Your job feed is the foundation of every campaign. If the data going in isn’t complete or well-structured, it affects everything, from ad quality to audience targeting.
That’s where the Data Source Recommendations dashboard comes in. It gives you a clear overview of the health of your job feed and offers actionable suggestions to improve its structure and performance.
In this article, we will walk you through what each section means, why it matters, and how to turn insights into results.
✅ Profile Matching Rate
This shows how many job titles in your feed are automatically matched to the correct candidate profiles using AI.
Why it matters
When a job title is matched to the right profile, Wonderkind can:
Target the right audience
Select relevant assets from your Asset Library
Improve ad quality and performance
Example: In the screenshot, 194 profiles were successfully matched, resulting in a 100% profile matching rate.
👉 Tip: If some jobs are unmatched, click See unmatched jobs to review and fix them.
📊 Quality Score
The quality score checks whether your job feed includes all the required fields needed to run campaigns effectively.
Required fields:
Job title
Company name
Reference number
URL
Location or Country
Recommended field:
Language (optional but advised for correct language targeting)
👉🏻 Why it matters: Missing required fields can prevent your jobs from being processed or targeted correctly.
Example: In the screenshot, 0 jobs are missing required fields, so the score is 100%.
🚀 Optimisation Score
This score shows how well your job feed uses recommended fields, which aren’t mandatory, but dramatically improve ad performance.
Recommended fields
Description – Essential for profile matching and creative relevance.
Salary – Can be used in ads and overlays to increase interest.
Education – Filters for specific qualifications.
Experience – Helps refine by seniority level.
Postal Code – Improves location-based targeting within cities.
👉🏻 Why it matters: The more data you include, the better we can tailor your ad creatives and target the right candidates
📌 Additional Insights
Beneath your main scores, you’ll also see extra data points to give you a snapshot of your feed’s diversity and coverage:
Minor groups – Number of unique role categories present.
Companies – Number of different employers in your feed.
Locations – Total count of unique job locations.
These insights help you assess whether your campaigns are covering the right range of roles, locations, and clients.
🛠 How to Use These Insights
Once you've reviewed your dashboard, here’s how to take action:
1️⃣ Check profile matching
Ensure most (if not all) jobs are mapped to the correct candidate profiles for better targeting and asset usage.
2️⃣ Fix required fields
Review any jobs missing critical information like title, company, or location. These must be present for your campaigns to run.
3️⃣ Improve your optimisation score
Enrich your feed by adding recommended fields such as salary, education, and experience. These help generate higher-quality, more compelling job ads.
4️⃣ Use postal codes
Wherever possible, include postal codes to enable more precise location-based targeting—especially useful in larger cities.
📌 Summary
The Data Source Recommendations dashboard gives you a snapshot of:
✅ How well your jobs are matched to candidate profiles
📄 Whether your required job fields are complete
🚀 Opportunities to improve ad performance with additional data
By following the dashboard's suggestions, you can:
Deliver more relevant and engaging job ads
Improve campaign targeting
Attract higher-quality candidates