In addition to the Sentiment Overview analytics, the platform also provides more concentrated views of the sentiment within the business. Specifically, those with access to the analytics area can choose to view sentiment relating only to Forms, including individual forms, Goal Comments, Diary Notes and Performance Management Issues.
The value of this is that as the system administrator you can really narrow down where the positive or negative sentiment is originating from. Is it the Goals that are causing negative sentiment? Is one particular form generating negative sentiment? Is positive sentiment being created through the use of diary notes for recognition?
Whatever the answers may be, your ability to identify the cause of sentiment will allow you to act on this information to provide a motivated and productive workforce.
Why is 'Sentiment - Performance Management Issues' important?
Establishing effective performance management systems can have significant benefits for your business, as it can lead to happier, more motivated and better performing employees. Reviewing, refining and implementing performance management systems are ways of helping achieve these significant benefits.
When performance management issues arise it's important important to act quickly. Managers shouldn't sit back and hope the problem will fix itself. However, raising performance issues with employees can be challenging for both employees and managers. Often, the suggestion of performance management will bring with it negative sentiment. The performance management feature of the platform has been designed with the goal of re-framing the performance issue into an opportunity to grow professionally. If managers are working with their employees to assist them to reach their potential, rather than managing their poor performance, then the process will be more positive for all.
The 'Sentiment - Performance Management Issues' provides an overview of the positive and negative sentiment reported through performance management issues over time. These are then weighted by significance and frequency so you can identify trending themes across your business.
Importantly, although there is a dedicated area for Performance Management in analytics the sentiment analysis of performance management issues allows you to delve deeper into how employees are feeling about their performance management and whether this is a positive or negative experience for the employee and the business.
Using the Sentiment - Performance Management Issues:
1. Reporting Period: Choose the period you wish to report on using the calendar at the top right of the screen.
2. Cross-filtering: You can cross filter your data to provide more specific insight into the sentiment related to performance management issues.
- Performance Issue Category : The categories are based on the same list as the diary notes categories promoting consistency within your reporting and as with the diary notes this list can be amended to suit your needs as a business. Monitoring the categories of performance management issues can be extremely beneficial in spotting trends within the business. For example, consistently negative sentiment relating to a 'Skills Gap' may suggest that the training delivered during performance management is not being received positively by employees and therefore changes to training selection or the method of delivery may need to be further evaluated.
- Sentiment Score Distribution: The distribution of sentiment across all performance management issues within your specified time period.
- Business Unit / Supervisor / Pay Grade / Gender / Work Type / Tenure: Using any of these cross filters will let you investigate specific groups within your organisation to see if positive or negative sentiment is directly correlated with employees meeting the criteria identified by your use of filters. If sentiment in any of these areas is significantly more positive or negative than average these anomalies can then be investigated further.
- Row Data: The row dataset provides every instance of data generated by the sentiment related to performance management issues over your specified time period. The strength of the row dataset is that each row links back to the original comment where the sentiment originated allowing you to pinpoint the exact sentence that created the sentiment.
By cross-filtering in Sentiment - Performance Management Issues, Lyanna noticed that negative sentiment was often reported when the employee working on their performance management action plan was supervised by William Bowen.
Lyanna then reviewed the row data visible when this had been filtered to display only data from employees with the supervisor William Bowen. Upon checking the performance management issues was able to identify the cause of the negative sentiment.
Although William had correctly reported performance management issues for several employees, having only joined the company in the last six month William was not aware of all the training resources available to help employees reach their potential. As such, William had identified performance issues and offered his time to help create plans to help the employees improve, but without specifically suggesting training courses to help his employees improve and develop some of those working on their action plan had reported no clear understanding on how they could reach their potential.
The business spoke to William about the learning and development opportunities offered to employees to aid professional development and he was then able to provide a clear pathway to improvement for his direct reports. They subsequently reported more positive sentiments regarding their ongoing improvement and development, this was a sentiment mirrored in William's comments about the employees' improved performance.