Overview of Attribution: Digital Attribution, Online to Offline Attribution and TV Attribution. We’ll cover the real-life examples, best practices, implementation details and results obtained.
6. 5
Attribution
The purpose of attribution is to quantify the influence each advertising
touchpoint has on a consumer’s decision to make a purchase decision, or
convert.
7. 6
”
Aim for better, not for perfect
Improving focus by increasing data quality, extending the scope of channel
measurement and media budget allocation.
It`s a process of technology and service which provides a clearer view on
marketing performance and enables value driven optimization.
11. 10
FABB
● is a constant process of media optimization
● assigns fractional contribution at granular and actionable level
● exports fractional contribution into bidding systems
12. Proprietary + Confidential
Process, products and features
Data driven modeling
(DDA + unified channel grouping)
X-Channel measurement
(auto tagging, utm`s, filters)
Automated bidding
(ROAS bid Strategy)
Data access / export
(unsampled report)
FABB
Import Conversion credits
(Offline Conversion Import)
14. Proprietary + Confidential
All signals per click are stored here
value
click IDs)
Unique ID
used by bid managers to track
ads and refer back in the
system
per ad / user / time / auctionURL?gclid=value
15. Proprietary + Confidential
Signals used in autom. bidding stored in a Click ID
+/-
XX%
Smartphone
Noon EST
Location
BrowserOS
Remarketing
list
Ad creative App
Language
Actual
query
Search
partner
Bid adjustment based on prioritized
combinations of signals
Click ID
Google
Stores auction
signals/info
16. Impact on ROAS performance
Pre Post
ROAS - SEA all
(Adwords All campaigns)
145%
(proportional increase)
77%
(proportional increase)
ROAS Top 10 generics
(Adwords Top 10 Generic campaigns)
Case study: https://goo.gl/r6RHgb
19. Context
153 stores in France
36 days of store data loaded in
Google Analytics
In-store buyers
with loyalty cards
A high % of transactions’ volumes
are made through the loyalty card
program
In-store buyers with loyalty
cards that log-in on the
website
Logged-in users represent a high % of
online traffic that can be matched
with offline transactions made with
loyalty cards
Online to Offline - Context & Methodology
1 2 3
18
22. 21
Login
Login
1 User
(persistent ID based)
› User-Centric Measurement
› Works on Web, mWeb & Apps and other devices
User ID: 4Q321
Cookie (clientID)
512955.2424231
Cookie (clientID)
123456.429834
Cookie (clientID)
123456.429834
User ID: 4Q321
23. 22
UserID Tracking in Analytics
user login
UserID (UID)
assigned
<UID
>
<UID
>
<UID
>
<UID
>
User ID
User ID
25. 24
Implementation guide: http://goo.gl/cMkBv7
UserID Tracking - Session Unification
PAGE 1 PAGE 2 PAGE 3
LOGGED INNOT LOGGED IN
1 SESSION
Login
With Session Unification enabled, all
login and pre-login hits in the same
session (only) are reported in the User
ID View
4
27. 26
Online to Offline Tracking in Analytics
Loyalty Card
purchase
Measurement Protocol
user login
UserID (UID)
assigned
<UID
>
<UID
>
<UID
>
<UID
>
User ID
User ID
28. 27
Measurement Protocol for Online to Offline
Measurement Protocol
allows you to send data to Google
Analytics from anything with
an Internet connection.
The data is sent via HTTP Requests,
a very common way to transfer
data online, to:
http://www.google-analytics.com/collect
http://ssl.google-analytics.com/collect
Name Parameter Example Description
Protocol
Version
v v=1 Protocol version - the value should be 1
Tracking ID tid tid=UA-123456-1 Google Analytics Property ID
User ID uid uid=123456 Persistent/authenticated user id, unique
to a particular user
Hit Type t t=event The type of interaction collected for a
particular user
30. Return on AdWords spend is multiplied by 6.4
when considering in-store transactions
Online return
on ad spend (€)
Online to in-store return
on ad spend (€)
x6.4
31. Proprietary + Confidential
More online preparation is done,
when the basket value is high
Low
28%
33%
39%
46%
58% 57%
66%
73%
87% 86%
x3
HighStore average basket value
O2S effect1
by basket size (%)
1
In-store buyers who visited the site before making a purchase (the standard lookback window of this study is 7 days
32. Proprietary + Confidential
Key Findings
44% x3
of in-store buyers visited
the site before making a
purchase
x6.4
Is where the O2S effect
is maximized
Mobile
O2S1
effect when
average basket value is
high
AdWords ROAS when
in-store sales are
considered
1
In-store buyers who visited the site before making a purchase (the standard lookback window of this study is 7 days)
Case study: https://goo.gl/sKw1Ii
34. How would you like to...
Identify TV Spot performance and optimise towards it?
TV Attribution helps you identify low performing
TV spot activity, and optimise its budget into
higher performing activity
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COSTEFFECTIVENESS