The two key measurements you should look for in a CV parser are Coverage and Accuracy.
Describes WHAT a parser actually tries to extract.
All parsers try to extract the contact information for the candidates, and most of them also extract skills, work histories and qualifications. The most advanced CV parsers (including DaXtra's) are even able to extract referees, hobbies, candidate summary, desired salary, desired location, nationality, visa status, and various other fields.
All of this information is required to create a full record for the candidate, so in general the more information a parser extracts, the better.
Describes HOW good a parser is at identifying information from a CV/Resume.
Accuracy measures how often the parser is actually right. For example, a precision of 95% on identifying names means that the CV parser correctly extracts the name of the candidate in 95% of all incoming resumes. This measure is important because the lower the accuracy, the more it costs to correct the errors that the resume parser makes.
In general, if a parser is less than about 90% accurate, the number of errors will be too large to permit it to load data into a resume database without extensive human supervision.
Interesting fact: Although the difference between 89% and 95% may not seem huge, it represents more than a doubling in the rate of errors that will need to be corrected, and hence a doubling of associated costs.
CV Parsing Claims: Fact or Fiction? You decide....