1 / 11

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

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”

camden
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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.

More Related