Data Driven Attribution: The Future of Intelligent Measurement By Simon Poulton
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Transcript of Data Driven Attribution: The Future of Intelligent Measurement By Simon Poulton
#SMX #31C @SPoulton
Dissecting The Evolution From Rules Based To Data-Driven Attribution
DATA-DRIVEN ATTRIBUTION:
THE FUTURE OF INTELLIGENT
MEASUREMENT
#SMX #31C @SPoulton
Dissecting The Evolution From Rules Based To Data-Driven Attribution
DATA-DRIVEN ATTRIBUTION:
THE FUTURE OF INTELLIGENT
MEASUREMENT
#SMX #31C @SPoulton
#SMX #31C @SPoulton
Dan Carter
#SMX #31C @SPoulton
Dan Carter – New Zealand All Black
106
1,598
89§ Matches Played
§ Percent Of Games Won
§ Points Scored (All Time)
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§ Matches Played
§ Percent Of Games Won
§ Points Scored (All Time)
Owen Franks – New Zealand All Black
95
088
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§ Owen Franks is a view-through conversion on Facebook.
§ Daniel Carter is an affiliate taking last-click attribution in Google Analytics.
§ Who should we invest in to keep winning?
Rugby At-TRY-bution
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§ March 2008: Avinash Kaushik publishes first article explaining basic rules-based models.
§ April 2009: Adam Goldberg challenges marketers to think about complexity.
Multi-Touch Attribution As A Concept
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Standard Position & Rules-Based Models
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§ Great article by Aaron Levy: http://searchengineland.com/whats-best-attribution-model-ppc-252374
Rules-Based Models – Further Reading
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§ First started appearing in articles & social conversations in late 2012.
The Rise Of Data-Driven Attribution
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Google’s Data-Driven Attribution
§ Google Analytics Premium: Launched on August 20th, 2013 (or 1,529 days ago!)
§ Additional Roll Outs:– DoubleClick: Feb 2016
– AdWords: May 2016– Google Attribution: ~Q1 2018
#SMX #31C @SPoultonMethodology & Purpose
DATA-DRIVEN ATTRIBUTION: UNDER THE HOOD
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Shapely Values
§ Created by Lloyd Shapely in 1953
§ Solution concept in Cooperative Game Theory
§ A way to assign credit among a group of “players” who cooperate for a certain end.
Lloyd Shapely
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Shapely Values – Glove Example§ Example:– 3 “Players”.– Player 1 receives a left-hand glove.
– Players 2 & 3 receive a right-hand glove.
§ Task:– Form a pair.– Credit assigned to each player after
forming a pair.
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Brand§ 3 AdWords Campaigns
§ Minimum 15,000 Clicks & 600 Conversion Actions In Past 30 Days
Shapely Values – AdWords Example
Shopping Non-Brand
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Problem: 3 AdWords Campaigns had 4 sales of $1. How can we distribute the total credit of $4 to the individuals?
Brand Shopping Non-Brand
$4
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Step 1: Compute Normalizing Factors (NF), for different sizes of sub-teams.
NF Formula** NF Team Permutations
Number of Campaigns
NF Formula NF Team Permutations
1 NF: (0! * 2!) / 3! = 2/6 = 1/3 33%
2 NF: (1! * 1!) / 3! = 1/6 = 1/6 16%
3 NF: (2! * 0!) / 3! = 2/6 = 1/3 33%
Brand Shopping Non-Brand
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Step 2: Performance data-points for individuals.
Brand Shopping Non-Brand
$2Sales
$1Sales
$0Sales
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Step 3: Performance data-points for campaigns as part of teams.
Brand Shopping
$4
§ Counterfactual Gain– Brand alone makes $2 in sales.
– Shopping alone makes $1 in sales.
– The two of them make $4 in sales.
§ Brand’s counterfactual gain, i.e. what Brand brings as a value add, is therefore the total sales, minus what Shopping would have achieved on its own.
§ Brand’s counterfactual gain, in a group with Shopping, is $4 - $1 = $3.– Similarly Shopping’s counterfactual is the total sales, minus
what Brand would have had on its own.
§ Shopping’s counterfactual gain, in a team with Brand, is $4 - $2 = $2.
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Step 3: Performance data-points for individuals as part of teams.
