Day 7

Use Cases, Metrics and Models - Part 2

• Presentation on October 11, 2023, focused on use cases, metrics, and models related to performance management.
• A quick recap was presented.
• Questions included metrics for workforce planning, attrition vs. turnover, recruitment, time to fill vs. time to hire, and learning & development.
• An index was provided for performance UC, employee voice/experience UC, inclusion/diversity UC, and compensation UC.
• The presentation covered performance management, highlighting use cases for implementing the performance management process in organizations

Performance Management Use Cases

• Using analytics to understand employee performance in meeting business objectives.
• Using generative AI to streamline performance review processes and improve outcomes.
• Assessing high performers and high-potential employees, and identifying differentiation strategies.
• Evaluating the impact of digital collaboration tools on performance improvement.
• Examining the connection between performance, engagement, and training.

Performance Metrics

• Feedback sent and received: real-time, continuous, and context-based employee feedback exchanges.
• Promotions: frequency of promotions categorized by employment type, level, department, organization, country, and location. A flag system for 'eligible for promotion' is mentioned.
• Performance results: final yearly evaluation ratings, including categories for exceptional performance, meets expectations, and requires improvement.

Performance Metrics (Continued)

• Objectives: defined objectives/priorities; data cuts by department, level, organization, market, and location. Questions are raised about defining yearly, individual, and team objectives.
• Engagement: engagement index at company level and departmental/organizational/level/market/location level. Engagement Index and survey participation data are used to compare to turnover, performance, etc.
• Absenteeism: hours of absenteeism categorized by department, level, organization, market, and location.
• Overtime: hours of overtime categorized by department, level, organization, market, and location.

Performance Tools

• Different tools and measures tailored to specific audiences (performance team, performance leads, HR executives).
• Performance operational reports: detailed reports on performance process status, feedback, promotions, and actions; provide high-level statistics across performance areas.
• Performance achievement analytics tools: visual display of connected information for deep analysis of performance outcomes and relevant metrics (metrics related to diversity, organization, country, level, engagement, account, skills, training).
• Ad-hoc analysis/models: examine the influence of engagement on performance factors; identify influencers, high-potential employees, collaboration patterns, useful information from feedback forms (skills, achievements, employee sentiments); organization network analysis.

Performance Tools (Continued)

• Generative AI examples for analysis: analyzing communication and collaboration patterns to improve performance and inclusivity; summarizing performance for managers; creating understandable and actionable employee feedback; performance goal creation; personalized learning plans.
• Key employee data types (compensation) include hire date, job start date; minimum, midpoint, and maximum salary range; base salary; bonus target; target cash compensation; Employee Total Compensation compared to market; salary and years in job (in the same structure).

Employee Voice & Employee Experience

• Focuses on gaining visibility into how employees feel in various aspects, with experience measured at each stage.
• Use cases: exploring the metaverse's impact on employee training experience, comparing current sentiment regarding employee training, examining the employee experience during role/position changes, analyzing how candidate experience impacts hiring numbers.
• Metrics: employee sentiment measured through surveys and feedback. The net promoter score (NPS) measures employee willingness to recommend the workplace. The score subtracts detractors (0-6 ratings) from promoters (9-10 ratings).
• Tools: text analyzer tool uses NLP for monitoring/visualizing sentiment in various processes; survey solutions provide insights based on comment text fields and quantitative fields; segmentation enables insightful analysis of employee groups based on specific criteria.

Compensation

• Focuses on ensuring compensation fairness aligned with business objectives.
• Use Cases: investigating trends in total rewards spending; assessing gender-based pay gaps within management levels; evaluating the effects of varied rewards/career opportunities on employee retention; examining pay equity for emerging skills.
• Metrics: pay range (salary bands); average salary; market index (comparison with external salary data); compensation costs; pay gap measure of organizational inequality.
• Additional data points: data on employee total compensation relative to the market, salary/job duration, salary comparisons within the organization and with the market.

Inclusion and Diversity

• Importance of analytics for ensuring equality, inclusivity, and fairness within organizations.
• Use cases: investigating organizational structure; assess pay equity; identify potential biases in recruitment and personnel decisions; assess the impacts of biases on inclusion and diversity targets.
• Metrics: ethnic mix; gender mix; generational mix; measures of these metrics broken down by process.
• Tools: I&D dashboard providing visibility into inclusion/diversity goals; other dashboards incorporate information about inclusion and diversity.

Recommendations

• When evaluating metrics and tools, use judgment and define new metrics and tools as necessary.
• Evaluating feedback from different organizations.

Use Cases vs Algorithms

• Various methods from classification methods to social network analysis and topic detection to explain use cases and the algorithms used.

Practice of the Day

• Analysis of feedback data and collaboration patterns including use cases, explanation of the practice, and opportunities to act on the practice.