Candidate matching helps consultants identify relevant profiles in their existing database. RecSphere ranks candidates against vacancy criteria, providing a starting point for the shortlist. Consultants then confirm interest, availability and suitability, with manual search available for additional sourcing.
Matching does not replace knowing your candidates. It is a faster way to see who in the database fits a requirement before the phone calls start, so consultants spend less time searching and more time talking.
Check the input information
Matching depends on clear vacancy criteria and accurate candidate profiles. Vacancy capture and CV parsing reduce manual entry, but missing experience or inconsistent speciality details can affect results. Correct those gaps and keep availability current.
The problems are usually predictable. Take a request for an intensive care nurse to cover nights next week. Nurses with intensive care experience but no speciality recorded will not be scored at all. Those whose availability was last updated months ago may rank below less suitable candidates with current records. A few routine checks keep profiles usable:
- Grade and speciality set from the agency's standard lists rather than free text.
- Availability, leave and sickness kept up to date.
- Rate expectations and preferred locations confirmed recently.
- Client pre-approvals recorded when a client has approved a candidate.
Small gaps compound. One missing speciality removes a candidate from every search for that role, not just the one in front of you.
Understand the factors behind the score
RecSphere's matching only considers candidates who hold the vacancy's grade and speciality, then scores them on rate, shift availability, location, preferences, margin, skills, client pre-approval and experience, with adjustable weights and a clear factor breakdown. Use those scores to focus candidate conversations and refine the matching criteria when the desk needs a different emphasis.
Because grade and speciality decide who is scored at all, those two fields matter most. The weighting should then reflect the desk. A short-notice nights desk may weight shift availability and location heavily, while a longer allied health contract may put more weight on experience and subspeciality. This kind of matching is a scoring model the agency controls, not AI, so it uses no AI credits. Review the weights with each desk every few months, as client mix and shift patterns change.
Confirm suitability with the candidate
Use the ranked list to guide candidate conversations. Confirm interest, availability and practical client requirements before progressing a submission. The matching score supports the shortlist; consultants make the qualification and suitability decisions.
A high score means the record fits. Only a conversation confirms that the person does. Before submitting, check that the candidate is still free on those dates, willing to travel to the site and has the documents this client requires in place. If the candidate declines, record why. A pattern of declines for one site, such as travel time or parking, is useful material for the next client conversation.
Use manual search alongside the list
Some requirements need a wider net than the ranked list provides: a rare subspeciality, a client asking for someone they have worked with before, or a candidate who registered recently and whose profile is still being completed. Manual search covers those cases. Treat it as a complement to matching, and record what you find so the next search starts from better information.
Use feedback to improve records
If a recommendation is weak, check whether the criteria or profile information needs correcting. Keep those updates on the shared record so colleagues benefit from the improved information.
When a call reveals something new, such as a changed preferred location or a lapsed qualification, update the profile straight away. A common mistake is keeping that knowledge in personal notes, where the next consultant to search cannot see it.
Measure the shortlist outcome
Measure time previously spent searching and assembling shortlists, then track relevant submissions and placement progress from the matches. Use the findings to improve the criteria and the consultant activity that follows the ranked list.
Look at where successful submissions came from. If most placements come from manual searches rather than the ranked list, review the vacancy criteria, weights and record quality before concluding that matching is at fault. It also helps to note how far down the list successful candidates usually sit. If placements regularly come from well below the top few, the weights may not suit that desk.