hr analytics recent developments and applications john p hausknecht ph d cornell university n.
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HR Analytics: Recent Developments and Applications John P. Hausknecht, Ph.D. Cornell University. HR Analytics. Systematic data collection and analysis designed to improve talent and business-related decisions Interest in analytics growing substantially HBR article on “Talent Analytics”

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Presentation Transcript
hr analytics
HR Analytics
  • Systematic data collection and analysis designed to improve talent and business-related decisions
  • Interest in analytics growing substantially
    • HBR article on “Talent Analytics”
    • Special issue on HR analytics in People + Strategy
    • Company profiles in New York Times
    • Flurry of consulting firm offerings
    • Cornell/CAHRS partner meeting
    • Four HR analytics working groups over past several years
  • Rapid changes in the last 5 years…
where are companies today
Where are companies today?
  • Still heavy on reporting, moving toward analytics
    • Some companies split these activities
    • Reporting skill set ≠ analytics skill set
  • Systems enhance/constrain opportunities
    • Data access issues
    • Relevance and accuracy of data
    • Different versions of the “truth”
    • Attention to data governance issues
  • Better org. design to support analytics
    • Teams with analytics as core responsibility
    • Diverse skill set needed; consulting, data analysis, HR, business
    • Background not always HR (oftentimes not)
where do companies seem to be heading
Where do companies seem to be heading?
  • Better systems/greater linkage ability
  • Hiring analytical talent (ideal profile difficult to find)
  • More sophisticated designs
  • Moving beyond employee surveys
  • Talent data as business data
  • Pilot testing and experimentation
academic practice partnerships
Academic-Practice partnerships
  • Partnerships with Cornell
    • Relation to HR faculty research
    • “Live cases” with student teams
  • Why it works
    • Faculty knowledge of literature and current practice
    • Expertise in research design and data analysis
    • Data source for research
    • Not selling solutions
  • Three brief examples
    • Key points, results, insights
same turnover rates different impact
Same turnover rates, different impact
  • Examined data from 5,631 employees and 75 work units in hospitality sector
  • Linked turnover rates with customer service quality scores and engagement survey questions

Key Insight: High turnover is only problematic under certain conditions

sometimes engagement doesn t matter
Sometimes engagement doesn’t matter
  • Studied 12,500 employees in 115 work units in large transportation organization
  • Data from climate survey, absenteeism rates (HRIS), unemployment (BLS)

Key Insight: Engagement scores are not predictive of absences in times of job market uncertainty (dampening effect)

experience needed but not too much
Experience needed. But not too much…
  • Collected data from 350+ stores of a major U.S. retailer
  • Predicting store revenue per square foot
  • Examined effects of attrition and store manager tenure; controls for structural characteristics
  • Tenure positively related to sales, but only to a point; after which it is negative

Key Insight: More management experience is not necessarily better.

current project rethinking attrition metrics
Current project: Rethinking attrition metrics
  • Working with several hundred locations of a large service organization
  • Going beyond assumption that each departure is the same
  • New measure to account for who leaves, who remains, and the timing of departures

Key Insight (TBD): We can improve the accuracy of talent-related metrics that are used to forecast operational performance.