Part 2 of 3
In Part 1, we discussed how to focus your efforts and develop a compelling case for change. In Part 2, we will discuss how to analyze the data and identify the most significant problems driving the performance gap.
Analyzing the right data was the biggest challenge. Each organization had relied on employee climate surveys, exit interviews, and anecdotal stories learned from industry conferences and publications to address retention in the past. The problem with these types of data are that they’re either opinions, unreliable, or fables. Surveys are just opinions, and not every departing employee has an exit interview. And many interviewed employees won’t tell the truth for fear of reprisal. So, what kind of data is the right data? Before collecting any additional data, it’s wise to define and understand the process producing the performance measure and gap. In this case, teams documented the “journey of a new hire” which started with “Vacant Position Identified” and concluded with “Vacant Position Was Filled for 12 Months”. The flow of steps for “the journey” was documented showing decision points, participating departments, and potential trouble spots. This helped inform the best approach for collecting reliable data, which was to obtain the start date and termination date of each person who departed in the past 12 months. For each team, this was more than 1400 employees, so samples of 300 employees were collected to achieve 95% confidence levels and 5% margins of error for the data sets to be analyzed. Next, histograms were created for each showing the proportion of employees with lengths of service ranging from one day to 20 years. Since a successful hire was defined as one with at least 12 months’ service, we focused on those who departed within the first 12 months. For team #1, the histogram revealed that 4% left within 30 days of hire, 22% within 90 days, and 53% within 12 months. This directed our attention on two cohorts – the 22% who left within 90 days of hire, and the 31% who left between 91 days and 12 months of their hire dates. Next, Pareto analysis was performed on the two cohorts indicating that RNs were the most significant problem in each. SMART Problem Statements were developed to address each problem, for example: “Reduce the number of RNs leaving between 91 days and 12 months of the hire date by 50% by December 31, 2019, thus reducing the overall employee turnover rate from 24% to 19%”. Another Problem Statement was developed to address the employees terminating during the first 90 days of employment thus reducing overall turnover another 3 percentage points to 16%. This was shy of the overall 14% target but accepted by senior leadership as a great first-start.
For team #2, the data revealed that RNs were not the significant problem, but rather Millennials. This was a first for me after 30 years of consulting and training, but indicative of the power of data.
End of Part 2 – To be continued. In Part 3 we will discuss how to select and verify root causes, develop permanent solutions, and document quantitative and impactful results.