Imagine the internet became a fine-tuned talent pipeline for you?

Especially if you have more candidates for a job than you wish to handle.

• Candidates look the same on paper or on online profiles. How to further rank candidates that have similar skills and knowledge?

• How to spend more time on interviewing the right candidates and spending less on interviewing those that will prove the wrong candidates for the job, anyway?

StoryMatcher helps you to easily select from your general talent pool a highly targetet shortlist for a specific job or department, fast and with relevant results.

How it works:

The manager of the unit with the vacancy, maybe together with some of the colleagues around the job in question selects scenarios from the pool of stories in the storymatcher base that relate to the operational success they want the future collegue to achieve.

The job advertisement or job description or your search form for your talent pool search is tagged by these story selections.

As a result you receive a prioritised shortlist of most matching people corresponding to your desired success at work for this specific job. Now up to you to invite them into conversations. We feel that with such a story-based shortlisting your selection interviews will be among the most exciting conversations your managers and staff will have.

On the other end, in your talent pool application, the candidates qualify their on-line-CV or online-profile with story-tags.

For each work experience ("episode") in the CV, candidates relate to the question: “which of these (typical) success stories remind you of your work episode?” and tag this episode with success story links.

For their job search, candidates choose their own success version out of the range of stories that the your organisation offers: “Which episode would you like to add to your career?” Matching of candidates (CV's) to jobs happens through the stories that both the department and candidates have selected and tagged to their CV or their vacancy ad. The matching is not only done on having tagged the same stories (content), but on similarity on the dimensions of level of development and typical characteristics across a range of different stories.