A business cannot run effectively unless future leaders are identified and developed. Succession planning requires prediction. To make good predictions requires analytical data, including HR and company operational data.
Succession planning also requires the use of metrics. Metrics are important as indicators of whether the company has meet key performance measures or not. For example, a metric can show you someone’s current job performance, but that may not be a good predictor of how someone will conduct himself or herself as a supervisor. Additionally, if the employee’s performance is viewed through the eyes of favoritism or the employee’s goals were not accurate, even the metric can be flawed.
Succession planning requires solid analytics and specific, accurate metric benchmarks for the business. It is critical that this data cannot be just HR data. If succession planning is defined as having the talent necessary to meet business needs, the business data cannot be disregarded.
This webinar will cover the use of metrics and analytics in succession planning by working through the following three practical case studies regarding succession planning.
1. High turnover/low skill company.
2. Rapidly growing, tech company with low turnover/high skill.
3. Company with a mix of high/medium/low skill jobs with regular turnover whose business model is to promote from within.
Areas Covered in the Session :
- The difference between metrics and analytics. What metrics to use, when and for what purpose.
- How HR metrics and business metrics must be used together to solve HR problems and develop HR programs.
- Understanding and appreciating the utility of analytics — even if you hate math.
- The three important things you have to understand about analytics.
- How the CEO would approach the three case studies and how you should too.
- Understanding the language of business — numbers. Develop confidence using, depending upon and speaking about analytics and metrics.