This article relates mainly to admin-level users.
Across the Weekly10 platform all users will have access to data, depending on their role:
- Employees can see personal-level data such as their check-in history and number of mentions.
- Managers can see team-level data such as team check-in rates and goal progress.
- Admins can see company-wide data such as 10Pulse engagement scores, sentiment values, participation rates and manager review rates.
Most data sets have charts to give you an at-a-glance understanding of the data. You can filter and group this data to make it easier to view, understand and use it.
There are five main filter and grouping options for your data
1. None
The default option for all charts. This option shows all data collected across the organisation with no filters or groups applied. (Like the chart below)
2. Department
Departmental filtering or grouping sorts data by department (e.g. HR, Sales, Leadership). It's extremely useful for working out if certain departments' behaviours within Weekly10 differ from others.
Example use cases:
- Compare IT's engagement scores against HR's to see where correlations or differences lie.
- Compare your Glasgow sales team against you Madrid sales team - is the difference noticeable, can you learn anything from their management style or company-sub culture?
3. Team
Team-level filters and groups sort data by team, using the team manager’s name as the unique identifier (e.g., John Doe, Jane Smith). It's used often when trying to identify the triggers for dramatic changes or seeing which teams are causing department/organisation sentiment to fall.
Example use cases:
- Compare manager review rates across departments to see if one team's low engagement score mirrors a low manager review rate
4. Individual
Individual filters and groups are only available for a small number of data types due to regulations relating to employee profiling. Data for sentiment for example cannot be filtered at an individual level.
Example use cases:
- Look at check-in rates across teams to see if a manager might need extra support
5. Score component
Score component relates specifically to 10Pulse employee engagement data. 10Pulse is calculated using an AI algorithm looking at 5 key components of employee engagement:
- job satisfaction
- feeling valued
- discretionary effort
- pride
- advocacy
Each of these datasets can be split out into these 5 components to help admins better understand where their strengths and weaknesses lie when it comes to improving employee engagement. (See the graph below).
Example use cases:
- Job satisfaction scores have noticeably decreased in your Paris office. Is this across the board or just one department?
- Pride has increased noticeably. Which department or team is fuelling this - and what can you learn from them or their manager?
To learn more about 10Pulse, please see this helpful article.
Outside of these defaults, you can also filter using person properties
Person property
This option allows admins to assign custom person properties such as role, country, and seniority to employees and then filter based on these.
Custom properties need to be assigned before any filtering can happen based on them.
It is also possible to have custom filters and groups applied such as those specific to location. Please get in touch if you need additional filter options and we’ll help if able.