Example approaches Unifying Marketing Data & Multi-Touch Attribution Analysis Many businesses on a free Google Analytics aren’t leveraging existing capabilities to its full potentials, and marketers feel it is not enough to support their MTA reporting needs. And most of the MTA solutions/services are a huge jump in terms of costs and capabilities for many businesses. The cost of enterprise-grade attribution tools are not reasonable for many businesses investing in a digital marketing campaign. Most of the tools that support MTA rely heavily on the marketer’s experience and leadership. The majority of the enterprise-grade MTA solutions require a large monetary commitment from setup to analyzing performance. Lacks the bridge between data and recommendations.

1. Date Prepared by
Media Mix Optimization
Unifying Marketing Data
& Multi Touch Attribution Analysis
2019/07/01 Principle Co., Ltd.

2. Copyright (C) Principle Co, Ltd. All Rights Reserved
Overview
• This document highlights some of Principle’s work in delivering custom databases that
power multi-touch attribution reporting.
• The examples highlighted in the slides are one of many Principle’s use cases in which we
helped clients optimize their digital marketing campaigns.
• The data in the dashboards is dummy data generated by Principle for demo purpose.
• This document is for marketers responsible for managing paid media budget and
execution.
1

3. Copyright (C) Principle Co, Ltd. All Rights Reserved
Current Landscape of Analytics
2
source: Bill Schmarzo “Big Data MBA” Course Curriculum
● BI is widely adopted to understand what happened in the past.
● More businesses are using Data Science to predict what will happen based on historical data trends.
● Businesses want to know what they should be doing.

4. Copyright (C) Principle Co, Ltd. All Rights Reserved
What is MTA？
MTA = Multi-Touch Attribution
A method of marketing measurement that evaluates the impact of each touchpoint in driving a
conversion, thereby determining the value of that specific touchpoint.
3
source: nielsen

5. Copyright (C) Principle Co, Ltd. All Rights Reserved
What is MMM？
MMM = Marketing Mix Modeling
• Technique which helps in quantifying the impact of several marketing inputs on sales or
Market Share.
• The purpose of using MMM is to understand how much each marketing input contributes to
sales, and how much to spend on each marketing input.
4
Example Images of MMM results

6. Copyright (C) Principle Co, Ltd. All Rights Reserved
B2C Attribution Model Examples
5
First Touch
Last Touch
Linear
U Shape or Position Based
Time Decay
Custom
X% X% X% X%

7. Copyright (C) Principle Co, Ltd. All Rights Reserved
100%
B2B Attribution Model Examples
6
Examples of B2B Sales & Marketing Milestones
B2B Attribution Models:
Conversion value for each milestones are evaluated
First or Last Touch
100%or
OR
First Touch → Lead Generated → Opportunity Created → Deal Closed
U-Shaped
W-Shaped
Custom or Full Path

8. Copyright (C) Principle Co, Ltd. All Rights Reserved
Current Landscape: B2B Marketer’s Adoption of Attribution
7
• Roughly 60% of B2B Marketers are not using multi-touch attribution model
• About 28% of B2B marketers don’t even have an attribution model.

9. Copyright (C) Principle Co, Ltd. All Rights Reserved
Current Landscape of MTA
• Advanced attribution tools are making it easier for businesses to bring together the data, and
analyze performance. However, marketers also need the right leadership and partner.
• According to Google’s survey, only 17% of advertisers surveyed said they are looking at the
performance of all their digital channels together.
• Marketer needs an attribution model beyond last touch.
• To address the shortcomings of last-click, many marketers are building custom MTA models
either through 3rd party tools or internal analytics resources.
• MTA is key to gain visibility into marketing impact within short time frame. The need for
marketing agility favor the models that are immediately actionable.
8
Source: Advertiser Perceptions/Google, Measurement Survey, U.S., September 2017, (n of 197 marketer and agency contacts who are fully involved in media brand selection decisions)

10. Copyright (C) Principle Co, Ltd. All Rights Reserved
Challenges with MTA tools
9
$ $$$$
Data Scientists / PhDSelf Service Support via
Call Center
Domain experts
Very long
time to value
Resource
intensive
• Many businesses on a free Google Analytics aren’t leveraging existing capabilities to its full potentials,
and marketers feel it is not enough to support their MTA reporting needs.
• And most of the MTA solutions/services are a huge jump in terms of costs and capabilities for many
businesses.
• The cost of enterprise-grade attribution tools are not reasonable for many businesses investing in a
digital marketing campaign.
• Most of the tools that support MTA rely heavily on the marketer’s experience and leadership.
• The Majority of the enterprise-grade MTA solutions require a large monetary commitment from setup
to analyzing performance. Lacks the bridge between data and recommendations.

