The Digital Marketing tech stack is expending every day. With so many tools at our fingertips we have the potential to accomplish more than ever before. But hidden issues with the people, processes, and technology in your dept can lead too lost time, lost opportunity, and invalid decisions. Using real world examples from leading companies around the globe, we'll teach you how to identify gaps in your data landscape, find solutions, and turn them into opportunities using resources you already have.

1. KEYNOTE
Cody Crumrine
PRINCIPAL CONSULTANT
DATA APTITUDE
How to Master
Your Marketing Data
PHILADELPHIA, PA ~ MAY 2 - 3, 2022
DIGIMARCONMIDATLANTIC.COM | #DigiMarConMidAtlantic

2. Master Your Marketing Data
Analyzing Accuracy, Efficiency and Trust to identify
and prioritize problems and solutions.

3. Goals for this session:
• Provide a framework for identifying issues.
• Provide a method for prioritizing what to solve and how.
• Focus on what you can do with existing resources.

4. Who's going to use this?
You can get start applying these evaluation techniques with resources you already
have. You'll need to pull together a team with at least these three roles:
• The Leader: Brings vision, organizes efforts, own outcomes,
communicates (up and down hill).
• The Executor: Wrangles help from others, conducts
interviews/calls/surveys, gathers information.
• The Champion: VP/Director position or higher, advocates for
making data evaluation a priority, enables resources to dedicate
time.
• All Three: Work together to analyze information to drive decision
making.

5. Efficiency, Accuracy and Trust
When trust is low – accuracy and
efficiency are in opposition.
When trust is high, accuracy and
efficiency can co-exist.

6. Efficiency, Accuracy and Trust
• Efficiency and Accuracy are often symptoms.
• We'll use those to identify issues in our data pipeline.
• We can often get more "bang for our buck" fixing trust issues.
• We'll use trust to prioritize problems and solutions.

7. Real World Scenarios
• The recruitment marketing department at "Blue Lizard Logistics"
• (Made up company and team – examples from real past projects).
• We're going to walk through a three-phase process:
• Building a Data Landscape
• Using Efficiency and Accuracy to identify issues
• Using Trust to prioritize solutions

8. Building a Data Landscape
• Take stock of People, Process and Technology.
• Keep it simple, use whatever tools are easy.
• Focus on a good representation, not an exhaustive list.

9. Data Landscape - Technology
• Project Management Software: Planning new Campaigns
• Ad Serving Platform: Used to deploy paid search and display ads
• Analytics Software: Used to report on website user activity
• Hiring Software: Used to manage applications/interviews/offers
• Data Management Platform: Used to
• Collect Campaign Metadata via forms
• Generate Tracking Codes for ad links
• Move data between platforms

10. Data Landscape - Processes | Automated
• Hiring -> Analytics: Direct integration IDs visitors who have applied
• Hiring -> DMP -> Analytics: Data about step in hiring process
extracted from Hiring system and uploaded to Analytics
• Ads -> Analytics: Analytics Software detects tracking codes in ad links
• DMP -> Analytics: Campaign meta data collected and uploaded to
Analytics.

11. Data Landscape - Processes | Manual
• Campaign Planning: Team plans campaigns, documents plans and
assigns tasks in PM software.
• Campaign Tagging/Approval: Team members enter campaign meta
data in DMP and receive tracking code:
• Approver must sign off on all submitted data before tracking codes are
generated.
• Tagged links with Tracking Codes used in Ad Platform.
• Ad Performance Reporting: Team combines performance reports from
Ad and Analytics platforms to calculate key metrics by campaign.
• Viable Candidate Reporting: Team combines report from analytics
platform and hiring platform to report on quality candidates delivered.

12. Data Landscape - People
For each platform and process identify the following:
• Users: People who follow a process or submit information.
• Owners: People who manage a platform or oversee a process.
• Consumers: People who receive reports or other outputs from a
process or platform.
(These can be individuals, roles or groups)

13. Data Landscape - Summary
Goal is to map out a data landscape that details the following:
• Technology: Individual software/platforms used.
• Process: Manual and automated ways that data is collected, moved
or evaluated.
• People: Users, owners and consumers for each platform and
process.

14. Evaluating Accuracy & Efficiency
• Simple conversations are the best way to get insights.
• People will be happy to share their struggles!
• Owners and Users will often have efficiency insights.
• Owners and Consumers will often have accuracy insights.

15. Example Accuracy & Efficiency Issues
• Efficiency – The campaign approval process is slow and takes up a lot
of time for both team members and approvers.
• Efficiency – The Viable Candidate report involves a large amount of
manual effort and is frequently late.
• Accuracy – The visitors reported by the Ad Platform and by the
Analytics platform almost never match up. This makes these numbers
difficult to act on.

16. Evaluating Trust
• For Efficiency and Accuracy issues you've identified, look for
underlying trust issue.
• Trust issues may be harder to identify.
• Start by asking "why does this process exist".
• Dig deeper with questions like:
• Do I trust the users of this process to complete it correctly?
• Do I feel competant using this software?
• Do I believe the current process accurately reflects my performance?
• Would I make decisions based on this report without checking another
source?
• Surveys can be helpful with large teams.

17. Trust Issues - Examples
• Efficency Issue: The approval workflow is a burden.
• Treat the Symptom Solutions:
• Make the approval workflow simpler?
• Add more approval staff?
• Underlying Trust Issue:
• Leadership doesn't trust submitters to enter correct info.
• Approvers check metadata against plans in PM tool. (often wrong)
• Trust Issue Solution:
• Build integration between DMP and PM tool.
• Allow users to enter less information.
• Saves time and decreases chance of error.

18. Trust Issues - Examples
• Efficency Issue: The Viable Candidate report is time consuming and
often late.
• Treat the Symptom Solutions:
• Automate the data collection and report creation.
• Underlying Trust Issue:
• Automated solution through analytics integration already existed!
• Didn't account for unaccepted offers.
• Trust Issue Solution:
• Adjust Hiring -> DMP -> Analytics process to create/track a new "highest
value" field.
• Significantly smaller time/cost investment.

19. Trust Issues - Examples
• Accuracy Issue: The Ad Platform and Analytics Platform numbers
don't match up.
• Treat the Symptom Solutions:
• Reconcilliation Reporting
• Use data from only one platform
• Underlying Trust Issue:
• Analytics platform was well trusted and understood.
• Ads platform was newer, less well understood, less of a relationship.
• Trust Issue Solution:
• New Training, better engagement?
• Consider other ad platforms?

20. Using Trust to Prioritize
• Viable Candidate Report
• Biggest impact with least cost/time
• Act on this first
• Campaign Approval Process
• Big impact
• Higher investments
• Plan an prioritize for later.

21. Summary
• Take stock of your data landscape
• Identify Technology
• Identify Automated & Manual Processes
• Identify Users, Owners and Consumers
• Work with Users, Owners, and Consumers to:
• Identify Accuracy and Efficiency issues
• Analyze Trust of people, processes and technology
• Use Trust issues to:
• Identify solutions that go beyond treating symptoms
• Prioritize efforts with the best impact/investment ratio.

22. Thanks for listening!
Cody Crumrine
ccrumrine@data-aptitude.com