how to determine mobile roi with multi touch attribution
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MULTI TOUCH ATTRIBUTION AND DETERMINING ROI
DAVID PEREZ
CMO, CONVERTRO
1
2004 2007 2010
2010
2010
2012
2012
2013
2013
2014
2014
Display
Demand
Side
Platform
Display
Supply
Side
Platform
Video
Demand &
Supply Side
Platform
Real
Time
AttributionContent
Personalization
Engine
Dynamic
Creative
Optimization
for
Product
Level
Retargeting
Rich
Media
Platform
Premium
Formats
NetworkVideo
Ad
Network
Ad
ServerDisplay Ad
Network
AOL PLATFORM EXPANSION
2
DMPAUDIENCE MGMT & ANALYTICS
MULTI-TOUCH ATTRIBUTION
Video, Display, Retargeting, PPC
ONE ENTERPRISE SUITE
3
Programmatic Buying Tools
MA
RK
ET
ER
SC
ON
SU
ME
RS
• Track all offline and online
media
• Track online & offline sales
• Automated spend
integration
PC, MOBILE, SOCIAL, TV, RADIO,
CATALOG
MEASURE
USER LEVEL
$1
7
DISPLAY
$3
3
VIDEO SOCIAL EMAIL RETAIL
$8 $4
2
• Algorithm assigns spend and $
value to each touch point
• Top down & bottom up
• Data updated every 24 hrs
$1
00
ATTRIBUTE
• Optimize media spend
factoring diminishing
returns
• Scenario planner for
“what if” mix analysis
• Integrate into buying tools
OPTIMAL
SPEND
BUYING
TOOLS
OPTIMIZE
OUR APPROACH TO ATTRIBUTION MEASUREMENT
MEASURE
5
CASE STUDY: CROSS-DEVICE TRACKING
6
Problem: Unsure if mobile campaigns were producing results
Result: Quantified how mobile campaigns were driving desktop sales
• Discovered that of multi-device users were switching
from mobile to desktop before converting
• Optimized mobile based on overall results
CROSS DEVICE TRACKING
Email Address = The New Cookie…
User ID = Non PII Track & Sync
7
CROSS DEVICE TRACKING
8
• Conversion Events (Sale) – Pass User ID
• Login – Pass User ID
• Overlay – Pass User ID via email
capture
FIRST PARTY
CROSS DEVICE TRACKING
• Convertro 2nd party
client pool device
mappings
• DFA Android + Gmail
• Atlas Facebook
9
SECOND PARTY
• Cookie sync with 3rd
party data providers that
track cross-device
• DrawBridge
• LiveRamp
• TapAd
THIRD PARTY
ATTRIBUTE
10
• Problem: Bonobos relied on last-click
attribution and didn’t know which
channels were more effective in
driving new customers
• Result: Model shifted more credit to
effective channels such as Facebook
and away from navigational sources11
CASE STUDY: ATTRIBUTION MODELING
CONVERTRO ALGORITHM
12
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Event Chaining
• Attribute events
preceding sale to assess
their predictive value
• Addresses “new” vs.
“repeat” purchases
• Handles multiple steps in
conversion funnel
• Allows touch-points to
exhibit various forms
of non-linear effects
like diminishing
returns
• Analyzes
“frequency” of
touch-points vs. just
indicators
Continuous
CovariatesNon-linear Effects
MTA MODEL ENHANCEMENTS
14
• Measures true incremental lift
of paid and unpaid marketing
on sales relative to base
probability
• Brand Equity - How much
revenue would I get if I shut off
all marketing spend?
• What is the combined impact
of frequency & recency
• Estimate period over which
“impressions” and “clicks” by
channel/tactic decay over time
Base/lift Time Decay
MTA MODEL ENHANCEMENTS
MODEL VALIDATION
• Randomly splitting user click trails into training and test sets
(80/20 split)
• Train a set of models on the training set
• Then Run a prediction on the previously-unseen test sets (with
the actual conversion events removed from the clicktrail).
15
OPTIMIZE
16
TV worked, but only on specific
channels with specific creative.
Convertro helped DSC identify
these opportunities.
With Convertro, Dollar Shave Club
was able to use TV to profitably
acquire customers online.
17
CASE STUDY: OPTIMIZATION
18
SHIFT SPEND AT CHANNEL LEVEL
OPTIMIZING - TACTICAL ALLOCATIONS
KEYWORDS
HIDDEN
19
SCENARIO PLANNING
20
• Track all offline and online
media
• Track online & offline sales
• Automated spend
integration
PC, MOBILE, SOCIAL, TV, RADIO,
CATALOG
MEASURE
USER LEVEL
$1
7
DISPLAY
$3
3
VIDEO SOCIAL EMAIL RETAIL
$8 $4
2
• Algorithm assigns spend and $
value to each touch point
• Top down & bottom up
• Data updated every 24 hrs
$1
00
ATTRIBUTE
• Optimize media spend
factoring diminishing
returns
• Scenario planner for
“what if” mix analysis
• Integrate into buying tools
OPTIMAL
SPEND
BUYING
TOOLS
OPTIMIZE
RECAP