Post on 15-Jan-2015
description
transcript
David Perez
CMO
"Measuring the Mobile Lead"
Cross-Device Attribution and Determining ROI
#SMX @Convertro
97% 95%85%
78%71%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Jan-10 Jan-11 Jan-12 Jan-13 Jan-14
Mobile
Tablet
Desktop
Source: Same set of 50 Convertro ecommerce clients over 5 year period
eCommerce % Visits By Device
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eCommerce % Revenue by Device
71%15%
14%
Desktop
Mobile/TabletWeb
In-App
Source: Convertro eCommerce clients with & without iOS/Android Apps - using algorithmic attribution – Jan 2014
80%
20%
Revenue by Device – No Apps Revenue by Device – w/ Apps
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Mobile, Tablet and Cross-Device Interactions
5.7 internet connected devices per U.S. HH according to
NPD (March 2013)
Convertro See 1.4 Devices Per Conversion (Jan 2014)
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Case Study: Cross-device tracking, Indochino
Problem: Unsure if mobile campaigns were producing
results
Result: Quantified how mobile campaigns were driving
desktop sales
1. Discovered that of multi-device users were
switching from mobile to desktop before converting
2. Optimized mobile based on overall results
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Tracking Cross Device
Email Address = The New Cookie…
User ID = The New Cookie
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Cross Device Tracking: 1st Party Data
1. Transaction – Get User ID
2. Login – Get User ID
3. ESP – Transactional Email – Get User ID
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Cross Device Tracking: 2nd Party Data
Convertro 2nd party client device mappings
DoubleClick Android + Gmail
Atlas Facebook
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Cross Device Tracking: 3rd Party Data
Cookie sync with 3rd party data providers that track
cross-device
• LiveRamp
• TapAd
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Holistic Attribution
• Need to track all channels, offline to online, for a holistic picture
of your marketing spend and conversion events
• Need transparency, verification and accuracy in order to take
control of your advertising performance
• Ability of tracking across devices is crucial for correct data
TV Radio Direct Mail Call
Centers
In-store
Purchases
Phone
Web/App
Tablet
Web/App
SocialEmailWebsite
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Convertro Algorithm
10
0.1
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1
Exposure to Marketing
Pro
ba
bili
ty o
f C
on
ve
rsio
n
Logistic Function
Events
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In Matrix Format (Example: Four Unique Paths)
Convs Total
10 1000
5 100
10 100
30 1000
Prob
Conv
1%
5%
10%
3%
Org. PPC TV Emai
l
Disp.
1 0 1 1 0
0 1 1 0 0
1 0 0 0 1
0 1 1 1 0
YConversion & Frequency
Counts of Paths X
=1
.10 .20 .30 .10 .30Resulting Weights
i
Convertro Algorithm
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Advanced Modeling: Base / Lift
Measures true incremental lift of paid marketing on sales:1. Define a “base” probability of conversion (probability that a customer
who was not exposed to marketing converts)
2. Measure the revenue contribution of other marketing sources relative to
the base probability as “lifts” or “increments” above that base
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Advanced Modeling: Event Chaining
• Addresses repeat purchases and multiple steps in
conversion funnel
• Attribute preceding events
• First conversion gets credit for the next conversion
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Advanced Modeling: Source Decay• Estimate period over which source exposure effect
decays over time
• Plot for views shows that < 25% of conversions
occurred by end of 1 day
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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).
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Model validation and testing
TRUSTED BY:
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Apply Cost Data
"A lot of that is around being able to allocate our
spend where it's most effective.”
Attribution + Cost = ROI, CPA
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Optimizing - Cross-Channel Allocations • Optimal spend against each marketing channel to maximize goal (e.g.
ROI)
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Optimizing - Sub-Channel Allocations
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Optimizing - Tactical Allocations
KEYWORDS
HIDDEN
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Summary
• Mobile/Tablet Traffic & Conversions Growing
• 1.4 Devices Per Conversion
• Mobile is Top of Funnel – Not Last Click or Last
Device 85% of time
• Use 1st party, 2nd party and 3rd party data to sync
cross-device
• Use Algorithm to allocate credit based upon
influence on conversion
• Determine optimal spend allocations
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