Every customer support manager needs to comprehensively know the agents’ performance. As decisions are taken to improve the team, it becomes important to identify the training needs of agents and areas where capacity can be optimized. This blog is part of the blog series ‘Customer Support Metrics’ shows how customer support managers can make strategic decisions by segmenting their data.
Two common pitfalls while analyzing agent performance
Decision-makers desire to make decisions objectively using the vast amount of data. Here are two common pitfalls that affect decision-making.
- Analyzing data in silos: Whether it is resolution time, backlogs, or any other attribute, trying to measure performance without considering other variables may lead to suboptimal decisions.
- Decisions based on limited data: While you know that there are problems to be solved, taking decisions based on a limited number of data points may not give accurate results.
Key benefits of segmenting your Agent Performance data
The Decision-making matrix helps you to quickly spot trends and provides insights and awareness.
- Promotes strategic thinking: Whatever attributes you are analyzing, it provides four key areas that are related to Agent performance. And you could tailor your decisions and approach each segment in a specific manner based on your needs.
- Flexibility in analysis: As you begin analyzing agent performance across multiple parameters like response time, resolution time, reply count, and so on, you could iteratively revisit your analysis and detect agent behaviors and trends.
- Responding to change: The decision-making matrix is a great way to uncover bottlenecks and better manage your customer support teams.
How HappyFox BI helps you segment agents
HappyFox BI provides you all the necessary tools that you need while analyzing your agent performance. As you create your decision-making matrix, you begin by including the dimensions of your interest.
- The right choice of attributes & dimensions: You could choose agents as the attribute that needs to be segmented. You can specify two numerical dimensions by which you want to segment your data. Examples are resolution time, ticket counts, response time, agent reply count, and so on.
- Marking the boundaries of each quadrant: HappyFox BI provides you enormous flexibility to create the boundaries of each segment. It can be done either by the average, percentile, or a static value.
Strategic decisions based on the segmented data
The decision-making matrix gives you enormous opportunities to intuitively understand your agent’s performance and make the right decisions.
- Reward the High Performers: You notice agents who handle higher than average ticket volume and also resolve tickets within relatively less time. Rewarding them would ensure continued excellence.
- Ease the bottlenecked Resources: These are agents who are unable to handle an increased volume of tickets resulting in higher than normal resolution times. You need to pay attention to these agents and find root causes for increased resolution time.
- Train your Slow Performers: These are agents who take an unduly long time to resolve a lesser number of tickets. These agents need training and support.
- Leverage Available Capacity: These agents resolve their tickets quickly. However, the number of tickets that they handle is lesser in comparison to others. You can probably increase their ticket volume.
As you see, for each of the quadrants, focussed initiatives to help the agents are taken. This clarity is enabled primarily by segmenting your agent data.
The possibilities of the above decision-making matrix are endless in the context of support analytics. You can continue analyzing your agent data by any number of variables and derive strategic insights from it.
With HappyFox BI, you will be able to perform advanced analytics and reporting. You get the tools and techniques in order to make the right decisions based on data and step up your organizational growth.
‘Get a demo’ of HappyFox BI and accelerate your analytics journey.