From the SMX Advanced Conference in Seattle, Washington, June 2-3, 2015. SESSION: Visualizing Attribution In Living Color. PRESENTATION: Visualizing Attribution in Living Color - Given by Charles Midwinter, @Minderwinter - Globe Education Network, Director of Paid Digital Media & Analytics. #SMX #21C1

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Charles Midwinter, Collegis Education
Visualizing Attribution
in Living Color

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 When multiple channels or tactics assist
with a conversion, an attribution model is
the set of rules we use to “attribute”
portions of the conversion to each assisting
touch-point.
 But you already knew that…
What is Attribution (review, obviously)?

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 Last Interaction
 Last Non-direct Click
 Last AdWords Click
 First Interaction
 Linear
 Time Decay
 Position Based
Google Analytics Attribution Models

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Almost anything is better than “Last Click,”
but black boxes aren’t much better.
 No visibility on the details of the attribution
calculation
 Possible pitfalls with certain channels
 Too many groundless assumptions
required
The Problem with Out-of-the-Box Attribution Models

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If you want to understand multi-channel
attribution, the “multi-channel attribution
funnel” reports in Google Analytics are your
first stop.
 Take a look at the “top conversion paths”
report
 This is great information, but how to
summarize it at a high level?
Google Analytics & Channel/Tactic Interactions

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The object that can summarize these
conversion paths is called an “edge matrix.”
 Usually used for the analysis of networks
(eg. social networks)
 Encodes the connections among entities
 Can be visualized as a “node graph” with
open source software (Gephi)
Edge Matrices

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Consider the following conversion paths:
 A > C > B > C
 A > B
 B > C
Edge Matrix Example 1/3

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In words
 A
 referred to C once
 referred to B once
 B
 referred to C twice
 C
 referred to B once
Edge Matrix Example 2/3

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As an “Edge Matrix”
Edge Matrix Example 3/3
A B C
A 0 1 1
B 0 0 2
C 0 1 0

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Just use my handy dandy Python script.
 Go to:
 traffictheory.org/smx-2015
 Download the script
 Make sure you have Python 2.7 installed
(not Python 3!)
 Follow the instructions at the URL above to
run.
MCF Top Conversion Paths to Edge Matrix

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To visualize the “Edge Matrix” as a Node
Graph, you’ll need Gephi, open source
graph software.
 Open the “edge_matrix.csv” file created by the
Python script (see website for more details)
 Import the “last_click.csv” file created by the
Python script (see website for more details)
Turning an Edge Matrix into a Node Graph

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 How do we turn this spaghetti into
something useful?
The Raw Node Graph

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 A layout algorithm uses the weights of the
connections/edges to re-arrange the
nodes.
 Usually physics-based, involving a
gravitation-like attraction that scales with
the edge weights between nodes, and
often a repulsion that separates weakly
connected nodes.
Layout Algorithms

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 Nodes that refer to
each other often are
now placed close
together in 2D space.
 Two central
communities of nodes
are identifiable
(“direct/(none)” and
“google/organic”)
The Result of Layout Algorithm
“Force Atlas 2”

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 To make this graph more useful, we’d like
to map a metric to node size
 The metric should give us some indication
of the node’s importance to the conversion
process
 In order to proceed, we should understand
a bit more about the node graph
Measuring Node Importance

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 Degree: the number of a node’s
connections.
 In-Degree: the number of a node’s
incoming connections
 Out-Degree: the number of a node’s out-
going connections
Degree

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 A
 Degree = 2
 In-Degree = 0
 Out-Degree = 2
Degree Example
A B C
A 0 1 1
B 0 0 2
C 0 1 0

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 B
 Degree = 1
 In-Degree = 0
 Out-Degree = 1
Degree Example
A B C
A 0 1 1
B 0 0 2
C 0 1 0

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 Weighted Degree: the number of a node’s
connections multiplied by their weights.
 In-Degree: the number of a node’s
incoming connections multiplied by their
weights.
 Out-Degree: the number of a node’s out-
going connections multiplied by their
weights.
Weighted Degree

