Call Center Capacity and Service Analytics Sigma Dashboard Example
Call center leaders are making decisions constantly to course correct, and they need relevant analytics available to them at any moment to make those decisions well. The cost of getting it wrong is high, as you might spend much more in overtime to make up missed service levels than investing to anticipate staffing gaps in advance.Â
This dashboard gives insight into the service performance of a call center, its capacity and demand requirements over time, and any level of detail from the overall call center down to individual teams within it. Â
Analytics like this facilitate more effective communication with operations to prepare playbook strategies and build trust and agreement before those misses impact results.
About the Data
This dataset was generated from scratch by creating a simulated call center with a real staffing model. The data is integrated and modeled in the Snowflake Data Cloud and for visualization and analysis in Sigma Computing. This example call center has three physical locations, three service channels, and 15 total staffing groups between them.Â
The example data reflects the staffing and performance dynamics you would expect between occupancy and service level results. Your call center data may be different, but will likely have a similar structure when integrating from platform sources, and the dashboard can scale to a company of any size.
Who Is This Dashboard For?
This dashboard is meant for call center operations and workforce management and planning leaders who need insights into the health and stability of their call center’s service performance. Â
Ultimately, this dashboard will show variance from plan and process stability across each link in the chain of the staffing model, so call center leaders can understand the quantitative causes of service performance and see and respond to future risk before it impacts results.
What You Can Accomplish With This Dashboard
1. Navigation
Click on any button along the top row to navigate the dashboard sections.
2. Filter Controls
On each page of the dashboard, the user may filter for a subset of the data for exploratory analysis by selecting certain Segments, Sites, Teams, or individual Staffing Plans. These controls will filter all of the rest of the data shown, and will persist when navigating between pages.
3. Date Controls
For all views, you can select a date range preset from the list shown, or pick a custom date range and date grouping to see different levels of historical and future forecast data represented.
4. Forecast Selection
The call center data modeled in this dashboard uses two different forecasts: Service Forecast, and Financial Forecast. You can toggle between these two forecasts using the button in the upper right corner of each dashboard page. The service forecast would be regularly updated by a short- to mid-range planning team to reflect the true forecasted demand and solve for risk to service outcomes. The Financial Forecast is updated multiple times through the year by the long-range planning team, and represents financial or budget risk.
5. Dashboard Sections
Summary View
This is a high-level view of current performance, with run rate calculations to show what level of future performance is needed to hit service goals.
Planning View
This dashboard section has all the metrics a call center might need in detail in one spot. Click any value to see that metric’s trend visualized below, and export the data quickly with the button at the top.
Root Cause Analysis
This area explains how results varied favorably or unfavorably against the financial and service forecasts for each component of the plan. For example, it will show the impact in required production hours that any variance in average handle time had from the plan, and how demand requirements and staffing capacity meet in the middle to reflect the actual service results for a date or period.
Individual KPI Trend Views
Navigation buttons for Call Volume, Average Handle Time, Shrinkage, Occupancy, and Headcount all lead the user to pages with the customizable trend and variance charts for each respective metric and any related KPIs.
Other Notes
Your call center may focus on slightly different performance metrics, like average speed of answer (ASA) and abandonment rate, so this could be tailored to meet your particular objectives. You’re likely to have different groupings and hierarchies of how your data is structured (segment, site, team, etc.) so those may be tailored to the needs of your data model as well.
Conclusion
This dashboard provides must-have analytics and insight for any call center. If you are being asked to do more with less, you need analytics tools like this one to identify opportunities and strategies to be more effective with your resources, as well as communicate effectively and quantitatively to ensure you can source the staffing and resources you really need to be successful.
We hope you have found this dashboard example useful in your pursuit of service excellence for your organization. If you have any questions, need help, or are interested in having a team of Sigma and Snowflake experts design dashboards for you, please reach out!
