Google Analytics Konferenz 2018_Rock your Data - Aktiviere deine Daten_ Thomas Reichel (Commanders Act)

Rock your data
Aktiviere Deine Daten!
GAC 2018 | Thomas Reichel

Genesis of tracking
From 2000 to today
© 2018 - COMMANDERS ACT | GAC Workshop 2

Tracking in the early 2000s
THE FIRST STEPS OF MEASURERING
• Web Analytics (web log analyzer, pixel tracking …)
• To justify invoices for clicks and ad impressions
• Marketing teams can measure ROI for the first time
• Emails
• Appearance of massive SPAM
• Need to frame the use with laws
First sponsored links (23rd October 2000)
4© 2018 - COMMANDERS ACT | GAC Workshop

Tracking in the early 2000s
THE FIRST STEPS OF MEASURERING

Tracking in the years 2005 – 2010
TRACKING EVOLVES WITH THE MARKET
• First steps of mobile tracking
• Acceleration of advertising fraud
• First optimizing algorithms Youtube was born
(2005)
iPhone 1
(June 29th 2007)

Tracking since 2010
TRACKING (TOO) SYSTEMATIC?
• IOT (Internet Of Things)
• Massive collections
• Simplified identifications
• A safe harbor ... poorly protected
October 6th 2015

Tracking today
THE ERA OF BIG DATA & TRACKING THE INDIVIDUAL
• More and more data to process
• Emergence of technologies capable of handling large volumes
• An online-offline reconciliation more and more present (connected cash
registers and counters, loyalty cards, …)
• ... and a constantly growing ecosystem!

947
Lösungen
2014
100
Lösungen
2011
350
Lösungen
2012
1876
Lösungen
2015
3874
Lösungen
2016
5381
Lösungen
2017
© 2018 FJORD TECHNOLOGIES SAS 12© 2018 - COMMANDERS ACT | GAC Workshop

Tracking today
3 major challanges
© TAG COMMANDER

Tracking today
THE 3 MAJOR CHALLENGES
1. To address the right person…
• Who is the individual currently surfing my webpage?
2. … at the right time …
• What are the habits of this person?
• What is her/his current situation in life? (e.g.: birth of a child to come,
need to buy a new car, ...)
• How often have we already tried to address her or him? (capping)
• What does she or he plan to do next? (predictive and machine learning)
3. … with the right message
• On which device is she or he navigating?
• What does she or he like?(analytics and recommandations)
• What are the needs of users with a similar profile? (lookalike)

• How often have we already tried to adress her or him? (capping)
• What does she or he like?(analytics and recommandations)
Tracking today

• How often have we already tried to adress her or him? (capping)
• What does she or he like?(analytics and recommendations)
Tracking today

Harris Interactive 2016
For 59% of users digital advertising
does not match their needs and desires

One Channel Multi Channel Cross Channel Omni Channel
Tracking today
18
FROM ONE BUDGET PER CHANNEL TO OMNI CHANNEL
© 2018 - COMMANDERS ACT | GAC Workshop

19
Tracking today
NO OMNI CHANNEL
SEARCH
STORE
E-MAIL
SOCIAL
NATURAL / SEO
DISPLAY

MERGE YOUR DATA!
20
ACTIVATE YOUR DATA!

21
Merge your data!
WHY AND HOW?
Tag Management
(TMS)DMP / CDP
Customer Journey
Tracking & Attribution
CRM
Centric
Site
Centric
Ad
Centric
Cross
Device

44 countries | 900 stores | 11 websites

23
Merge your data!
CUSTOMER JOURNEY TRACKING & ATTRIBUTION
Tag Management
(TMS)DMP / CDP
Customer Journey
CRM
Centric
Site
Centric
Ad
Centric
Cross
Device

24
Merge your data!
OFFLINE
ONLINE

Cookie
ID
Touch
point
Event
type
Channel Source Device Date
ID 12345 1 View Display Adform Desktop 01/03/2018 16:09
ID 12345 2 Click Search Google Mobile 03/03/2018 22:34
… … … … … …
ID 12345 8 ORDER Desktop 18/03/2018 11:17
ID 12345
Merge your data!

Merge your data!

Merge your data!
Last touch Linear flat U model
Linear increasing Custom Data driven

28
Merge your data!
TAG MANAGEMENT SYSTEM (TMS)
Tag Management
(TMS)DMP / CDP
Customer Journey
CRM
Centric
Site
Centric
Ad
Centric
Cross
Device

29
Of the marketing tags are fired
based on a visitor profile.5 %
95 % Of all marketing tags are fired
with every page load.

