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HR Analytics

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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HR Analytics

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  1. HR Analytics: Recent Developments and Applications John P. Hausknecht, Ph.D.Cornell University

  2. 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…

  3. Evolution of HR Analytics

  4. Recent applications

  5. 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)

  6. 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

  7. 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

  8. 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

  9. 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)

  10. 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.

  11. 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.

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