Traditionally HR search and match is a fairly simple affair which involves indexing the candidate data in a database or search engine, then querying that data with various keywords or phrases.
Whilst this works, mostly, it is both time-consuming and prone to error or missed results.
For example, if you had an opening for a “customer service assistant” then the natural way, using the above, would be to query “customer service assistant”. If you wanted to broaden results then you might query “customer service” but then you’d have to wade through the “customer service managers” and “customer service directors” too. If you were a bit smarter you might also search for a synonym, e.g. “customer service assistant, customer service asst”. But you will miss a great candidate for the role who has “customer svc assistant” on their CV
See where we are going with this? To search for all common variations of “customer service assistant” would require around 10k (yes, ten thousand) synonyms (according to our taxonomy), including abbreviations, typos and role synonyms. Simple text search just doesn’t cut it any more.
But even if you are lucky enough to find some candidates using traditional search, how do you rank them? Typically the highest ranked will be those who have mentioned “customer service assistant” the most times on their profile. Not necessarily that useful.
As for matching jobs to candidates, or vice versa, then almost everything on the market is pretty poor and extremely prone to false positives and true negatives!
A good search and match suite should be able to:
A poor search and match means that you, or your users, will spend hours trawling through irrelevant results, or that you will miss the perfect candidate or job. A good search and match will save time and money by quickly finding the candidates/jobs you are looking for and quite possibly reactivate the 90% of dormant candidate data sitting around in your database.