Project Summary:

As a Data Consultant, one of my specialties is analyzing client/ customer data. I wanted to add an additional project in my portfolio that dived deeper into Customer Operations data and explored Service Level Agreement (SLA) compliance. In order to have happy customers, Customer Success teams and analysts must ensure that any service agreements such as responding to messages within 24 hours or resolving a ticket within 48 hours are fulfilled. This dashboard would allow the CS team and stakeholders to monitor their SLA compliance to find potential gaps in their service. Using AI, I created synthetic SLA data from 2023-2024 to identify year-over-year trends. I chose to use PowerBI for ease of 2025 forecasting, compliance, and correlation rates.

key insights

• SLA compliance rates are extremely low at 30.77% for responding within 4 hours and 38.72% of resolutions reached within 24 hours. There is a strong negative correlation between response/ resolution time and SLA success. Meaning the long it takes to respond or resolve a client ticket, the less likely it is for the SLA compliance rate will be met.

  • Recommendation: Touch base with Customer Success team. Review performance for employees. Upon reviewing employee performance and expectations, hire additional employees if it is not possible for current staff to reach all tickets within a timely manner. If hiring additional employees is not possible, then revisit SLA compliance rates to allow more time to resolve and respond to clients.

• Both East and West teams show the largest fluctuations in their performance. Technical issues and billing inquiries show the longest resolution times across all teams.

  • Recommendation: Gather more data on the East and West teams to discover if they are having issues with lack of staffing or an unexpected surplus in customer tickets that exceeds current employee capabilities to resolve. Revisit training for technical issues and billing inquiries to rule out instances of knowledge gaps. Review processes for solving technical and billing issues to identify efficiency gaps. Identify specific repetitive technical and billing issues for the engineering team to fully resolve.

• The forecasted SLA compliance trends show that performance would stay consistent throughout the next year with a large cone of variability.

  • Recommendation: We do not want performance to stay consistent to the same current averages, as the team is not meeting SLA compliance rates. The large variability means that without intervention, performance could stagnate or get worse. Follow the recommendation above to address lack of compliance rates being met.

process

Objective:

Create a dashboard to be used by the Customer Success Team to monitor SLA compliance with clients. Provide year-over-year historical insights, as well as 2025 forecasting.

Step-by-Step Process:

1. CSV files with synthetic data generated by AI were downloaded and imported directly into PowerBI. Included fields such as Date, Team, Category, Response time, Resolution time, and SLA Met.

   For ongoing, live reporting, I would use a tool like DirectQuery, to pull data directly from the database.

2. Created calculated columns to convert SLA Met values (previously Yes/ No) into numeric format (Yes = 1, No = 0) . Verified all data types (date, numeric, Boolean)

3. Used DAX to create new measures to calculate rate of compliance for Response and Resolution time, and correlation between Response/ Resolution time and SLA Met.

4. Began building visualizations and finding the best way to show each KPI within the dashboard.

   KPI callout cards: to display compliance rate percentages and correlation values, along with a Correlation Guide to explain the 1 to -1 value scale.

   Line Charts: to show trends over time such as SLA compliance and average response/ resolution times, as well as future forecasts.

   Bar Charts: to compare average response/ resolution times by SLA outcome (if a resolution achieved or not achieved) and to compare response/ resolution min, max, and average times per team and business category.

   Scatter Plot: to visually show the volume of tickets according to SLA Met status (whether the ticket was resolved or not) and the time the company took to respond and resolve (or not resolve) the ticket. This was the best way to show correlation of time and resolution.

5. I arranged the charts to split across two dashboard pages, the first focusing on overall compliance rates while page 2 focuses on team/ company category performance.