30
Of your users are identified.
5 %
95 % Of your users stay anonymous.

31
Of your data is used at every
site view.5 %
95 % Of your data stays unused.

32
Of the relevant data is stored on
your servers5 %
95 % Of your relevant data is owned by
3rd party providers

Merge your data!
TMS
DMP / CDP
33
Data layer

Merge your data!

36
Merge your data!
DMP / CDP
Tag Management
(TMS)DMP / CDP
Customer Journey
CRM
Centric
Site
Centric
Ad
Centric
Cross
Device

ID 12345
DMP / CDP
CRM
TMS
Merge your data!
DMP / CDP

Cookie
ID
Touch
point
Event
type
Channel Source Interest Gender Device Date
ID 12345 1 View Display Adform Shoes female Desktop 01/03/2018 16:09
ID 12345 2 Click Search Google Shoes female Mobile 03/03/2018 22:34
… … … … … female …
ID 12345 7 Pageview Dresses female 18/03/2018 11:17
ID 12345 8 ORDER Store female Desktop 18/03/2018 11:17
Merge your data!
DMP / CDP

Demographic
Data
Technical
Data
Web
Analytics
Data
Lifetime
Value
Customer
Relation
Marketing
Channels
Post
Purchase
Client
Preference
Personal
Interest
Social
Media
COMMON
USER ID
Merge your data!
COMMON USER ID

40
ACTIVATE YOUR DATA!

Client
Was in funnel
>16€
<22 PV
>22 PV
<16€
Not
in funnel
#UseCase 1: Media Spend Optimization
BY USING PREDICTION TREES
Prediction TreeData collection Media Spend Optimization
89%
Purchase-
probability
••••••
••••••••••••••••••
•••
DMP / CDP

42
BY USING PREDICTION TREES

RESULTS
61% Conversions despite omitting retargeting
Advertising budget distributed to other (acquisition) channels
91% Reliability of predictions of the prediction tree

#UseCase 2: Offline / Online Integration
ONLINE
•••••••••••••••••••••••••••••••
••••••••••••••
••••
Dress
OFFLINE
••••••••••••••
•••••
•••••••••••••••••
••••••••••••••
••••••
••••••
DMP / CDP
BY USING A UNIQUE USER ID
44
•••• ••••

#UseCase 2: Offline / Online Integration
RESULTS AND ADVANTAGES
40 % of the online customer journeys have converted offline
Return frequency of visits to the stores and the website has almost
doubled
Increase in CLTV up to 30%

#UseCase 3: Customer Engagement
•••••••
•••••••
Dress
Shoes
••••••• ••••••
••••••
••••••
ONSITE PERSONALISATION

#UseCase 3: Customer Engagement
RESULTS AND ADVANTAGES
10 % uplift in conversions
20% faster coversions

Visitor
TCID:123
email-hash =
hzdfhdifuh7kjh49
TCID:123
Brand interest
Category interest
Bad Customers
…
Landing
on site
CRM database
1
2
3
User score = 3
User group = high CLV
Brand awareness = Brand
XYZ
4
#UseCase 4: Personalization of Campaigns
KNOWN CUSTOMER

Visitor
TCID:123
email-hash =
hzdfhdifuh7kjh49
TCID:123
Brand interest
Category interest
Bad Customers
Onsite behavior
Landing
on site
CRM database
1
2
3
4
KNOWN CUSTOMER

Visitor
TCID:123
Brand interest
Category interest
…
2
4
1
UNKNOWN CUSTOMER
50
3
5
TCID:123 TCID:123
SOCIAL
TCID:123
Brand interest
Category interest
Source = Social Media

CONECTORS TO SHARE USER SEGMENTS

1. USERS ARE NOT ADDRESSED THE RIGHT WAY
2. ONE DIMENSIONAL OM APPROACHES
3. MERGE YOUR DATA!
4. USE CJT + ATTRIBUTION
5. USE TMS
6. USE DMP / CDP
7. ACTIVATE YOUR DATA!
52
Learnings

THANKS FOR LISTENING
THOMAS.REICHEL@COMMANDERSACT.COM

Google Analytics Konferenz 2018_Rock your Data - Aktiviere deine Daten_ Thomas Reichel (Commanders Act)

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