High Level Design
Overview
Google Analytics 360 is Google’s flagship for digital reporting and analytics. It has reporting and tracking abilities and it includes features, such as multi-channel funnel reports, enhanced ecommerce, and attribution modeling
One of the greatest premium features of GA 360 is unsampled data.
With the BigQuery integration feature, all your raw data can be automatically imported into Google BigQuery where you can then query it to your heart's delight.
Among the areas that GA 360 covers are Traffic, Content, E-Commerce, Revenues, Sources, Geo, Google Ads and more.
(Attribution modeling uses machine learning to assign weights to each touchpoint of a user’s journey. It spans different devices and channels, combining data from Google Analytics, AdWords, and Doubleclick to comprehensively determine Digital Attribution.)
- The sample data set is of Google’s Ecommerce shop
- BigQuery Export schema
Goals
Improve ROI from data analytics, by improving conversion rates, optimize ecommerce accessibility for visitors, focus on core countries and recognize developing markets.
Objectives
- Monitoring total revenue and transactions on the eshop
- Calculating conversion rates between transaction’s phases
- Understanding clients trends and sources
- Discovering the clients’ geo distribution
KPIs Architecture
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Objectives |
KPIs |
Measures |
Data Source |
|
Monitoring total revenue and transactions on the eshop |
Month To Date Revenue (Indicator) MTD Revenue Change from last month |
([Total hits.transaction.transactionRevenue] )/1000000 GrowthPastMONTH(([Total hits.transaction.transactionRevenue] )/1000000) |
ga_sessions |
|
Month To Date Transactions (Indicator) MTD # Transactions Change from last month |
[# of hits.transaction.transactionRevenue]
GrowthPastMONTH([# of hits.transaction.transactionRevenue]) |
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Daily Heat Map Revenue (Calendar Heatmap)
Date Color |
Days in VisitDateAdjusted sum([Totals.total Transaction Revenue])/1000000 |
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Calculating conversion rates between transaction’s phases |
Conversion Funnel (bar chart) |
All below values stacked in 1 category to simulate a funnel effect |
|
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Visitor Could not be measured (Indicator)
Could not be measured
% from Total Visits |
( sum([Totals.visits]), [ActionTypeforFunnel1]=Could not be measured)
[Could not be measured] / [Totals.visits] |
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|
Click through of product lists (Indicator)
Click through of product lists
% from Total Visits |
( sum([Totals.visits]), [ActionTypeforFunnel1]=Click through of product lists)
[Click through of product lists] / [Totals.visits] |
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|
Product Detail Views (Indicator)
Product Detail Views
% from Total Visits |
( sum([Totals.visits]), [ActionTypeforFunnel1]=Product Detail Views)
[Product Detail Views] / [Totals.visits] |
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|
Add product(s) to cart (Indicator)
Add product(s) to cart
% from Total Visits |
( sum([Totals.visits]), [ActionTypeforFunnel1]=Add product(s) to cart)
[Add product(s) to cart] / [Totals.visits] |
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|
Check Out (Indicator)
Check Out
% from Total Visits |
( sum([Totals.visits]), [ActionTypeforFunnel1]=Check Out)
[Check Out] / [Totals.visits] |
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|
Completed Purchase (Indicator)
Completed Purchase
% from Total Visits |
( sum([Totals.visits]), [ActionTypeforFunnel1]=Completed Purchase)
[Completed Purchase] / [Totals.visits] |
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Understanding clients trends and sources
|
Monthly Trend of Revenue by Medium (Column Chart)
Categories Values Break by |
Months in VisitDateAdjusted sum([Totals.total Transaction Revenue])/1000000 Medium |
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|
Monthly Trend of Revenue by Source (top 5) (Column Chart)
Categories Values Break by |
Months in VisitDateAdjusted sum([Totals.total Transaction Revenue])/1000000 Source |
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Hourly Trend of Revenue and Hits (Line Chart)
Categories: Revenue # Of Hits |
Hours sum([Totals.total Transaction Revenue])/1000000 dupcount([Hits.hour]) |
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Total Revenue by City (Scatter Map)
Location Size
|
sum([Totals.total Transaction Revenue])/1000000 City |
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Plugins
Scripts
Data Connectors