Brand ShoppingNon-
BrandShopping + Non-Brand
Non-Brand +Brand
Shopping +Brand
Brand +Shopping + Non-Brand
Sales $2 $1 $0 $2 $1 $4 $4
Brand $2 - - - $1 $3 $3
Shopping - $1 - $2 - $2 $2
Non-Brand - - $0 $0 $0 - $0
Coun
terf
actu
al G
ain
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Step 4: Computing payoff for individuals from counterfactual gains, using Normalizing Factors (NFs).
Group of 1 Group of 2 Group of 3 Attributed Payout
NF 33% 16% 33% 100%
Brand $2 $2+$3=$5 $333%*$2 + 16%*$5 +
33%*$3 = $2.5
Shopping $1 $1+$2=$3 $233%*$1 + 16%*$3 +
33%*$2 = $1.5
Non-Brand $0 $0 $0 $0
#SMX #31C @SPoultonCase Studies & Examples
DATA-DRIVEN ATTRIBUTION: ADWORDS
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Conversion Shifts To Non-Brand
Non-Brand
Brand
Conversions244
+64%
Cost / Conv$190
+12%
Click Conv. Rate0.7%
-13%
Conversions69
-63%
Cost / Conv$111
+58%
Click Conv. Rate0.7%
-24%
§ Client Type: Auto-Parts Client with a complex path to purchase.
§ What Happened? Attribution weight shifted from remarketing and brand to non-brand upper funnel terms, allowing for a focus on non-brand to drive growth.
*Date Range: 30 days Pre & Post Attribution Model Change.
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Conversion Shifts To Brand
Non-Brand
Brand
Conversions1,246
-7.1%
Cost / Conv$31
-12.1%
Click Conv. Rate2%
+13.4%
Conversions1,450
+8.4%
Cost / Conv$1
+0.2%
Click Conv. Rate12%
+4.8%
§ Client Type: Wedding Personalization Company with a strong focus on brand search.
§ What Happened? Brand campaign has started to see more credit. May be an indicator of over-reliance on lower funnel activity.
*Date Range: 30 days Pre & Post Attribution Model Change.
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Conversion Shifts To Mobile
Mobile
Desktop/Tablet
Conversions551
+10.4%
Cost / Conv$86
-32.4%
Click Conv. Rate1%
+52.2%
Conversions522
-13.4%
Cost / Conv$60
-6.1%
Click Conv. Rate3%
+6.7%
§ Client Type: Furniture store with a long consumer research phase pre-purchase.
§ What Happened? Heavier weighting of earlier touch points (on mobile devices) drove a number of mobile bid optimizations & increases.
*Date Range: 30 days Pre & Post Attribution Model Change.
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Conversion Shifts To Search From Shopping
Search
Shopping
Conversions827
+2.6%
Cost / Conv$74
-19.6%
Click Conv. Rate2%
+25.6%
Conversions218
-19.8%
Cost / Conv$442
+8.5%
Click Conv. Rate<1%
-9.4%
§ Client Type: Fast-Fashion Brand with short consideration phase.
§ What Happened? Client was overly reliant on shopping campaigns as they were close to bottom of funnel.
*Date Range: 30 days Pre & Post Attribution Model Change.
#SMX #31C @SPoultonThings to Consider
CRITICISM
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§ Google Only– While the methodology is exciting, the
model is still limited by the inputs provided. Specifically, accounting for 3rd
party ad networks (inc. Facebook) is non-existent.
§ Black Box– Without the ability to view Transaction IDs
associated with AdWords conversions, we are still trusting Google that they are fairly claiming credit across campaigns.
Criticism
#SMX #31C @SPoultonTLDR
KEY TAKEAWAYS
#SMX #31C @SPoulton
§ Attribution Modeling is about looking forward to determine how to grow, not about looking back.
§ Standard Position & Rules-Based Attribution Models: Still very useful, but inherently contain bias & limit actionable insights.
§ Data-Driven Attribution– Uses Shapely Values & Counterfactual Gains to constantly adjust based
on new information available.
– Available to all AdWords accounts with at least 15,000 clicks, and 600 conversion events in past 30 days.
– Limitations: Still only ingests data from Google platforms, with no insights provided into Display or Facebook interactions.
Key Takeaways
#SMX #31C @SPoulton
§ Director of Digital Intelligence @ Wpromote
§ Let’s Connect:– Twitter: @SPoulton
– LinkedIn: /smpoulton/– Email: [email protected]
Speaker: Simon Poulton
#SMX #31C @SPoulton
LEARN MORE: UPCOMING @SMX EVENTS
THANK YOU! SEE YOU AT THE NEXT #SMX