Digital & Analytics Dialogue UK event, 26 Apr. 2018 Pestana Chelsea Bridge - London, UK Website: http://goo.gl/kbDfkW Sjaun goes through the engineering projects to build a framework and infrastructure to overcome the most frustrating issues his data science teams experience on marketing campaign projects (data preparation and automating activated data across 3rd party sites). Agenda: • Define and compare - Marketing Mix, Attribution Modelling & Customer 360 degree view strategy aka Customer Journey Analytics. • Outline the value Customer 360 degree view strategy engineering brings to both models by improving data quality matching off-site web data. • The complexity of tracking customer journeys in Customer 360 degree view strategy. • Explain the engineering solution and a quick example.

1. Attribution Modelling or Customer
360⁰ view engineering:
Which comes first & how to automate
for intervention strategies
Sjaun Wong – 26 Apr. 2018
Digital & Analytics Dialogue UK

2. • Former Global Head of Business Insights for GVC, a FTSE 100
gaming company in the UK.
• Brands: Ladbrokes, Coral, Party Poker and many more.
• I was responsible for the data architecture, visualisation and
data transformation strategies.
SJAUN WONG
Data Strategist
Previous roles for Comic Relief, Havas (advertising),
Betfair, CallCredit and Rightster (Brave Bison)
Highlights:
• Rightster founding team (14) grew to 224 staff and IPOed in 2.5 yrs.
• I’ve worked in both analytics, web development team roles,
advertising and SEO agency roles – experienced with multiple CRM
& marketing tools and Data Management Platforms.
@SjaunWong
SjaunWong

3. Agenda
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SjaunWong
• Define and compare - Marketing Mix, Attribution Modelling &
Customer 360 degree view strategy (C360o) aka Customer Journey
Analytics.
• Outline the value C360o engineering brings to both models by
improving data quality matching off-site web data.
• The complexity of tracking customer journeys in C360o.
• Explain the engineering solution and a quick example.

4. Gartner Forrester
Attribution
Modelling
Individual-level data rather than
aggregated data and as a result is
usually confined to digital marketing
channels. 1
The purpose of attribution is to give fair
credit to the tactics – placements, creative
ideas, formats – that work. 3
The practice of using advanced
statistical approaches to allocate
proportional credit to marketing
communications and media
activity across all channels, which
ultimately leads to the desired
customer action. 4
Gartner Forrester
Marketing
mix
modelling
Aggregate data, not user-level data, and
explores the impact of a wider range of
channels and factors, including non-
digital media such as broadcast television
and radio. 1
The process of using statistical
analysis to estimate,
optimize, and predict the impact of
paid, owned, and earned
multichannel marketing tactics on
future business revenue
or any other key metric. 2
Defining the challenge
1 Source: "Clarify marketing impact with attributions and marketing mix modelling", Chris Pemberton, Gartner, Inc., 6 Jan. 2017
2 Source: "Webinar: How Agencies Play A Role In Attribution", Tina Moffett and Ari Osur, Forrester Research, Inc., 8 May 2012
3 Source: Blog article: "Is Accurate Attribution Even Possible?“, Martin Kihn, Gartner, Inc., 29 Sep. 2016
4 Source: Blog article: "Just published: The Forrester Wave: Cross-Channel Attribution Providers“, Tina Moffett, Forrester Research, Inc., 7 Nov. 2014
5 Source: "Market Guide for Customer Journey Analytics", Gartner, Inc., 30 August 2017
Gartner
C360⁰ view
engineering
4 distinct phases of journey analysis:
• Gathering
• Connecting
• Visualizing
• Acting
Customer journey analytics (Cust. 360⁰
view) is the process of tracking and
analyzing the way customers use a
combination of available channels to
interact with an organization
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5. What value does C360⁰ view engineering bring
to Marketing Mix and Attribution Modelling?
Data time-
range
Long-term:
Months – years.
Short-term:
Days-months.
Short to Medium-term:
Weeks-months.
Prediction
accuracy
Better for evaluating
overall media ROI.
Lacks insight into
customer off-site
transactions.
Improves unified MMM by
delivering ROI per user (ad)
impression.
Data
collection
time-lag
Months – years. Hourly-daily. Usually longer than
Attribution Modelling but
less than MMM.
Marketing
Automation
Media macro allocation
– no automation.
Channel allocation –
no automation.
Digital User allocation
+ campaign automation.
Marketing mix
modelling
Attribution Modelling
(siloed approach)
C360⁰ view Attribution
modelling
Channel /
User Scope
Macro-channel:
Economic/Market
factors, media mix &
consumer demand KPIs
e.g. sales.
Digital Micro-channel:
Usually cookie-based
/ device IDs not
customer ID.
Unified approach:
Multi-device & cookie
unification matched to
customer ID.
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6. WebsiteAffiliates
Mobile
Retargeting (Search & Display)
Display Ads App Store
Search
Web
Personalisation
Customer
Retention
Optimisation
Loyalty Programmes
Word of
Mouth
Sample Customer Journey
Search EngineSearch Ads
Social Ads
Reviews
Comparison sites
News / Blogs /
Social Media
Social
Networks
Reviews
E-commerce
PR
Radio,
TV, Print
Customer
Service
PURCHASE
A typical customer journey
MACRO: MARKETING MIX MODELLING
CUSTOMER 360 DEGREE ENGINEERING = CUSTOMER JOURNEY ANALYTICS
ATTRIBUTION MODELLING CUSTOMER DATA
–
overlaying the approaches
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7. Customer journeys are complex
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8. Wouldn’t it be nice to have an ID Graph that
joins 3rd party Users IDs to Customer IDs?
Customer journeys are complex
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9. Digital Marketing Landscape
Source: "Gartner Digital Marketing Transit Map", Gartner, Inc., 2 Feb. 2018
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Without an ID graph – this compounds the complexity!!

10. Framework for an ID Graph:
connecting users’ cookies and device IDs per session
Unified User ID
cookie / device per session unified
deterministically
first and then
probablistically
2nd Party Data
1st Party Data
DWH / ERP data
IoT
CRM
Email
SMS
Digital Analyics
Push notifications
Social Media Analytics
DSP / Ad Exchanges
Publisher Ad Private Marketplace
Data brokers
Marketing Channel activation
targeting per Customer
DSPs
Email
Mobile Push
Notifications
Social
Personalisation
Affiliates
CRM
Test & Learn
SMS
Attribution Modelling &
SegmentationAffiliates
Partners
3rd Party Data
@SjaunWong
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11. How does this ID Graph work in practice –
a simple example
Tag Manager
Machine Learning /
Deep Learning
DoubleClick
Campaign Manager
• Match GID
• Return Ad Bid Price
• Creative & Ad position
• URL
• Targeted keywords
Web Analytics
• Match GID
• Page URL
• Conversion clicks
• AB/Testing
• Site Personalisation
Google Ad
pixel / tag
Google Cookie
Matching Service
Blog Advertiser Website
Customer 360 Data
Storage Repository

12. Thanks!!
Email: sjaun@ukprofessionals.com
@SjaunWong
SjaunWong
Sjaun Wong
Data Strategist & Marketing Activator