Day 9

Use Cases, Metrics, and Models - Part 2

• Presentation date: October 11, 2023
• Focuses on performance management, employee voice, employee experience, compensation, and inclusion/diversity.

Quick Recap

• No specific details provided in the recap section.

Questions

• Metrics for workforce planning
• Difference between attrition and turnover
• Metrics for recruitment
• Comparing time to fill vs time to hire
• Metrics for learning and development

Index

• Performance use cases, metrics, and models
• Employee voice and employee experience metrics and models
• Inclusion and diversity metrics and models
• Compensation metrics and models

Performance Management

• Performance management use cases, processes, and strategies for implementation
• Analyzes employee performance to meet business objectives
• Uses generative AI to save time for managers in administrative tasks
• Determines the percentage of high performers and high potentials
• Identifies differentiation factors, such as promotion, growth, compensation for high performers
• Investigates if investments in digital tools improve performance.

Performance Metrics

• Feedback sent and received: real-time, continuous, context-based feedback from/to employees
• Regular / annual feedback; equal-to-equal feedback; informal, on-the-job feedback; avoiding ratings; setting objectives for feedback
• Performance results: yearly evaluation examples: exceptional, meets, requires improvement
• Promotions: different category levels (by employment type, level, department, organization, country, location )
• Promotion eligibility flags and gender/ethnicity promotion percentages for target evaluation

Performance Metrics (continued)

• Objectives: defined objectives/priorities; additional cuts of data (by department, level, organization, market, location)
• Does it make sense to define yearly objectives or team objectives? - Asking questions to ensure the objectives are the best for the workforce
• Absenteeism: absenteeism hours (by department, level, organization, market, location)
• Overtime: overtime hours (by department, level, organization, market, location)

Performance Tools

• Performance operational reports: details of the performance process to monitor objectives, feedback, promotions, and actions. Provides high-level statistics.
• Performance achievement analytics tools: connected information for deep analysis of performance outcomes, metrics, and additional data (inclusion, diversity, organization, country, level, engagement, account, skills, training)
• Ad-hoc analysis and models: impact of engagement on performance factors, organizational network analysis for influencers, high potentials, and collaboration patterns, and NLP models for feedback (skills, achievements, sentiment)

Performance Tools (continued)

• Samples of how General AI can analyze and help:
• Analyze communication and collaboration patterns in companies to help boost performance and inclusiveness
• Create performance summaries
• Make feedback more understandable and actionable
• Generate more comprehensive and cohesive performance reviews.
• Design personalized learning plans
• Prepare employees for performance reviews

Employee Performance Assessment Dashboard

• Key performance indicators: absenteeism, overtime hours, training costs, overall labor effectiveness
• A dashboard reviewing training costs, absenteeism, overtime hours, and overall labor effectiveness.
• Recognition dashboard, including the number of recognitions sent, percentage recognition, and average overtime per week.

Employee Voice/ Employee Experience

• Use cases by HR function, data usage for employee feeling
• Employee voice and employee experience use cases.
• Measures employee feedback/sentiment for all stages.
• Includes sections on the metaverse training experience, comparing sentiments to previous years, and employee experience when changing roles/positions.
• Employee experience when changing roles/positions, in the onboarding process, on the first year, and if the candidate experience impacts the number of hires.

Employee Voice & Employee Experience Metrics

• Employee sentiment/experience: Surveys and Feedback to measure overall sentiment in specific areas/processes.
• Net Promoter Score (NPS): measures employee willingness to recommend the workplace
• Methods to understand the likelihood of recommending the workplace (0 to 10 scale).

Employee Voice & Employee Experience Tools

• Text Analyzer Tool: Natural Language Processing (NLP) tool to monitor and visualize employee sentiment
• Survey Solutions: Survey tool for gathering employee insights through open-ended comments and quantitative data
• Segmentation: Segmenting employees based on criteria to get insights at a segment level
• Employee Voice/Employee Experience Tools

Compensation & Rewards

• Compensation and reward use cases by HR function
• Determines the trend and total spend on rewards.
• Investigates gender pay gap, measures for fairness and equity across an organization.
• Understands the relationship between rewards, career opportunity, and employee retention.
• Measures for emerging skill pay equity, organizational inequality in payment.
• Data showing compensation in comparison with the market and compensation over time.

Compensation Metrics

• Pay Range: salary bands for a level, role, etc.
• Average Salary: average salaries by level
• Market Index: compare base salary with the external marketplace.
• Compensation Costs: total cost to pay employees' salaries
• Pay Gap: organizational inequality in payment measurement

Inclusion and Diversity

• Inclusion and diversity use cases by HR function
• Investigates and determines if the organization has an equitable pay system
• Determines if the organizations has biases in the recruitment process; if gender/ethnicity bias with respect to talent decisions.
• Inclusion and diversity metrics include Percentage breakdown by ethnicity of employees, Percentage of female employees and mix for different generations.

Inclusion and Diversity Tools

• I&D dashboard: visibility into inclusion and diversity goals and information.
• Diversity dashboard: metrics of the gender, ethnicity, level, and age distributions.
• Important information for diversity tools and measures is normally included in other dashboards.

Recommendations

• Define new metrics and tools as needed when discussing metrics and tools.

Use Cases vs Algorithms

• Method and description of classification, clustering, statistical modeling, factorial analysis, social network analysis, topic detection, and association ruling
• Use case examples for each method, such as determining if employees are likely to leave, classifying management styles, predicting productivity levels, identifying employee clusters, relating sales to engagement/absenteeism, identifying elements correlated to turnover, and more.