Wanderly is a travel healthcare staffing marketplace: 663,000+ registered candidates, 500+ agencies. Recruiters there had one recurring question: of the thousands of candidates in my list, who do I call first?
We built a Readiness Score to answer it. This is the version-one model, the reasoning behind its shape, and what we deliberately left out.
## Three signals, not eight
The full design had eight signals: recency, job views, clicks, applications, email and SMS engagement, an opt-out penalty, campaign participation, profile completeness. It was a good model. We shipped three:
- Campaign engagement, 50%
- Application activity, 30%
- Profile completeness, 20%
Fewer signals meant the score was explainable to a recruiter in one sentence, and it meant we could validate the model against sixty days of real pilot data before adding anything.
## Recency decay
Campaign engagement is decayed by recency: activity in the last 30 days counts at 1.0x, 31 to 90 days at 0.7x, 91 to 180 days at 0.4x, and nothing beyond the configured window. A reply from last week and a reply from five months ago are not the same signal.
## Hard overrides
Some things are switches, not inputs.
- Opt-out (a STOP reply): score forced to 0 and the candidate is removed from every campaign audience, regardless of any other signal.
- Twelve months of total inactivity: score capped at 25.
- New candidate with no history: scored on profile completeness only, shown with a New badge.
Compliance sits outside the model. A weighted penalty can be outvoted by other signals; a hard override cannot.
## Tiers
Hot 70 to 100. Warm 40 to 69. Cold 20 to 39. Inactive 0 to 19. Minimum campaign-eligible score: 20. Weights are locked for v1; admins configure the time windows and the tier thresholds.
## What the recruiter sees
The number, a colour, and a breakdown: Campaign engagement 42/50, Application activity 24/30, Profile 18/20, based on the last 6 months. Opted-out candidates are flagged, not hidden.
## What we left for v2
Behavioural events (job views, time on job detail, pay comparisons, saves), agency-specific weights, and a second scoring mode for agencies that use the marketplace without the campaign product. All of it waits on real data from v1.
Originally published at decipheringlogic.com
