seedlink tech x recruiters united · *prelimary rr for 2018 after full seedlink ramp up indicate...
TRANSCRIPT
Seedlink Tech x Recruiters United
Problem: Inefficient methods and selection process noise Recruiting decisions for business are critical because of the high costs of bad people decisions
Inefficient selection methodsSelection process
discrepancies
0,18
0,38 0,37
ResumeScreening
Psychometrics Interviews
Ine
ffic
ien
t 0.50
Correlation to job performance1
High costs
Attrition/turnover2
Replacements costs up to 200% of salary
Inefficiency3
Replacements costs up to 200% of salary
Opportunity cost4
82% of hiring decisions miss the mark
20% of job candidates have CV discrepancies
– Kroll Research
Only 10% of candidates are qualified for key areas at MNC
– PWC Research
1. Schmidt & Hunter, 1998; 2. Cascio, 2000 ; 3. Harvard Business Rev iew; 4. Gallup
Solution: Predictive Artificial Intelligence interviewPeople Science driven by computational linguistics to move from feelings to data driven insights
1. In development; 2. Results from client case studies
Use of AI and language to predict company & job fit...
Data input Feature
extraction Model building Prediction
Predictive Artificial Intelligence interview
Computational linguistics
Text
Audio
Video1 NaturalLanguageProcessing
MachineLearning
Big Data
Competence
Creativity Leadership
ResultDriven
Personality
Potential
Faster+70% reduce time to offer
1
Better+33% diversity+83% predict best hires+95% improve experience
2
Cheaper+25% reduce turnover3
Individual candidate reports People analytics
Automated ranking
Skill Insights into Candidates/Employees
Digital Interview
From feelings to data driven insightsPredictive insights in the right hire, development areas and people analytics
10BB+
WORDS
6M +
ANSWERS
100K+
PERSONALITY
LABELS
20K+
JOB PERFORMANCE
COMPETENCY LABELS
9
LANGUAGES
100K+
VIDEO’S
Based on proprietary datasets and tech Millions of answers to open ended questions in 9 languages
Predictions are built based on layers of dataExisting employees serve as benchmark to train the custom models
Language + Traits
· Video
· Audio· Text
· Behavior
· Personality· Preferences
of
Store Managers
Beauty Advisors
Young Professionals
Marketing
Trained
Model
Validation
Refining Production
Machine
Learning
Predictive Model
BEHAVIORAL LANGUAGEINTELLIGENCE
Computational Linguistics Deep Learning Organizational Culture
• Professor of Artificial Sociality at Wageningen University
• Pioneer in the study of organizational culture computational modelling of adaptive systems.
• Leading researcher at the junction of computer science, psychology and culture with over 200 publications, and over 7,000 citations.
• Professor of computational linguistics at University of Antwerp.
• Pioneered the use of machine learning in natural language processing.
• Head of Research at Computational Linguistics & Psycholinguistics Research Center.
• Over 8,000 citations h-index: 44
Gert Jan HofstedeCultural Research Director
Walter DaelemansAI Research Director
Seedlink uses ML to lead in cutting edge researchCombining multiple fields of science
Language, audio and video analysisSeedlink makes it possible to extract information from input from candidates
Text Behavior
Video Personality
Audio Preferences
1 Since using Seedlink, Global FMCG has removed artificial barriers such as top university and specific degree requirements, allowing greater inclusivity and expanding the talent pool during pre-screening.
3,000 Hours candidate
processing time
10 HoursCandidate
processing time
42,000 Candidates1
15,000 Candidates
Before Seedlink After Seedlink
180%
80%selection efficiency
number of applicants (new groups)
Results are exponentially more efficientClient case study shows 80% selection efficiency
Client Case #1
42%*
More Revenue Generated on avg. Per Month PER HIRE
Ramp-Up + ROIJustifies Client Investment
723%**
*Prelimary RR for 2018 after full Seedlink ramp up indicate compound performance growth, exceeding 42% outperformance. ** Based on a non-discounted, full priced AI model.
And increases revenue of individual contributorsClient case study shows 42% higher productivity
Client Case #2
Methods:
The gender of each application was determined using a voice-based gender classifier (GMM of MFCCs). After exclusions (no Seedlinkscore, no audio), there were 8147 people in the dataset. 2240 (27.5%) males and 5907 (72.5%) females. Next, the dataset was slicedinto ten percentile segments of Seedlink score, i.e. from 0th to 10th percentile, from 10th to 20th percentile, …, and from 90th to100th percentile, and the gender distribution in each segment was visualized. Seedlink score percentile curves for Males and Femaleswere computed as well. Implementation on Github (leerix/audio/genderclassification/gender_seedlinkscore.ipynb).
Results:
Conclusion:
Seedlink score appears independent of gender.
Audio analysis proof gender neutral resultsClient case study: no bias in the results
Client Case #3
Pitch
Conclusion:
Females with high pitched voices have higher probability to get hired. Males with ‘normal’ pitched voices have higher probability to get hired. Also note the different probability to get hired (or go to AC) between males and females, indicated by red lines and blue lines.
Females Males
Audio intonation is layered on top of contentMore context into how people intend their words
Seedlink helps global leaders across industriesIn 9 languages and in over 30 countries
Thank you