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Predictive Analytics in Human Resource Management: A Hands-on Approach

A hands-on, step-by-step guide showing HR managers how to model business problems and apply predictive analytics tools like artificial neural networks and K-nearest neighbour to forecast HR outcomes such as turnover and candidate selection.

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What it’s about

Predictive Analytics in Human Resource Management: A Hands-on Approach demystifies HR analytics for managers, teachers, and students without requiring prior expertise in statistics or programming. Written by two analytics scholars, it presents a 'holistic approach'—a seven-step framework spanning problem identification, business modelling, tool selection, application, validation, recommendation, and future exploration—illustrated with executable R scripts on real employee data. Using accessible language, corporate examples, and worked cases from the Indian IT industry, the book demonstrates how firms can move from intuition-based decisions toward data-driven, fact-based, predictive HR management. It covers data sourcing and quantification, model building with dependent/independent variables and systems thinking, and applies ANN and KNN to predict turnover intent and screen applicants, while surveying emerging trends like people analytics, IoT, voice analytics, Big Data, and Industry 4.0 disruption of HRM.

The through-line

Who it’s for
An HR manager, student, or practitioner who wants to make data-driven, fact-based HR decisions and demonstrate HR's strategic contribution to business outcomes.
The problem
HR departments collect vast employee data but lack the skills and framework to model business problems and apply predictive analytics to forecast outcomes like turnover and hiring quality. They feel intimidated by statistics and programming, uncertain where to begin, and pressured to justify HR's value in a fiercely competitive talent market.
The plan
  1. Understand business analytics and the need and role of HR analytics.
  2. Identify and define the HR business problem using a systems/process view.
  3. Model the problem by identifying variables and building a theoretical foundation.
  4. Select an appropriate analytical tool based on data and purpose.
  5. Apply the tool using hands-on R scripts on real data.
The payoff
The manager makes proactive, fact-based HR decisions that lower turnover and improve hiring quality. · HR demonstrates quantified contribution (hROI) to overall business strategy and outcomes. · The firm gains a sustainable competitive edge through effective talent acquisition, development, and retention.

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Additional reading

  • Investing in People: Financial Impact of Human Resource Initiatives · Wayne Cascio and John Boudreau

    The book cites this work as the source for the LAMP framework, a core model presented for connecting HR metrics and analytics to strategic organizational change.

  • Competing on Analytics: The New Science of Winning · Thomas H. Davenport and Jeanne G. Harris

    The authors use a definition of analytics from this foundational book, situating their own hands-on approach within the broader business analytics movement it helped popularize.

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