BACKGROUND

Rodrigo, the Head of IT Team came to my desk and asked me to create a report to review his team performance in 2020. He'd like to know what should be improved for his team to face 2021. He gave me some data:

1. Tickets, which means any formal request from the Employee to Agents for solving the issue.
2. Agents' Profile

ABOUT THE DATA

Let's have a look at the data. We have Tickets and Agents Profile data.

Tickets

In Tickets data, we have 10 fields:

1. ID Ticket
2. Issue Date
3. Employee ID
4. Agent ID
5. Request Category (Login Access, Hardware, Software, and System)
6. Issue Type (IT Request and IT Error)
7. Severity Level
8. Priority Level
9. Resolution Time (in Days)
10. Satisfaction Rate

Agents' Profile

In Agents' Profile data, we have 6 fields:

1. Agent ID
2. Full Name
3. Email
4. Year of Birth
5. Month of Birth
6. Day of Birth

ADDITIONAL CONTEXT

We have several additional contexts to make our analysis clearer.

Target

The average of resolution time is 4.5 days. Therefore, this value will be determined as our monthly target. We also add the target for satisfaction rate, that must be ranging between 4.5 - 5.0 points.

Service Level Agreement

Rodrigo has set the Service Level Agreement (SLA), where the Agents should solve the employees' issue within a particular time. Therefore, we'd like to categorize the issues based on SLA.

1. Within SLA (Where the agents can solve the issue less than 3.5 days)
2. Outside SLA (Where the agents can solve the issue more than or equal to 3.5 days)

Assessing Agents' Performance

To assess agents' performance, Rodrigo has set the KPIs; % Achievement of The Average of Resolution Time (KPI Process) and % Achievement of The Average of Satisfaction Rate (KPI Result). Rodrigo has given the weight for each KPI, 55% KPI Result and 45 % KPI Process.

Total Score = 55% KPI Result + 45% KPI Process

KEY INSIGHTS

Login Access

Let's see the number of cases based on request category (Hardware, Login Access, Software, and System). Through this chart, I'd like to show the % of Outside SLA cases to Total cases in 2020. I created a measure to calculate this one. Where # of Outside SLA Cases is the number of cases that are included in Outside SLA category. I used color formatting to highlight the category that is frequently solved within our SLA. After seeing the chart, I'm curious why Login Access issue become the most frequent asked issue after the System issue. Then, I checked % Login Access Issue based on Issue Type (User Request and IT Error) and the number of employees who requested login access.

Assessing Agents' Performance

I also listed the agents who have total score more than or equal to 100%. As you can see, we have 10 agents who perform really well this year. Moreover, we have 20 agents who need improvement. There are 3 agents that are underperform. I think we need to take action of these 3 agents.

SUMMARY

We have some points that need to be done in 2021.

1. We need to reduce Login Access cases by step-by-step guides (video tutorials or something similar) within the self-service portal to assist users in handling basic access related tasks without the need to submit formal request. This action can shift our focus and energy to the other cases.
2. Developing a comprehensive self-service portal to perform routine login access tasks independently, such as password resets, account unlocking, and profile updates.
3. We can also provide training session to users.
4. We need to improve our agents' skills. We also need to decide whether we upskill the 3 underperform agents or find the new competent agents.