11. Copyright (C) Principle Co, Ltd. All Rights Reserved
How we are thinking about attribution reporting and approach
10
$ $$$$
Data/Marketing experts
Very long
time to value
Resource
intensive
• Will work with your existing Google Analytics data.
• Help business clean up and normalize their Google Analytics and ad tech tracking for good
data foundation for MTA reporting.
• Custom data warehouse, queries, attribution model, and reports. (Will work with your data
warehouse)
• Client can retain marketing agility, test and learn before committing to a very expensive
solution and services.

12. Copyright (C) Principle Co, Ltd. All Rights Reserved
Building Custom Database for Attribution Analysis
11
Client’s Challenges
● Commercial real estate company
targeting millennials, wanted to
establish a CDP (customer data
platform) to unify data to better
understand how effective the
campaigns reached and influenced the
target audience.
Principle Delivered
● Unified Web Analytics, Digital
Responses from various marketing
sources, ESP, CRM, Store Management
System data to track marketing
influence at an individual level.
○ User level data being managed
○ Tracked Traffic sources by action
○ Offline conversion events
○ User device type is tracked

13. Copyright (C) Principle Co, Ltd. All Rights Reserved
Example Approach
12
● Integrated data into BigQuery and used Tableau to provide dashboards and insights
● Client had the ability to use the dashboards via Tableau
● Client also has the ability to migrate the DMP into their environment. Client owns the data.

14. Copyright (C) Principle Co, Ltd. All Rights Reserved 13
Result
* Charts shown here only represent a part of the entire project
● Client is able to visualize the data by
individual users and the marketing channel
influencing converting users
● Client understands the mobile’s reach and
channel mix that influenced the user’s path
to conversion
● Client is now able to take further marketing
action and proactive response based on
behavior, event, intent

15. Copyright (C) Principle Co, Ltd. All Rights Reserved
Use Case // Attribution Model & Reporting
14
Client’s Challenges
● Lack of visibility in marketing performance
outside of last click attribution
● Assess ways to optimize marketing budget
allocation
Our Approach
● Assessed KPI using additional attribution
models
● Based on the findings, Principle recommended
to readjust spend on tactics that aren’t driving
results in both first & last click influenced
conversions (Quadrant 3 in right image)

16. Copyright (C) Principle Co, Ltd. All Rights Reserved
Old Approach To Tracking Marketing ROI
15
*Example Image Only

17. Copyright (C) Principle Co, Ltd. All Rights Reserved
New Approach To Managing Marketing, Customer Data,
Integrate Data, Measure ROI, and Activate Campaigns
16
Arm Treasure Data is Principle’s CDP partner
A “Customer Data Platform” is a marketer-controlled integrated customer database that can support coordinated
programs across multiple channels. -- Gartner

18. Copyright (C) Principle Co, Ltd. All Rights Reserved
User Level MTA Analysis
Google Analytics + Tableau Prep + Tableau Desktop

19. Copyright (C) Principle Co, Ltd. All Rights Reserved
Data Preparation: What analysis data could look like
18
User ID
Campaign Response Date
Traffic Source, medium, campaign detail
Attribution Model
Output Data

20. Copyright (C) Principle Co, Ltd. All Rights Reserved
Data Preparation workflow with Tableau Prep
19
Transaction Data
User level touch point data
with sources
Channel and campaign level
spend data
Defined attribution
models and it’s logic
Aggregated data for
analyzing the output with
Tableau

21. Copyright (C) Principle Co, Ltd. All Rights Reserved
Comparing KPI per Attribution Model
20
First Touch Last Touch Custom Model
• First and last touch models are available in GA reporting, but depending on business and it’s industry, custom model
may be the way to go.
• There are many benefits for analyzing MTA using Tableau due it’s flexibility and customizations working.
Filter: # of user
touches
Filter: User ID

22. Copyright (C) Principle Co, Ltd. All Rights Reserved
Example of comparing MTA models
21
First Touch
Last Touch
Custom Model
DateTime

23. Copyright (C) Principle Co, Ltd. All Rights Reserved
Example of MTA Dashboard w/ Custom Attribution
22
Last TouchCustom Model
Same Spend
across channels
CPA results are
different per
model
• Our client can now see ROI per marketing channels and tactics in a way they couldn’t see before.

