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How to Create a Customer Service Dashboard in Looker

Cody Schneider•

A great customer service dashboard in Looker turns messy support data into a clear nerve center for your entire team. It spots trends, highlights what’s working, and flags issues before they spiral out of control. This guide walks you through defining the right metrics, preparing your data, and building your first customer service dashboard, tile by tile.

Start with a Plan: The Key Customer Service Metrics to Track

Before you build a single chart, you need a clear plan. A dashboard is only as good as the questions it answers. Think about what your team, your managers, and company leadership need to know. Most essential customer service metrics fall into three categories.

Team Performance Metrics

These KPIs measure the efficiency and effectiveness of your support agents. They help with resource allocation, performance coaching, and identifying process bottlenecks.

  • First Response Time (FRT): How long customers wait for an initial reply. A low FRT shows you're attentive, even if the final resolution takes more time.

  • Average Resolution Time (ART): The total time taken from when a ticket is opened to when it's marked as solved. It's a key indicator of your team's overall efficiency.

  • Ticket Volume: The total number of new support inquiries. Track this daily, weekly, and monthly by channel (email, chat, phone) to understand demand and staffing needs.

  • Tickets Solved per Agent: A simple productivity metric. Compare it alongside CSAT to ensure that speed isn't coming at the cost of quality.

  • Ticket Backlog: The number of unresolved tickets at any given time. A rising backlog is a classic early warning sign that your team is overloaded or a new product issue is causing a spike in inquiries.

Customer Satisfaction Metrics

These metrics tell you how customers feel about the support they received. They are direct measures of the quality of your service.

  • Customer Satisfaction (CSAT): Typically measured with a post-interaction survey asking, "How satisfied were you with your support experience?" It gives you ticket-level feedback on agent performance.

  • Customer Effort Score (CES): Asks customers how easy it was to get their issue resolved. A lower effort score is highly correlated with customer loyalty.

  • Net Promoter Score (NPS): While a broader company metric measuring overall loyalty, a sudden drop in NPS can often be traced back to a string of poor support experiences. It’s useful for high-level context.

Operational & Channel Metrics

These provide a helicopter view of your support operations, helping you make smarter strategic decisions about channels and resources.

  • Ticket Volume by Channel: Are most of your tickets coming from email, live chat, phone calls, or social media? This helps you staff your most popular channels effectively.

  • Cost Per Conversation: Calculated by dividing your total support operating costs by the number of conversations. This helps you understand the financial impact of your support operations.

  • Self-Service Usage: How many customers are finding answers in your knowledge base or help center without creating a ticket? A high self-service rate can significantly reduce ticket volume and cost per conversation.

Preparing Your Data & Setting Up Your LookML Model

Looker is a powerful BI tool, but it doesn't store your data. It connects to your existing SQL database or data warehouse (like BigQuery, Snowflake, or Redshift) and uses a modeling layer called LookML to define your business logic. For a customer service dashboard, your data likely comes from tools like Zendesk, Salesforce Service Cloud, or Intercom.

1. Connect Your Data Source

The first step is connecting Looker to the database where your support data lives. If your support data from Zendesk (for example) is already being piped into your data warehouse via a tool like Fivetran or Stitch, you're halfway there. A Looker Admin in your company would establish this connection under Admin > Database > Connections.

2. Create a LookML Project and Views

Once connected, it's time to build the LookML model. A developer will usually set up a new LookML project and generate a base model from your database schema. This creates "Views," which are LookML files that correspond to the tables in your database (e.g., tickets, users, comments).

In a tickets.view.lkml, a developer will define your dimensions and measures. Dimensions are the "group by" fields - things you can filter or pivot on like status, agent name, or creation date. Measures are the aggregate calculations, like counts, sums, or averages.

3. Define Custom Metrics and Join Views

A good LookML model brings all your data together. The next step is joining your tickets view with other relevant views, like users (to get customer details) or agents. A LookML developer will also create custom dimensions and more complex measures for things like First Response Time. This centralizes business logic so that everyone in your company can use the same definitions.

Building Your Customer Service Dashboard, Step by Step

1. Start Exploring

Start by navigating to the Explore section in Looker. Here, you can build queries to create visualizations. Select the tickets Explore as your starting point.

2. Build Simple KPI Tile: Total Open Tickets

  1. In the Explore section, go to your Dimensions list on the left and select "Tickets Status." Go to the Measures list and select "Tickets Count."

  2. Click Run. You'll see a data table showing the count of tickets for each status (new, open, pending, solved, etc.).

  3. Next, click the Filter icon next to the "Status" dimension. Filter for the value "open."

  4. Now go to the Visualization tab above and select "Single Value" to see your result as a number.

  5. Click Save to add your chart to the dashboard. Choose "Add to Dashboard" and select your new or existing dashboard.

3. Add Trend Line: Ticket Volume

In the same Explore session, add another measure for "Ticket Created Date", and then visualize it as a line chart to see trends over time. Select the Line Chart option to visualize the trend.

4. Add Performance Comparison: Agent Productivity

To compare agent productivity, add a bar chart visualization. Select "Agent Name" from the dimensions and "Tickets Solved" from the measures. Run the query to see each agent's performance.

5. Arrange and Customize Your Dashboard

Once all your visualizations are ready, arrange them in a logical order on your dashboard. You can customize colors and layout to make data easier to interpret.

Final Thoughts

Building a comprehensive customer service dashboard in Looker is a step-by-step process that helps transform raw support data into actionable insights. Once your dashboard is set up, it can become a pivotal tool for your team's strategy and performance analysis, enhancing your ability to deliver excellent customer support.