Supporting your customers in the pre-purchase, buying or post purchase stages allows you to get to know your customer, their expectations and level of satisfaction with your products and services. With the core functions of cloud for customer you can a set this process to success. Connecting with your customers through different channels, from low touch to high touch support models and leveraging from a service catalogue with different service categories, soon you go live you can start understanding your business trends in the service process.
When it comes to service teams some essential metrics will support Service Managers to take the necessary actions on improving team efficiency, finding the right balance between human and machine interactions, and even the level of satisfaction from your customer.
When you monitor a service department it is key to optimize the effort spent there, and as a service manager you will want to look to your team’s impact and check their activities from different angles.
As an administrator you can build a home page for service managers. Think of this example: you have a service manager who wants to have a “Daily view of Workforce Performance”. What KPIs would come to mind for this scenario?
From here users can explore different analysis, for instance they may want to check only tickets which are in process for more then 2 days, or they may want to check how many tickets were closed in the last day. When building the homepage tiles think on how you will connect them to a view that facilitates the exploration of data and help users to jump directly from a single tile into the data behind. Think about possible filters to show the data related to certain segments or offer a report view which allow slice and dice of data to help understand in more detail the situation.
As a manager this will help you determine what performance improvements are required and how to address them. To address the results you may need to improve ticket routing by automating this process based on employee or org unit. Maybe you find that multiple tickets relate to the same issue, and therefore you may combine them and centralize their resolution. If you find knowledge is missing at service agent level you may decide to empower your workforce by providing ease of access to the knowledge base tool which offer a way to find the best solution in previous similar issues. All these capabilities offered by cloud for customer can help you influence those business outcomes which you are expected to achieve.
Besides the daily views Service Managers can also ask about service effectiveness of their teams as well as the best combination of channels according to service performance and customer satisfaction. In this case you can think about different KPIs:
When placing this information in home page you can think about a tile with the KPI that is connected to a predefined dashboard. For instance, if you consider the average handle time it may be worth to break it dive into analysis like: how does AHT compare by service organization, or by team, and going further by agent? What is the AHT by service category, incident category or by product?
As you try to improve effectiveness you may decide to activate machine learning models that help reduce repetitive tasks or speed up service response times or connect additional channels that allow you to offer some self-service capacity. If one of the scenarios you are working is related to ticket categorization automation you may think of preparing a specific dashboard for it. We suggest you to also read dashboard for the ML based Ticket Categorization scenario.
If you are a service provider you have certainly defined Service Level Agreements (SLA) between you and your customers. In those cases there are additional KPIs to consider:
A report on SLA compliance may help provide this information and can be synthetized with a KPI like the one below.
We have walked you through different reports and for each you need to define which data sources you need when planning Service Analytics. We have provided you examples that will pull from different data sources, search them with words like Ticket, SLA, social media, Ticket Intelligence etc. Start by exploring all reports available for each data sources, and if the standard views can provide what you need for your dashboards and KPIs.
Now once you plan your service analytics and assess the out of the box capabilities you may find the need to build custom reports. In this case you will probably want to create a join data source, custom key figures (in case of join data sources those will be created in the final join), or restricted key figures, you can work on all these capabilities and need to explore after you decide what you need and confirm what is available out of the box.
We encourage you to attend our on demand webinar about Service Analytics for Smart Decision Making Webcast while going through this process.
We hope you enjoy the reading!
CX Intelligent Adoption & Consumption Team
When it comes to service teams some essential metrics will support Service Managers to take the necessary actions on improving team efficiency, finding the right balance between human and machine interactions, and even the level of satisfaction from your customer.
When you monitor a service department it is key to optimize the effort spent there, and as a service manager you will want to look to your team’s impact and check their activities from different angles.
- What is the best channel? – Understand if there are preferred channels and which are more efficient?
- How effective are your service teams? – Understand if there is proper categorization, how fast or accurately are tickets addressed or which problems in service categories need a different approach in the process.
- How is the workforce performing? – Understand if the work is properly distributed, if service reps are sufficiently supported by a solid knowledge base, is the average handle and response time adequate at individual level.
- What is the customer experience? – Understand if customer expectations are met, or if there is compliance with SLAs.
As an administrator you can build a home page for service managers. Think of this example: you have a service manager who wants to have a “Daily view of Workforce Performance”. What KPIs would come to mind for this scenario?
- Team or agent load - # of tickets per team/agent and priorities
- Tickets from a period - # of tickets per status
- Overdue tickets per agent - # of tickets overdue
- # Unassigned tickets
- # Unassigned emails
From here users can explore different analysis, for instance they may want to check only tickets which are in process for more then 2 days, or they may want to check how many tickets were closed in the last day. When building the homepage tiles think on how you will connect them to a view that facilitates the exploration of data and help users to jump directly from a single tile into the data behind. Think about possible filters to show the data related to certain segments or offer a report view which allow slice and dice of data to help understand in more detail the situation.
As a manager this will help you determine what performance improvements are required and how to address them. To address the results you may need to improve ticket routing by automating this process based on employee or org unit. Maybe you find that multiple tickets relate to the same issue, and therefore you may combine them and centralize their resolution. If you find knowledge is missing at service agent level you may decide to empower your workforce by providing ease of access to the knowledge base tool which offer a way to find the best solution in previous similar issues. All these capabilities offered by cloud for customer can help you influence those business outcomes which you are expected to achieve.
Besides the daily views Service Managers can also ask about service effectiveness of their teams as well as the best combination of channels according to service performance and customer satisfaction. In this case you can think about different KPIs:
- % tickets with single agent handling
- Ticket closure rate
- Average Handle Time (AHT)
- Overall Machine Learning (ML) prediction
- SLA compliance by agent
- Interaction by channel
- Top 10 agents by # of social media interactions
When placing this information in home page you can think about a tile with the KPI that is connected to a predefined dashboard. For instance, if you consider the average handle time it may be worth to break it dive into analysis like: how does AHT compare by service organization, or by team, and going further by agent? What is the AHT by service category, incident category or by product?
Average Handle Time
As you try to improve effectiveness you may decide to activate machine learning models that help reduce repetitive tasks or speed up service response times or connect additional channels that allow you to offer some self-service capacity. If one of the scenarios you are working is related to ticket categorization automation you may think of preparing a specific dashboard for it. We suggest you to also read dashboard for the ML based Ticket Categorization scenario.
If you are a service provider you have certainly defined Service Level Agreements (SLA) between you and your customers. In those cases there are additional KPIs to consider:
- Number of Compliant Tickets by Initial Review
- Number of Compliant Tickets by Completion
- Number of Compliant Tickets
- Number of Missed Responses
- Percent Compliance Rate for Initial Review
A report on SLA compliance may help provide this information and can be synthetized with a KPI like the one below.
SLA Compliance Ticket Details
We have walked you through different reports and for each you need to define which data sources you need when planning Service Analytics. We have provided you examples that will pull from different data sources, search them with words like Ticket, SLA, social media, Ticket Intelligence etc. Start by exploring all reports available for each data sources, and if the standard views can provide what you need for your dashboards and KPIs.
Now once you plan your service analytics and assess the out of the box capabilities you may find the need to build custom reports. In this case you will probably want to create a join data source, custom key figures (in case of join data sources those will be created in the final join), or restricted key figures, you can work on all these capabilities and need to explore after you decide what you need and confirm what is available out of the box.
We encourage you to attend our on demand webinar about Service Analytics for Smart Decision Making Webcast while going through this process.
We hope you enjoy the reading!
CX Intelligent Adoption & Consumption Team
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