24. Copyright (C) Principle Co, Ltd. All Rights Reserved
Optimizing Media Mix with Linear Regression
BigQuery ML + Tableau Prep + Tableau Desktop

25. Copyright (C) Principle Co, Ltd. All Rights Reserved
Data Preparation Example Using Tableau Prep
24
Data manipulation
- Change string to dates
- Remove unnecessary fields
- Grouping, etc.
Join many data files
to create one
aggregated dataset.

26. Copyright (C) Principle Co, Ltd. All Rights Reserved
Data Preparation Example Using Tableau Prep
25
CSV file output for
Google BigQuery
Hyper extract file for
analyzing in Tableau
Normalized data output for analysis

27. Copyright (C) Principle Co, Ltd. All Rights Reserved
Building Machine Learning Model in Google BigQuery ML
• BigQuery allows us to run standard SQL against the dataset and build machine learning model.
• Because we don’t have to move data across different environments, speed to building model is fast.
• Supported models are: Linear regression, logistic regression, K means clustering, and TensorFlow model import
26

28. Copyright (C) Principle Co, Ltd. All Rights Reserved
Evaluating the Model
• It is important to evaluate the model, using ML.EVALUATE function.
• We’ll evaluate using R-Square score. Shows how fit the model is if it’s over 0.80 (best is over 0.95).
27

29. Copyright (C) Principle Co, Ltd. All Rights Reserved
Use the model and build query that predicts the outcome
• Running this model/query in
Tableau
• Generate query or data to run as
an input for modeling the
prediction.
• Define what your inputs would be
(example here is spend data).
Using parameter so we can
adjust the spend from Tableau.
28

30. Copyright (C) Principle Co, Ltd. All Rights Reserved
Spend allocation and simulating the results
29
Spend / Transaction
$4.20 (Predicted) Vs. $4.10 (Actual)
Campaign
Spend as inputs

31. Copyright (C) Principle Co, Ltd. All Rights Reserved
Spend allocation and simulating the results
30
Spend / Transaction got worse
$4.60 (Predicted) Vs. $4.10 (Actual)
Significantly
Increased spend

32. Copyright (C) Principle Co, Ltd. All Rights Reserved
B2B

33. Copyright (C) Principle Co, Ltd. All Rights Reserved
Tracking & Integration: GTM + GA + CRM + CDP + DMP
32
Website
+
GTM
User_ID
Global_ID
CRM
Customer data
Demo / Profile
User_ID
Client_ID
CampaignID
DMP/DSP
Campaign Activation
Customers
Client ID: 1234.XXX
User ID: ABC.123
1
2
3
4
Customer Data Platform
Global_ID
per User_ID
Customer
Segment
1. Google Tag Manager (GTM) manages digital touchpoints and behavior tracking. Conversion event generates User_ID.
2. CRM data with customer detail and demographic info captures and stores User_ID.
3. Customer Data Platform (CDP) manages Global_ID, a universal ID tag can identify a user and their User_ID.
4. CDP integrates with the DMP/DSP where Ad activations take place with improved ad targeting.
5. Use Tableau to build custom attribution reporting.
Reporting
5

34. Copyright (C) Principle Co, Ltd. All Rights Reserved
Example of B2B MTA Dashboard
33
Filter data by
specific MTA
model
FT: First Touch
LC: Lead Creation
Opty: Opportunity Created
CW: Closed Won
Minor: Minor Touch in
between key milestones

35. Copyright (C) Principle Co, Ltd. All Rights Reserved
Example of B2B MTA Dashboard
34
• Business can compare MTA models and evaluate KPI (e.g. Conversion Amount, CPA, etc.)
Filter and compare data
by marketing channels
Filter data by
User levelFilter data by
specific MTA
model

36. Copyright (C) Principle Co, Ltd. All Rights Reserved
End Of Document