<p>HR decisions typically rely on a combination of experience, intuition and basic workforce metrics. As organisations grow, they accumulate vast amounts of digital data on recruitment and performance, learning, engagement and attrition. Often this data is dispersed and unorganised. Used effectively, HR analytics can convert this expanding pool of data into forward-looking decisions. This month’s newsletter examines five areas in which analytics can make a tangible difference: talent acquisition, employee retention, employee wellbeing and productivity, skills development and D&I.</p>.<p>HR analytics can be broadly divided into four categories, each answering a different question. </p><ol><li><p><strong>Descriptive:</strong> Historical data that helps the organisation understand what happened in a particular area (headcount, absenteeism, attrition) in a specific period</p></li><li><p><strong>Diagnostic:</strong> Examines why certain outcomes occurred, such as increased turnover within a particular team</p></li><li><p><strong>Predictive:</strong> Uses statistical models and current data to forecast future trends, such as employee behaviour, a candidate’s cultural fit, retention etc.</p></li><li><p><strong>Prescriptive:</strong> Provides recommendations on what actions should be taken to achieve desired outcomes </p></li></ol><p>Organisations may use several of these approaches together instead of confining it to one function. </p>.<h2><strong>Talent Acquisition </strong></h2><p>Advanced analytics can substantially increase the value of the talent acquisition function. Leading companies no longer rely solely on applicant tracking systems (ATS) for hiring, rather integrating recruitment data with information from performance, learning, engagement and operational information to assess which hiring channels produce successful employees. Predictive models can provide insights on a candidate’s likelihood of succeeding in a role. Yet, while technology is gaining a foothold in the world of talent acquisition, the most widely used solutions are in candidate sourcing and screening. More mature analytics capabilities can therefore improve candidate selection and recruitment processes while connecting talent acquisition more directly to business outcomes.</p><p><strong><a href="https://www.deloitte.com/us/en/services/consulting/services/talent-acquisition-analytics.html">Read More</a></strong></p>.<h2><strong>Turnover and Retention</strong></h2><p>Deloitte examined how workforce analytics can help limited-service restaurants address high attrition by identifying the factors that cause employees to leave. Integrating data on compensation, scheduling, manager quality, training, performance and local labour-market conditions revealed patterns that conventional turnover figures miss. For example, diagnostic analytics can reveal whether resignations are associated with unpredictable shifts in their roles, limited development opportunities or particular management practices can allow organisations to move from broad retention programs to targeted interventions. While this article is focused on restaurants, the approach is relevant to any business with a large frontline workforce, demonstrating how retention analytics can improve workforce stability while reducing recruitment and training costs.</p><p><strong><a href="https://www.deloitte.com/us/en/industries/consumer/articles/predictive-analytics-ai-restaurant-employee-retention.html">Read More</a></strong></p>.<h2><strong>Wellbeing and Engagement </strong></h2><p>With companies moving to hybrid and remote models, it has become harder for managers to assess employee wellbeing and detect signs of burnout. HR analytics can help spot shifting patterns in employee behaviour and engagement, which can otherwise go unnoticed. Descriptive analytics systems let companies look at real-time workplace activity through emails, messages, virtual meetings, or the work done on shared documents, and help spot changes in engagement levels by looking at how people communicate. These patterns reveal productivity and engagement levels, while predictive models can flag when an employee is at the risk of burnout. AI and prescriptive analytics can then help turn these data points into valuable insights for the organisation. </p><p><strong><a href="https://techrseries.com/featured/how-hrtech-platforms-are-detecting-early-signs-of-employee-burnout-through-behavioral-data/">Read More</a></strong></p>.<h2><strong>Skills-Gap and Training-Needs </strong></h2><p>As skill requirements evolve rapidly with changing market demands and business requirements, organisations need a more dynamic view of the skills available within their workforce. Analytics can examine job descriptions, performance reviews, employment histories and training records to create an inventory of existing capabilities and compare this with anticipated business needs. Predictive analytics can identify emerging skills gaps, including specific employees who need to be upskilled or reskilled. Prescriptive tools can recommend relevant learning and career opportunities. <strong>IBM</strong> uses AI analytics to infer employees skills and proficiency levels and recommend personalised learning and career opportunities. In 2024, this boosted employee engagement by an estimated 20%. Used strategically, skills analytics can therefore go beyond determining training investment to inform internal mobility, recruitment and longer-term workforce planning.