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 B
 Weighted Degree = 2
 In-Degree = 0
 Out-Degree = 2
Weighted Degree Example
A B C
A 0 1 1
B 0 0 2
C 0 1 0

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 The most important nodes are the ones generating incremental
conversions
 Conceptually, they generate a net output.
 A node that gets no in-bound connections, but has many out-
bound connections is a source of conversions, and should be
highly valued.
 A node that generates a lot of last-click conversions has value, but
its net output should be adjusted so that in-bound connections are
subtracted.
 A node that has as many in-bound connections as it does last-
click/out-bound connections is adding little value from an
incremental perspective.
Assessing Node (Campaign or Source/Medium)
Importance

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(Weighted Out-degree + Last Click) – Weighted In-Degree
 This metric gives us an indication of node
importance from an incremental conversion
perspective.
Net Output

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 Nodes that generate
more incremental
conversions are
larger
 Caveat: flawed
tracking means this
metric is far from
perfect
Mapping “Net Output” to Node Size

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 Positioning tells us which nodes are closely
connected, and size tells us how well
nodes generate incremental conversions
 It would also be nice to know how each
node tends to assist in the conversion
process: does it produce last clicks, or is it
higher in the funnel?
Assessing Node Function

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 The lower a node is in the conversion
funnel, the more last clicks it should have
 The higher a node is in the funnel, the
more likely it is to push traffic to other
nodes (high weighted out-degree)
Funnel Position 1/2

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Last Click / (Weighted Out-degree + Last Click)
 0 for nodes with no last click
 1 for nodes with all last click
 Varies from 0 to 1 as ratio of last click to
weighted out-degree increases
Funnel Position 2/2

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 Nodes high in the
funnel are redder
 Nodes lower in the
funnel are bluer
 In-between nodes
are lighter in color,
sometimes almost
white.
Mapping Funnel Position to Node Color

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The Final Result

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 Proximity tells you how often channels
interact
 Color tells you a channel/campaign’s
position in the funnel
 Size tells you how many incremental
conversions are likely generated by a
channel/campaign
How to Interpret the Result

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 Identify “sinks”
 Sinks are blueish.
 These kinds of channels
are at the end of the
conversion path
 They are lynch pins in
the network, fed by
channels higher in the
funnel
 Overvalued by last click
Sinks

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 Identify “sources”:
 Reddish
 Tend to be earlier in
the conversion path
 Undervalued by last
click
Sources

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 Identify “assistors”:
 Pale, or sometimes
white
 Beware of small
assistors
 Tend to be midway in
the conversion path
 Undervalued by last
click, but can be
overvalued by other
models
Assistors

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 Display
 Retargeting
 Direct Buy
 Behavioral
 Paid Search
 Branded
 Unbranded
 Organic Search
 Referral
 Social
 Direct
Source, Sink, or Assistor?

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 Display
 Retargeting (Assistor)
 Direct Buy (Source)
 Behavioral
(Source/Assistor)
 Paid Search
 Branded (Sink)
 Unbranded
(Source/Assistor)
 Organic Search
(Assistor/Sink)
 Referral
(Source/Assistor)
 Social (Assistor)
 Direct
(Assistor/Sink)
Source, Sink, or Assistor?

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 Depending on your sales cycle, channels &
campaigns may function differently in the
conversion funnel
Results May Vary

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 Nodes with little visibility are hard to
interpret:
 Organic: because of (not provided), its a mix of branded and
unbranded. Its “Funnel Position” will be determined by the
strength of your brand and the amount of unbranded organic
traffic you receive.
 Direct: can skew your results. We know it contains all kinds of
poorly tracked traffic. Sometimes, I just go ahead and remove
direct from the graph.
Caveats

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 Select an attribution model that fits your
conversion process
 Sources are under valued by both last click
and time decay, for example.
 Identify outliers and understand what they
say about your mix (discover fraud)
 Use the visualization rhetorically to justify
budget for exposure tactics
How to Make This Actionable

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THANK YOU!
Charles Midwinter
Associate Director of Marketing Strategy
Collegis Education
traffictheory.org/smx-2015