This document discusses 5 case studies of implementing data solutions: (1) linking CRM and web data to optimize marketing, (2) creating personalized dynamic remarketing banners, (3) integrating web sales into an existing database, (4) matching online leads to offline sales, and (5) infusing external weather API data into analytics. Each case study outlines the business need, solution, challenges, and multi-step architecture to understand, build, run, analyze, and optimize the data implementation through various platforms and tools. The document emphasizes starting small, leveraging existing infrastructure, and building solutions in phases to close loops between online and offline data sources and channels.

1. MAKING
DATA
YOUR
BEST
ASSET

2. Why should you care about user data?
Digital
advertising
Digital
business
intelligence
Data
Synergies
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3. Traffic acquired
Engaged visitors
Converted visitors
Why is data important for advertisers?

4. Turning behavior into data points
Building anchor points,
gates or micro-conversion
Traffic acquired
Engaged visitors
Converted visitors

5. Closing the loop, optimizing acquisition
Traffic acquired
Engaged visitors
Converted visitors

6. Data in an digital marketing world today
Digital
advertising
Digital
business
intelligence
Data
Synergies
</>
Online

7. Data in an digital marketing world tomorrow
Digital
advertising
Digital
business
intelligence
Data
Synergies
</>
User

8. Relying on a model

9. The implementation model
#1 Understand
#2 Build
#3 Run
#4 Analyze
#5 Optimize

10. The implementation model
#1 Understand
What’s already in place?
What’s meaningful to your user?
What’s meaningful to you?
Who are the stakeholders at the company?

11. The implementation model
#1 Understand
#2 Build
What does your architecture need to look like?
What are your touch points?
Which phases do we need?
What is critical and what is luxury?

12. The implementation model
#1 Understand
#2 Build
#3 Run Validation period
Does the data come in correctly?
Can we identify the noise?

13. The implementation model
#1 Understand
#2 Build
#3 Run
#4 Analyze What works?
What doesn’t work?
Where do users encounter issues?
Drill down in the data

14. The implementation model
#1 Understand
#2 Build
#3 Run
#4 Analyze
#5 Optimize
Apply our findings
Iterate
Initiate next phase?
Did the technology evolve?

15. #1 - CRM TO WEB
Industry
Retail
What the business needed
Increase growth by optimizing the touch points for high-end
customers.
Solution
Apply external (rich) CRM segments to acquisition channels in order to
optimize touch points for high AOV customers and increase buying
rate.
Challenges
Acquisition channels cannot be linked to rich CRM data directly.

16. #1 - CRM TO WEB
Solution Architecture
#1 – User signs in
#2 – Backend and CRM interact
#3 – Values are pushed in DataLayer
#4 – Map customer segments as custom dimensions in Universal Analytics
#5 – Segments can be transformed into remarketing lists for acquisition channels

17. #2 – 121 Dynamic RMKT
Industry
Automotive
What the business needed
Introduce new potential customers to the brand.
Solution
Create an engaging and new format fuelled by market data that
stands out compared to the other online advertising formats.
Challenges
Create market intent to offer data
Create architecture to customize banners in real time

18. #2 – 121 Dynamic RMKT
Solution Architecture – Phase 1
#1 – Child subscribes to the campaign, providing info on parents
#2 – Link is created
#3 – Email is sent to parent
#4 – Upon clicking the link the parent is directed to the landing page
#5 – Based on the link the information is pushed to the DataLayer & picked up by GTM
#5 – A cookie is pushed containing all of the URL parameters

19. #2 – 121 Dynamic RMKT
Solution Architecture – Phase 2
#6 – Create banners in DoubleClick Studio (platform 1)
#7 – Create audiences in DoubleClick Campaign Manager (platform 2)
#8 – Retargeting campaigns managed by DoubleClick Bidding Manager (platform 3)
#9 – Parents see personalized banners populated with personal information
Studio
DCM
DBM

20. #3 - WEB TO DATABASE
Industry
Travel
What the business needed
360 degree overview of where sales are being generated.
Solution
Integrate online sales data in the existing database.
Challenges
Match up with the data format used in the existing database
infrastructure.

21. #3 - WEB TO DATABASE
Solution Architecture
#1 – Job runs at 2AM and exports the data to a designated intermediary server
#2 – Job runs at 5AM and picks up the data, transforms it and infuses it in the database
#3 – The client has all necessary data available in the existing infrastructure
#4 – Databse serves as a one-stop-shop to report on sales channels + act accordingly

22. #4 - OFFLINE TO WEB
Industry
Insurance
What the business needed
Close the loop from online lead generation to signed contract offline
Solution
Create unique identifier to match online leads with offline sales
Ensure confirmed offline sale data is uploaded to the web analytics platform
Challenges
Create unique identifier
Create technology to upload offline data and match it with online leads

23. #4 - OFFLINE TO WEB
Solution Architecture – Phase 1
#1 – Implement Enhanced Ecommerce / TransactionID (Contract#)
#2 – Implement ClientID in order to create a unique key value
#3 – Upon online conversion this information is stored in GA and is considered a lead

24. #4 - OFFLINE TO WEB
Solution Architecture – Phase 2
#4 – Create new ‘offline conversion’ goal in GA
#5 – The Contract# is passed on to the broker
#6 – Contract is signed and Contract# is confirmed
#7 – Retrieve ClientID of contract signed based on Contract#
#8 – Upload ClientID and the Contract# to GA through the Measurement Protocol
#9 – The lead is considered as a sale and the loop is closed.

25. #5 – INTRODUCTION OF EXTERNAL API DATA
Industry
Retail
What the business needed
Investigate if external factors are impacting online sales and how this
knowledge could be leveraged
Solution
Infuse weather data from an external API into Google Analytics to verify if
behavior changes given weather circumstances
Challenges
Determine the impact of weather on user behavior and optimize digital
acquisition channels

26. Solution Architecture – Phase 1
#1 – Push the current weather state of the user’s location to the DataLayer
#2 – The weather state is picked up through GTM and pushed to GA
#3 – A custom dimension is created so the weather state display in the reports
#5 – INTRODUCTION OF EXTERNAL API DATA

27. Solution Architecture – Phase 2
#4 – Collection & analysis of the data in order to identify different scenario’s
#5 – Define bid modifiers and campaign states according to the different scenario’s
#6 – Automate the actions required in acquisition platforms based on the different scenario’s
through custom scripting
#5 – INTRODUCTION OF EXTERNAL API DATA

28. Key takeaways
#1 Understand your objectives & goals
#2 Understand the infrastructure in place
#3 Map your needs versus the technical requirements
#4 Start small & build upon
#5 Leverage your data