</p><p><strong><a href="https://www.businessinsider.com/ai-improving-workplace-how-to-use-ai-identify-skill-gaps-2025-12">Read More</a></strong><a href="https://www.businessinsider.com/ai-improving-workplace-how-to-use-ai-identify-skill-gaps-2025-12"> </a></p>.<h2><strong>D&I </strong></h2><p>Analytics focused on D&I can help organisations move beyond headline hiring targets to understand what happens during an employee lifecycle. Many companies have deployed descriptive analytics to track representation across functions and diagnostic analysis to uncover hidden biases, helping HR develop more equitable hiring processes. <strong>Mastercard</strong> used AI analytics to identify obstacles to women’s advancement, contributing to a 35% increase in female representation in senior leadership over 3 years, while <strong>Accenture’s</strong> audit of its hiring process contributed to a 20% increase in the recruitment of underrepresented groups in a single year. By tracking differences in promotion, pay, retention and employee experience, organisations can identify where inequities persist and target interventions more effectively.</p><p><strong><a href="https://hr.economictimes.indiatimes.com/news/workplace-4-0/diversity-and-inclusion/data-driven-dei-measuring-progress-and-driving-inclusive-leadership/122358128">Read More</a></strong> </p>.<p>Before implementing HR analytics, CHROs should begin by asking the following questions:</p><ol><li><p>What specific workforce or business objective do we want HR analytics to achieve?</p></li><li><p>Which workforce metrics are most relevant to that objective and its business outcomes?</p></li><li><p>Do we have the appropriate technology to integrate and analyse workforce data effectively?</p></li><li><p>Do HR teams and managers have the analytical skills required to interpret the findings and act on them?</p></li><li><p>What safeguards are in place to protect employee data and ensure its secure and responsible use?</p></li></ol>
<p>HR decisions typically rely on a combination of experience, intuition and basic workforce metrics. As organisations grow, they accumulate vast amounts of digital data on recruitment and performance, learning, engagement and attrition. Often this data is dispersed and unorganised. Used effectively, HR analytics can convert this expanding pool of data into forward-looking decisions. This month’s newsletter examines five areas in which analytics can make a tangible difference: talent acquisition, employee retention, employee wellbeing and productivity, skills development and D&I.</p>.<p>HR analytics can be broadly divided into four categories, each answering a different question. </p><ol><li><p><strong>Descriptive:</strong> Historical data that helps the organisation understand what happened in a particular area (headcount, absenteeism, attrition) in a specific period</p></li><li><p><strong>Diagnostic:</strong> Examines why certain outcomes occurred, such as increased turnover within a particular team</p></li><li><p><strong>Predictive:</strong> Uses statistical models and current data to forecast future trends, such as employee behaviour, a candidate’s cultural fit, retention etc.</p></li><li><p><strong>Prescriptive:</strong> Provides recommendations on what actions should be taken to achieve desired outcomes </p></li></ol><p>Organisations may use several of these approaches together instead of confining it to one function. </p>.<h2><strong>Talent Acquisition </strong></h2><p>Advanced analytics can substantially increase the value of the talent acquisition function. Leading companies no longer rely solely on applicant tracking systems (ATS) for hiring, rather integrating recruitment data with information from performance, learning, engagement and operational information to assess which hiring channels produce successful employees. Predictive models can provide insights on a candidate’s likelihood of succeeding in a role. Yet, while technology is gaining a foothold in the world of talent acquisition, the most widely used solutions are in candidate sourcing and screening. More mature analytics capabilities can therefore improve candidate selection and recruitment processes while connecting talent acquisition more directly to business outcomes.</p><p><strong><a href="https://www.deloitte.com/us/en/services/consulting/services/talent-acquisition-analytics.html">Read More</a></strong></p>.