We provide a robust checklist to further your journey toward seamless zero-touch support that will enhance the end-user customer experience.
As part of the journey to zero-touch service desks, there are several facets to measure the adoption of self-service and automation. The first and most prominent of these is creating an overall baseline of the ticket types and volumes before the initiative, at the beginning, and regularly during the initiative. Many metrics that provide insights into the adoption and effectiveness of initiatives are very familiar and commonly used as part of operational delivery. These may include the following:
Volume of tickets per channel, recognizing that not all channels are the immediate go-to for some end-users and an amount of encouragement and support may be needed to help them with adoption.
Volume of tickets per user per month—this is like the metric above, with a reduction of tickets to the service desk in favor of other channels, or successful resolution by other methods such as self-heal.
Average number of tickets per agent—the current industry average is 1.1 tickets logged by end-user support per month, according to MetricNet. The number will reduce significantly with the increase in automation and adoption of self-service, leaving the more complex tickets to be addressed, leading to longer transaction times.
Average time to resolve a ticket—these times will likely increase at the service desk as the type and volume of tickets will change, leading to agents needing more skills and knowledge.
Percentage of tickets resolved without support team intervention—this is crucial to indicate progress toward zero touch.
Customer and service desk analyst experience is vital in understanding the transformed service’s overall impression and adoption rate.
Customer satisfaction measures still need to be gathered, and these form part of the evaluation step and as input into the initiative’s success, with overall customer satisfaction continuing to be closely monitored and tracked against adoption rates. A contributing factor is demonstrating how any end-user automation issues encountered are effectively identified and promptly addressed, with a communication loop back to the end-user community.
The attrition rates of service desk analysts need to be carefully monitored and compared with 12 months of data before the zero-touch journey began. These factors impact the analyst’s overall job satisfaction.
Where there is a service change, this should also be supported by organizational change management (OCM) messaging to help the end-user appreciate any impacts. This OCM messaging will assist when publishing the following metrics:
First-time resolution rate: This measure will likely reduce significantly for reasons like those described in the previous section. All that remains is addressing the more complex issues through automation.
Ticket reopen rate: Resolving issues the first time is essential, so this measure indicates how often end users are dissatisfied with the response.
Other considerations which may be more challenging to measure during a transformation include:
Accuracy: How precise the self-diagnosis suggestions provided to the end-user are in resolving their issue, allocating a ticket to a support group, or understanding voice recognition. Compare a baseline with results achieved through manual intervention by a service desk analyst carrying out similar routines.
Automated task performance: This looks at how well AI or ML responds to completing a task against the original objectives. This could include using a minimum performance metric as an acceptable baseline for a task. Balancing performance against the overall quality also needs to be considered.
Return on investment metrics are needed to ensure the outlay delivers the value outlined within the business case. Value can mean different things to organizations, including increased sales, profits, enhanced productivity or better customer engagement and experience.
Baselining or benchmarking metrics mentioned above help to identify focus areas and future investments and understand what will deliver value over time. Example metrics include:
Cost per ticket: Where there is an investment upfront in automation, average ticket costs will reduce over time, including those associated with service desk operations, as the number of agents required will reduce, and complex issues will be offset by an increased number fixed without their intervention.
Adoption: Usage over time of any AI or ML solution by the end-user or the service desk analyst where the option is available.
