5 data-driven strategies for maximizing agent productivity in Zendesk | Geckoboard blog

KPIs can be a blessing and a curse.

Customer support is–in comparison to many other industries–extremely easy to measure. KPIs provide crucial insights into the customer experience and can provide a lot of direction for you.

That said, an overly rigid focus on individual metrics can lead teams to chase numbers at the expense of genuine customer success. The best way to avoid that is by taking a holistic approach. Understanding how these metrics work together is essential in crafting a data-driven strategy.

The good news is that Zendesk provides a wealth of reports that make it easy to track all the data you need. It also offers a ton of features that you can tweak to implement strategies that balance productivity with customer satisfaction.

Key customer support KPIs and how they influence each other

The most widely used support KPIs include:

• First Response Time (FRT): The time it takes for an agent to respond to a customer's inquiry for the first time. A faster response should correlate with higher CSAT and a low resolution time.
• First Contact Resolution Rate (FCR): The percentage of tickets resolved in the first interaction without the need for follow-ups. FCR should also lead to low resolution times and higher CSAT–and ideally a lower first response time in the long-run as well, since fewer tickets require a second response.
• Customer Satisfaction (CSAT): The percentage of customers who are satisfied with the support they receive, typically based on post-interaction surveys. This can sometimes be measured in a scale as well.
• Contact Rate: The percentage of customers submitting support tickets. An increasing number of knowledge base views or interactions with an AI solution should result in a lower contact rate.
• Ratio of Knowledge Base Views vs. Tickets submitted: A measure of how often customers turn to the self-service knowledge base compared to submitting tickets. Ideally, if the number of knowledge base views increases in relation to tickets submitted, it should also come with good helpfulness ratings on the articles.
• Deflection Rate: The percentage of contacts that are resolved using self-service options (such as a chatbot or knowledge base), without a ticket being submitted. This works best when combined with another qualitative metric like CSAT or a Customer Effort Score.

Each metric provides only part of the picture.

Aiming for a fast response time should mean that customers are more satisfied because they’re getting their answers solved faster. But it could also mean that customers are getting low quality answers.

A low contact rate should mean that customers are managing to solve their questions before they reach out, but it could also mean that customers are struggling to find contact options. Even CSAT, which might feel like a good catch-all metric, can suffer from low response rates and fluctuate.

Considering how these metrics interact will help you make informed decisions that genuinely improve agent productivity and the customer experience, rather than chasing numbers for their own sake.

Five proven strategies for maximizing productivity in Zendesk

Optimizing ticket routing

Ticket routing is often the foundation of the entire Zendesk setup. Most teams start by creating a few basic triggers and automations that categorize incoming tickets and assign them to the correct people.

Zendesk has a few key features for ticket routing:

• Skills-based routing is the most impactful, allowing you to match tickets with agents based on their specific expertise, language abilities, or product knowledge.
• Triggers and automations enable you to create rules based on ticket properties like priority, channel, customer segment, or custom fields.

The best metrics that would indicate if there are opportunities here are:

• The escalate or reassignment rate looks at the percentage of tickets that have to get reassigned.
• First assignment time will display how long tickets spend in the queue before getting assigned. You can also see if there’s a large gap between first assignment time and first reply time.
• Full resolution time, broken down by agent group.

Adjusting coverage to meet SLAs

SLAs are a great tool for maximizing productivity in general. Psychologically, the design of having a countdown attached to a ticket that turns red when the SLA is breached is very effective for most teams.

Zendesk has a ton of time-based reports for volume and SLA breaches, so it’s easy to identify recurring patterns–if 80% of SLA breaches happen at 3pm on Wednesdays and if peak volume comes in at 2pm on Fridays, these reports will show that.

Improving macros based on analytics

Macros are powerful efficiency tools, but their real value emerges when you refine them based on usage analytics.

Zendesk has a few features that are great for macros:

• It automatically suggests macros for agents, which should increase how often a macro is used and improve its adoption rate.
• Macro suggestions for admins help create macros and suggest relevant actions.

Digging into response times

Zendesk's analytics tools can break down ticket handling time into specific components: first response time, time between agent responses, time spent waiting for customer replies, and total resolution time.

Implement knowledge-centered service (KCS)

The KCS methodology is a structured framework for making every member of your team responsible for creating, using, and updating your knowledge base and documentation.

Implementing the KCS framework can take time, but it leads to massive gains in productivity across your organization.

Refine your Zendesk workflows over time

Maximizing productivity isn’t about working harder but working smarter. When agents have the right tools, efficient workflows, and clear priorities, they can focus on what matters most: solving problems and building long-term relationships with your customers.

Share your goals, metrics, and data on a live dashboard

Geckoboard is the easiest way to make key information visible for your team.