<h2><strong>Turnover and Retention</strong></h2><p>Deloitte examined how workforce analytics can help limited-service restaurants address high attrition by identifying the factors that cause employees to leave. Integrating data on compensation, scheduling, manager quality, training, performance and local labour-market conditions revealed patterns that conventional turnover figures miss. For example, diagnostic analytics can reveal whether resignations are associated with unpredictable shifts in their roles, limited development opportunities or particular management practices can allow organisations to move from broad retention programs to targeted interventions. While this article is focused on restaurants, the approach is relevant to any business with a large frontline workforce, demonstrating how retention analytics can improve workforce stability while reducing recruitment and training costs.</p><p><strong><a href="https://www.deloitte.com/us/en/industries/consumer/articles/predictive-analytics-ai-restaurant-employee-retention.html">Read More</a></strong></p>.<h2><strong>Wellbeing and Engagement </strong></h2><p>With companies moving to hybrid and remote models, it has become harder for managers to assess employee wellbeing and detect signs of burnout. HR analytics can help spot shifting patterns in employee behaviour and engagement, which can otherwise go unnoticed. Descriptive analytics systems let companies look at real-time workplace activity through emails, messages, virtual meetings, or the work done on shared documents, and help spot changes in engagement levels by looking at how people communicate. These patterns reveal productivity and engagement levels, while predictive models can flag when an employee is at the risk of burnout. AI and prescriptive analytics can then help turn these data points into valuable insights for the organisation. </p><p><strong><a href="https://techrseries.com/featured/how-hrtech-platforms-are-detecting-early-signs-of-employee-burnout-through-behavioral-data/">Read More</a></strong></p>.<h2><strong>Skills-Gap and Training-Needs </strong></h2><p>As skill requirements evolve rapidly with changing market demands and business requirements, organisations need a more dynamic view of the skills available within their workforce. Analytics can examine job descriptions, performance reviews, employment histories and training records to create an inventory of existing capabilities and compare this with anticipated business needs. Predictive analytics can identify emerging skills gaps, including specific employees who need to be upskilled or reskilled. Prescriptive tools can recommend relevant learning and career opportunities. <strong>IBM</strong> uses AI analytics to infer employees skills and proficiency levels and recommend personalised learning and career opportunities. In 2024, this boosted employee engagement by an estimated 20%. Used strategically, skills analytics can therefore go beyond determining training investment to inform internal mobility, recruitment and longer-term workforce planning.</p><p><strong><a href="https://www.businessinsider.com/ai-improving-workplace-how-to-use-ai-identify-skill-gaps-2025-12">Read More</a></strong><a href="https://www.businessinsider.com/ai-improving-workplace-how-to-use-ai-identify-skill-gaps-2025-12"> </a></p>.<h2><strong>D&I </strong></h2><p>Analytics focused on D&I can help organisations move beyond headline hiring targets to understand what happens during an employee lifecycle. Many companies have deployed descriptive analytics to track representation across functions and diagnostic analysis to uncover hidden biases, helping HR develop more equitable hiring processes. <strong>Mastercard</strong> used AI analytics to identify obstacles to women’s advancement, contributing to a 35% increase in female representation in senior leadership over 3 years, while <strong>Accenture’s</strong> audit of its hiring process contributed to a 20% increase in the recruitment of underrepresented groups in a single year. By tracking differences in promotion, pay, retention and employee experience, organisations can identify where inequities persist and target interventions more effectively.</p><p><strong><a href="https://hr.economictimes.indiatimes.com/news/workplace-4-0/diversity-and-inclusion/data-driven-dei-measuring-progress-and-driving-inclusive-leadership/122358128">Read More</a></strong> </p>.<p>Before implementing HR analytics, CHROs should begin by asking the following questions:</p><ol><li><p>What specific workforce or business objective do we want HR analytics to achieve?</p></li><li><p>Which workforce metrics are most relevant to that objective and its business outcomes?</p></li><li><p>Do we have the appropriate technology to integrate and analyse workforce data effectively?</p></li><li><p>Do HR teams and managers have the analytical skills required to interpret the findings and act on them?</p></li><li><p>What safeguards are in place to protect employee data and ensure its secure and responsible use?</p></li></ol>
