date
  , order_code
  , giftcard_only_order
  , order_line_id
  , store_order_line_id
  , store_product_id
  , store_variant_id
  , original_currency
  , currency_conversion_rate
  , converted_currency
  , product_name
  , listing_sku
  , sku
  , quantity
  , price
  , gross_sales
  , discount_amount
  , shipping_amount
  , fulfillment_amount
  , shipping_cost
  , fulfillment_cost
  , tax_amount
  , refund_amount
  , net_sales
  , sku_cost
  , duties
  , __shop_id
  , __uos_integration_id
  , __loaded_at
  , __synced_at

March 11, 2025

improved

Transformation

Visualization

Network Type added to Vendor-Reported Marketing explore

The explore that allows you to analyze all of your vendor-reported marketing KPIs has a new dimension — Network Type — which will help you understand performance metrics based on the targeting and network delivery settings for your ads.

For example, if you have some adsets on Meta that are set up to run only on Instagram vs other adsets that are set up to run only on Facebook, this new dimension will allow you to do so.

March 10, 2025

fixed

Visualization

Updated default filters on Customer Retention dashboard

Some of the tiles on the Customer Retention dashboard were filtering for outdated Customer Segment default values. This resulted in blank results being returned for the tile. This updates the filter on the tile so that they use the new defaults:

1. Active Multi-Buyer
2. Lapsed Multi-Buyer
3. Active Single Buyer
4. Lapsed Single Buyer
5. Non-Buyer

March 6, 2025

improved

Visualization

Stage-specific measures in Shopping Stage explore

Our Shopping Stage explore previously required you to pivot by the Shopping Stage dimension to get useful metrics. This worked for basic use cases, but made it difficult to create measures that show continuation rates from one step to another, and also made it difficult to plot these measures over time.

This update makes it easier to see how your ecommerce funnel is performing over time, like in the visualization below:

(Click here to enlarge)

The update adds the following measures to the explore:

Percent Visit -> Purchase
Percent Visit -> Product View
Percent Product View -> Add to Cart
Percent Add to Cart -> Begin Checkout
Percent Begin Checkout -> Purchase
Total Sessions w/ Visit
Total Sessions w/ Product View
Total Sessions w/ Add to Cart
Total Sessions w/ Begin Checkout
Total Sessions w/ Purchase

It also:

Hides the old "Shopping Stage" dimension, since combining it with the new measures would give confusing results, and
Hides the old "Total Sessions" measure, since using it without the Shopping Stage dimension would result in inflated and misleading metrics

March 5, 2025

improved

Transformation

TikTok Shops conversions

All places in our data model that contained vendor-reported conversions for TikTok were including only on-site purchases (i.e. purchases on your ecommerce site). They will now contain on-site purchases as well as purchases through TikTok Shops.

February 19, 2025

improved

Visualization

Order Line Revenue measure re-organization (Enterprise)

We have just released an update that will group measures in the Order Line Revenue explore by aggregation type. This means all count, total, average, and percent measures will be grouped together
(Click here to view full-size image)

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