Campaign monitoring, data quality, and action priorities.
MY CONTRIBUTION
Operational requirements, feature priorities, and workflow validation.
WHAT YOU WILL SEE
The project story and a portfolio demo using synthetic data.
Open the part you want to read.
Context & contribution+
01 / CONTEXT & ROLE
Plenty of data. A need for clear priorities.
In campaign operations, numbers that look normal may not provide enough information for a decision. Campaigns need to be reviewed against targets, remaining time, data quality, and earlier findings.
The project started with a need to find campaigns that need attention and explain why they should take priority. For example, a filter showing only Critical campaigns can miss At Risk campaigns that also need review.
Contribution and scope of my role
I translated operational needs into feature priorities, directed the review workflow, and validated dashboard behavior. Development was supported by AI. My contribution focused on campaign context, clear priorities, and a usable workflow.
Design decisions+
02 / DECISIONS THAT SHAPED THE PRODUCT
Every feature answers an operational question.
01 / MONITOR
What needs to be reviewed first?
Bringing Critical, At Risk, and No Spend together in a “Needs action” view while retaining the source risk classifications.
One starting point for priority reviews.
02 / INVESTIGATE
Can these numbers be relied on?
Distinguishing the snapshot date, campaign period, and serving status. Old data or a calendar status alone does not establish that ads are serving.
Context and limits of interpretation are visible.
03 / ACT
What should be checked next?
Providing reasoning, a review sequence, and follow-up notes. Recording an action must be distinguished from actually resolving an issue.
Follow-up actions have reasoning that can be reviewed.
Results & learnings+
05 / RESULTS & LEARNINGS
Results that can be explained. Lessons that can be applied.
Functional project outcomes
The priority view groups categories that need action, with sorting by campaign end date and risk.
Data freshness indicators distinguish snapshot information from the browser date.
Failed updates can retain the last valid data while showing its status.
This summary refers to documented behavior in the Campaign Health Monitor project. The separate demo shows part of the workflow. These outcomes describe product capabilities; business impact has not been quantified.
What I learned
A dashboard that supports decisions needs to explain what the numbers mean, how old the data is, and how it can be interpreted. Clear filters and information order matter as much as metric coverage.
Validation with operational scenarios helps uncover issues that may not be obvious at first glance, such as missed risk categories or a review status being mistaken for resolution.