case studies

A case study: Forvis Mazars x Empion

Together with renowned HR professor Prof. Dr. Fabiola Gerpott and Lorenz Rogall, Head of Talent Acquisition at our client Forvis Mazars, we have published the results of years of collaboration in Harvard Business Manager.

The results:

500x

Higher Reach

9

Days Time-to-Hire

-62%

Cost-per-Hire

The problem

When searching for talents, recruiters frequently focus exclusively on technical skills, often overlooking the critical importance of cultural fit between candidates and organizations. A misalignment in this regard can result in performance that falls short of expectations.

The reason

Many HR departments remain cautious about artificial intelligence. In the four-level AI-driven recruiting model developed by the authors, most organizations are still positioned at Level 0.

The solution

Mit einem KI-Ansatz lässt sich das Recruiting effizienter und gün­s­tiger gestalten. Dabei werden Bewerberprofile mit Fokus auf die kulturelle ­Passung analysiert. Das Ergebnis: Es sinken Time-to-Hire und Cost-per-Hire und dank Multi-Channel-Approach wird auch der passive Bewerbermarkt berücksichtigt.

Qualified applicants
Previously, Mazars used the “post and pray” principle to generate over 300 applications, of which only 16 were suitable. Through targeted, data-driven outreach - including the passive applicant market - the reach increased from 10,000 to 5 million. AI-based matching selected ten people from this pool, half of whom were invited for interviews.
500x
higher reach
Cost-per-hire
Previously, the average cost per hire was €8,100, but this has been reduced by 62% through the targeted use of Empion.
62%
Cost reduction
Time-to-hire
While the time-to-hire without Empion was previously 22 days, the new approach reduced it to nine days - a significant efficiency gain in filling the open position.
9 Days
Time-to-hire
Empion Case Study in Harvard Business Manager

HR professionals often claim that recruiting is largely a matter of gut feeling and that AI should be kept out of the process. Yet, the technology could actually help find urgently needed talent.

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