2. Analysing Digital Campaigns:
Current Situation
• Google Analytics and Facebook Analytics offer
limited, standardized metrics for analysis
• To push efficiencies, deeper and more actionable
analytics needed
• Integrating custom analysis parameters within
existing platforms has limitations
• Deeper analysis is time consuming, as it involves
considerable back and forth between Google
Analytics, Facebook Analytics and Custom
Parameters
2
4. How It Works?
• All metrics in one, customizable pivot table
• Set date ranges, & custom ranges such as weekdays/
holidays
• Create custom metrics on-the-fly (For example -
Revenue per 1000 impressions)
• Custom properties such as Price ranges, Ad themes,
Headlines, Ad copy, Visual elements, etc. available
within the table
• Organize & track A/B tests with Custom A/B Test
Dimensions
• 10+ Dimensions in a flat table (vs. 2 for Google Analytics)
• 15+ Drilldowns for Dimensions (vs. 5 for Google Analytics)
4
5. Facebook
Analytics
Google
Analytics
Integrated Analytics Platform
Dimensions Campaign, Ad,
Ad Set,
Audience, Age,
City
Landing Page,
Source, Medium,
Campaign, Ad
Content, Date,
Week, Month,
Devices
All FB & Google dimensions +
custom dimensions such as
Headlines, Adunit Types, Price
Ranges, Product Names,
Campaign Themes, Duration,
Acquisition
Metrics
CPM, CTR,
Reach, Freq,
Spend
Visits, Pageviews,
Unique Visitors
All FB & Google metrics in one
place
Behaviour
Metrics
n/a Bounce Rates,
Pages per visit,
Visit Duration
All FB & Google metrics +
Custom metrics such as:
%Dropoffs, %Interested
Visitors, %Product Page Views
Conversio
n Metrics
Conversions,
CPAs, Revenue,
based on 1, 7,
30 day
attribution
Conversions,
Revenue, CPAs,
Product View%,
Cart View% and
other Custom
Goals
All FB & Google + custom
metrics such as Revenue per
1000 impressions, %Click to
Transaction, %Spillover to
‘Direct’
5
6. Results
• Analysis time reduced by up to 90% with no back
and forth between different platforms and custom
parameters
• Test Campaign Budgets decreased by 75%
• Minimum Sample sizes decreased by 75%
• Conversion Rates increased by 100%
• Avg. Campaign pausing time decreased from 4
days to 1 day
6
7. Next Steps (In Progress)
• By observing patterns based on Behavioural metrics for Ad
Campaigns, we can predict which Ad Campaigns are likely to
perform even before any Conversions are observed. For
example:
o Facebook Newsfeed campaigns having Bounce Rate below X%, ProductView
% above Y% and Product-Page-Drop-Off% less than Z% are likely to perform
well
o Facebook Right Column Ad Campaigns landing on Product Pages, having
Pages per visit lesser than ‘X’ and Product Page Dropoff% more than Y% should
be discontinued as they are not likely to perform
• Create Daily Dashboards that show current status and predict
which campaigns are likely to do well
• Automate and Create mini-algorithms for Pausing Ads,
thereby increasing efficiencies
7
