Send Volume Analytics
Monitors weekly email delivery volume with KPI cards (Total Delivered, Open %, Click %, Click-to-Open %), trend line, and top/bottom 20 deployment type tables. Includes delivery breakdowns by brand and designation.
A multi-page Power BI report built for the Audience Team to monitor email marketing performance across brands, deployment types, and promo codes. Covers send volume, opt-out trends, and promotional efforts — each with dedicated drill-down and drill-through pages and AI-generated insights and recommendations.
Overview
AM Analytics is a comprehensive Power BI report designed for the Audience Team to track and analyse email campaign performance on a weekly basis. The report surfaces key engagement metrics — delivered volume, open rate, click rate, click-to-open rate, opt-outs, and subscription activity — across multiple brands, designations, and deployment types.
Each analytics module includes an Insights & Recommendation panel powered by AI, providing automated narrative summaries of weekly performance and actionable recommendations for stakeholders without requiring manual interpretation.
Report pages
Send Volume Analytics
Monitors weekly email delivery volume with KPI cards (Total Delivered, Open %, Click %, Click-to-Open %), trend line, and top/bottom 20 deployment type tables. Includes delivery breakdowns by brand and designation.
Send Volume Analytics Breakdown
Drill-down page showing granular row-level data — date, brand, designation, deployment type, title, total delivered, click rate, open rate, and click-to-open rate.
Opt-Outs Analytics
Tracks weekly unsubscribe trends with KPI cards, opt-out trend line, and highest/lowest opt-out deployment type tables — broken down by brand and designation.
Opt-Outs Analytics Breakdown
Drill-down page showing row-level opt-out data with date, brand, designation, deployment type, title, total opt-outs, and opt-out rate.
Opt-Outs Alert Monitor
Compares current opt-outs vs monthly average and vs same day last month. Flags critical alerts to stakeholders when thresholds are exceeded, with a full historical monthly breakdown by brand.
Promotional Efforts Analytics
Monitors promo code performance with KPIs for total, new, and renewed subscribers. Includes subscriber volume trends over time, top/bottom performing promo codes, and breakdowns by brand and product.
Promotional Efforts Analytics Breakdown
Drill-down page showing promo code performance by brand, product type, product, promo code, total subscribers, new, and renewed counts.
Promo Code Details
Drill-through page showing individual customer records for a selected promo code — date, product, subscription ID, customer ID, name, and renewal status.
Interactive slicers
All pages support dynamic filtering via slicers. Common slicers are available across all modules; additional slicers are exclusive to the Promotional Efforts Analytics section.
Common slicers
Promotional Efforts exclusive
Technology stack
BI & Visualisation
Data & Analytics
Security & Access
Report Features
Enterprise features
Power BI App
Published as a dedicated Power BI App with controlled access. Users must request access from the report owner — ensuring only authorised Audience Team members can view the report.
Row-Level Security (RLS)
RLS applied at the app level to restrict data visibility per user role. Access is managed by the report owner, preventing unauthorised data exposure across teams.
OAuth2 + Databricks Connection
Connected to Databricks SQL Warehouse via OAuth2 authentication — providing secure, token-based access to the underlying data without storing credentials.
Daily Scheduled Refresh
Configured for automated daily refresh at 8:30 PM PHT — ensuring the Audience Team always works with the latest data without manual intervention.
Data model
The Power BI data model consists of two fact tables (Deployment Details and Promo Efforts) connected to a shared Dates Table for time intelligence, plus a Measures Table to centralise all DAX calculations.
Power BI model view showing Deployment Details and Promo Efforts fact tables joined to a shared Dates Table via date keys.
SQL scripts (Databricks)
Both Power BI datasets are powered by custom SQL queries written in Databricks SQL Warehouse. These scripts extract, join, and transform raw data from Omeda and internal tables into clean, report-ready datasets.
SELECT DISTINCT CAST(sent_date_pt AS DATE) AS sent_date , odd.brand , odd.email_subject AS title , odd.deployment_name , oot.type_name AS deployment_type , oot.DESIGNATION_NAME AS designation , odd.delivered_total , odd.opens_total , odd.opens_unique , odd.opens_rate , odd.clicks_total , odd.clicks_unique , odd.clicks_rate , odd.optouts_total , odd.optouts_rate FROM schema.bi_analytics.v_deployment_details AS odd INNER JOIN schema.virtual_db.omail_deployment AS ood ON odd.deployment_name = ood.NAME INNER JOIN schema.virtual_db.omail_type AS oot ON ood.DEPLOYMENT_TYPE_ID = oot.DEPLOYMENT_TYPE_ID ORDER BY sent_date DESC
WITH base AS ( SELECT ocp.PRODUCT_SUBSCRIPTION_ID , ocp.CUSTOMER_ID , ocp.PRODUCT_ID , ocp.PRODUCT_ORIGINAL_ORDER , ocp.VERIFICATION_DATE , ocp.PRODUCT_VERSION_TYPE , ocp.PRODUCT_PROMO_CODE , ocp.AMOUNT , DATEADD(month, 1, ocp.product_original_order) AS month_after_original_order FROM schema.virtual_db.customer_product ocp WHERE NULLIF(ocp.PRODUCT_PROMO_CODE, '') IS NOT NULL AND ocp.PRODUCT_PROMO_CODE != 'Conv' ) , brand_map AS ( SELECT DISTINCT Brand_Finance , Brand_Abbr FROM schema.silver.brand_mapping ) , demo AS ( SELECT oc.CUSTOMER_ID , oc.FIRST_NAME , oc.LAST_NAME FROM schema.virtual_db.customer oc ) SELECT b.product_subscription_id , b.customer_id , d.first_name , d.last_name , bm.Brand_Finance AS brand_map , op.Name AS product , op.product_type_name AS product_type , CASE WHEN b.product_version_type = 'P' THEN 'Print' WHEN b.product_version_type = 'D' THEN 'Digital' WHEN b.product_version_type = 'B' THEN 'Both' ELSE b.product_version_type END AS version_type , b.product_original_order AS original_order_date , b.verification_date , CASE WHEN DATEADD(year, 1, b.verification_date) <= CURRENT_DATE THEN 'Already 1YR' WHEN b.verification_date > b.month_after_original_order THEN 'Renewed' WHEN b.verification_date <= b.month_after_original_order THEN 'New' ELSE 'Unknown' END AS status , b.product_promo_code AS promo_code , b.amount FROM base b INNER JOIN schema.virtual_db.product op ON b.PRODUCT_ID = op.PRODUCT_ID INNER JOIN demo d ON b.customer_id = d.customer_id LEFT JOIN brand_map bm ON UPPER(op.NAME) LIKE CONCAT('NL-', UPPER(bm.Brand_Abbr), '%')
Report screenshots
Send Volume Analytics — Main Page
Weekly KPIs, delivered volume trend, top/bottom 20 deployment types, and AI-generated insights & recommendation panel.
Send Volume Analytics — Breakdown (Drill-Down)
Row-level delivery data showing date, brand, deployment type, title, and all engagement metrics.
Opt-Outs Analytics
Weekly opt-out trend with highest/lowest deployment type tables.
Opt-Outs Breakdown
Granular opt-out data with opt-out rate per deployment.
Opt-Outs Alert Monitor
Critical alert flags comparing current opt-outs vs monthly average and same day last month, with full historical brand breakdown.
Promotional Efforts Analytics
Subscriber KPIs, volume trends, and top/bottom promo code performance.
Promotional Efforts Breakdown
Promo code performance by brand, product, new vs renewed subscribers.
Promo Code Details — Drill-Through
Individual customer-level records for a selected promo code, accessible via right-click drill-through from the breakdown page.
AM Analytics consolidates email marketing performance data into a single, interactive Power BI report — giving the Audience Team instant visibility into delivery health, audience churn, and subscription growth without manual data preparation or spreadsheet reporting.
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